WorldWideScience

Sample records for spatial data

  1. Spatial Data Management

    CERN Document Server

    Mamoulis, Nikos

    2011-01-01

    Spatial database management deals with the storage, indexing, and querying of data with spatial features, such as location and geometric extent. Many applications require the efficient management of spatial data, including Geographic Information Systems, Computer Aided Design, and Location Based Services. The goal of this book is to provide the reader with an overview of spatial data management technology, with an emphasis on indexing and search techniques. It first introduces spatial data models and queries and discusses the main issues of extending a database system to support spatial data.

  2. Marine Spatial Data Infrastruktur

    DEFF Research Database (Denmark)

    Stigsen, Tino Kastbjerg; Weber, Michael; Hvingel, Line Træholt

    2011-01-01

    En bæredygtig fremtid har stået højt på den politiske dagsorden siden Brundtlandsrapporten udkom i 1987. Geodata spiller en væsentlig rolle i opfyldelse af dette mål. Med udgangspunkt i geodata kan der skabes en datainfrastruktur, der kan være med til at understøtte den planlægning, administratio...... Enabled Society, såvel som i teorier om digital forvaltning (eGovernment). Alle diskurser anerkender vigtigheden af Spatial Data Infrastructure (SDI), og dermed af geodata, som et redskab og katalysator for processen....

  3. Elements of spatial data quality

    CERN Document Server

    Guptill, SC

    1995-01-01

    Elements of Spatial Data Quality outlines the need and suggests potential categories for the content of a comprehensive statement of data quality that must be imbedded in the metadata that accompanies the transfer of a digital spatial data file or is available in a separate metadata catalog. Members of the International Cartographic Association's Commission on Spatial Data Quality have identified seven elements of data quality: positional accuracy, attribute accuracy, completeness, logical consistency, lineage, semantic accuracy and temporal information. In the book the authors describe: compo

  4. GCCS Spatial Data Base Module

    National Research Council Canada - National Science Library

    Bell, Paul

    1998-01-01

    .... JMTK is divided into three primary areas: (1) Visual, (2) Analysis (non-visual), and (3) Spatial Data Base (SDBM). The primary objective of the SDBM effort is to define, design, develop and test mapping, charting and geodesy...

  5. Spatial Data Management System (SDMS)

    Science.gov (United States)

    Hutchison, Mark W.

    1994-01-01

    The Spatial Data Management System (SDMS) is a testbed for retrieval and display of spatially related material. SDMS permits the linkage of large graphical display objects with detail displays and explanations of its smaller components. SDMS combines UNIX workstations, MIT's X Window system, TCP/IP and WAIS information retrieval technology to prototype a means of associating aggregate data linked via spatial orientation. SDMS capitalizes upon and extends previous accomplishments of the Software Technology Branch in the area of Virtual Reality and Automated Library Systems.

  6. Spatial Statistical Data Fusion (SSDF)

    Science.gov (United States)

    Braverman, Amy J.; Nguyen, Hai M.; Cressie, Noel

    2013-01-01

    As remote sensing for scientific purposes has transitioned from an experimental technology to an operational one, the selection of instruments has become more coordinated, so that the scientific community can exploit complementary measurements. However, tech nological and scientific heterogeneity across devices means that the statistical characteristics of the data they collect are different. The challenge addressed here is how to combine heterogeneous remote sensing data sets in a way that yields optimal statistical estimates of the underlying geophysical field, and provides rigorous uncertainty measures for those estimates. Different remote sensing data sets may have different spatial resolutions, different measurement error biases and variances, and other disparate characteristics. A state-of-the-art spatial statistical model was used to relate the true, but not directly observed, geophysical field to noisy, spatial aggregates observed by remote sensing instruments. The spatial covariances of the true field and the covariances of the true field with the observations were modeled. The observations are spatial averages of the true field values, over pixels, with different measurement noise superimposed. A kriging framework is used to infer optimal (minimum mean squared error and unbiased) estimates of the true field at point locations from pixel-level, noisy observations. A key feature of the spatial statistical model is the spatial mixed effects model that underlies it. The approach models the spatial covariance function of the underlying field using linear combinations of basis functions of fixed size. Approaches based on kriging require the inversion of very large spatial covariance matrices, and this is usually done by making simplifying assumptions about spatial covariance structure that simply do not hold for geophysical variables. In contrast, this method does not require these assumptions, and is also computationally much faster. This method is

  7. Fundamentals of spatial data quality

    CERN Document Server

    Devillers, Rodolphe

    2010-01-01

    This book explains the concept of spatial data quality, a key theory for minimizing the risks of data misuse in a specific decision-making context. Drawing together chapters written by authors who are specialists in their particular field, it provides both the data producer and the data user perspectives on how to evaluate the quality of vector or raster data which are both produced and used. It also covers the key concepts in this field, such as: how to describe the quality of vector or raster data; how to enhance this quality; how to evaluate and document it, using methods such as metadata;

  8. The spatial glaciological data infrastructure

    Directory of Open Access Journals (Sweden)

    T. Y. Khromova

    2014-01-01

    Full Text Available Substantial and rapid environmental changes require developing methods which could be able to manage huge information flows, to optimize processes of the data acquisition, storage, analysis, and exchange. Such facilities can be provided by the newly developed GIS technologies. Digital data bases are used as the key component of the GIS methods. We present the system of glaciological data management, developed in the Institute of Geography of Russian Academy of Sciences (IGRAS. Digital Atlas «Snow and Ice on the Earth», glacier inventories and digital library are the basic structures making possible objective presentation of the glaciological knowledge and data. The system provides the data integration, access to the data base, and makes possible using the GIS techniques for analysis. Data integration technologies are designed to form the united information space of subject areas of the spatial data. The objects of integration in our study are the information resources of glaciology, accumulated in a distributed system of data on the IGRAS web servers and geoportals in forms of data and metadata bases, structured (in a particular format data files, object data files (plain text, documents, images, etc., and electronic atlases. The best option for formation of a large-scale distributed environment, integration of many information resources of glaciology is to provide the so-called interoperability of data. This refers to compliance with certain rules or usage of additional software tools that allows interaction between various spatial data. These are standards to which the integrated information resources of glaciology should satisfy. The result of integration of the glaciological data technology application is the series of software and technology solutions. The main result of this work is creation of geoportals «Electronic Earth» (www.webgeo.ru, «The Nature and Resources of the Russian North» (www.north.webgeo.ru, «IPY-IGRAS» (www

  9. Perspectives on spatial data analysis

    CERN Document Server

    Rey, Sergio

    2010-01-01

    This book takes both a retrospective and prospective view of the field of spatial analysis by combining selected reprints of classic articles by Arthur Getis with current observations by leading experts in the field. Four main aspects are highlighted, dealing with spatial analysis, pattern analysis, local statistics as well as illustrative empirical applications. Researchers and students will gain an appreciation of Getis' methodological contributions to spatial analysis and the broad impact of the methods he has helped pioneer on an impressively broad array of disciplines including spatial epidemiology, demography, economics, and ecology. The volume is a compilation of high impact original contributions, as evidenced by citations, and the latest thinking on the field by leading scholars. This makes the book ideal for advanced seminars and courses in spatial analysis as well as a key resource for researchers seeking a comprehensive overview of recent advances and future directions in the field.

  10. Assessment of spatial data infrastructures

    African Journals Online (AJOL)

    bases, networks, Web services and portals to facilitate and coordinate the availability, ... need for an SDI to support the spatial and land development planning .... inform integrated and development planning ... provincial and regional planning.

  11. The 3-D global spatial data model foundation of the spatial data infrastructure

    CERN Document Server

    Burkholder, Earl F

    2008-01-01

    Traditional methods for handling spatial data are encumbered by the assumption of separate origins for horizontal and vertical measurements. Modern measurement systems operate in a 3-D spatial environment. The 3-D Global Spatial Data Model: Foundation of the Spatial Data Infrastructure offers a new model for handling digital spatial data, the global spatial data model or GSDM. The GSDM preserves the integrity of three-dimensional spatial data while also providing additional benefits such as simpler equations, worldwide standardization, and the ability to track spatial data accuracy with greater specificity and convenience. This groundbreaking spatial model incorporates both a functional model and a stochastic model to connect the physical world to the ECEF rectangular system. Combining horizontal and vertical data into a single, three-dimensional database, this authoritative monograph provides a logical development of theoretical concepts and practical tools that can be used to handle spatial data mo...

  12. Rasdaman for Big Spatial Raster Data

    Science.gov (United States)

    Hu, F.; Huang, Q.; Scheele, C. J.; Yang, C. P.; Yu, M.; Liu, K.

    2015-12-01

    Spatial raster data have grown exponentially over the past decade. Recent advancements on data acquisition technology, such as remote sensing, have allowed us to collect massive observation data of various spatial resolution and domain coverage. The volume, velocity, and variety of such spatial data, along with the computational intensive nature of spatial queries, pose grand challenge to the storage technologies for effective big data management. While high performance computing platforms (e.g., cloud computing) can be used to solve the computing-intensive issues in big data analysis, data has to be managed in a way that is suitable for distributed parallel processing. Recently, rasdaman (raster data manager) has emerged as a scalable and cost-effective database solution to store and retrieve massive multi-dimensional arrays, such as sensor, image, and statistics data. Within this paper, the pros and cons of using rasdaman to manage and query spatial raster data will be examined and compared with other common approaches, including file-based systems, relational databases (e.g., PostgreSQL/PostGIS), and NoSQL databases (e.g., MongoDB and Hive). Earth Observing System (EOS) data collected from NASA's Atmospheric Scientific Data Center (ASDC) will be used and stored in these selected database systems, and a set of spatial and non-spatial queries will be designed to benchmark their performance on retrieving large-scale, multi-dimensional arrays of EOS data. Lessons learnt from using rasdaman will be discussed as well.

  13. A precategorical spatial-data metamodel

    OpenAIRE

    Steven A Roberts; G Brent Hall; Paul H Calamai

    2006-01-01

    Increasing recognition of the extent and speed of habitat fragmentation and loss, particularly in the urban fringe, is driving the need to analyze qualitatively and quantitatively regional landscape structure for decision support in land-use planning and environmental-policy implementation. The spatial analysis required in this area is not well served by existing spatial-data models. In this paper a new theoretical spatial-data metamodel is introduced as a tool for addressing such needs and a...

  14. Spatial data quality and coastal spill modelling

    International Nuclear Information System (INIS)

    Li, Y.; Brimicombe, A.J.; Ralphs, M.P.

    1998-01-01

    Issues of spatial data quality are central to the whole oil spill modelling process. Both model and data quality performance issues should be considered as indispensable parts of a complete oil spill model specification and testing procedure. This paper presents initial results of research that will emphasise to modeler and manager alike the practical issues of spatial data quality for coastal oil spill modelling. It is centred around a case study of Jiao Zhou Bay in the People's Republic of China. The implications for coastal oil spill modelling are discussed and some strategies for managing the effects of spatial data quality in the outputs of oil spill modelling are explored. (author)

  15. Spatial normalization of array-CGH data

    Directory of Open Access Journals (Sweden)

    Brennetot Caroline

    2006-05-01

    Full Text Available Abstract Background Array-based comparative genomic hybridization (array-CGH is a recently developed technique for analyzing changes in DNA copy number. As in all microarray analyses, normalization is required to correct for experimental artifacts while preserving the true biological signal. We investigated various sources of systematic variation in array-CGH data and identified two distinct types of spatial effect of no biological relevance as the predominant experimental artifacts: continuous spatial gradients and local spatial bias. Local spatial bias affects a large proportion of arrays, and has not previously been considered in array-CGH experiments. Results We show that existing normalization techniques do not correct these spatial effects properly. We therefore developed an automatic method for the spatial normalization of array-CGH data. This method makes it possible to delineate and to eliminate and/or correct areas affected by spatial bias. It is based on the combination of a spatial segmentation algorithm called NEM (Neighborhood Expectation Maximization and spatial trend estimation. We defined quality criteria for array-CGH data, demonstrating significant improvements in data quality with our method for three data sets coming from two different platforms (198, 175 and 26 BAC-arrays. Conclusion We have designed an automatic algorithm for the spatial normalization of BAC CGH-array data, preventing the misinterpretation of experimental artifacts as biologically relevant outliers in the genomic profile. This algorithm is implemented in the R package MANOR (Micro-Array NORmalization, which is described at http://bioinfo.curie.fr/projects/manor and available from the Bioconductor site http://www.bioconductor.org. It can also be tested on the CAPweb bioinformatics platform at http://bioinfo.curie.fr/CAPweb.

  16. Visual data mining for quantized spatial data

    Science.gov (United States)

    Braverman, Amy; Kahn, Brian

    2004-01-01

    In previous papers we've shown how a well known data compression algorithm called Entropy-constrained Vector Quantization ( can be modified to reduce the size and complexity of very large, satellite data sets. In this paper, we descuss how to visualize and understand the content of such reduced data sets.

  17. GCCS Spatial Data Base Module Extensions

    National Research Council Canada - National Science Library

    Bell, Paul

    1998-01-01

    .... JMTK is divided into three primary areas: (1) Visual, (2) Analysis (nonvisual), and (3) Spatial Data Base (SDBM). The primary objective of the SDBM effort is to define, design, develop and test mapping, charting and geodesy...

  18. Spatial data modelling and maximum entropy theory

    Czech Academy of Sciences Publication Activity Database

    Klimešová, Dana; Ocelíková, E.

    2005-01-01

    Roč. 51, č. 2 (2005), s. 80-83 ISSN 0139-570X Institutional research plan: CEZ:AV0Z10750506 Keywords : spatial data classification * distribution function * error distribution Subject RIV: BD - Theory of Information

  19. City of Zagreb Spatial Data Infrastructure

    Directory of Open Access Journals (Sweden)

    Darko Šiško

    2011-12-01

    Full Text Available Through the establishment of the Coordination Group for the City of Zagreb Spatial Management IT System, the City of Zagreb has become actively involved in the wider global community by setting up the Zagreb Spatial Data Infrastructure (ZSDI service. In the City of Zagreb, many bodies of city administration use and create spatial data and services daily in their work. All are ZSDI users and obviously have to make data mutually available. Without spatial data and services, it would be impossible to manage space effectively, plan city development, monitor the situation on the ground, or carry out many other activities. This paper gives an overview of ZSDI set-up activities so far, as well as plans for the future. 

  20. Hierarchical modeling and analysis for spatial data

    CERN Document Server

    Banerjee, Sudipto; Gelfand, Alan E

    2003-01-01

    Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, or written at a level often inaccessible to those lacking a strong background in mathematical statistics.Hierarchical Modeling and Analysis for Spatial Data is the first accessible, self-contained treatment of hierarchical methods, modeling, and dat

  1. Data management on the spatial web

    DEFF Research Database (Denmark)

    Jensen, Christian S.

    2012-01-01

    Due in part to the increasing mobile use of the web and the proliferation of geo-positioning, the web is fast acquiring a significant spatial aspect. Content and users are being augmented with locations that are used increasingly by location-based services. Studies suggest that each week, several...... billion web queries are issued that have local intent and target spatial web objects. These are points of interest with a web presence, and they thus have locations as well as textual descriptions. This development has given prominence to spatial web data management, an area ripe with new and exciting...... opportunities and challenges. The research community has embarked on inventing and supporting new query functionality for the spatial web. Different kinds of spatial web queries return objects that are near a location argument and are relevant to a text argument. To support such queries, it is important...

  2. Spatial occupancy models for large data sets

    Science.gov (United States)

    Johnson, Devin S.; Conn, Paul B.; Hooten, Mevin B.; Ray, Justina C.; Pond, Bruce A.

    2013-01-01

    Since its development, occupancy modeling has become a popular and useful tool for ecologists wishing to learn about the dynamics of species occurrence over time and space. Such models require presence–absence data to be collected at spatially indexed survey units. However, only recently have researchers recognized the need to correct for spatially induced overdisperison by explicitly accounting for spatial autocorrelation in occupancy probability. Previous efforts to incorporate such autocorrelation have largely focused on logit-normal formulations for occupancy, with spatial autocorrelation induced by a random effect within a hierarchical modeling framework. Although useful, computational time generally limits such an approach to relatively small data sets, and there are often problems with algorithm instability, yielding unsatisfactory results. Further, recent research has revealed a hidden form of multicollinearity in such applications, which may lead to parameter bias if not explicitly addressed. Combining several techniques, we present a unifying hierarchical spatial occupancy model specification that is particularly effective over large spatial extents. This approach employs a probit mixture framework for occupancy and can easily accommodate a reduced-dimensional spatial process to resolve issues with multicollinearity and spatial confounding while improving algorithm convergence. Using open-source software, we demonstrate this new model specification using a case study involving occupancy of caribou (Rangifer tarandus) over a set of 1080 survey units spanning a large contiguous region (108 000 km2) in northern Ontario, Canada. Overall, the combination of a more efficient specification and open-source software allows for a facile and stable implementation of spatial occupancy models for large data sets.

  3. Landscape Modelling and Simulation Using Spatial Data

    Directory of Open Access Journals (Sweden)

    Amjed Naser Mohsin AL-Hameedawi

    2017-08-01

    Full Text Available In this paper a procedure was performed for engendering spatial model of landscape acclimated to reality simulation. This procedure based on combining spatial data and field measurements with computer graphics reproduced using Blender software. Thereafter that we are possible to form a 3D simulation based on VIS ALL packages. The objective was to make a model utilising GIS, including inputs to the feature attribute data. The objective of these efforts concentrated on coordinating a tolerable spatial prototype, circumscribing facilitation scheme and outlining the intended framework. Thus; the eventual result was utilized in simulation form. The performed procedure contains not only data gathering, fieldwork and paradigm providing, but extended to supply a new method necessary to provide the respective 3D simulation mapping production, which authorises the decision makers as well as investors to achieve permanent acceptance an independent navigation system for Geoscience applications.

  4. Generalised recurrence plot analysis for spatial data

    International Nuclear Information System (INIS)

    Marwan, Norbert; Kurths, Juergen; Saparin, Peter

    2007-01-01

    Recurrence plot based methods are highly efficient and widely accepted tools for the investigation of time series or one-dimensional data. We present an extension of the recurrence plots and their quantifications in order to study recurrent structures in higher-dimensional spatial data. The capability of this extension is illustrated on prototypical 2D models. Next, the tested and proved approach is applied to assess the bone structure from CT images of human proximal tibia. We find that the spatial structures in trabecular bone become more recurrent during the bone loss in osteoporosis

  5. Ontology Based Quality Evaluation for Spatial Data

    Science.gov (United States)

    Yılmaz, C.; Cömert, Ç.

    2015-08-01

    Many institutions will be providing data to the National Spatial Data Infrastructure (NSDI). Current technical background of the NSDI is based on syntactic web services. It is expected that this will be replaced by semantic web services. The quality of the data provided is important in terms of the decision-making process and the accuracy of transactions. Therefore, the data quality needs to be tested. This topic has been neglected in Turkey. Data quality control for NSDI may be done by private or public "data accreditation" institutions. A methodology is required for data quality evaluation. There are studies for data quality including ISO standards, academic studies and software to evaluate spatial data quality. ISO 19157 standard defines the data quality elements. Proprietary software such as, 1Spatial's 1Validate and ESRI's Data Reviewer offers quality evaluation based on their own classification of rules. Commonly, rule based approaches are used for geospatial data quality check. In this study, we look for the technical components to devise and implement a rule based approach with ontologies using free and open source software in semantic web context. Semantic web uses ontologies to deliver well-defined web resources and make them accessible to end-users and processes. We have created an ontology conforming to the geospatial data and defined some sample rules to show how to test data with respect to data quality elements including; attribute, topo-semantic and geometrical consistency using free and open source software. To test data against rules, sample GeoSPARQL queries are created, associated with specifications.

  6. Reference Data as a Basis for National Spatial Data Infrastructure

    Directory of Open Access Journals (Sweden)

    Tomáš Mildorf

    2012-12-01

    Full Text Available Spatial data are increasingly being used for a range of applications beyond their, traditional uses. Collection of such data and their update constitute a substantial part of the total costs for their maintenance. In order to ensure sustainable development in the area of geographic information systems, efficient data custody and coordination mechanisms for data sharing must be put in place. This paper shows the importance of reference data as a basis for national spatial data infrastructure that serves as a platform for decision making processes in society. There are several European initiatives supporting the wider use of spatial data. An example is the INSPIRE Directive. Its principles and the main world trends in data integration pave the way to successful SDI driven by stakeholders and coordinated by national mapping agencies.

  7. Towards a Spatial Data Infrastructure in Croatia

    Directory of Open Access Journals (Sweden)

    Vlado Cetl

    2003-09-01

    Full Text Available The term Spatial Data Infrastructure (SDI is not new and has already been present in the world for quite a long time. President Clinton's Executive Order 12906 from April 1994 played a crucial role and was an initiative in establishing National Spatial Data Infrastructure (NSDI. This Order induced briskly the building of NSDI and also of all additional counterparts in the USA and around the whole world. Besides NSDI, various other initiatives at regional (EUROGI, PCGIAP, … and global level (GSDI were also launched.In this paper, an overview of different initiatives and efforts in establishing SDI in Croatia will be presented. State bodies such as the Government and State Geodetic Administration have the main role in it in collaboration with public and commercial sector and also with academic community. As the main factor in creating a future SDI, State Geodetic Administration has launched several initiatives the goal of which is the installation of new technologies, equipment and procedures in map production and the establishment of digital topographic and cadastre databases. The arrangement and modernization of spatial records and the establishment of NSDI make the key factors for sustainable physical planning and land development at local and national level.In the next few years Croatia must solve numerous duties to arrange spatial records. These duties must be solved very conscientiously and in a reasonable period of time. It is very important for Croatian prosperity and for the fulfilment of the conditions set in the process of entering European and international integrations.

  8. A Spatial Data Infrastructure for Environmental Noise Data in Europe.

    Science.gov (United States)

    Abramic, Andrej; Kotsev, Alexander; Cetl, Vlado; Kephalopoulos, Stylianos; Paviotti, Marco

    2017-07-06

    Access to high quality data is essential in order to better understand the environmental and health impact of noise in an increasingly urbanised world. This paper analyses how recent developments of spatial data infrastructures in Europe can significantly improve the utilization of data and streamline reporting on a pan-European scale. The Infrastructure for Spatial Information in the European Community (INSPIRE), and Environmental Noise Directive (END) described in this manuscript provide principles for data management that, once applied, would lead to a better understanding of the state of environmental noise. Furthermore, shared, harmonised and easily discoverable environmental spatial data, required by the INSPIRE, would also support the data collection needed for the assessment and development of strategic noise maps. Action plans designed by the EU Member States to reduce noise and mitigate related effects can be shared to the public through already established nodes of the European spatial data infrastructure. Finally, data flows regarding reporting on the state of environment and END implementation to the European level can benefit by applying a decentralised e-reporting service oriented infrastructure. This would allow reported data to be maintained, frequently updated and enable pooling of information from/to other relevant and interrelated domains such as air quality, transportation, human health, population, marine environment or biodiversity. We describe those processes and provide a use case in which noise data from two neighbouring European countries are mapped to common data specifications, defined by INSPIRE, thus ensuring interoperability and harmonisation.

  9. Developing a modelling for the spatial data infrastructure

    CSIR Research Space (South Africa)

    Hjelmager, J

    2005-07-01

    Full Text Available The Commission on Spatial Data Standards of the International Cartographic Association (ICA) is working on defining spatial models and technical characteristics of a Spatial Data Infrastructure (SDI). To date, this work has been restricted...

  10. Spatial data requirements for emergency response

    International Nuclear Information System (INIS)

    Walker, H.

    1985-01-01

    The Atmospheric Release Advisory Capability (ARAC) provides real-time assessments of the consequences resulting from an atmospheric radioactive material. In support of this operation, a system has been created which integrates numerical models, data acquisition systems, data analysis techniques, and professional staff. Components of this system rely to large degree on spatial data of various kinds. Of particular importance is the rapid generation of digital terrain models for any area in the continental U.S. The digital terrain models are used as input to atmospheric models and serve to familiarize assessors to new areas by presenting the terrain surface as a graphical image. In addition, base map data (roads, rivers, political boundaries) must also be supplied as an overlay to ARAC graphical products. A terrain data base and an associated acquisition system have been developed that provide the required terrain data within ten minutes. This terrain data base was derived from the Defense Mapping Ageny's planar data. A digital base map data base is currently being developed from the U.S. Geographical Survey's 1:2,000,000 Digital Line Graph data as well as their Geographic Names Information System. This base map data base improves ARAC's response to its mapping needs anywhere in the continental U.S

  11. Spatial Data Web Services Pricing Model Infrastructure

    Science.gov (United States)

    Ozmus, L.; Erkek, B.; Colak, S.; Cankurt, I.; Bakıcı, S.

    2013-08-01

    The General Directorate of Land Registry and Cadastre (TKGM) which is the leader in the field of cartography largely continues its missions which are; to keep and update land registry and cadastre system of the country under the responsibility of the treasure, to perform transactions related to real estate and to establish Turkish national spatial information system. TKGM a public agency has completed many projects. Such as; Continuously Operating GPS Reference Stations (TUSAGA-Aktif), Geo-Metadata Portal (HBB), Orthophoto-Base Map Production and web services, Completion of Initial Cadastre, Cadastral Renovation Project (TKMP), Land Registry and Cadastre Information System (TAKBIS), Turkish National Spatial Data Infrastructure Project (TNSDI), Ottoman Land Registry Archive Information System (TARBIS). TKGM provides updated map and map information to not only public institutions but also to related society in the name of social responsibility principals. Turkish National Spatial Data Infrastructure activities have been started by the motivation of Circular No. 2003/48 which was declared by Turkish Prime Ministry in 2003 within the context of e-Transformation of Turkey Short-term Action Plan. Action No. 47 in the mentioned action plan implies that "A Feasibility Study shall be made in order to establish the Turkish National Spatial Data Infrastructure" whose responsibility has been given to General Directorate of Land Registry and Cadastre. Feasibility report of NSDI has been completed in 10th of December 2010. After decision of Steering Committee, feasibility report has been send to Development Bank (old name State Planning Organization) for further evaluation. There are two main arrangements with related this project (feasibility report).First; Now there is only one Ministry which is Ministry of Environment and Urbanism responsible for establishment, operating and all national level activities of NSDI. And Second arrangement is related to institutional Level. The

  12. Spatial big data for disaster management

    Science.gov (United States)

    Shalini, R.; Jayapratha, K.; Ayeshabanu, S.; Chemmalar Selvi, G.

    2017-11-01

    Big data is an idea of informational collections that depicts huge measure of information and complex that conventional information preparing application program is lacking to manage them. Presently, big data is a widely known domain used in research, academic, and industries. It is utilized to store substantial measure of information in a solitary brought together one. Challenges integrate capture, allocation, analysis, information precise, visualization, distribution, interchange, delegation, inquiring, updating and information protection. In this digital world, to put away the information and recovering the data is enormous errand for the huge organizations and some time information ought to be misfortune due to circulated information putting away. For this issue the organization individuals are chosen to actualize the huge information to put away every one of the information identified with the organization they are put away in one enormous database that is known as large information. Remote sensor is a science getting data used to distinguish the items or break down the range from a separation. It is anything but difficult to discover the question effortlessly with the sensor. It makes geographic data from satellite and sensor information so in this paper dissect what are the structures are utilized for remote sensor in huge information and how the engineering is vary from each other and how they are identify with our investigations. This paper depicts how the calamity happens and figuring consequence of informational collection. And applied a seismic informational collection to compute the tremor calamity in view of classification and clustering strategy. The classical data mining algorithms for classification used are k-nearest, naive bayes and decision table and clustering used are hierarchical, make density based and simple k_means using XLMINER and WEKA tool. This paper also helps to predicts the spatial dataset by applying the XLMINER AND WEKA tool and

  13. Land Administration and Spatial Data Infrastructure

    DEFF Research Database (Denmark)

    Parker, John R.; Enemark, Stig

    2005-01-01

    of developing land policies that effectively and efficiently incorporate appropriate spatial data infrastructures, including an understanding of the value of integrating the land administration/cadastre/land registration function with the topographic mapping function. This paper presents an overview...... of “The Development of Land Information Policies in the Americas”. FIG was tasked with taking the lead role in planning and arranging the Special Forum. The objective of this inter-regional forum was to establish an awareness of the economic and social value for decision makers, of the importance...

  14. Factor Copula Models for Replicated Spatial Data

    KAUST Repository

    Krupskii, Pavel

    2016-12-19

    We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.

  15. Factor Copula Models for Replicated Spatial Data

    KAUST Repository

    Krupskii, Pavel; Huser, Raphaë l; Genton, Marc G.

    2016-01-01

    We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.

  16. Reducing Spatial Data Complexity for Classification Models

    International Nuclear Information System (INIS)

    Ruta, Dymitr; Gabrys, Bogdan

    2007-01-01

    Intelligent data analytics gradually becomes a day-to-day reality of today's businesses. However, despite rapidly increasing storage and computational power current state-of-the-art predictive models still can not handle massive and noisy corporate data warehouses. What is more adaptive and real-time operational environment requires multiple models to be frequently retrained which further hinders their use. Various data reduction techniques ranging from data sampling up to density retention models attempt to address this challenge by capturing a summarised data structure, yet they either do not account for labelled data or degrade the classification performance of the model trained on the condensed dataset. Our response is a proposition of a new general framework for reducing the complexity of labelled data by means of controlled spatial redistribution of class densities in the input space. On the example of Parzen Labelled Data Compressor (PLDC) we demonstrate a simulatory data condensation process directly inspired by the electrostatic field interaction where the data are moved and merged following the attracting and repelling interactions with the other labelled data. The process is controlled by the class density function built on the original data that acts as a class-sensitive potential field ensuring preservation of the original class density distributions, yet allowing data to rearrange and merge joining together their soft class partitions. As a result we achieved a model that reduces the labelled datasets much further than any competitive approaches yet with the maximum retention of the original class densities and hence the classification performance. PLDC leaves the reduced dataset with the soft accumulative class weights allowing for efficient online updates and as shown in a series of experiments if coupled with Parzen Density Classifier (PDC) significantly outperforms competitive data condensation methods in terms of classification performance at the

  17. Smart POI: Open and linked spatial data

    Science.gov (United States)

    Cerba, Otakar; Berzins, Raitis; Charvat, Karel; Mildorf, Tomas

    2016-04-01

    The Smart Point of Interest (SPOI) represents an unique seamless spatial data set based on standards recommended for Linked and open data, which are supported by scientist and researchers as well as by several government authorities and European Union. This data set developed in cooperation of partners of SDI4Apps project contains almost 24 millions points of interest focused mainly on tourism, natural features, transport or citizen services. The SPOI data covers almost all countries and territories over the world. It is created as a harmonized combination of global data resources (selected points from OpenStreetMap, Natural Earth and GeoNames.org) and several local data sets (for example data published by the Citadel on the Move project, data from Posumavi region in the Czech Republic or experimental ontologies developed in the University of West Bohemia including ski regions in Europe or historical sights in Rome). The added value of the SDI4Apps approach in comparison to other similar solutions consists in implementation of linked data approach (several objects are connected to DBpedia or GeoNames.org), using of universal RDF format, using of standardized and respected properties or vocabularies (for example FOAF or GeoSPARQL) and development of the completely harmonized data set with uniform data model and common classification (not only a copy of original resources). The SPOI data is published as SPARQL endpoint as well as in the map client. The SPOI dataset is a specific set of POIs which could be "a data fuel" for applications and services related to tourism, local business, statistics or landscape monitoring. It can be used also as a background data layer for thematic maps.

  18. Reducing Spatial Data Complexity for Classification Models

    Science.gov (United States)

    Ruta, Dymitr; Gabrys, Bogdan

    2007-11-01

    Intelligent data analytics gradually becomes a day-to-day reality of today's businesses. However, despite rapidly increasing storage and computational power current state-of-the-art predictive models still can not handle massive and noisy corporate data warehouses. What is more adaptive and real-time operational environment requires multiple models to be frequently retrained which further hinders their use. Various data reduction techniques ranging from data sampling up to density retention models attempt to address this challenge by capturing a summarised data structure, yet they either do not account for labelled data or degrade the classification performance of the model trained on the condensed dataset. Our response is a proposition of a new general framework for reducing the complexity of labelled data by means of controlled spatial redistribution of class densities in the input space. On the example of Parzen Labelled Data Compressor (PLDC) we demonstrate a simulatory data condensation process directly inspired by the electrostatic field interaction where the data are moved and merged following the attracting and repelling interactions with the other labelled data. The process is controlled by the class density function built on the original data that acts as a class-sensitive potential field ensuring preservation of the original class density distributions, yet allowing data to rearrange and merge joining together their soft class partitions. As a result we achieved a model that reduces the labelled datasets much further than any competitive approaches yet with the maximum retention of the original class densities and hence the classification performance. PLDC leaves the reduced dataset with the soft accumulative class weights allowing for efficient online updates and as shown in a series of experiments if coupled with Parzen Density Classifier (PDC) significantly outperforms competitive data condensation methods in terms of classification performance at the

  19. Prospective surveillance of multivariate spatial disease data

    Science.gov (United States)

    Corberán-Vallet, A

    2012-01-01

    Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this article, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application to diseases of the respiratory system in South Carolina is finally presented. PMID:22534429

  20. Database modeling to integrate macrobenthos data in Spatial Data Infrastructure

    Directory of Open Access Journals (Sweden)

    José Alberto Quintanilha

    2012-08-01

    Full Text Available Coastal zones are complex areas that include marine and terrestrial environments. Besides its huge environmental wealth, they also attracts humans because provides food, recreation, business, and transportation, among others. Some difficulties to manage these areas are related with their complexity, diversity of interests and the absence of standardization to collect and share data to scientific community, public agencies, among others. The idea to organize, standardize and share this information based on Web Atlas is essential to support planning and decision making issues. The construction of a spatial database integrating the environmental business, to be used on Spatial Data Infrastructure (SDI is illustrated by a bioindicator that indicates the quality of the sediments. The models show the phases required to build Macrobenthos spatial database based on Santos Metropolitan Region as a reference. It is concluded that, when working with environmental data the structuring of knowledge in a conceptual model is essential for their subsequent integration into the SDI. During the modeling process it can be noticed that methodological issues related to the collection process may obstruct or prejudice the integration of data from different studies of the same area. The development of a database model, as presented in this study, can be used as a reference for further research with similar goals.

  1. Big Data analytics in the Geo-Spatial Domain

    NARCIS (Netherlands)

    R.A. Goncalves (Romulo); M.G. Ivanova (Milena); M.L. Kersten (Martin); H. Scholten; S. Zlatanova; F. Alvanaki (Foteini); P. Nourian (Pirouz); E. Dias

    2014-01-01

    htmlabstractBig data collections in many scientific domains have inherently rich spatial and geo-spatial features. Spatial location is among the core aspects of data in Earth observation sciences, astronomy, and seismology to name a few. The goal of our project is to design an efficient data

  2. Assessing the Development of Kenya National Spatial Data ...

    African Journals Online (AJOL)

    Spatial data plays a vital role in developmental activities, whether natural resource management or socio-economic development. Spatial Data Infrastructures (SDIs) facilitate access, sharing and dissemination of spatial data necessary for complex decision-making processes of the future. Thus, conducting SDI assessment ...

  3. Assessing the development of Kenya National Spatial Data Infrastructure (KNSDI)

    NARCIS (Netherlands)

    Okuku, J.; Bregt, A.K.; Grus, L.

    2014-01-01

    Spatial data plays a vital role in developmental activities, whether natural resource management or socio-economic development. Spatial Data Infrastructures (SDIs) facilitate access, sharing and dissemination of spatial data necessary for complex decision-making processes of the future. Thus,

  4. Mature e-Government based on spatial data

    DEFF Research Database (Denmark)

    Hvingel, Line; Baaner, Lasse; Schrøder, Lise

    2014-01-01

    The relation of spatial data and e-Government is important, but not always acknowledged in the development and implementation of e-Government. The implementation of the INSPIRE directive pushed this agenda towards a growing awareness of the role of spatial data and the need for a spatial data...... infrastructure to support e-Government. With technology, policies, data and infrastructure in place, new iterations of this relationship are needed, in order to reach a higher level of maturity. This paper analyses and discusses the need for the differentiated roles of spatial data as an important step towards...... of these data is the wording of the law and the spatial data are just visualisation thereof. Under other circumstances, the spatial data themselves represent the legal status. Compliance between spatial data and the legal administrative framework is necessary, to obtain a mature e-Government. A preliminary test...

  5. Review of Spatial Indexing Techniques for Large Urban Data Management

    DEFF Research Database (Denmark)

    Azri, Suhaibah; Ujang, Uznir; Anton, François

    Pressure on land development in urban areas causes progressive efforts in spatial planning and management. The physical expansion of urban areas to accommodate rural migration implies a massive impact to social, economical and political situations of major cities. Most of the models used...... in managing urban areas are moving towards sustainable urban development in order to fulfill current necessities while preserving the resources for future generations. However, in order to manage large amounts of urban spatial data, an efficient spatial data constellation method is needed. With the ease...... of three dimensional (3D) spatial data usage in urban areas as a new source of data input, practical spatial data indexing is necessary to improve data retrieval and management. Current two dimensional (2D) spatial indexing approaches seem not applicable to the current and future spatial developments...

  6. Applications of Spatial Data Using Business Analytics Tools

    Directory of Open Access Journals (Sweden)

    Anca Ioana ANDREESCU

    2011-12-01

    Full Text Available This paper addresses the possibilities of using spatial data in business analytics tools, with emphasis on SAS software. Various kinds of map data sets containing spatial data are presented and discussed. Examples of map charts illustrating macroeconomic parameters demonstrate the application of spatial data for the creation of map charts in SAS Enterprise Guise. Extended features of map charts are being exemplified by producing charts via SAS programming procedures.

  7. Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor

    OpenAIRE

    Liu Xin; Zhang Shaoliang; Zheng Pulin

    2015-01-01

    Spatial local outlier factor (SLOF) algorithm was adopted in this study for spatial outlier detection because of the limitations of the traditional static threshold detection. Based on the spatial characteristics of CO2 monitoring data obtained in the carbon capture and storage (CCS) project, the K-Nearest Neighbour (KNN) graph was constructed using the latitude and longitude information of the monitoring points to identify the spatial neighbourhood of the monitoring points. Then ...

  8. National Spatial Data Clearinghouses: Worldwide development and impact

    NARCIS (Netherlands)

    Crompvoets, J.W.H.C.

    2006-01-01

    Over the last few years many countries have spent considerable resources on developing their own National Spatial Data Infrastructure (NSDI) in order to manage and utilise spatial data assets more efficiently, reduce the cost of data production, and eliminate duplication of data acquisition. One of

  9. Spatial Econometric data analysis: moving beyond traditional models

    NARCIS (Netherlands)

    Florax, R.J.G.M.; Vlist, van der A.J.

    2003-01-01

    This article appraises recent advances in the spatial econometric literature. It serves as the introduction too collection of new papers on spatial econometric data analysis brought together in this special issue, dealing specifically with new extensions to the spatial econometric modeling

  10. Integrating the statistical analysis of spatial data in ecology

    Science.gov (United States)

    A. M. Liebhold; J. Gurevitch

    2002-01-01

    In many areas of ecology there is an increasing emphasis on spatial relationships. Often ecologists are interested in new ways of analyzing data with the objective of quantifying spatial patterns, and in designing surveys and experiments in light of the recognition that there may be underlying spatial pattern in biotic responses. In doing so, ecologists have adopted a...

  11. Adding temporal data enhancements to the advanced spatial data infrastructure platform

    CSIR Research Space (South Africa)

    Sibolla, B

    2014-10-01

    Full Text Available Users of Spatial Data Infrastructure (SDI) increasingly require provision to data holdings beyond the traditional static raster, map or vector based data sets within their organisations. The modern GIS practitioner and Spatial Data Scientist...

  12. Panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable

    NARCIS (Netherlands)

    Elhorst, J. Paul

    2001-01-01

    This paper surveys panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable. In particular, it focuses on the specification and estimation of four panel data models commonly used in applied research: the fixed effects model, the random effects model, the

  13. Social networks in spatial data infrastructures

    NARCIS (Netherlands)

    Oort, van P.A.J.; Hazeu, G.W.; Kramer, H.; Bregt, A.K.; Rip, F.I.

    2010-01-01

    Data sharing over the internet is increasingly efficient and increasingly anonymous. This growing anonymity makes it difficult for data users to exchange feedback: interesting ideas, innovations, ancillary data, concerns about data quality, etcetera. For data producers not knowing the end-users

  14. Spatial data content standards for Africa

    CSIR Research Space (South Africa)

    Cooper, Antony K

    2005-11-01

    Full Text Available , they selected 14 standards containing data dictionaries or feature catalogues, and compared their feature types. They have also provided some advice and recommendations on data content standards (particularly for data dictionaries and feature catalogues...

  15. Searching for spatial data resources by fitness for use

    NARCIS (Netherlands)

    Ivanova, I.; Morales, J.M.; de By, R.A.; Beshe, T.S.; Gebresilassie, M.A.

    2013-01-01

    During the search for spatial data resources, users, both experts and non-experts in the geoinformation field, are expected to know what type of spatial data resource they need, and in which clearinghouse or geoportal to search. In the case of success, they are still left with the decision on

  16. The object of mobile spatial data, the subject in mobile spatial research

    Directory of Open Access Journals (Sweden)

    Jim Thatcher

    2016-09-01

    Full Text Available With an estimated one billion smartphones producing over 5 petabytes of data a day, the spatially aware mobile device has become a near ubiquitous presence in daily life. Cogent, excellent research in a variety of fields has explored what the spatial data these devices produce can reveal of society, such as analysis of Foursquare check-ins to reveal patterns of mobility for groups through a city. In such studies, the individual intentions, motivations, and desires behind the production of said data can become lost through computational aggregation and analysis. In this commentary, I argue for a rethinking of the epistemological leap from individual to data point through a (reseating of the reflexive, self-eliciting subject as an object for spatial big data research. To do so, I first situate current research on spatial big data within a computational turn in social sciences that relies overly on the data produced as a stand-in for the subject producing said data. Second, I argue that a recent shift within geography and cognate disciplines toward viewing spatial big data as a form of spatial media allows for study of the sociotechnical processes that produce modern assemblages of data and society. As spatial media, the spatial big data created through mobile device use can be understood as the data of everyday life and as part of the sociotechnical processes that produce individuals, data, and space. Ultimately, to understand the data of everyday life, researchers must write thick descriptions of the stories we tell ourselves about the data we give off to others.

  17. Spatial reconstruction of single-cell gene expression data.

    Science.gov (United States)

    Satija, Rahul; Farrell, Jeffrey A; Gennert, David; Schier, Alexander F; Regev, Aviv

    2015-05-01

    Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.

  18. Adaptive proxy map server for efficient vector spatial data rendering

    Science.gov (United States)

    Sayar, Ahmet

    2013-01-01

    The rapid transmission of vector map data over the Internet is becoming a bottleneck of spatial data delivery and visualization in web-based environment because of increasing data amount and limited network bandwidth. In order to improve both the transmission and rendering performances of vector spatial data over the Internet, we propose a proxy map server enabling parallel vector data fetching as well as caching to improve the performance of web-based map servers in a dynamic environment. Proxy map server is placed seamlessly anywhere between the client and the final services, intercepting users' requests. It employs an efficient parallelization technique based on spatial proximity and data density in case distributed replica exists for the same spatial data. The effectiveness of the proposed technique is proved at the end of the article by the application of creating map images enriched with earthquake seismic data records.

  19. Scene Classification Using High Spatial Resolution Multispectral Data

    National Research Council Canada - National Science Library

    Garner, Jamada

    2002-01-01

    ...), High-spatial resolution (8-meter), 4-color MSI data from IKONOS provide a new tool for scene classification, The utility of these data are studied for the purpose of classifying the Elkhorn Slough and surrounding wetlands in central...

  20. Approximate inference for spatial functional data on massively parallel processors

    DEFF Research Database (Denmark)

    Raket, Lars Lau; Markussen, Bo

    2014-01-01

    With continually increasing data sizes, the relevance of the big n problem of classical likelihood approaches is greater than ever. The functional mixed-effects model is a well established class of models for analyzing functional data. Spatial functional data in a mixed-effects setting...... in linear time. An extremely efficient GPU implementation is presented, and the proposed methods are illustrated by conducting a classical statistical analysis of 2D chromatography data consisting of more than 140 million spatially correlated observation points....

  1. Modeling Spatial Data within Object Relational-Databases

    Directory of Open Access Journals (Sweden)

    Iuliana BOTHA

    2011-03-01

    Full Text Available Spatial data can refer to elements that help place a certain object in a certain area. These elements are latitude, longitude, points, geometric figures represented by points, etc. However, when translating these elements into data that can be stored in a computer, it all comes down to numbers. The interesting part that requires attention is how to memorize them in order to obtain fast and various spatial queries. This part is where the DBMS (Data Base Management System that contains the database acts in. In this paper, we analyzed and compared two object-relational DBMS that work with spatial data: Oracle and PostgreSQL.

  2. Protecting location privacy for outsourced spatial data in cloud storage.

    Science.gov (United States)

    Tian, Feng; Gui, Xiaolin; An, Jian; Yang, Pan; Zhao, Jianqiang; Zhang, Xuejun

    2014-01-01

    As cloud computing services and location-aware devices are fully developed, a large amount of spatial data needs to be outsourced to the cloud storage provider, so the research on privacy protection for outsourced spatial data gets increasing attention from academia and industry. As a kind of spatial transformation method, Hilbert curve is widely used to protect the location privacy for spatial data. But sufficient security analysis for standard Hilbert curve (SHC) is seldom proceeded. In this paper, we propose an index modification method for SHC (SHC(∗)) and a density-based space filling curve (DSC) to improve the security of SHC; they can partially violate the distance-preserving property of SHC, so as to achieve better security. We formally define the indistinguishability and attack model for measuring the privacy disclosure risk of spatial transformation methods. The evaluation results indicate that SHC(∗) and DSC are more secure than SHC, and DSC achieves the best index generation performance.

  3. Spatial correlation length of normalized cone data in sand

    DEFF Research Database (Denmark)

    Firouzianbandpey, Sarah; Griffiths, D. V.; Ibsen, Lars Bo

    2014-01-01

    The main topic of this study is to assess the anisotropic spatial correlation lengths of a sand layer deposit based on cone penetration testing with pore pressure measurement (CPTu) data. Spatial correlation length can be an important factor in reliability analysis of geotechnical systems, yet it...

  4. Visualization techniques for spatial probability density function data

    Directory of Open Access Journals (Sweden)

    Udeepta D Bordoloi

    2006-01-01

    Full Text Available Novel visualization methods are presented for spatial probability density function data. These are spatial datasets, where each pixel is a random variable, and has multiple samples which are the results of experiments on that random variable. We use clustering as a means to reduce the information contained in these datasets; and present two different ways of interpreting and clustering the data. The clustering methods are used on two datasets, and the results are discussed with the help of visualization techniques designed for the spatial probability data.

  5. Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data

    Directory of Open Access Journals (Sweden)

    Daniel A. Griffith

    2016-06-01

    Full Text Available Virtually all remotely sensed data contain spatial autocorrelation, which impacts upon their statistical features of uncertainty through variance inflation, and the compounding of duplicate information. Estimating the nature and degree of this spatial autocorrelation, which is usually positive and very strong, has been hindered by computational intensity associated with the massive number of pixels in realistically-sized remotely-sensed images, a situation that more recently has changed. Recent advances in spatial statistical estimation theory support the extraction of information and the distilling of knowledge from remotely-sensed images in a way that accounts for latent spatial autocorrelation. This paper summarizes an effective methodological approach to achieve this end, illustrating results with a 2002 remotely sensed-image of the Florida Everglades, and simulation experiments. Specifically, uncertainty of spatial autocorrelation parameter in a spatial autoregressive model is modeled with a beta-beta mixture approach and is further investigated with three different sampling strategies: coterminous sampling, random sub-region sampling, and increasing domain sub-regions. The results suggest that uncertainty associated with remotely-sensed data should be cast in consideration of spatial autocorrelation. It emphasizes that one remaining challenge is to better quantify the spatial variability of spatial autocorrelation estimates across geographic landscapes.

  6. Optimizing ISOCAM data processing using spatial redundancy

    Science.gov (United States)

    Miville-Deschênes, M.-A.; Boulanger, F.; Abergel, A.; Bernard, J.-P.

    2000-11-01

    Several instrumental effects of the Long Wavelength channel of ISOCAM, the camera on board the Infrared Space Observatory, degrade the processed images. We present new data-processing techniques that correct these defects, taking advantage of the fact that a position in the sky has been observed by several pixels at different times. We use this redundant information (1) to correct the long-term variation of the detector response, (2) to correct memory effects after glitches and point sources, and (3) to refine the deglitching process. As an example we have applied our processing to the gamma-ray burst observation GRB 970402. Our new data-processing techniques allow the detection of faint extended emission with contrast smaller than 1% of the zodiacal background. The data reduction corrects instrumental effects to the point where the noise in the final map is dominated by the readout and the photon noises. All raster ISOCAM observations can benefit from the data processing described here. This includes mapping of solar system extended objects (comet dust trails), nearby clouds and star forming regions, images from diffuse emission in the Galactic plane and external galaxies. These techniques could also be applied to other raster type observations (e.g. ISOPHOT). Based on observations with ISO, an ESA project with instruments funded by ESA Member States (especially the PI countries: France, Germany, The Netherlands and the UK) and with the participation of ISAS and NASA.

  7. A Statistical Toolbox For Mining And Modeling Spatial Data

    Directory of Open Access Journals (Sweden)

    D’Aubigny Gérard

    2016-12-01

    Full Text Available Most data mining projects in spatial economics start with an evaluation of a set of attribute variables on a sample of spatial entities, looking for the existence and strength of spatial autocorrelation, based on the Moran’s and the Geary’s coefficients, the adequacy of which is rarely challenged, despite the fact that when reporting on their properties, many users seem likely to make mistakes and to foster confusion. My paper begins by a critical appraisal of the classical definition and rational of these indices. I argue that while intuitively founded, they are plagued by an inconsistency in their conception. Then, I propose a principled small change leading to corrected spatial autocorrelation coefficients, which strongly simplifies their relationship, and opens the way to an augmented toolbox of statistical methods of dimension reduction and data visualization, also useful for modeling purposes. A second section presents a formal framework, adapted from recent work in statistical learning, which gives theoretical support to our definition of corrected spatial autocorrelation coefficients. More specifically, the multivariate data mining methods presented here, are easily implementable on the existing (free software, yield methods useful to exploit the proposed corrections in spatial data analysis practice, and, from a mathematical point of view, whose asymptotic behavior, already studied in a series of papers by Belkin & Niyogi, suggests that they own qualities of robustness and a limited sensitivity to the Modifiable Areal Unit Problem (MAUP, valuable in exploratory spatial data analysis.

  8. MULTI-TEMPORAL SPATIAL DATA AFRICA

    Directory of Open Access Journals (Sweden)

    R. Becker

    2017-11-01

    Full Text Available To plan for the future, it is essential to understand the present as well as the past. Hindsight is the most valuable asset when examining how to proceed. Apart from natural catastrophes, conditions do not happen, they develop! This paper proposes that in order to change or influence conditions, not only must their present state be considered, but it is essential to investigate what triggered them in the first place. This requires access to records that might have to reach back for as much as many years. In Africa, such records are hard, if not impossible, to retrieve. Moreover, whatever records can be located are likely to have been compiled by a variety of different agencies, each presenting their own versions of events and consequently, often contradictory. To overcome such problems, it is proposed to use aerial photography that has been taken and hopefully archived over the past decades. Such aerial imagery can provide temporal geospatial data to trace the course, interaction and consequences of events. Combined with more recent satellite imagery, they provide unbiased evidence of past developments that can now be analyzed and assessed based on our awareness of today. This presentation deals with advanced data conversion and automated processing procedures conceived for convenient and user-friendly information access, as well as data mining and bigdata processing.

  9. Geostatistics and Analysis of Spatial Data

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    2007-01-01

    , specifically the spherical, the exponential and the Gaussian models. Equations to carry out simple og ordinary kriging are deduced. Other types of kriging are mentioned, and references to international literature, Internet addresses and state-of-the-art software in the field are given. A very simple example...... to illustrate the computations and a more realistic example with height data from an area near Slagelse, Denmark, are given. Finally, a series of attractive characteristics of kriging are mentioned, and a simple sampling strategic consideration is given based on the dependence of the kriging variance...

  10. Remote Sensing Technologies and Spatial Data Applications.

    Science.gov (United States)

    1987-12-01

    multispectral or thermal infrared techniques. Radar (active) or passive microwave data may strongly assist because of the "relatively" unique emissivity ...UW w 00 4w 0. C:’- " Oh C -4 cd 04 : w -0 *4 v o 0 4 H J r4 "I Uw 0 0 uJ 04) hI u u 93d b e 4 04) 00., 4).- - HHO " 0 𔃺 UO 014 -4 W 4)4 U -4 0d O P-4P

  11. Spatially explicit spectral analysis of point clouds and geospatial data

    Science.gov (United States)

    Buscombe, Daniel D.

    2015-01-01

    The increasing use of spatially explicit analyses of high-resolution spatially distributed data (imagery and point clouds) for the purposes of characterising spatial heterogeneity in geophysical phenomena necessitates the development of custom analytical and computational tools. In recent years, such analyses have become the basis of, for example, automated texture characterisation and segmentation, roughness and grain size calculation, and feature detection and classification, from a variety of data types. In this work, much use has been made of statistical descriptors of localised spatial variations in amplitude variance (roughness), however the horizontal scale (wavelength) and spacing of roughness elements is rarely considered. This is despite the fact that the ratio of characteristic vertical to horizontal scales is not constant and can yield important information about physical scaling relationships. Spectral analysis is a hitherto under-utilised but powerful means to acquire statistical information about relevant amplitude and wavelength scales, simultaneously and with computational efficiency. Further, quantifying spatially distributed data in the frequency domain lends itself to the development of stochastic models for probing the underlying mechanisms which govern the spatial distribution of geological and geophysical phenomena. The software packagePySESA (Python program for Spatially Explicit Spectral Analysis) has been developed for generic analyses of spatially distributed data in both the spatial and frequency domains. Developed predominantly in Python, it accesses libraries written in Cython and C++ for efficiency. It is open source and modular, therefore readily incorporated into, and combined with, other data analysis tools and frameworks with particular utility for supporting research in the fields of geomorphology, geophysics, hydrography, photogrammetry and remote sensing. The analytical and computational structure of the toolbox is

  12. Spatially explicit spectral analysis of point clouds and geospatial data

    Science.gov (United States)

    Buscombe, Daniel

    2016-01-01

    The increasing use of spatially explicit analyses of high-resolution spatially distributed data (imagery and point clouds) for the purposes of characterising spatial heterogeneity in geophysical phenomena necessitates the development of custom analytical and computational tools. In recent years, such analyses have become the basis of, for example, automated texture characterisation and segmentation, roughness and grain size calculation, and feature detection and classification, from a variety of data types. In this work, much use has been made of statistical descriptors of localised spatial variations in amplitude variance (roughness), however the horizontal scale (wavelength) and spacing of roughness elements is rarely considered. This is despite the fact that the ratio of characteristic vertical to horizontal scales is not constant and can yield important information about physical scaling relationships. Spectral analysis is a hitherto under-utilised but powerful means to acquire statistical information about relevant amplitude and wavelength scales, simultaneously and with computational efficiency. Further, quantifying spatially distributed data in the frequency domain lends itself to the development of stochastic models for probing the underlying mechanisms which govern the spatial distribution of geological and geophysical phenomena. The software package PySESA (Python program for Spatially Explicit Spectral Analysis) has been developed for generic analyses of spatially distributed data in both the spatial and frequency domains. Developed predominantly in Python, it accesses libraries written in Cython and C++ for efficiency. It is open source and modular, therefore readily incorporated into, and combined with, other data analysis tools and frameworks with particular utility for supporting research in the fields of geomorphology, geophysics, hydrography, photogrammetry and remote sensing. The analytical and computational structure of the toolbox is described

  13. A composite likelihood approach for spatially correlated survival data

    Science.gov (United States)

    Paik, Jane; Ying, Zhiliang

    2013-01-01

    The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory. PMID:24223450

  14. A composite likelihood approach for spatially correlated survival data.

    Science.gov (United States)

    Paik, Jane; Ying, Zhiliang

    2013-01-01

    The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory.

  15. Assessing the Development of Kenya National Spatial Data

    African Journals Online (AJOL)

    okuku

    Keywords: Spatial data infrastructure, Kenya NSDI, development, .... calculated based on the value of the 16 indicators of SDI readiness (Table 1). .... instance, majority of the staff at Survey of Kenya; the National Mapping Agency are GIS and.

  16. A nonparametric spatial scan statistic for continuous data.

    Science.gov (United States)

    Jung, Inkyung; Cho, Ho Jin

    2015-10-20

    Spatial scan statistics are widely used for spatial cluster detection, and several parametric models exist. For continuous data, a normal-based scan statistic can be used. However, the performance of the model has not been fully evaluated for non-normal data. We propose a nonparametric spatial scan statistic based on the Wilcoxon rank-sum test statistic and compared the performance of the method with parametric models via a simulation study under various scenarios. The nonparametric method outperforms the normal-based scan statistic in terms of power and accuracy in almost all cases under consideration in the simulation study. The proposed nonparametric spatial scan statistic is therefore an excellent alternative to the normal model for continuous data and is especially useful for data following skewed or heavy-tailed distributions.

  17. Characterizing spatial uncertainty when integrating social data in conservation planning.

    Science.gov (United States)

    Lechner, A M; Raymond, C M; Adams, V M; Polyakov, M; Gordon, A; Rhodes, J R; Mills, M; Stein, A; Ives, C D; Lefroy, E C

    2014-12-01

    Recent conservation planning studies have presented approaches for integrating spatially referenced social (SRS) data with a view to improving the feasibility of conservation action. We reviewed the growing conservation literature on SRS data, focusing on elicited or stated preferences derived through social survey methods such as choice experiments and public participation geographic information systems. Elicited SRS data includes the spatial distribution of willingness to sell, willingness to pay, willingness to act, and assessments of social and cultural values. We developed a typology for assessing elicited SRS data uncertainty which describes how social survey uncertainty propagates when projected spatially and the importance of accounting for spatial uncertainty such as scale effects and data quality. These uncertainties will propagate when elicited SRS data is integrated with biophysical data for conservation planning and may have important consequences for assessing the feasibility of conservation actions. To explore this issue further, we conducted a systematic review of the elicited SRS data literature. We found that social survey uncertainty was commonly tested for, but that these uncertainties were ignored when projected spatially. Based on these results we developed a framework which will help researchers and practitioners estimate social survey uncertainty and use these quantitative estimates to systematically address uncertainty within an analysis. This is important when using SRS data in conservation applications because decisions need to be made irrespective of data quality and well characterized uncertainty can be incorporated into decision theoretic approaches. © 2014 Society for Conservation Biology.

  18. Unleashing spatially distributed ecohydrology modeling using Big Data tools

    Science.gov (United States)

    Miles, B.; Idaszak, R.

    2015-12-01

    Physically based spatially distributed ecohydrology models are useful for answering science and management questions related to the hydrology and biogeochemistry of prairie, savanna, forested, as well as urbanized ecosystems. However, these models can produce hundreds of gigabytes of spatial output for a single model run over decadal time scales when run at regional spatial scales and moderate spatial resolutions (~100-km2+ at 30-m spatial resolution) or when run for small watersheds at high spatial resolutions (~1-km2 at 3-m spatial resolution). Numerical data formats such as HDF5 can store arbitrarily large datasets. However even in HPC environments, there are practical limits on the size of single files that can be stored and reliably backed up. Even when such large datasets can be stored, querying and analyzing these data can suffer from poor performance due to memory limitations and I/O bottlenecks, for example on single workstations where memory and bandwidth are limited, or in HPC environments where data are stored separately from computational nodes. The difficulty of storing and analyzing spatial data from ecohydrology models limits our ability to harness these powerful tools. Big Data tools such as distributed databases have the potential to surmount the data storage and analysis challenges inherent to large spatial datasets. Distributed databases solve these problems by storing data close to computational nodes while enabling horizontal scalability and fault tolerance. Here we present the architecture of and preliminary results from PatchDB, a distributed datastore for managing spatial output from the Regional Hydro-Ecological Simulation System (RHESSys). The initial version of PatchDB uses message queueing to asynchronously write RHESSys model output to an Apache Cassandra cluster. Once stored in the cluster, these data can be efficiently queried to quickly produce both spatial visualizations for a particular variable (e.g. maps and animations), as well

  19. Awareness as a foundation for developing effective spatial data infrastructures

    DEFF Research Database (Denmark)

    Clausen, Christian Bech; Rajabifard, Abbas; Enemark, Stig

    2006-01-01

    data. But what makes collaboration effective and successful? For example people often resist sharing data across organizational boundaries due to loss of control, power and independency. In the spatial community, the term awareness is often used when discussing issues concerned with inter-organizational...... addresses the problems spatial organizations currently encounter. As a result, the focus of this paper is on the nature and role of awareness. It explores why and how awareness plays a fundamental role in overcoming organizational constraints and in developing collaboration between organizations. The paper...... discusses the concept of awareness in the area of organizational collaboration in the spatial community, explains the important role awareness plays in the development of spatial data infrastructures, and introduces a methodology to promote awareness. Furthermore, the paper aims to make people...

  20. Spatial data infrastructures at work analysing the spatial enablement of public sector processes

    CERN Document Server

    Dessers, Ezra

    2013-01-01

    In 'Spatial Data Infrastructures at Work', Ezra Dessers introduces spatial enablement as a key concept to describe the realisation of SDI objectives in the context of individual public sector processes. Drawing on four years of research, Dessers argues that it has become essential, even unavoidable, to manage and (re)design inter-organisational process chains in order to further advance the role of SDIs as an enabling platform for a spatially enabled society. Detailed case studies illustrate that the process he describes is the setting in which one can see the SDI at work.

  1. GIS-Assisted Spatial Data Management for Corps of Engineers Real Estate Activities: Spatial Data Conversion Options

    National Research Council Canada - National Science Library

    Dove, Linda

    2002-01-01

    ...; and scanning hardcopy maps and then using heads up digitizing or automated vectorization. These methods are discussed in this report. Four Corps District Offices have provided examples of their spatial data conversion methods.

  2. DEVELOPING A SPATIAL DATA INFRASTRUCTURE FOR THE HANFORD SITE

    Energy Technology Data Exchange (ETDEWEB)

    RUSH SF

    2009-11-06

    Summary of this report is: (1) aggressive implementation of metadata; (2) higher confidence in spatial data and organizational structure; (3) improved data sharing between Hanford and neighboring government agencies; and (4) improved data sharing and management reduce unnecessary cost to DOE and the American taxpayer.

  3. Spatial data analysis for exploration of regional scale geothermal resources

    Science.gov (United States)

    Moghaddam, Majid Kiavarz; Noorollahi, Younes; Samadzadegan, Farhad; Sharifi, Mohammad Ali; Itoi, Ryuichi

    2013-10-01

    Defining a comprehensive conceptual model of the resources sought is one of the most important steps in geothermal potential mapping. In this study, Fry analysis as a spatial distribution method and 5% well existence, distance distribution, weights of evidence (WofE), and evidential belief function (EBFs) methods as spatial association methods were applied comparatively to known geothermal occurrences, and to publicly-available regional-scale geoscience data in Akita and Iwate provinces within the Tohoku volcanic arc, in northern Japan. Fry analysis and rose diagrams revealed similar directional patterns of geothermal wells and volcanoes, NNW-, NNE-, NE-trending faults, hotsprings and fumaroles. Among the spatial association methods, WofE defined a conceptual model correspondent with the real world situations, approved with the aid of expert opinion. The results of the spatial association analyses quantitatively indicated that the known geothermal occurrences are strongly spatially-associated with geological features such as volcanoes, craters, NNW-, NNE-, NE-direction faults and geochemical features such as hotsprings, hydrothermal alteration zones and fumaroles. Geophysical data contains temperature gradients over 100 °C/km and heat flow over 100 mW/m2. In general, geochemical and geophysical data were better evidence layers than geological data for exploring geothermal resources. The spatial analyses of the case study area suggested that quantitative knowledge from hydrothermal geothermal resources was significantly useful for further exploration and for geothermal potential mapping in the case study region. The results can also be extended to the regions with nearly similar characteristics.

  4. Police Spatial Big Data Location Code and Its Application Prospect

    Directory of Open Access Journals (Sweden)

    HU Xiaoguang

    2016-12-01

    Full Text Available The rich decision-making basis are provided for police work by police spatial big data. But some challenges are also brought by it, such as:large data integration complex, multi scale information related difficulties, the location identification is not unique. Thus, how to make the data better service to the police work reform and development is a problem need to be study. In this paper, we propose location identification method to solve the existing problems. Based on subdivision grid, we design the location encoding method of police spatial big data, and choose domicile location identification as a case. Finally, the prospect of its application is presented. So, a new idea is proposed to solve the problem existing in the police spatial data organization and application.

  5. A Versatile and Efficient GPU Data Structure for Spatial Indexing

    KAUST Repository

    Schneider, Jens

    2016-08-10

    In this paper we present a novel GPU-based data structure for spatial indexing. Based on Fenwick trees—a special type of binary indexed trees—our data structure allows construction in linear time. Updates and prefixes can be computed in logarithmic time, whereas point queries require only constant time on average. Unlike competing data structures such as summed-area tables and spatial hashing, our data structure requires a constant amount of bits for each data element, and it offers unconstrained point queries. This property makes our data structure ideally suited for applications requiring unconstrained indexing of large data, such as block-storage of large and block-sparse volumes. Finally, we provide asymptotic bounds on both run-time and memory requirements, and we show applications for which our new data structure is useful.

  6. A Versatile and Efficient GPU Data Structure for Spatial Indexing

    KAUST Repository

    Schneider, Jens; Rautek, Peter

    2016-01-01

    In this paper we present a novel GPU-based data structure for spatial indexing. Based on Fenwick trees—a special type of binary indexed trees—our data structure allows construction in linear time. Updates and prefixes can be computed in logarithmic time, whereas point queries require only constant time on average. Unlike competing data structures such as summed-area tables and spatial hashing, our data structure requires a constant amount of bits for each data element, and it offers unconstrained point queries. This property makes our data structure ideally suited for applications requiring unconstrained indexing of large data, such as block-storage of large and block-sparse volumes. Finally, we provide asymptotic bounds on both run-time and memory requirements, and we show applications for which our new data structure is useful.

  7. A spatial scan statistic for compound Poisson data.

    Science.gov (United States)

    Rosychuk, Rhonda J; Chang, Hsing-Ming

    2013-12-20

    The topic of spatial cluster detection gained attention in statistics during the late 1980s and early 1990s. Effort has been devoted to the development of methods for detecting spatial clustering of cases and events in the biological sciences, astronomy and epidemiology. More recently, research has examined detecting clusters of correlated count data associated with health conditions of individuals. Such a method allows researchers to examine spatial relationships of disease-related events rather than just incident or prevalent cases. We introduce a spatial scan test that identifies clusters of events in a study region. Because an individual case may have multiple (repeated) events, we base the test on a compound Poisson model. We illustrate our method for cluster detection on emergency department visits, where individuals may make multiple disease-related visits. Copyright © 2013 John Wiley & Sons, Ltd.

  8. Bayesian spatial transformation models with applications in neuroimaging data.

    Science.gov (United States)

    Miranda, Michelle F; Zhu, Hongtu; Ibrahim, Joseph G

    2013-12-01

    The aim of this article is to develop a class of spatial transformation models (STM) to spatially model the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) and a set of covariates. The proposed STM include a varying Box-Cox transformation model for dealing with the issue of non-Gaussian distributed imaging data and a Gaussian Markov random field model for incorporating spatial smoothness of the imaging data. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations and real data analysis demonstrate that the STM significantly outperforms the voxel-wise linear model with Gaussian noise in recovering meaningful geometric patterns. Our STM is able to reveal important brain regions with morphological changes in children with attention deficit hyperactivity disorder. © 2013, The International Biometric Society.

  9. 5th Regional Study on Cadastre and Spatial Data Infrastructure

    Directory of Open Access Journals (Sweden)

    Ivica Skender

    2012-12-01

    Full Text Available In the aftermath of the 5th Regional Conference on Cadastre and Spatial Data Infrastructure (Banja Luka and Laktaši, Bosnia and Herzegovina, June 6–8, 2012, the Republic Authority for Geodetic and Property Affairs of the Republic of Srpska and the Federal Administration for Geodetic and Real Property Affairs published the 5th Regional Study on Cadastre and Spatial Data Infrastructure. The study was produced in the frame of the Project INSPIRATION – Spatial Data Infrastructure in the Western Balkans, which is being realized for the benefit and with cooperation of representatives of eight geodetic administrations in the region (Albania, Bosnia and Herzegovina, Montenegro, Croatia, Kosovo, Macedonia, Serbia by consortium led by German company GFA of Hamburg, in cooperation with GDi GISDATA of Zagreb, experts from the Austrian Environmental Agency and German company con terra GmbH and financed from the European Union IPA funding programme for 2010.

  10. Generalized index for spatial data sets as a measure of complete spatial randomness

    Science.gov (United States)

    Hackett-Jones, Emily J.; Davies, Kale J.; Binder, Benjamin J.; Landman, Kerry A.

    2012-06-01

    Spatial data sets, generated from a wide range of physical systems can be analyzed by counting the number of objects in a set of bins. Previous work has been limited to equal-sized bins, which are inappropriate for some domains (e.g., circular). We consider a nonequal size bin configuration whereby overlapping or nonoverlapping bins cover the domain. A generalized index, defined in terms of a variance between bin counts, is developed to indicate whether or not a spatial data set, generated from exclusion or nonexclusion processes, is at the complete spatial randomness (CSR) state. Limiting values of the index are determined. Using examples, we investigate trends in the generalized index as a function of density and compare the results with those using equal size bins. The smallest bin size must be much larger than the mean size of the objects. We can determine whether a spatial data set is at the CSR state or not by comparing the values of a generalized index for different bin configurations—the values will be approximately the same if the data is at the CSR state, while the values will differ if the data set is not at the CSR state. In general, the generalized index is lower than the limiting value of the index, since objects do not have access to the entire region due to blocking by other objects. These methods are applied to two applications: (i) spatial data sets generated from a cellular automata model of cell aggregation in the enteric nervous system and (ii) a known plant data distribution.

  11. Multivariate Receptor Models for Spatially Correlated Multipollutant Data

    KAUST Repository

    Jun, Mikyoung

    2013-08-01

    The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air pollutant data measured at a single monitoring site or measurements of a single pollutant collected at multiple monitoring sites. Despite the growing availability of multipollutant data collected from multiple monitoring sites, there has not yet been any attempt to incorporate spatial dependence that may exist in such data into multivariate receptor modeling. We propose a spatial statistics extension of multivariate receptor models that enables us to incorporate spatial dependence into estimation of source composition profiles and contributions given the prespecified number of sources and the model identification conditions. The proposed method yields more precise estimates of source profiles by accounting for spatial dependence in the estimation. More importantly, it enables predictions of source contributions at unmonitored sites as well as when there are missing values at monitoring sites. The method is illustrated with simulated data and real multipollutant data collected from eight monitoring sites in Harris County, Texas. Supplementary materials for this article, including data and R code for implementing the methods, are available online on the journal web site. © 2013 Copyright Taylor and Francis Group, LLC.

  12. Evaluation of Deep Learning Representations of Spatial Storm Data

    Science.gov (United States)

    Gagne, D. J., II; Haupt, S. E.; Nychka, D. W.

    2017-12-01

    The spatial structure of a severe thunderstorm and its surrounding environment provide useful information about the potential for severe weather hazards, including tornadoes, hail, and high winds. Statistics computed over the area of a storm or from the pre-storm environment can provide descriptive information but fail to capture structural information. Because the storm environment is a complex, high-dimensional space, identifying methods to encode important spatial storm information in a low-dimensional form should aid analysis and prediction of storms by statistical and machine learning models. Principal component analysis (PCA), a more traditional approach, transforms high-dimensional data into a set of linearly uncorrelated, orthogonal components ordered by the amount of variance explained by each component. The burgeoning field of deep learning offers two potential approaches to this problem. Convolutional Neural Networks are a supervised learning method for transforming spatial data into a hierarchical set of feature maps that correspond with relevant combinations of spatial structures in the data. Generative Adversarial Networks (GANs) are an unsupervised deep learning model that uses two neural networks trained against each other to produce encoded representations of spatial data. These different spatial encoding methods were evaluated on the prediction of severe hail for a large set of storm patches extracted from the NCAR convection-allowing ensemble. Each storm patch contains information about storm structure and the near-storm environment. Logistic regression and random forest models were trained using the PCA and GAN encodings of the storm data and were compared against the predictions from a convolutional neural network. All methods showed skill over climatology at predicting the probability of severe hail. However, the verification scores among the methods were very similar and the predictions were highly correlated. Further evaluations are being

  13. U.S. Geological Survey spatial data access

    Science.gov (United States)

    Faundeen, John L.; Kanengieter, Ronald L.; Buswell, Michael D.

    2002-01-01

    The U.S. Geological Survey (USGS) has done a progress review on improving access to its spatial data holdings over the Web. The USGS EROS Data Center has created three major Web-based interfaces to deliver spatial data to the general public; they are Earth Explorer, the Seamless Data Distribution System (SDDS), and the USGS Web Mapping Portal. Lessons were learned in developing these systems, and various resources were needed for their implementation. The USGS serves as a fact-finding agency in the U.S. Government that collects, monitors, analyzes, and provides scientific information about natural resource conditions and issues. To carry out its mission, the USGS has created and managed spatial data since its inception. Originally relying on paper maps, the USGS now uses advanced technology to produce digital representations of the Earth’s features. The spatial products of the USGS include both source and derivative data. Derivative datasets include Digital Orthophoto Quadrangles (DOQ), Digital Elevation Models, Digital Line Graphs, land-cover Digital Raster Graphics, and the seamless National Elevation Dataset. These products, created with automated processes, use aerial photographs, satellite images, or other cartographic information such as scanned paper maps as source data. With Earth Explorer, users can search multiple inventories through metadata queries and can browse satellite and DOQ imagery. They can place orders and make payment through secure credit card transactions. Some USGS spatial data can be accessed with SDDS. The SDDS uses an ArcIMS map service interface to identify the user’s areas of interest and determine the output format; it allows the user to either download the actual spatial data directly for small areas or place orders for larger areas to be delivered on media. The USGS Web Mapping Portal provides views of national and international datasets through an ArcIMS map service interface. In addition, the map portal posts news about new

  14. Spatial Indexing for Data Searching in Mobile Sensing Environments

    Directory of Open Access Journals (Sweden)

    Yuchao Zhou

    2017-06-01

    Full Text Available Data searching and retrieval is one of the fundamental functionalities in many Web of Things applications, which need to collect, process and analyze huge amounts of sensor stream data. The problem in fact has been well studied for data generated by sensors that are installed at fixed locations; however, challenges emerge along with the popularity of opportunistic sensing applications in which mobile sensors keep reporting observation and measurement data at variable intervals and changing geographical locations. To address these challenges, we develop the Geohash-Grid Tree, a spatial indexing technique specially designed for searching data integrated from heterogeneous sources in a mobile sensing environment. Results of the experiments on a real-world dataset collected from the SmartSantander smart city testbed show that the index structure allows efficient search based on spatial distance, range and time windows in a large time series database.

  15. Spatial Indexing for Data Searching in Mobile Sensing Environments.

    Science.gov (United States)

    Zhou, Yuchao; De, Suparna; Wang, Wei; Moessner, Klaus; Palaniswami, Marimuthu S

    2017-06-18

    Data searching and retrieval is one of the fundamental functionalities in many Web of Things applications, which need to collect, process and analyze huge amounts of sensor stream data. The problem in fact has been well studied for data generated by sensors that are installed at fixed locations; however, challenges emerge along with the popularity of opportunistic sensing applications in which mobile sensors keep reporting observation and measurement data at variable intervals and changing geographical locations. To address these challenges, we develop the Geohash-Grid Tree, a spatial indexing technique specially designed for searching data integrated from heterogeneous sources in a mobile sensing environment. Results of the experiments on a real-world dataset collected from the SmartSantander smart city testbed show that the index structure allows efficient search based on spatial distance, range and time windows in a large time series database.

  16. The Bayesian group lasso for confounded spatial data

    Science.gov (United States)

    Hefley, Trevor J.; Hooten, Mevin B.; Hanks, Ephraim M.; Russell, Robin E.; Walsh, Daniel P.

    2017-01-01

    Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the spatial random effects can make computation challenging and influence inference. We present a Bayesian group lasso prior with a single tuning parameter that can be chosen to optimize predictive ability of the SGLMM and jointly regularize the regression coefficients and spatial random effect. We implement the group lasso SGLMM using efficient Markov chain Monte Carlo (MCMC) algorithms and demonstrate how multicollinearity among covariates and the spatial random effect can be monitored as a derived quantity. To test our method, we compared several parameterizations of the SGLMM using simulated data and two examples from plant ecology and disease ecology. In all examples, problematic levels multicollinearity occurred and influenced sampling efficiency and inference. We found that the group lasso prior resulted in roughly twice the effective sample size for MCMC samples of regression coefficients and can have higher and less variable predictive accuracy based on out-of-sample data when compared to the standard SGLMM.

  17. Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor

    Directory of Open Access Journals (Sweden)

    Liu Xin

    2015-12-01

    Full Text Available Spatial local outlier factor (SLOF algorithm was adopted in this study for spatial outlier detection because of the limitations of the traditional static threshold detection. Based on the spatial characteristics of CO2 monitoring data obtained in the carbon capture and storage (CCS project, the K-Nearest Neighbour (KNN graph was constructed using the latitude and longitude information of the monitoring points to identify the spatial neighbourhood of the monitoring points. Then SLOF was adopted to calculate the outlier degrees of the monitoring points and the 3σ rule was employed to identify the spatial outlier. Finally, the selection of K value was analysed and the optimal one was selected. The results show that, compared with the static threshold method, the proposed algorithm has a higher detection precision. It can overcome the shortcomings of the static threshold method and improve the accuracy and diversity of local outlier detection, which provides a reliable reference for the safety assessment and warning of CCS monitoring.

  18. Book Review. Mapping the determinants of spatial data sharing By ...

    African Journals Online (AJOL)

    Book Review. Mapping the determinants of spatial data sharing. By Uta Wehn de Montalvo (2003). Yoichi Mine. Abstract. Aldershot: Ashgate. Africa Development Vol. XXX(3) 2005: 145-146. http://dx.doi.org/10.4314/ad.v30i3.22237 · AJOL African Journals Online. HOW TO USE AJOL... for Researchers · for Librarians ...

  19. Analysis of spatial count data using Kalman smoothing

    DEFF Research Database (Denmark)

    Dethlefsen, Claus

    2007-01-01

    We consider spatial count data from an agricultural field experiment. Counts of weed plants in a field have been recorded in a project on precision farming. Interest is in mapping the weed intensity so that the dose of herbicide applied at any location can be adjusted to the amount of weed present...

  20. Periodicity in spatial data and geostatistical models: autocorrelation between patches

    Science.gov (United States)

    Volker C. Radeloff; Todd F. Miller; Hong S. He; David J. Mladenoff

    2000-01-01

    Several recent studies in landscape ecology have found periodicity in correlograms or semi-variograms calculated, for instance, from spatial data of soils, forests, or animal populations. Some of the studies interpreted this as an indication of regular or periodic landscape patterns. This interpretation is in disagreement with other studies that doubt whether such...

  1. Challenges in Spatial Data Infrastructure research: a role for transdisciplinarity?

    NARCIS (Netherlands)

    Bregt, A.K.; Crompvoets, J.W.H.C.; Man, de E.; Grus, L.

    2009-01-01

    The field of Spatial Data Infrastructure (SDI) is developing and approaches rapidly a critical masss of more or less operational SDIs. The purpose of the paper is to anticipate the possible impact of the maturing SDI field on its research agenda. Initial initiatives were predominantly techno centred

  2. Challenges in Coastal Spatial Data Infrastructure implementation: A ...

    African Journals Online (AJOL)

    The ability to cope with the complexity surrounding the coastal zone requires an integrated approach for sustainable socio-economic development and environmental management. The concept of integrated coastal zone management (ICZM) was advanced in response to this. In line with the success story of spatial data ...

  3. Assessing the Development of Kenya National Spatial Data

    African Journals Online (AJOL)

    okuku

    Keywords: Spatial data infrastructure, Kenya NSDI, development, assessment, ... of a nation can be used for; network survey of coordinates, waterways, ... to European community (INSPIRE) at the European national, regional and .... Digitisation efforts were spearheaded by the joint cooperation of JICA (Japan international.

  4. A Multi-view Framework to Assess Spatial Data Infrastructures

    NARCIS (Netherlands)

    Crompvoets, J.W.H.C.; Rajabifard, A.; Loenen, van B.; Delgado Fernandez, T.

    2008-01-01

    There is growing interest internationally in the role that Spatial Data Infrastructures SDIs play as key tools in supporting sustainable development. SDIs, as defined in the context of this book, are network-based national solutions to provide easy, consistent and effective access to geographic

  5. SAGA GIS based processing of spatial high resolution temperature data

    International Nuclear Information System (INIS)

    Gerlitz, Lars; Bechtel, Benjamin; Kawohl, Tobias; Boehner, Juergen; Zaksek, Klemen

    2013-01-01

    Many climate change impact studies require surface and near surface temperature data with high spatial and temporal resolution. The resolution of state of the art climate models and remote sensing data is often by far to coarse to represent the meso- and microscale distinctions of temperatures. This is particularly the case for regions with a huge variability of topoclimates, such as mountainous or urban areas. Statistical downscaling techniques are promising methods to refine gridded temperature data with limited spatial resolution, particularly due to their low demand for computer capacity. This paper presents two downscaling approaches - one for climate model output and one for remote sensing data. Both are methodically based on the FOSS-GIS platform SAGA. (orig.)

  6. Unveiling Spatial Epidemiology of HIV with Mobile Phone Data

    Science.gov (United States)

    Brdar, Sanja; Gavrić, Katarina; Ćulibrk, Dubravko; Crnojević, Vladimir

    2016-01-01

    An increasing amount of geo-referenced mobile phone data enables the identification of behavioral patterns, habits and movements of people. With this data, we can extract the knowledge potentially useful for many applications including the one tackled in this study - understanding spatial variation of epidemics. We explored the datasets collected by a cell phone service provider and linked them to spatial HIV prevalence rates estimated from publicly available surveys. For that purpose, 224 features were extracted from mobility and connectivity traces and related to the level of HIV epidemic in 50 Ivory Coast departments. By means of regression models, we evaluated predictive ability of extracted features. Several models predicted HIV prevalence that are highly correlated (>0.7) with actual values. Through contribution analysis we identified key elements that correlate with the rate of infections and could serve as a proxy for epidemic monitoring. Our findings indicate that night connectivity and activity, spatial area covered by users and overall migrations are strongly linked to HIV. By visualizing the communication and mobility flows, we strived to explain the spatial structure of epidemics. We discovered that strong ties and hubs in communication and mobility align with HIV hot spots.

  7. Data Updating Methods for Spatial Data Infrastructure that Maintain Infrastructure Quality and Enable its Sustainable Operation

    Science.gov (United States)

    Murakami, S.; Takemoto, T.; Ito, Y.

    2012-07-01

    The Japanese government, local governments and businesses are working closely together to establish spatial data infrastructures in accordance with the Basic Act on the Advancement of Utilizing Geospatial Information (NSDI Act established in August 2007). Spatial data infrastructures are urgently required not only to accelerate computerization of the public administration, but also to help restoration and reconstruction of the areas struck by the East Japan Great Earthquake and future disaster prevention and reduction. For construction of a spatial data infrastructure, various guidelines have been formulated. But after an infrastructure is constructed, there is a problem of maintaining it. In one case, an organization updates its spatial data only once every several years because of budget problems. Departments and sections update the data on their own without careful consideration. That upsets the quality control of the entire data system and the system loses integrity, which is crucial to a spatial data infrastructure. To ensure quality, ideally, it is desirable to update data of the entire area every year. But, that is virtually impossible, considering the recent budget crunch. The method we suggest is to update spatial data items of higher importance only in order to maintain quality, not updating all the items across the board. We have explored a method of partially updating the data of these two geographical features while ensuring the accuracy of locations. Using this method, data on roads and buildings that greatly change with time can be updated almost in real time or at least within a year. The method will help increase the availability of a spatial data infrastructure. We have conducted an experiment on the spatial data infrastructure of a municipality using those data. As a result, we have found that it is possible to update data of both features almost in real time.

  8. Spatial capture-recapture models for search-encounter data

    Science.gov (United States)

    Royle, J. Andrew; Kery, Marc; Guelat, Jerome

    2011-01-01

    1. Spatial capture–recapture models make use of auxiliary data on capture location to provide density estimates for animal populations. Previously, models have been developed primarily for fixed trap arrays which define the observable locations of individuals by a set of discrete points. 2. Here, we develop a class of models for 'search-encounter' data, i.e. for detections of recognizable individuals in continuous space, not restricted to trap locations. In our hierarchical model, detection probability is related to the average distance between individual location and the survey path. The locations are allowed to change over time owing to movements of individuals, and individual locations are related formally by a model describing individual activity or home range centre which is itself regarded as a latent variable in the model. We provide a Bayesian analysis of the model in WinBUGS, and develop a custom MCMC algorithm in the R language. 3. The model is applied to simulated data and to territory mapping data for the Willow Tit from the Swiss Breeding Bird Survey MHB. While the observed density was 15 territories per nominal 1 km2 plot of unknown effective sample area, the model produced a density estimate of 21∙12 territories per square km (95% posterior interval: 17–26). 4. Spatial capture–recapture models are relevant to virtually all animal population studies that seek to estimate population size or density, yet existing models have been proposed mainly for conventional sampling using arrays of traps. Our model for search-encounter data, where the spatial pattern of searching can be arbitrary and may change over occasions, greatly expands the scope and utility of spatial capture–recapture models.

  9. Maritime Laser Scanning as the Source for Spatial Data

    Directory of Open Access Journals (Sweden)

    Szulwic Jakub

    2015-12-01

    Full Text Available The rapid development of scanning technology, especially mobile scanning, gives the possibility to collect spatial data coming from maritime measurement platforms and autonomous manned or unmanned vehicles. Presented solution is derived from the mobile scanning. However we should keep in mind that the specificity of laser scanning at sea and processing collected data should be in the form acceptable in Geographical Information Systems, especially typical for the maritime needs. At the same time we should be aware that data coming from maritime mobile scanning constitutes a new approach to the describing of maritime environment and brings a new perspective that is completely different than air and terrestrial scanning.

  10. Spatial Data Integration Using Ontology-Based Approach

    Science.gov (United States)

    Hasani, S.; Sadeghi-Niaraki, A.; Jelokhani-Niaraki, M.

    2015-12-01

    In today's world, the necessity for spatial data for various organizations is becoming so crucial that many of these organizations have begun to produce spatial data for that purpose. In some circumstances, the need to obtain real time integrated data requires sustainable mechanism to process real-time integration. Case in point, the disater management situations that requires obtaining real time data from various sources of information. One of the problematic challenges in the mentioned situation is the high degree of heterogeneity between different organizations data. To solve this issue, we introduce an ontology-based method to provide sharing and integration capabilities for the existing databases. In addition to resolving semantic heterogeneity, better access to information is also provided by our proposed method. Our approach is consisted of three steps, the first step is identification of the object in a relational database, then the semantic relationships between them are modelled and subsequently, the ontology of each database is created. In a second step, the relative ontology will be inserted into the database and the relationship of each class of ontology will be inserted into the new created column in database tables. Last step is consisted of a platform based on service-oriented architecture, which allows integration of data. This is done by using the concept of ontology mapping. The proposed approach, in addition to being fast and low cost, makes the process of data integration easy and the data remains unchanged and thus takes advantage of the legacy application provided.

  11. SPATIAL DATA INTEGRATION USING ONTOLOGY-BASED APPROACH

    Directory of Open Access Journals (Sweden)

    S. Hasani

    2015-12-01

    Full Text Available In today's world, the necessity for spatial data for various organizations is becoming so crucial that many of these organizations have begun to produce spatial data for that purpose. In some circumstances, the need to obtain real time integrated data requires sustainable mechanism to process real-time integration. Case in point, the disater management situations that requires obtaining real time data from various sources of information. One of the problematic challenges in the mentioned situation is the high degree of heterogeneity between different organizations data. To solve this issue, we introduce an ontology-based method to provide sharing and integration capabilities for the existing databases. In addition to resolving semantic heterogeneity, better access to information is also provided by our proposed method. Our approach is consisted of three steps, the first step is identification of the object in a relational database, then the semantic relationships between them are modelled and subsequently, the ontology of each database is created. In a second step, the relative ontology will be inserted into the database and the relationship of each class of ontology will be inserted into the new created column in database tables. Last step is consisted of a platform based on service-oriented architecture, which allows integration of data. This is done by using the concept of ontology mapping. The proposed approach, in addition to being fast and low cost, makes the process of data integration easy and the data remains unchanged and thus takes advantage of the legacy application provided.

  12. Spatially explicit data: stewardship and ethical challenges in science.

    Science.gov (United States)

    Hartter, Joel; Ryan, Sadie J; Mackenzie, Catrina A; Parker, John N; Strasser, Carly A

    2013-09-01

    Scholarly communication is at an unprecedented turning point created in part by the increasing saliency of data stewardship and data sharing. Formal data management plans represent a new emphasis in research, enabling access to data at higher volumes and more quickly, and the potential for replication and augmentation of existing research. Data sharing has recently transformed the practice, scope, content, and applicability of research in several disciplines, in particular in relation to spatially specific data. This lends exciting potentiality, but the most effective ways in which to implement such changes, particularly for disciplines involving human subjects and other sensitive information, demand consideration. Data management plans, stewardship, and sharing, impart distinctive technical, sociological, and ethical challenges that remain to be adequately identified and remedied. Here, we consider these and propose potential solutions for their amelioration.

  13. Spatial data processing for the purpose of video games

    Directory of Open Access Journals (Sweden)

    Chądzyńska Dominika

    2016-03-01

    Full Text Available Advanced terrain models are currently commonly used in many video/computers games. Professional GIS technologies, existing spatial datasets and cartographic methodology are more widely used in their development. This allows for achieving a realistic model of the world. On the other hand, the so-called game engines have very high capability of spatial data visualization. Preparing terrain models for the purpose of video games requires knowledge and experience of GIS specialists and cartographers, although it is also accessible for non-professionals. The authors point out commonness and variety of use of terrain models in video games and the existence of a series of ready, advanced tools and procedures of terrain model creating. Finally the authors describe the experiment of performing the process of data modeling for “Condor Soar Simulator”.

  14. Bayesian Inference of Ecological Interactions from Spatial Data

    Directory of Open Access Journals (Sweden)

    Christopher R. Stephens

    2017-11-01

    Full Text Available The characterization and quantification of ecological interactions and the construction of species’ distributions and their associated ecological niches are of fundamental theoretical and practical importance. In this paper, we discuss a Bayesian inference framework, which, using spatial data, offers a general formalism within which ecological interactions may be characterized and quantified. Interactions are identified through deviations of the spatial distribution of co-occurrences of spatial variables relative to a benchmark for the non-interacting system and based on a statistical ensemble of spatial cells. The formalism allows for the integration of both biotic and abiotic factors of arbitrary resolution. We concentrate on the conceptual and mathematical underpinnings of the formalism, showing how, using the naive Bayes approximation, it can be used to not only compare and contrast the relative contribution from each variable, but also to construct species’ distributions and ecological niches based on an arbitrary variable type. We also show how non-linear interactions between distinct niche variables can be identified and the degree of confounding between variables accounted for.

  15. Spatial and temporal epidemiological analysis in the Big Data era.

    Science.gov (United States)

    Pfeiffer, Dirk U; Stevens, Kim B

    2015-11-01

    Concurrent with global economic development in the last 50 years, the opportunities for the spread of existing diseases and emergence of new infectious pathogens, have increased substantially. The activities associated with the enormously intensified global connectivity have resulted in large amounts of data being generated, which in turn provides opportunities for generating knowledge that will allow more effective management of animal and human health risks. This so-called Big Data has, more recently, been accompanied by the Internet of Things which highlights the increasing presence of a wide range of sensors, interconnected via the Internet. Analysis of this data needs to exploit its complexity, accommodate variation in data quality and should take advantage of its spatial and temporal dimensions, where available. Apart from the development of hardware technologies and networking/communication infrastructure, it is necessary to develop appropriate data management tools that make this data accessible for analysis. This includes relational databases, geographical information systems and most recently, cloud-based data storage such as Hadoop distributed file systems. While the development in analytical methodologies has not quite caught up with the data deluge, important advances have been made in a number of areas, including spatial and temporal data analysis where the spectrum of analytical methods ranges from visualisation and exploratory analysis, to modelling. While there used to be a primary focus on statistical science in terms of methodological development for data analysis, the newly emerged discipline of data science is a reflection of the challenges presented by the need to integrate diverse data sources and exploit them using novel data- and knowledge-driven modelling methods while simultaneously recognising the value of quantitative as well as qualitative analytical approaches. Machine learning regression methods, which are more robust and can handle

  16. From mobile phone data to the spatial structure of cities

    Science.gov (United States)

    Louail, Thomas; Lenormand, Maxime; Cantu Ros, Oliva G.; Picornell, Miguel; Herranz, Ricardo; Frias-Martinez, Enrique; Ramasco, José J.; Barthelemy, Marc

    2014-01-01

    Pervasive infrastructures, such as cell phone networks, enable to capture large amounts of human behavioral data but also provide information about the structure of cities and their dynamical properties. In this article, we focus on these last aspects by studying phone data recorded during 55 days in 31 Spanish cities. We first define an urban dilatation index which measures how the average distance between individuals evolves during the day, allowing us to highlight different types of city structure. We then focus on hotspots, the most crowded places in the city. We propose a parameter free method to detect them and to test the robustness of our results. The number of these hotspots scales sublinearly with the population size, a result in agreement with previous theoretical arguments and measures on employment datasets. We study the lifetime of these hotspots and show in particular that the hierarchy of permanent ones, which constitute the ‘heart' of the city, is very stable whatever the size of the city. The spatial structure of these hotspots is also of interest and allows us to distinguish different categories of cities, from monocentric and “segregated” where the spatial distribution is very dependent on land use, to polycentric where the spatial mixing between land uses is much more important. These results point towards the possibility of a new, quantitative classification of cities using high resolution spatio-temporal data. PMID:24923248

  17. Web Platform for Sharing Spatial Data and Manipulating Them Online

    Science.gov (United States)

    Bachelet, Dominique; Comendant, Tosha; Strittholt, Jim

    2011-04-01

    To fill the need for readily accessible conservation-relevant spatial data sets, the Conservation Biology Institute (CBI) launched in 2010 a Web-based platform called Data Basin (http://www.databasin.org). It is the first custom application of ArcGIS technology, which provides Web access to free maps and imagery using the most current version of Environmental Systems Research Institute (ESRI; http://www.esri.com/) geographic information system (GIS) software, and its core functionality is being made freely available. Data Basin includes spatial data sets (Arc format shapefiles and grids, or layer packages) that can be biological (e.g., prairie dog range), physical (e.g., average summer temperature, 1950-2000), or socioeconomic (e.g., locations of Alaska oil and gas wells); based on observations as well as on simulation results; and of local to global relevance. They can be uploaded, downloaded, or simply visualized. Maps (overlays of multiple data sets) can be created and customized (e.g., western Massachusetts protected areas, time series of the Deep Water Horizon oil spill). Galleries are folders containing data sets and maps focusing on a theme (e.g., sea level rise projections for the Pacific Northwest region from the National Wildlife Federation, soil data sets for the conterminous United States).

  18. Quantifying spatial and temporal patterns of flow intermittency using spatially contiguous runoff data

    Science.gov (United States)

    Yu (于松延), Songyan; Bond, Nick R.; Bunn, Stuart E.; Xu, Zongxue; Kennard, Mark J.

    2018-04-01

    River channel drying caused by intermittent stream flow is a widely-recognized factor shaping stream ecosystems. There is a strong need to quantify the distribution of intermittent streams across catchments to inform management. However, observational gauge networks provide only point estimates of streamflow variation. Increasingly, this limitation is being overcome through the use of spatially contiguous estimates of the terrestrial water-balance, which can also assist in estimating runoff and streamflow at large-spatial scales. Here we proposed an approach to quantifying spatial and temporal variation in monthly flow intermittency throughout river networks in eastern Australia. We aggregated gridded (5 × 5 km) monthly water-balance data with a hierarchically nested catchment dataset to simulate catchment runoff accumulation throughout river networks from 1900 to 2016. We also predicted zero flow duration for the entire river network by developing a robust predictive model relating measured zero flow duration (% months) to environmental predictor variables (based on 43 stream gauges). We then combined these datasets by using the predicted zero flow duration from the regression model to determine appropriate 'zero' flow thresholds for the modelled discharge data, which varied spatially across the catchments examined. Finally, based on modelled discharge data and identified actual zero flow thresholds, we derived summary metrics describing flow intermittency across the catchment (mean flow duration and coefficient-of-variation in flow permanence from 1900 to 2016). We also classified the relative degree of flow intermittency annually to characterise temporal variation in flow intermittency. Results showed that the degree of flow intermittency varied substantially across streams in eastern Australia, ranging from perennial streams flowing permanently (11-12 months) to strongly intermittent streams flowing 4 months or less of year. Results also showed that the

  19. Making Temporal Search More Central in Spatial Data Infrastructures

    Science.gov (United States)

    Corti, P.; Lewis, B.

    2017-10-01

    A temporally enabled Spatial Data Infrastructure (SDI) is a framework of geospatial data, metadata, users, and tools intended to provide an efficient and flexible way to use spatial information which includes the historical dimension. One of the key software components of an SDI is the catalogue service which is needed to discover, query, and manage the metadata. A search engine is a software system capable of supporting fast and reliable search, which may use any means necessary to get users to the resources they need quickly and efficiently. These techniques may include features such as full text search, natural language processing, weighted results, temporal search based on enrichment, visualization of patterns in distributions of results in time and space using temporal and spatial faceting, and many others. In this paper we will focus on the temporal aspects of search which include temporal enrichment using a time miner - a software engine able to search for date components within a larger block of text, the storage of time ranges in the search engine, handling historical dates, and the use of temporal histograms in the user interface to display the temporal distribution of search results.

  20. Enhancing discovery in spatial data infrastructures using a search engine

    Directory of Open Access Journals (Sweden)

    Paolo Corti

    2018-05-01

    Full Text Available A spatial data infrastructure (SDI is a framework of geospatial data, metadata, users and tools intended to provide an efficient and flexible way to use spatial information. One of the key software components of an SDI is the catalogue service which is needed to discover, query and manage the metadata. Catalogue services in an SDI are typically based on the Open Geospatial Consortium (OGC Catalogue Service for the Web (CSW standard which defines common interfaces for accessing the metadata information. A search engine is a software system capable of supporting fast and reliable search, which may use ‘any means necessary’ to get users to the resources they need quickly and efficiently. These techniques may include full text search, natural language processing, weighted results, fuzzy tolerance results, faceting, hit highlighting, recommendations and many others. In this paper we present an example of a search engine being added to an SDI to improve search against large collections of geospatial datasets. The Centre for Geographic Analysis (CGA at Harvard University re-engineered the search component of its public domain SDI (Harvard WorldMap which is based on the GeoNode platform. A search engine was added to the SDI stack to enhance the CSW catalogue discovery abilities. It is now possible to discover spatial datasets from metadata by using the standard search operations of the catalogue and to take advantage of the new abilities of the search engine, to return relevant and reliable content to SDI users.

  1. The Fundamental Spatial Data in the Public Administration Registers

    Science.gov (United States)

    Čada, V.; Janečka, K.

    2016-06-01

    The system of basic registers was launched in the Czech Republic in 2012. The system provides a unique solution to centralize and keep actual most common and widely used information as a part of the eGovernment. The basic registers are the central information source for information systems of public authorities. In October 2014, the Czech government approved the conception of The Strategy for the Development of the Infrastructure for Spatial Information in the Czech Republic to 2020 (GeoInfoStrategy) that serves as a basis for the NSDI. The paper describes the challenges in building the National Spatial Data Infrastructure (NSDI) in the Czech Republic with focus on the fundamental spatial data and related basic registers. The GeoInfoStrategy should also contribute to increasing of the competitiveness of the economy. Therefore the paper also reflects the Directive 2014/61/EU of the European Parliament and of the Council on measures to reduce the cost of deploying high-speed electronic communication networks. The Directive states that citizens as well as the private and public sectors must have the opportunity to be part of the digital economy. A high quality digital infrastructure underpins virtually all sectors of a modern and innovative economy. To ensure a development of such infrastructure in the Czech Republic, the Register of passive infrastructure providing information on the features of passive infrastructure has to be established.

  2. Graphic display of spatially distributed binary-state experimental data

    International Nuclear Information System (INIS)

    Watson, B.L.

    1981-01-01

    Experimental data collected from a large number of transducers spatially distributed throughout a three-dimensional volume has typically posed a difficult interpretation task for the analyst. This paper describes one approach to alleviating this problem by presenting color graphic displays of experimental data; specifically, data representing the dynamic three-dimensional distribution of cooling fluid collected during the reflood and refill of simulated nuclear reactor vessels. Color-coded binary data (wet/dry) are integrated with a graphic representation of the reactor vessel and displayed on a high-resolution color CRT. The display is updated with successive data sets and made into 16-mm movies for distribution and analysis. Specific display formats are presented and extension to other applications discussed

  3. Spatial Forecast of Landslides in Three Gorges Based On Spatial Data Mining

    Directory of Open Access Journals (Sweden)

    Xianmin Wang

    2009-03-01

    Full Text Available The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc. China-Brazil Earth Resources Satellite (Cbers images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods.

  4. Spatial forecast of landslides in three gorges based on spatial data mining.

    Science.gov (United States)

    Wang, Xianmin; Niu, Ruiqing

    2009-01-01

    The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods.

  5. Indoor 3D Route Modeling Based On Estate Spatial Data

    Science.gov (United States)

    Zhang, H.; Wen, Y.; Jiang, J.; Huang, W.

    2014-04-01

    Indoor three-dimensional route model is essential for space intelligence navigation and emergency evacuation. This paper is motivated by the need of constructing indoor route model automatically and as far as possible. By comparing existing building data sources, this paper firstly explained the reason why the estate spatial management data is chosen as the data source. Then, an applicable method of construction three-dimensional route model in a building is introduced by establishing the mapping relationship between geographic entities and their topological expression. This data model is a weighted graph consist of "node" and "path" to express the spatial relationship and topological structure of a building components. The whole process of modelling internal space of a building is addressed by two key steps: (1) each single floor route model is constructed, including path extraction of corridor using Delaunay triangulation algorithm with constrained edge, fusion of room nodes into the path; (2) the single floor route model is connected with stairs and elevators and the multi-floor route model is eventually generated. In order to validate the method in this paper, a shopping mall called "Longjiang New City Plaza" in Nanjing is chosen as a case of study. And the whole building space is constructed according to the modelling method above. By integrating of existing path finding algorithm, the usability of this modelling method is verified, which shows the indoor three-dimensional route modelling method based on estate spatial data in this paper can support indoor route planning and evacuation route design very well.

  6. DESIGN FOR CONNECTING SPATIAL DATA INFRASTRUCTURES WITH SENSOR WEB (SENSDI

    Directory of Open Access Journals (Sweden)

    D. Bhattacharya

    2016-06-01

    Full Text Available Integrating Sensor Web With Spatial Data Infrastructures (SENSDI aims to extend SDIs with sensor web enablement, converging geospatial and built infrastructure, and implement test cases with sensor data and SDI. It is about research to harness the sensed environment by utilizing domain specific sensor data to create a generalized sensor webframework. The challenges being semantic enablement for Spatial Data Infrastructures, and connecting the interfaces of SDI with interfaces of Sensor Web. The proposed research plan is to Identify sensor data sources, Setup an open source SDI, Match the APIs and functions between Sensor Web and SDI, and Case studies like hazard applications, urban applications etc. We take up co-operative development of SDI best practices to enable a new realm of a location enabled and semantically enriched World Wide Web - the "Geospatial Web" or "Geosemantic Web" by setting up one to one correspondence between WMS, WFS, WCS, Metadata and 'Sensor Observation Service' (SOS; 'Sensor Planning Service' (SPS; 'Sensor Alert Service' (SAS; a service that facilitates asynchronous message interchange between users and services, and between two OGC-SWE services, called the 'Web Notification Service' (WNS. Hence in conclusion, it is of importance to geospatial studies to integrate SDI with Sensor Web. The integration can be done through merging the common OGC interfaces of SDI and Sensor Web. Multi-usability studies to validate integration has to be undertaken as future research.

  7. Efficient Spatial Data Structure for Multiversion Management of Engineering Drawings

    Directory of Open Access Journals (Sweden)

    Yasuaki Nakamura

    2004-08-01

    Full Text Available In the engineering database system, multiple versions of a design including engineering drawings should be managed efficiently. The paper proposes an extended spatial data structure for efficient management of multiversion engineering drawings. The R-tree is adapted as a basic data structure. The efficient mechanism to manage the difference between drawings is introduced to the R-tree to eliminate redundant duplications and to reduce the amount of storage required for the data structure. The extended data structures of the R-tree, MVR and MVR* trees, are developed and the performances of these trees are evaluated. A series of simulation tests shows that, compared with the basic R-tree, the amounts of storage required for the MVR and MVR* trees are reduced to 50% and 30%, respectively. The search efficiencies of the R, MVR, and MVR* trees are almost the same.

  8. Estimating range of influence in case of missing spatial data

    DEFF Research Database (Denmark)

    Bihrmann, Kristine; Ersbøll, Annette Kjær

    2015-01-01

    BACKGROUND: The range of influence refers to the average distance between locations at which the observed outcome is no longer correlated. In many studies, missing data occur and a popular tool for handling missing data is multiple imputation. The objective of this study was to investigate how...... the estimated range of influence is affected when 1) the outcome is only observed at some of a given set of locations, and 2) multiple imputation is used to impute the outcome at the non-observed locations. METHODS: The study was based on the simulation of missing outcomes in a complete data set. The range...... of influence was estimated from a logistic regression model with a spatially structured random effect, modelled by a Gaussian field. Results were evaluated by comparing estimates obtained from complete, missing, and imputed data. RESULTS: In most simulation scenarios, the range estimates were consistent...

  9. Science with High Spatial Resolution Far-Infrared Data

    Science.gov (United States)

    Terebey, Susan (Editor); Mazzarella, Joseph M. (Editor)

    1994-01-01

    The goal of this workshop was to discuss new science and techniques relevant to high spatial resolution processing of far-infrared data, with particular focus on high resolution processing of IRAS data. Users of the maximum correlation method, maximum entropy, and other resolution enhancement algorithms applicable to far-infrared data gathered at the Infrared Processing and Analysis Center (IPAC) for two days in June 1993 to compare techniques and discuss new results. During a special session on the third day, interested astronomers were introduced to IRAS HIRES processing, which is IPAC's implementation of the maximum correlation method to the IRAS data. Topics discussed during the workshop included: (1) image reconstruction; (2) random noise; (3) imagery; (4) interacting galaxies; (5) spiral galaxies; (6) galactic dust and elliptical galaxies; (7) star formation in Seyfert galaxies; (8) wavelet analysis; and (9) supernova remnants.

  10. SPATIAL DATA INFRASTRUCTURE OF THE BAIKAL REGION: PLACEMENT AND MAPPING

    Directory of Open Access Journals (Sweden)

    A. N. Beshentsev

    2016-01-01

    Full Text Available Spatial data infrastructure (SDI is created for the organization of information exchange in the country. SDI is the information- telecommunication system that provides access to the public and government authorities to spatial data resources, as well as the dissemination and exchange of data in order to improve the efficiency of their production and use [1]. SDI development is the result of society territorial activities informatization and represents a specific geographical phenomenon, which is characterized by the presence of specific natural and man-made structures, the virtual geographical environment and geoinformation resources and territorial processes of users interaction and movement of resources within the near-earth space. Reliable management of this phenomenon of the modern information society requires accurate and timely inventory data centers, telecommunication highways, reference features, geocoding of interaction participants, etc. A cartographic registration of SDI components and spatio-temporal analysis of their development will provide solution to these problems. In addition, mapping assessment of natural, social and economic conditions of accommodation SDI will establish physical and geographical features of the localization of its objects and will perform predictive modeling of their design.

  11. Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification.

    Science.gov (United States)

    Liu, Da; Li, Jianxun

    2016-12-16

    Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.

  12. Supplementary Material for: Factor Copula Models for Replicated Spatial Data

    KAUST Repository

    Krupskii, Pavel; Huser, Raphaë l; Genton, Marc G.

    2016-01-01

    We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.

  13. Extending Primitive Spatial Data Models to Include Semantics

    Science.gov (United States)

    Reitsma, F.; Batcheller, J.

    2009-04-01

    Our traditional geospatial data model involves associating some measurable quality, such as temperature, or observable feature, such as a tree, with a point or region in space and time. When capturing data we implicitly subscribe to some kind of conceptualisation. If we can make this explicit in an ontology and associate it with the captured data, we can leverage formal semantics to reason with the concepts represented in our spatial data sets. To do so, we extend our fundamental representation of geospatial data in a data model by including a URI in our basic data model that links it to our ontology defining our conceptualisation, We thus extend Goodchild et al's geo-atom [1] with the addition of a URI: (x, Z, z(x), URI) . This provides us with pixel or feature level knowledge and the ability to create layers of data from a set of pixels or features that might be drawn from a database based on their semantics. Using open source tools, we present a prototype that involves simple reasoning as a proof of concept. References [1] M.F. Goodchild, M. Yuan, and T.J. Cova. Towards a general theory of geographic representation in gis. International Journal of Geographical Information Science, 21(3):239-260, 2007.

  14. Spatial Sampling of Weather Data for Regional Crop Yield Simulations

    Science.gov (United States)

    Van Bussel, Lenny G. J.; Ewert, Frank; Zhao, Gang; Hoffmann, Holger; Enders, Andreas; Wallach, Daniel; Asseng, Senthold; Baigorria, Guillermo A.; Basso, Bruno; Biernath, Christian; hide

    2016-01-01

    Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio-temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982-2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50, 100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated. The results showed differences in simulated yields among crop models but all models reproduced well the pattern of the stratification. Importantly, the regional mean of simulated yields based on full coverage could already be reproduced by a small sample of 10 points. This was also true for reproducing the temporal variability in simulated yields but more sampling points (about 100) were required to accurately reproduce spatial yield variability. The number of sampling points can be smaller when a stratified sampling is applied as compared to a random sampling. However, differences between crop models were observed including some interaction between the effect of sampling on simulated yields and the model used. We concluded that stratified sampling can considerably reduce the number of required simulations. But, differences between crop models must be considered as the choice for a specific model can have larger effects on simulated yields than the sampling strategy. Assessing the impact of sampling soil and crop management

  15. AN EXTENDED SPECTRAL–SPATIAL CLASSIFICATION APPROACH FOR HYPERSPECTRAL DATA

    Directory of Open Access Journals (Sweden)

    D. Akbari

    2017-11-01

    Full Text Available In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1 unsupervised feature extraction methods including principal component analysis (PCA, independent component analysis (ICA, and minimum noise fraction (MNF; (2 supervised feature extraction including decision boundary feature extraction (DBFE, discriminate analysis feature extraction (DAFE, and nonparametric weighted feature extraction (NWFE; (3 genetic algorithm (GA. The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

  16. An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

    Science.gov (United States)

    Akbari, D.

    2017-11-01

    In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

  17. Spatial resolution enhancement of satellite image data using fusion approach

    Science.gov (United States)

    Lestiana, H.; Sukristiyanti

    2018-02-01

    Object identification using remote sensing data has a problem when the spatial resolution is not in accordance with the object. The fusion approach is one of methods to solve the problem, to improve the object recognition and to increase the objects information by combining data from multiple sensors. The application of fusion image can be used to estimate the environmental component that is needed to monitor in multiple views, such as evapotranspiration estimation, 3D ground-based characterisation, smart city application, urban environments, terrestrial mapping, and water vegetation. Based on fusion application method, the visible object in land area has been easily recognized using the method. The variety of object information in land area has increased the variation of environmental component estimation. The difficulties in recognizing the invisible object like Submarine Groundwater Discharge (SGD), especially in tropical area, might be decreased by the fusion method. The less variation of the object in the sea surface temperature is a challenge to be solved.

  18. a Bottom-Up Geosptial Data Update Mechanism for Spatial Data Infrastructure Updating

    Science.gov (United States)

    Tian, W.; Zhu, X.; Liu, Y.

    2012-08-01

    Currently, the top-down spatial data update mechanism has made a big progress and it is wildly applied in many SDI (spatial data infrastructure). However, this mechanism still has some issues. For example, the update schedule is limited by the professional department's project, usually which is too long for the end-user; the data form collection to public cost too much time and energy for professional department; the details of geospatial information does not provide sufficient attribute, etc. Thus, how to deal with the problems has become the effective shortcut. Emerging Internet technology, 3S technique and geographic information knowledge which is popular in the public promote the booming development of geoscience in volunteered geospatial information. Volunteered geospatial information is the current "hotspot", which attracts many researchers to study its data quality and credibility, accuracy, sustainability, social benefit, application and so on. In addition to this, a few scholars also pay attention to the value of VGI to support the SDI updating. And on that basis, this paper presents a bottom-up update mechanism form VGI to SDI, which includes the processes of match homonymous elements between VGI and SDI vector data , change data detection, SDI spatial database update and new data product publication to end-users. Then, the proposed updating cycle is deeply discussed about the feasibility of which can detect the changed elements in time and shorten the update period, provide more accurate geometry and attribute data for spatial data infrastructure and support update propagation.

  19. Enabling search services on outsourced private spatial data

    KAUST Repository

    Yiu, Man Lung

    2009-10-30

    Cloud computing services enable organizations and individuals to outsource the management of their data to a service provider in order to save on hardware investments and reduce maintenance costs. Only authorized users are allowed to access the data. Nobody else, including the service provider, should be able to view the data. For instance, a real-estate company that owns a large database of properties wants to allow its paying customers to query for houses according to location. On the other hand, the untrusted service provider should not be able to learn the property locations and, e. g., selling the information to a competitor. To tackle the problem, we propose to transform the location datasets before uploading them to the service provider. The paper develops a spatial transformation that re-distributes the locations in space, and it also proposes a cryptographic-based transformation. The data owner selects the transformation key and shares it with authorized users. Without the key, it is infeasible to reconstruct the original data points from the transformed points. The proposed transformations present distinct trade-offs between query efficiency and data confidentiality. In addition, we describe attack models for studying the security properties of the transformations. Empirical studies demonstrate that the proposed methods are efficient and applicable in practice. © 2009 Springer-Verlag.

  20. Spatial Data Analysis: Recommendations for Educational Infrastructure in Sindh

    Directory of Open Access Journals (Sweden)

    Abdul Aziz Ansari

    2017-06-01

    Full Text Available Analysing the Education infrastructure has become a crucial activity in imparting quality teaching and resources to students. Facilitations required in improving current education status and future schools is an important analytical component. This is best achieved through a Geographical Information System (GIS analysis of the spatial distribution of schools. In this work, we will execute GIS Analytics on the rural and urban school distributions in Sindh, Pakistan. Using a reliable dataset collected from an international survey team, GIS analysis is done with respect to: 1 school locations, 2 school facilities (water, sanitation, class rooms etc. and 3 student’s results. We will carry out analysis at district level by presenting several spatial results. Correlational analysis of highly influential factors, which may impact the educational performance will generate recommendations for planning and development in weak areas which will provide useful insights regarding effective utilization of resources and new locations to build future schools. The time series analysis will predict the future results which may be witnessed through keen observations and data collections.

  1. A Big Spatial Data Processing Framework Applying to National Geographic Conditions Monitoring

    Directory of Open Access Journals (Sweden)

    F. Xiao

    2018-04-01

    Full Text Available In this paper, a novel framework for spatial data processing is proposed, which apply to National Geographic Conditions Monitoring project of China. It includes 4 layers: spatial data storage, spatial RDDs, spatial operations, and spatial query language. The spatial data storage layer uses HDFS to store large size of spatial vector/raster data in the distributed cluster. The spatial RDDs are the abstract logical dataset of spatial data types, and can be transferred to the spark cluster to conduct spark transformations and actions. The spatial operations layer is a series of processing on spatial RDDs, such as range query, k nearest neighbor and spatial join. The spatial query language is a user-friendly interface which provide people not familiar with Spark with a comfortable way to operation the spatial operation. Compared with other spatial frameworks, it is highlighted that comprehensive technologies are referred for big spatial data processing. Extensive experiments on real datasets show that the framework achieves better performance than traditional process methods.

  2. A Big Spatial Data Processing Framework Applying to National Geographic Conditions Monitoring

    Science.gov (United States)

    Xiao, F.

    2018-04-01

    In this paper, a novel framework for spatial data processing is proposed, which apply to National Geographic Conditions Monitoring project of China. It includes 4 layers: spatial data storage, spatial RDDs, spatial operations, and spatial query language. The spatial data storage layer uses HDFS to store large size of spatial vector/raster data in the distributed cluster. The spatial RDDs are the abstract logical dataset of spatial data types, and can be transferred to the spark cluster to conduct spark transformations and actions. The spatial operations layer is a series of processing on spatial RDDs, such as range query, k nearest neighbor and spatial join. The spatial query language is a user-friendly interface which provide people not familiar with Spark with a comfortable way to operation the spatial operation. Compared with other spatial frameworks, it is highlighted that comprehensive technologies are referred for big spatial data processing. Extensive experiments on real datasets show that the framework achieves better performance than traditional process methods.

  3. Patterns in the spatial distribution of Peruvian anchovy ( Engraulis ringens) revealed by spatially explicit fishing data

    Science.gov (United States)

    Bertrand, Sophie; Díaz, Erich; Lengaigne, Matthieu

    2008-10-01

    Peruvian anchovy ( Engraulis ringens) stock abundance is tightly driven by the high and unpredictable variability of the Humboldt Current Ecosystem. Management of the fishery therefore cannot rely on mid- or long-term management policy alone but needs to be adaptive at relatively short time scales. Regular acoustic surveys are performed on the stock at intervals of 2 to 4 times a year, but there is a need for more time continuous monitoring indicators to ensure that management can respond at suitable time scales. Existing literature suggests that spatially explicit data on the location of fishing activities could be used as a proxy for target stock distribution. Spatially explicit commercial fishing data could therefore guide adaptive management decisions at shorter time scales than is possible through scientific stock surveys. In this study we therefore aim to (1) estimate the position of fishing operations for the entire fleet of Peruvian anchovy purse-seiners using the Peruvian satellite vessel monitoring system (VMS), and (2) quantify the extent to which the distribution of purse-seine sets describes anchovy distribution. To estimate fishing set positions from vessel tracks derived from VMS data we developed a methodology based on artificial neural networks (ANN) trained on a sample of fishing trips with known fishing set positions (exact fishing positions are known for approximately 1.5% of the fleet from an at-sea observer program). The ANN correctly identified 83% of the real fishing sets and largely outperformed comparative linear models. This network is then used to forecast fishing operations for those trips where no observers were onboard. To quantify the extent to which fishing set distribution was correlated to stock distribution we compared three metrics describing features of the distributions (the mean distance to the coast, the total area of distribution, and a clustering index) for concomitant acoustic survey observations and fishing set positions

  4. Constraining the break of spatial diffeomorphism invariance with Planck data

    Science.gov (United States)

    Graef, L. L.; Benetti, M.; Alcaniz, J. S.

    2017-07-01

    The current most accepted paradigm for the early universe cosmology, the inflationary scenario, shows a good agreement with the recent Cosmic Microwave Background (CMB) and polarization data. However, when the inflation consistency relation is relaxed, these observational data exclude a larger range of red tensor tilt values, prevailing the blue ones which are not predicted by the minimal inflationary models. Recently, it has been shown that the assumption of spatial diffeomorphism invariance breaking (SDB) in the context of an effective field theory of inflation leads to interesting observational consequences. Among them, the possibility of generating a blue tensor spectrum, which can recover the specific consistency relation of the String Gas Cosmology, for a certain choice of parameters. We use the most recent CMB data to constrain the SDB model and test its observational viability through a Bayesian analysis assuming as reference an extended ΛCDM+tensor perturbation model, which considers a power-law tensor spectrum parametrized in terms of the tensor-to-scalar ratio, r, and the tensor spectral index, nt. If the inflation consistency relation is imposed, r=-8 nt, we obtain a strong evidence in favor of the reference model whereas if such relation is relaxed, a weak evidence in favor of the model with diffeomorphism breaking is found. We also use the same CMB data set to make an observational comparison between the SDB model, standard inflation and String Gas Cosmology.

  5. Constraining the break of spatial diffeomorphism invariance with Planck data

    Energy Technology Data Exchange (ETDEWEB)

    Graef, L.L.; Benetti, M.; Alcaniz, J.S., E-mail: leilagraef@on.br, E-mail: micolbenetti@on.br, E-mail: alcaniz@on.br [Departamento de Astronomia, Observatório Nacional, R. Gen. José Cristino, 77—São Cristóvão, 20921-400, Rio de Janeiro, RJ (Brazil)

    2017-07-01

    The current most accepted paradigm for the early universe cosmology, the inflationary scenario, shows a good agreement with the recent Cosmic Microwave Background (CMB) and polarization data. However, when the inflation consistency relation is relaxed, these observational data exclude a larger range of red tensor tilt values, prevailing the blue ones which are not predicted by the minimal inflationary models. Recently, it has been shown that the assumption of spatial diffeomorphism invariance breaking (SDB) in the context of an effective field theory of inflation leads to interesting observational consequences. Among them, the possibility of generating a blue tensor spectrum, which can recover the specific consistency relation of the String Gas Cosmology, for a certain choice of parameters. We use the most recent CMB data to constrain the SDB model and test its observational viability through a Bayesian analysis assuming as reference an extended ΛCDM+tensor perturbation model, which considers a power-law tensor spectrum parametrized in terms of the tensor-to-scalar ratio, r , and the tensor spectral index, n {sub t} . If the inflation consistency relation is imposed, r =−8 n {sub t} , we obtain a strong evidence in favor of the reference model whereas if such relation is relaxed, a weak evidence in favor of the model with diffeomorphism breaking is found. We also use the same CMB data set to make an observational comparison between the SDB model, standard inflation and String Gas Cosmology.

  6. Sustainable and Smart City Planning Using Spatial Data in Wallonia

    Science.gov (United States)

    Stephenne, N.; Beaumont, B.; Hallot, E.; Wolff, E.; Poelmans, L.; Baltus, C.

    2016-09-01

    Simulating population distribution and land use changes in space and time offer opportunities for smart city planning. It provides a holistic and dynamic vision of fast changing urban environment to policy makers. Impacts, such as environmental and health risks or mobility issues, of policies can be assessed and adapted consequently. In this paper, we suppose that "Smart" city developments should be sustainable, dynamic and participative. This paper addresses these three smart objectives in the context of urban risk assessment in Wallonia, Belgium. The sustainable, dynamic and participative solution includes (i) land cover and land use mapping using remote sensing and GIS, (ii) population density mapping using dasymetric mapping, (iii) predictive modelling of land use changes and population dynamics and (iv) risk assessment. The comprehensive and long-term vision of the territory should help to draw sustainable spatial planning policies, to adapt remote sensing acquisition, to update GIS data and to refine risk assessment from regional to city scale.

  7. Dissemination of spatial data infrastructure in Panamá

    Directory of Open Access Journals (Sweden)

    Sandra Yanet Velazco Florez

    2016-06-01

    Full Text Available Spatial Data Infrastructure deployed today, allow the dissemination of information that comes to an individual in a simple and accessible way for any organization whether it be public, private or citizens. The means by which messages arrive from one individual to another are called communication channels. Theories of diffusion of innovation can provide a very useful for the study and development of national and regional IDE framework. The diffusion model of innovation of Rogers, emphasizes the importance of interpersonal end communication and the role of social networks, through processes of disclosure to different societies, considering also that the media does not are the only channels of diffusion of innovations, most however, this communication need of leadership within the group to manage communication processes for the new product offered is recognized and accepted by the stakeholders to whom it is addressed.

  8. RADSS: an integration of GIS, spatial statistics, and network service for regional data mining

    Science.gov (United States)

    Hu, Haitang; Bao, Shuming; Lin, Hui; Zhu, Qing

    2005-10-01

    Regional data mining, which aims at the discovery of knowledge about spatial patterns, clusters or association between regions, has widely applications nowadays in social science, such as sociology, economics, epidemiology, crime, and so on. Many applications in the regional or other social sciences are more concerned with the spatial relationship, rather than the precise geographical location. Based on the spatial continuity rule derived from Tobler's first law of geography: observations at two sites tend to be more similar to each other if the sites are close together than if far apart, spatial statistics, as an important means for spatial data mining, allow the users to extract the interesting and useful information like spatial pattern, spatial structure, spatial association, spatial outlier and spatial interaction, from the vast amount of spatial data or non-spatial data. Therefore, by integrating with the spatial statistical methods, the geographical information systems will become more powerful in gaining further insights into the nature of spatial structure of regional system, and help the researchers to be more careful when selecting appropriate models. However, the lack of such tools holds back the application of spatial data analysis techniques and development of new methods and models (e.g., spatio-temporal models). Herein, we make an attempt to develop such an integrated software and apply it into the complex system analysis for the Poyang Lake Basin. This paper presents a framework for integrating GIS, spatial statistics and network service in regional data mining, as well as their implementation. After discussing the spatial statistics methods involved in regional complex system analysis, we introduce RADSS (Regional Analysis and Decision Support System), our new regional data mining tool, by integrating GIS, spatial statistics and network service. RADSS includes the functions of spatial data visualization, exploratory spatial data analysis, and

  9. Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data

    Directory of Open Access Journals (Sweden)

    Arvind Sharma

    2016-01-01

    Full Text Available There are many techniques available in the field of data mining and its subfield spatial data mining is to understand relationships between data objects. Data objects related with spatial features are called spatial databases. These relationships can be used for prediction and trend detection between spatial and nonspatial objects for social and scientific reasons. A huge data set may be collected from different sources as satellite images, X-rays, medical images, traffic cameras, and GIS system. To handle this large amount of data and set relationship between them in a certain manner with certain results is our primary purpose of this paper. This paper gives a complete process to understand how spatial data is different from other kinds of data sets and how it is refined to apply to get useful results and set trends to predict geographic information system and spatial data mining process. In this paper a new improved algorithm for clustering is designed because role of clustering is very indispensable in spatial data mining process. Clustering methods are useful in various fields of human life such as GIS (Geographic Information System, GPS (Global Positioning System, weather forecasting, air traffic controller, water treatment, area selection, cost estimation, planning of rural and urban areas, remote sensing, and VLSI designing. This paper presents study of various clustering methods and algorithms and an improved algorithm of DBSCAN as IDBSCAN (Improved Density Based Spatial Clustering of Application of Noise. The algorithm is designed by addition of some important attributes which are responsible for generation of better clusters from existing data sets in comparison of other methods.

  10. Remote sensing data handling to improve the system integration of indonesian national spatial data infrastructure

    International Nuclear Information System (INIS)

    Hari, G. R. V.

    2010-01-01

    With the usage of metadata as a reference for spatial data query, remote sensing images and other spatial datasets have been linked to their related semantic information. In the current catalogue systems, like those or satellite data provides, or clearinghouses, each remote sensing image is maintained as an independent entity. There is a very limited possibility to know the linkage of one image to another, even if one image has actually been derived from the other. It is an advantage for many purposes if the linkage among remote sensing image or other spatial data can be maintained or at least reconstructed. This research will explore how an image is linked to its related information, and how an image can be linked to another images. By exploring links among remote sensing images, a query of remote sensing data collection can be extended, for example, to find the answer of the query: 'which images are used to create certain dataset?', or 'which images have been created from a concrete dataset?', or 'is there a relationship between image A and image B based on their processing steps?'. By building links among spatial datasets in a collection based on their creation process, a further possibility of spatial data organization can be supported. The applicability and compatibility of the proposed method with the current platform is also considered. The proposed method can be implemented using the same standard and protocol and using the same metadata file as used by the existing system. This approach makes it also possible to be implemented in many countries which use the same infrastructure. To prove this purpose, we develop a prototype based on open source platform, including PostgreSQL, Apache Webserver, Mapserver WebGIS, and PHP programming environment. The output of this research leads to an improvement of spatial data handling, where an adjacency list is used to maintain spatial dataset history link. This improvement can enhance the query of spatial data in a

  11. Towards Geo-spatial Information Science in Big Data Era

    Directory of Open Access Journals (Sweden)

    LI Deren

    2016-04-01

    Full Text Available Since the 1990s, with the advent of worldwide information revolution and the development of internet, geospatial information science have also come of age, which pushed forward the building of digital Earth and cyber city. As we entered the 21st century, with the development and integration of global information technology and industrialization, internet of things and cloud computing came into being, human society enters into the big data era. This article covers the key features (ubiquitous, multi-dimension and dynamics, internet+networking, full automation and real-time, from sensing to recognition, crowdsourcing and VGI, and service-oriented of geospatial information science in the big data era and addresses the key technical issues (non-linear four dimensional Earth reference frame system, space based enhanced GNSS, space-air and land unified network communication techniques, on board processing techniques for multi-sources image data, smart interface service techniques for space-borne information, space based resource scheduling and network security, design and developing of a payloads based multi-functional satellite platform. That needs to be resolved to provide a new definition of geospatial information science in big data era. Based on the discussion in this paper, the author finally proposes a new definition of geospatial information science (geomatics, i.e. Geomatics is a multiple discipline science and technology which, using a systematic approach, integrates all the means for spatio-temporal data acquisition, information extraction, networked management, knowledge discovering, spatial sensing and recognition, as well as intelligent location based services of any physical objects and human activities around the earth and its environment. Starting from this new definition, geospatial information science will get much more chances and find much more tasks in big data era for generation of smart earth and smart city . Our profession

  12. A Spatial Data Infrastructure for the Global Mercury Observation System

    Directory of Open Access Journals (Sweden)

    Cinnirella S.

    2013-04-01

    Full Text Available The Global Mercury Observation System (GMOS Project includes a specific Work Package aimed at developing tools (i.e. databases, catalogs, services to collect GMOS datasets, harvest mercury databases, and offer services like search, view, and download spatial datasets from the GMOS portal (www.gmos.eu. The system will be developed under the framework of the Infrastructure for Spatial Information in the European Community (INSPIRE Directive and the Directive 2003/4/EC on public access to environmental information, which both aim to make relevant, harmonized, high-quality geographic information available to support the formulation, implementation, monitoring, and evaluation of policies and activities that have a direct or indirect impact on the environment. Three databases have been proposed (on emissions, field data and model results, and each will be equipped with state-of-the-art, open-source software to allow for the highest performance possible. Web-based user-interfaces and prototype applications will be developed to demonstrate the potential of blending different datasets from different servers for environmental assessment studies. Several services (i.e. catalog browsers, WMS and WCS services, web GIS services will be developed to facilitate data integration, data re-use, and data exchange within and beyond the GMOS project. Different types of measurement and model datasets provided by project partners and other sources will be integrated into PostgreSQL-PostGIS, harmonized by creating INSPIRE-compliant metadata and made available to a larger community of stakeholders, policy makers, scientists, and NGOs (as well as to other public and private institutions, as dictated by the Directive 2003/4/EC. Since interoperability is a central concept for the Global Earth Observation System of Systems (GEOSS, the Global Monitoring for Environmental and Security (GMES and the INSPIRE Directive, guidelines developed in these three frameworks will be

  13. Parameterizing the Spatial Markov Model From Breakthrough Curve Data Alone

    Science.gov (United States)

    Sherman, Thomas; Fakhari, Abbas; Miller, Savannah; Singha, Kamini; Bolster, Diogo

    2017-12-01

    The spatial Markov model (SMM) is an upscaled Lagrangian model that effectively captures anomalous transport across a diverse range of hydrologic systems. The distinct feature of the SMM relative to other random walk models is that successive steps are correlated. To date, with some notable exceptions, the model has primarily been applied to data from high-resolution numerical simulations and correlation effects have been measured from simulated particle trajectories. In real systems such knowledge is practically unattainable and the best one might hope for is breakthrough curves (BTCs) at successive downstream locations. We introduce a novel methodology to quantify velocity correlation from BTC data alone. By discretizing two measured BTCs into a set of arrival times and developing an inverse model, we estimate velocity correlation, thereby enabling parameterization of the SMM in studies where detailed Lagrangian velocity statistics are unavailable. The proposed methodology is applied to two synthetic numerical problems, where we measure all details and thus test the veracity of the approach by comparison of estimated parameters with known simulated values. Our results suggest that our estimated transition probabilities agree with simulated values and using the SMM with this estimated parameterization accurately predicts BTCs downstream. Our methodology naturally allows for estimates of uncertainty by calculating lower and upper bounds of velocity correlation, enabling prediction of a range of BTCs. The measured BTCs fall within the range of predicted BTCs. This novel method to parameterize the SMM from BTC data alone is quite parsimonious, thereby widening the SMM's practical applicability.

  14. The Role of NASA's Planetary Data System in the Planetary Spatial Data Infrastructure Initiative

    Science.gov (United States)

    Arvidson, R. E.; Gaddis, L. R.

    2017-12-01

    An effort underway in NASA's planetary science community is the Mapping and Planetary Spatial Infrastructure Team (MAPSIT, http://www.lpi.usra.edu/mapsit/). MAPSIT is a community assessment group organized to address a lack of strategic spatial data planning for space science and exploration. Working with MAPSIT, a new initiative of NASA and USGS is the development of a Planetary Spatial Data Infrastructure (PSDI) that builds on extensive knowledge on storing, accessing, and working with terrestrial spatial data. PSDI is a knowledge and technology framework that enables the efficient discovery, access, and exploitation of planetary spatial data to facilitate data analysis, knowledge synthesis, and decision-making. NASA's Planetary Data System (PDS) archives >1.2 petabytes of digital data resulting from decades of planetary exploration and research. The PDS charter focuses on the efficient collection, archiving, and accessibility of these data. The PDS emphasis on data preservation and archiving is complementary to that of the PSDI initiative because the latter utilizes and extends available data to address user needs in the areas of emerging technologies, rapid development of tailored delivery systems, and development of online collaborative research environments. The PDS plays an essential PSDI role because it provides expertise to help NASA missions and other data providers to organize and document their planetary data, to collect and maintain the archives with complete, well-documented and peer-reviewed planetary data, to make planetary data accessible by providing online data delivery tools and search services, and ultimately to ensure the long-term preservation and usability of planetary data. The current PDS4 information model extends and expands PDS metadata and relationships between and among elements of the collections. The PDS supports data delivery through several node services, including the Planetary Image Atlas (https

  15. A quality-aware spatial data warehouse for querying hydroecological data

    Science.gov (United States)

    Berrahou, L.; Lalande, N.; Serrano, E.; Molla, G.; Berti-Équille, L.; Bimonte, S.; Bringay, S.; Cernesson, F.; Grac, C.; Ienco, D.; Le Ber, F.; Teisseire, M.

    2015-12-01

    Addressing data quality issues in information systems remains a challenging task. Many approaches only tackle this issue at the extract, transform and load steps. Here we define a comprehensive method to gain greater insight into data quality characteristics within data warehouse. Our novel architecture was implemented for an hydroecological case study where massive French watercourse sampling data are collected. The method models and makes effective use of spatial, thematic and temporal accuracy, consistency and completeness for multidimensional data in order to offer analysts a "data quality" oriented framework. The results obtained in experiments carried out on the Saône River dataset demonstrated the relevance of our approach.

  16. A book review of Spatial data analysis in ecology and agriculture using R

    Science.gov (United States)

    Spatial Data Analysis in Ecology and Agriculture Using R is a valuable resource to assist agricultural and ecological researchers with spatial data analyses using the R statistical software(www.r-project.org). Special emphasis is on spatial data sets; how-ever, the text also provides ample guidance ...

  17. Parameterizing the Spatial Markov Model from Breakthrough Curve Data Alone

    Science.gov (United States)

    Sherman, T.; Bolster, D.; Fakhari, A.; Miller, S.; Singha, K.

    2017-12-01

    The spatial Markov model (SMM) uses a correlated random walk and has been shown to effectively capture anomalous transport in porous media systems; in the SMM, particles' future trajectories are correlated to their current velocity. It is common practice to use a priori Lagrangian velocity statistics obtained from high resolution simulations to determine a distribution of transition probabilities (correlation) between velocity classes that govern predicted transport behavior; however, this approach is computationally cumbersome. Here, we introduce a methodology to quantify velocity correlation from Breakthrough (BTC) curve data alone; discretizing two measured BTCs into a set of arrival times and reverse engineering the rules of the SMM allows for prediction of velocity correlation, thereby enabling parameterization of the SMM in studies where Lagrangian velocity statistics are not available. The introduced methodology is applied to estimate velocity correlation from BTCs measured in high resolution simulations, thus allowing for a comparison of estimated parameters with known simulated values. Results show 1) estimated transition probabilities agree with simulated values and 2) using the SMM with estimated parameterization accurately predicts BTCs downstream. Additionally, we include uncertainty measurements by calculating lower and upper estimates of velocity correlation, which allow for prediction of a range of BTCs. The simulated BTCs fall in the range of predicted BTCs. This research proposes a novel method to parameterize the SMM from BTC data alone, thereby reducing the SMM's computational costs and widening its applicability.

  18. Data, data everywhere: detecting spatial patterns in fine-scale ecological information collected across a continent

    Science.gov (United States)

    Kevin M. Potter; Frank H. Koch; Christopher M. Oswalt; Basil V. Iannone

    2016-01-01

    Context Fine-scale ecological data collected across broad regions are becoming increasingly available. Appropriate geographic analyses of these data can help identify locations of ecological concern. Objectives We present one such approach, spatial association of scalable hexagons (SASH), whichidentifies locations where ecological phenomena occur at greater...

  19. using gis for spatial exploratory analysis of borehole data

    African Journals Online (AJOL)

    PUBLICATIONS1

    Thus, understanding the spatial structure of aquifer characteristics could be used as a resourceful tool and as a .... x is position in one dimensional space. N(h) is Pairwise ... best fitted theoretical models, the spatial struc- ture of each variable ...

  20. Spatial fuel data products of the LANDFIRE Project

    Science.gov (United States)

    Matt Reeves; Kevin C. Ryan; Matthew G. Rollins; Thomas G. Thompson

    2009-01-01

    The Landscape Fire and Resource Management Planning Tools (LANDFIRE) Project is mapping wildland fuels, vegetation, and fire regime characteristics across the United States. The LANDFIRE project is unique because of its national scope, creating an integrated product suite at 30-m spatial resolution and complete spatial coverage of all lands within the 50...

  1. Spatial and temporal remote sensing data fusion for vegetation monitoring

    Science.gov (United States)

    The suite of available remote sensing instruments varies widely in terms of sensor characteristics, spatial resolution and acquisition frequency. For example, the Moderate-resolution Imaging Spectroradiometer (MODIS) provides daily global observations at 250m to 1km spatial resolution. While imagery...

  2. Evaluating water erosion prediction project model using Cesium-137-derived spatial soil redistribution data

    Science.gov (United States)

    The lack of spatial soil erosion data has been a major constraint on the refinement and application of physically based erosion models. Spatially distributed models can only be thoroughly validated with distributed erosion data. The fallout cesium-137 has been widely used to generate spatial soil re...

  3. Toolsets for Airborne Data (TAD): Enhanced Airborne Data Merging Functionality through Spatial and Temporal Subsetting

    Science.gov (United States)

    Early, A. B.; Chen, G.; Beach, A. L., III; Northup, E. A.

    2016-12-01

    NASA has conducted airborne tropospheric chemistry studies for over three decades. These field campaigns have generated a great wealth of observations, including a wide range of the trace gases and aerosol properties. The Atmospheric Science Data Center (ASDC) at NASA Langley Research Center in Hampton Virginia originally developed the Toolsets for Airborne Data (TAD) web application in September 2013 to meet the user community needs for manipulating aircraft data for scientific research on climate change and air quality relevant issues. The analysis of airborne data typically requires data subsetting, which can be challenging and resource intensive for end users. In an effort to streamline this process, the TAD toolset enhancements will include new data subsetting features and updates to the current database model. These will include two subsetters: temporal and spatial, and vertical profile. The temporal and spatial subsetter will allow users to both focus on data from a specific location and/or time period. The vertical profile subsetter will retrieve data collected during an individual aircraft ascent or descent spiral. This effort will allow for the automation of the typically labor-intensive manual data subsetting process, which will provide users with data tailored to their specific research interests. The development of these enhancements will be discussed in this presentation.

  4. Spatial Data Services for Interdisciplinary Applications from the NASA Socioeconomic Data and Applications Center

    Science.gov (United States)

    Chen, R. S.; MacManus, K.; Vinay, S.; Yetman, G.

    2016-12-01

    The Socioeconomic Data and Applications Center (SEDAC), one of 12 Distributed Active Archive Centers (DAACs) in the NASA Earth Observing System Data and Information System (EOSDIS), has developed a variety of operational spatial data services aimed at providing online access, visualization, and analytic functions for geospatial socioeconomic and environmental data. These services include: open web services that implement Open Geospatial Consortium (OGC) specifications such as Web Map Service (WMS), Web Feature Service (WFS), and Web Coverage Service (WCS); spatial query services that support Web Processing Service (WPS) and Representation State Transfer (REST); and web map clients and a mobile app that utilize SEDAC and other open web services. These services may be accessed from a variety of external map clients and visualization tools such as NASA's WorldView, NOAA's Climate Explorer, and ArcGIS Online. More than 200 data layers related to population, settlements, infrastructure, agriculture, environmental pollution, land use, health, hazards, climate change and other aspects of sustainable development are available through WMS, WFS, and/or WCS. Version 2 of the SEDAC Population Estimation Service (PES) supports spatial queries through WPS and REST in the form of a user-defined polygon or circle. The PES returns an estimate of the population residing in the defined area for a specific year (2000, 2005, 2010, 2015, or 2020) based on SEDAC's Gridded Population of the World version 4 (GPWv4) dataset, together with measures of accuracy. The SEDAC Hazards Mapper and the recently released HazPop iOS mobile app enable users to easily submit spatial queries to the PES and see the results. SEDAC has developed an operational virtualized backend infrastructure to manage these services and support their continual improvement as standards change, new data and services become available, and user needs evolve. An ongoing challenge is to improve the reliability and performance

  5. Including spatial data in nutrient balance modelling on dairy farms

    Science.gov (United States)

    van Leeuwen, Maricke; van Middelaar, Corina; Stoof, Cathelijne; Oenema, Jouke; Stoorvogel, Jetse; de Boer, Imke

    2017-04-01

    at Dutch dairy farms. We selected two dairy farms located on cover sands in the Netherlands. One farm was located on relatively homogeneous soil type, and one on many different soil types within the sandy soils. A full year of data of N and P inputs and outputs on farm and field level were provided by the farmers, including field level yields, yield composition, manure composition, degree of grazing and degree of mowing. Soil heterogeneity was defined as the number of soil units within the farm corrected for surface area, and quantified from the Dutch 1:50.000 soil map. N and P balances at farm and field level were determined, as well as differences in nutrient use efficiency, leaching, and N emission. We will present the effect of the spatial scale on nutrient balance analysis and discuss to which degree any differences are caused by within-farm land management and soil variation. This study highlights to which extent within-farm land management and soil variation should be taken into account when modelling nutrient flows and nutrient use efficiencies at farm level, to contribute to field-based decision making for improved land management.

  6. National Fish Habitat Action Plan (NFHAP) - Coastal Spatial Framework and Coastal Indicator Data

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The NFHAP Coastal Spatial Framework and Indicator Datasets consist of a geospatial base layer developed in ArcGIS, and associated data fields joined to the spatial...

  7. ‘Sensor’ship and Spatial Data Quality

    Directory of Open Access Journals (Sweden)

    Elisabeth Sedano

    2016-06-01

    Full Text Available This article describes a Los Angeles-based website that collects volunteered geographic information (VGI on outdoor advertising using the Google Street View interface. The Billboard Map website was designed to help the city regulate signage. The Los Angeles landscape is thick with advertising, and the city efforts to count total of signs has been stymied by litigation and political pressure. Because outdoor advertising is designed to be seen, the community collectively knows how many and where signs exist. As such, outdoor advertising is a perfect subject for VGI. This paper analyzes the Los Angeles community's entries in the Billboard Map website both quantitatively and qualitatively. I find that members of the public are well able to map outdoor advertisements, successfully employing the Google Street View interface to pinpoint sign locations. However, the community proved unaware of the regulatory distinctions between types of signs, mapping many more signs than those the city technically designates as billboards. Though these findings might suggest spatial data quality issues in the use of VGI for municipal record-keeping, I argue that the Billboard Map teaches an important lesson about how the public's conceptualization of the urban landscape differs from that envisioned by city planners. In particular, I argue that community members see the landscape of advertising holistically, while city agents treat the landscape as a collection of individual categories. This is important because, while Los Angeles recently banned new off-site signs, it continues to approve similar signs under new planning categories, with more in the works.

  8. Spatial Data Exploring by Satellite Image Distributed Processing

    Science.gov (United States)

    Mihon, V. D.; Colceriu, V.; Bektas, F.; Allenbach, K.; Gvilava, M.; Gorgan, D.

    2012-04-01

    Our society needs and environmental predictions encourage the applications development, oriented on supervising and analyzing different Earth Science related phenomena. Satellite images could be explored for discovering information concerning land cover, hydrology, air quality, and water and soil pollution. Spatial and environment related data could be acquired by imagery classification consisting of data mining throughout the multispectral bands. The process takes in account a large set of variables such as satellite image types (e.g. MODIS, Landsat), particular geographic area, soil composition, vegetation cover, and generally the context (e.g. clouds, snow, and season). All these specific and variable conditions require flexible tools and applications to support an optimal search for the appropriate solutions, and high power computation resources. The research concerns with experiments on solutions of using the flexible and visual descriptions of the satellite image processing over distributed infrastructures (e.g. Grid, Cloud, and GPU clusters). This presentation highlights the Grid based implementation of the GreenLand application. The GreenLand application development is based on simple, but powerful, notions of mathematical operators and workflows that are used in distributed and parallel executions over the Grid infrastructure. Currently it is used in three major case studies concerning with Istanbul geographical area, Rioni River in Georgia, and Black Sea catchment region. The GreenLand application offers a friendly user interface for viewing and editing workflows and operators. The description involves the basic operators provided by GRASS [1] library as well as many other image related operators supported by the ESIP platform [2]. The processing workflows are represented as directed graphs giving the user a fast and easy way to describe complex parallel algorithms, without having any prior knowledge of any programming language or application commands

  9. Spatial noise-aware temperature retrieval from infrared sounder data

    DEFF Research Database (Denmark)

    Malmgren-Hansen, David; Laparra, Valero; Nielsen, Allan Aasbjerg

    2017-01-01

    Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) for dimensionality reduction, and study the compactness and information content of the extracted features. Assessment of the results is done on a big dataset covering many spatial and temporal situations. PCA is widely used...... for these purposes but our analysis shows that one can gain significant improvements of the error rates when using MNF instead. In our analysis we also investigate the relationship between error rate improvements when including more spectral and spatial components in the regression model, aiming to uncover the trade...

  10. Parallel Landscape Driven Data Reduction & Spatial Interpolation Algorithm for Big LiDAR Data

    Directory of Open Access Journals (Sweden)

    Rahil Sharma

    2016-06-01

    Full Text Available Airborne Light Detection and Ranging (LiDAR topographic data provide highly accurate digital terrain information, which is used widely in applications like creating flood insurance rate maps, forest and tree studies, coastal change mapping, soil and landscape classification, 3D urban modeling, river bank management, agricultural crop studies, etc. In this paper, we focus mainly on the use of LiDAR data in terrain modeling/Digital Elevation Model (DEM generation. Technological advancements in building LiDAR sensors have enabled highly accurate and highly dense LiDAR point clouds, which have made possible high resolution modeling of terrain surfaces. However, high density data result in massive data volumes, which pose computing issues. Computational time required for dissemination, processing and storage of these data is directly proportional to the volume of the data. We describe a novel technique based on the slope map of the terrain, which addresses the challenging problem in the area of spatial data analysis, of reducing this dense LiDAR data without sacrificing its accuracy. To the best of our knowledge, this is the first ever landscape-driven data reduction algorithm. We also perform an empirical study, which shows that there is no significant loss in accuracy for the DEM generated from a 52% reduced LiDAR dataset generated by our algorithm, compared to the DEM generated from an original, complete LiDAR dataset. For the accuracy of our statistical analysis, we perform Root Mean Square Error (RMSE comparing all of the grid points of the original DEM to the DEM generated by reduced data, instead of comparing a few random control points. Besides, our multi-core data reduction algorithm is highly scalable. We also describe a modified parallel Inverse Distance Weighted (IDW spatial interpolation method and show that the DEMs it generates are time-efficient and have better accuracy than the one’s generated by the traditional IDW method.

  11. Hadoop-GIS: A High Performance Spatial Data Warehousing System over MapReduce.

    Science.gov (United States)

    Aji, Ablimit; Wang, Fusheng; Vo, Hoang; Lee, Rubao; Liu, Qiaoling; Zhang, Xiaodong; Saltz, Joel

    2013-08-01

    Support of high performance queries on large volumes of spatial data becomes increasingly important in many application domains, including geospatial problems in numerous fields, location based services, and emerging scientific applications that are increasingly data- and compute-intensive. The emergence of massive scale spatial data is due to the proliferation of cost effective and ubiquitous positioning technologies, development of high resolution imaging technologies, and contribution from a large number of community users. There are two major challenges for managing and querying massive spatial data to support spatial queries: the explosion of spatial data, and the high computational complexity of spatial queries. In this paper, we present Hadoop-GIS - a scalable and high performance spatial data warehousing system for running large scale spatial queries on Hadoop. Hadoop-GIS supports multiple types of spatial queries on MapReduce through spatial partitioning, customizable spatial query engine RESQUE, implicit parallel spatial query execution on MapReduce, and effective methods for amending query results through handling boundary objects. Hadoop-GIS utilizes global partition indexing and customizable on demand local spatial indexing to achieve efficient query processing. Hadoop-GIS is integrated into Hive to support declarative spatial queries with an integrated architecture. Our experiments have demonstrated the high efficiency of Hadoop-GIS on query response and high scalability to run on commodity clusters. Our comparative experiments have showed that performance of Hadoop-GIS is on par with parallel SDBMS and outperforms SDBMS for compute-intensive queries. Hadoop-GIS is available as a set of library for processing spatial queries, and as an integrated software package in Hive.

  12. Research on presentation and query service of geo-spatial data based on ontology

    Science.gov (United States)

    Li, Hong-wei; Li, Qin-chao; Cai, Chang

    2008-10-01

    The paper analyzed the deficiency on presentation and query of geo-spatial data existed in current GIS, discussed the advantages that ontology possessed in formalization of geo-spatial data and the presentation of semantic granularity, taken land-use classification system as an example to construct domain ontology, and described it by OWL; realized the grade level and category presentation of land-use data benefited from the thoughts of vertical and horizontal navigation; and then discussed query mode of geo-spatial data based on ontology, including data query based on types and grade levels, instances and spatial relation, and synthetic query based on types and instances; these methods enriched query mode of current GIS, and is a useful attempt; point out that the key point of the presentation and query of spatial data based on ontology is to construct domain ontology that can correctly reflect geo-concept and its spatial relation and realize its fine formalization description.

  13. Correction of Spatial Bias in Oligonucleotide Array Data

    Directory of Open Access Journals (Sweden)

    Philippe Serhal

    2013-01-01

    Full Text Available Background. Oligonucleotide microarrays allow for high-throughput gene expression profiling assays. The technology relies on the fundamental assumption that observed hybridization signal intensities (HSIs for each intended target, on average, correlate with their target’s true concentration in the sample. However, systematic, nonbiological variation from several sources undermines this hypothesis. Background hybridization signal has been previously identified as one such important source, one manifestation of which appears in the form of spatial autocorrelation. Results. We propose an algorithm, pyn, for the elimination of spatial autocorrelation in HSIs, exploiting the duality of desirable mutual information shared by probes in a common probe set and undesirable mutual information shared by spatially proximate probes. We show that this correction procedure reduces spatial autocorrelation in HSIs; increases HSI reproducibility across replicate arrays; increases differentially expressed gene detection power; and performs better than previously published methods. Conclusions. The proposed algorithm increases both precision and accuracy, while requiring virtually no changes to users’ current analysis pipelines: the correction consists merely of a transformation of raw HSIs (e.g., CEL files for Affymetrix arrays. A free, open-source implementation is provided as an R package, compatible with standard Bioconductor tools. The approach may also be tailored to other platform types and other sources of bias.

  14. Correction of Spatial Bias in Oligonucleotide Array Data

    Science.gov (United States)

    Lemieux, Sébastien

    2013-01-01

    Background. Oligonucleotide microarrays allow for high-throughput gene expression profiling assays. The technology relies on the fundamental assumption that observed hybridization signal intensities (HSIs) for each intended target, on average, correlate with their target's true concentration in the sample. However, systematic, nonbiological variation from several sources undermines this hypothesis. Background hybridization signal has been previously identified as one such important source, one manifestation of which appears in the form of spatial autocorrelation. Results. We propose an algorithm, pyn, for the elimination of spatial autocorrelation in HSIs, exploiting the duality of desirable mutual information shared by probes in a common probe set and undesirable mutual information shared by spatially proximate probes. We show that this correction procedure reduces spatial autocorrelation in HSIs; increases HSI reproducibility across replicate arrays; increases differentially expressed gene detection power; and performs better than previously published methods. Conclusions. The proposed algorithm increases both precision and accuracy, while requiring virtually no changes to users' current analysis pipelines: the correction consists merely of a transformation of raw HSIs (e.g., CEL files for Affymetrix arrays). A free, open-source implementation is provided as an R package, compatible with standard Bioconductor tools. The approach may also be tailored to other platform types and other sources of bias. PMID:23573083

  15. Data-driven inference for the spatial scan statistic

    Directory of Open Access Journals (Sweden)

    Duczmal Luiz H

    2011-08-01

    Full Text Available Abstract Background Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. Results A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. Conclusions A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.

  16. Using GIS for spatial exploratory analysis of borehole data: a ...

    African Journals Online (AJOL)

    Groundwater is an inimitable resource that provides water to communities especially in arid and semi-arid regions. However, the spatial variability of the resource as well as the heterogeneity and complex nature of aquifer systems that store groundwater presents difficulties for groundwater development. Thus ...

  17. Data-driven inference for the spatial scan statistic.

    Science.gov (United States)

    Almeida, Alexandre C L; Duarte, Anderson R; Duczmal, Luiz H; Oliveira, Fernando L P; Takahashi, Ricardo H C

    2011-08-02

    Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas) or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.

  18. Demonstration of Hadoop-GIS: A Spatial Data Warehousing System Over MapReduce.

    Science.gov (United States)

    Aji, Ablimit; Sun, Xiling; Vo, Hoang; Liu, Qioaling; Lee, Rubao; Zhang, Xiaodong; Saltz, Joel; Wang, Fusheng

    2013-11-01

    The proliferation of GPS-enabled devices, and the rapid improvement of scientific instruments have resulted in massive amounts of spatial data in the last decade. Support of high performance spatial queries on large volumes data has become increasingly important in numerous fields, which requires a scalable and efficient spatial data warehousing solution as existing approaches exhibit scalability limitations and efficiency bottlenecks for large scale spatial applications. In this demonstration, we present Hadoop-GIS - a scalable and high performance spatial query system over MapReduce. Hadoop-GIS provides an efficient spatial query engine to process spatial queries, data and space based partitioning, and query pipelines that parallelize queries implicitly on MapReduce. Hadoop-GIS also provides an expressive, SQL-like spatial query language for workload specification. We will demonstrate how spatial queries are expressed in spatially extended SQL queries, and submitted through a command line/web interface for execution. Parallel to our system demonstration, we explain the system architecture and details on how queries are translated to MapReduce operators, optimized, and executed on Hadoop. In addition, we will showcase how the system can be used to support two representative real world use cases: large scale pathology analytical imaging, and geo-spatial data warehousing.

  19. Kolmogorov-Smirnov test for spatially correlated data

    Science.gov (United States)

    Olea, R.A.; Pawlowsky-Glahn, V.

    2009-01-01

    The Kolmogorov-Smirnov test is a convenient method for investigating whether two underlying univariate probability distributions can be regarded as undistinguishable from each other or whether an underlying probability distribution differs from a hypothesized distribution. Application of the test requires that the sample be unbiased and the outcomes be independent and identically distributed, conditions that are violated in several degrees by spatially continuous attributes, such as topographical elevation. A generalized form of the bootstrap method is used here for the purpose of modeling the distribution of the statistic D of the Kolmogorov-Smirnov test. The innovation is in the resampling, which in the traditional formulation of bootstrap is done by drawing from the empirical sample with replacement presuming independence. The generalization consists of preparing resamplings with the same spatial correlation as the empirical sample. This is accomplished by reading the value of unconditional stochastic realizations at the sampling locations, realizations that are generated by simulated annealing. The new approach was tested by two empirical samples taken from an exhaustive sample closely following a lognormal distribution. One sample was a regular, unbiased sample while the other one was a clustered, preferential sample that had to be preprocessed. Our results show that the p-value for the spatially correlated case is always larger that the p-value of the statistic in the absence of spatial correlation, which is in agreement with the fact that the information content of an uncorrelated sample is larger than the one for a spatially correlated sample of the same size. ?? Springer-Verlag 2008.

  20. Development of spatial data guidelines and standards: spatial data set documentation to support hydrologic analysis in the U.S. Geological Survey

    Science.gov (United States)

    Fulton, James L.

    1992-01-01

    Spatial data analysis has become an integral component in many surface and sub-surface hydrologic investigations within the U.S. Geological Survey (USGS). Currently, one of the largest costs in applying spatial data analysis is the cost of developing the needed spatial data. Therefore, guidelines and standards are required for the development of spatial data in order to allow for data sharing and reuse; this eliminates costly redevelopment. In order to attain this goal, the USGS is expanding efforts to identify guidelines and standards for the development of spatial data for hydrologic analysis. Because of the variety of project and database needs, the USGS has concentrated on developing standards for documenting spatial sets to aid in the assessment of data set quality and compatibility of different data sets. An interim data set documentation standard (1990) has been developed that provides a mechanism for associating a wide variety of information with a data set, including data about source material, data automation and editing procedures used, projection parameters, data statistics, descriptions of features and feature attributes, information on organizational contacts lists of operations performed on the data, and free-form comments and notes about the data, made at various times in the evolution of the data set. The interim data set documentation standard has been automated using a commercial geographic information system (GIS) and data set documentation software developed by the USGS. Where possible, USGS developed software is used to enter data into the data set documentation file automatically. The GIS software closely associates a data set with its data set documentation file; the documentation file is retained with the data set whenever it is modified, copied, or transferred to another computer system. The Water Resources Division of the USGS is continuing to develop spatial data and data processing standards, with emphasis on standards needed to support

  1. Putting people on the map: protecting confidentiality with linked social-spatial data

    National Research Council Canada - National Science Library

    Panel on Confidentiality Issues Arising from the Integration of Remotely Sensed and Self-Identifying Data, National Research Council

    2007-01-01

    Precise, accurate spatial information linked to social and behavioral data is revolutionizing social science by opening new questions for investigation and improving understanding of human behavior...

  2. STATE OF THE ART OF THE LANDSCAPE ARCHITECTURE SPATIAL DATA MODEL FROM A GEOSPATIAL PERSPECTIVE

    OpenAIRE

    Kastuari, A.; Suwardhi, D.; Hanan, H.; Wikantika, K.

    2016-01-01

    Spatial data and information had been used for some time in planning or landscape design. For a long time, architects were using spatial data in the form of topographic map for their designs. This method is not efficient, and it is also not more accurate than using spatial analysis by utilizing GIS. Architects are sometimes also only accentuating the aesthetical aspect for their design, but not taking landscape process into account which could cause the design could be not suitable for its us...

  3. Continuous time modelling of dynamical spatial lattice data observed at sparsely distributed times

    DEFF Research Database (Denmark)

    Rasmussen, Jakob Gulddahl; Møller, Jesper

    2007-01-01

    Summary. We consider statistical and computational aspects of simulation-based Bayesian inference for a spatial-temporal model based on a multivariate point process which is only observed at sparsely distributed times. The point processes are indexed by the sites of a spatial lattice......, and they exhibit spatial interaction. For specificity we consider a particular dynamical spatial lattice data set which has previously been analysed by a discrete time model involving unknown normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared...... with discrete time processes in the setting of the present paper as well as other spatial-temporal situations....

  4. Aggregation-cokriging for highly multivariate spatial data

    KAUST Repository

    Furrer, R.; Genton, M. G.

    2011-01-01

    Best linear unbiased prediction of spatially correlated multivariate random processes, often called cokriging in geostatistics, requires the solution of a large linear system based on the covariance and cross-covariance matrix of the observations. For many problems of practical interest, it is impossible to solve the linear system with direct methods. We propose an efficient linear unbiased predictor based on a linear aggregation of the covariables. The primary variable together with this single meta-covariable is used to perform cokriging. We discuss the optimality of the approach under different covariance structures, and use it to create reanalysis type high-resolution historical temperature fields. © 2011 Biometrika Trust.

  5. Aggregation-cokriging for highly multivariate spatial data

    KAUST Repository

    Furrer, R.

    2011-08-26

    Best linear unbiased prediction of spatially correlated multivariate random processes, often called cokriging in geostatistics, requires the solution of a large linear system based on the covariance and cross-covariance matrix of the observations. For many problems of practical interest, it is impossible to solve the linear system with direct methods. We propose an efficient linear unbiased predictor based on a linear aggregation of the covariables. The primary variable together with this single meta-covariable is used to perform cokriging. We discuss the optimality of the approach under different covariance structures, and use it to create reanalysis type high-resolution historical temperature fields. © 2011 Biometrika Trust.

  6. Estimating Gross Primary Production in Cropland with High Spatial and Temporal Scale Remote Sensing Data

    Science.gov (United States)

    Lin, S.; Li, J.; Liu, Q.

    2018-04-01

    Satellite remote sensing data provide spatially continuous and temporally repetitive observations of land surfaces, and they have become increasingly important for monitoring large region of vegetation photosynthetic dynamic. But remote sensing data have their limitation on spatial and temporal scale, for example, higher spatial resolution data as Landsat data have 30-m spatial resolution but 16 days revisit period, while high temporal scale data such as geostationary data have 30-minute imaging period, which has lower spatial resolution (> 1 km). The objective of this study is to investigate whether combining high spatial and temporal resolution remote sensing data can improve the gross primary production (GPP) estimation accuracy in cropland. For this analysis we used three years (from 2010 to 2012) Landsat based NDVI data, MOD13 vegetation index product and Geostationary Operational Environmental Satellite (GOES) geostationary data as input parameters to estimate GPP in a small region cropland of Nebraska, US. Then we validated the remote sensing based GPP with the in-situ measurement carbon flux data. Results showed that: 1) the overall correlation between GOES visible band and in-situ measurement photosynthesis active radiation (PAR) is about 50 % (R2 = 0.52) and the European Center for Medium-Range Weather Forecasts ERA-Interim reanalysis data can explain 64 % of PAR variance (R2 = 0.64); 2) estimating GPP with Landsat 30-m spatial resolution data and ERA daily meteorology data has the highest accuracy(R2 = 0.85, RMSE MODIS 1-km NDVI/EVI product import; 3) using daily meteorology data as input for GPP estimation in high spatial resolution data would have higher relevance than 8-day and 16-day input. Generally speaking, using the high spatial resolution and high frequency satellite based remote sensing data can improve GPP estimation accuracy in cropland.

  7. Data Cubes Integration in Spatial OLAP for Agricultural Commodities

    Science.gov (United States)

    Putri, A. I.; Sitanggang, I. S.

    2017-03-01

    Ministry of Agriculture Indonesia collects data of agricultural commodities in Indonesia in the annual period. Agricultural commodities data include food crops, horticulture, plantations, and livestock. The data are available in the spreadsheet format. This study developed data cubes for food crops, plantations, and livestock using the galaxy schema of data warehouse and integrated the data cubes into the SOLAP Horticulture using SpagoBI. SOLAP is useful for data analysis and data visualization. The application displays agricultural commodities data in form of crosstab and chart. This study also developed the location intelligence module that visualizes agricultural commodities data on the map. The system was tested using the black box approach. The result showed that main functions including roll up, drill down, slice, dice, and pivot work properly. This application is expected to enable users to easily obtain data summaries of agricultural commodities.

  8. Opportunities for using spatial property assessment data in air pollution exposure assessments

    Directory of Open Access Journals (Sweden)

    Keller C Peter

    2005-10-01

    Full Text Available Abstract Background Many epidemiological studies examining the relationships between adverse health outcomes and exposure to air pollutants use ambient air pollution measurements as a proxy for personal exposure levels. When pollution levels vary at neighbourhood levels, using ambient pollution data from sparsely located fixed monitors may inadequately capture the spatial variation in ambient pollution. A major constraint to moving toward exposure assessments and epidemiological studies of air pollution at a neighbourhood level is the lack of readily available data at appropriate spatial resolutions. Spatial property assessment data are widely available in North America and may provide an opportunity for developing neighbourhood level air pollution exposure assessments. Results This paper provides a detailed description of spatial property assessment data available in the Pacific Northwest of Canada and the United States, and provides examples of potential applications of spatial property assessment data for improving air pollution exposure assessment at the neighbourhood scale, including: (1 creating variables for use in land use regression modelling of neighbourhood levels of ambient air pollution; (2 enhancing wood smoke exposure estimates by mapping fireplace locations; and (3 using data available on individual building characteristics to produce a regional air pollution infiltration model. Conclusion Spatial property assessment data are an extremely detailed data source at a fine spatial resolution, and therefore a source of information that could improve the quality and spatial resolution of current air pollution exposure assessments.

  9. Application of computer intensive data analysis methods to the analysis of digital images and spatial data

    DEFF Research Database (Denmark)

    Windfeld, Kristian

    1992-01-01

    Computer-intensive methods for data analysis in a traditional setting has developed rapidly in the last decade. The application of and adaption of some of these methods to the analysis of multivariate digital images and spatial data are explored, evaluated and compared to well established classical...... into the projection pursuit is presented. Examples from remote sensing are given. The ACE algorithm for computing non-linear transformations for maximizing correlation is extended and applied to obtain a non-linear transformation that maximizes autocorrelation or 'signal' in a multivariate image....... This is a generalization of the minimum /maximum autocorrelation factors (MAF's) which is a linear method. The non-linear method is compared to the linear method when analyzing a multivariate TM image from Greenland. The ACE method is shown to give a more detailed decomposition of the image than the MAF-transformation...

  10. Supporting spatial data harmonization process with the use of ontologies and Semantic Web technologies

    Science.gov (United States)

    Strzelecki, M.; Iwaniak, A.; Łukowicz, J.; Kaczmarek, I.

    2013-10-01

    Nowadays, spatial information is not only used by professionals, but also by common citizens, who uses it for their daily activities. Open Data initiative states that data should be freely and unreservedly available for all users. It also applies to spatial data. As spatial data becomes widely available it is essential to publish it in form which guarantees the possibility of integrating it with other, heterogeneous data sources. Interoperability is the possibility to combine spatial data sets from different sources in a consistent way as well as providing access to it. Providing syntactic interoperability based on well-known data formats is relatively simple, unlike providing semantic interoperability, due to the multiple possible data interpretation. One of the issues connected with the problem of achieving interoperability is data harmonization. It is a process of providing access to spatial data in a representation that allows combining it with other harmonized data in a coherent way by using a common set of data product specification. Spatial data harmonization is performed by creating definition of reclassification and transformation rules (mapping schema) for source application schema. Creation of those rules is a very demanding task which requires wide domain knowledge and a detailed look into application schemas. The paper focuses on proposing methods for supporting data harmonization process, by automated or supervised creation of mapping schemas with the use of ontologies, ontology matching methods and Semantic Web technologies.

  11. Deciphering Equine Evolution and Spatial Ancestry with Ancient Data

    DEFF Research Database (Denmark)

    Jónsson, Hákon

    High-throughput sequencing has opened ancient DNA research to genomics, revolutionizing the amount of genetic information retrievable from archaeological and paleontological remains. Paleogenomics is still in infancy and requires substantial improvements in computational methods tailored to the s......High-throughput sequencing has opened ancient DNA research to genomics, revolutionizing the amount of genetic information retrievable from archaeological and paleontological remains. Paleogenomics is still in infancy and requires substantial improvements in computational methods tailored...... in the analysis of environmental bacterial sequences, which generally dominate ancient DNA extracts, and in the first pipeline completely devoted to the computational analysis of raw ancient DNA sequences. We then develop a spatially explicit method for determining which extant populations show the greatest...... genetic anity to ancient individuals, which often represents the key question in human paleogenomic projects. We applied the computational infrastructure developed to complete the genomic characterization of extant members of the genus Equus, which is composed of horses, asses and zebras. We sequenced...

  12. A Framework for Evaluation of Marine Spatial Data Geoportals Using Case Studies

    Directory of Open Access Journals (Sweden)

    Tavra Marina

    2014-12-01

    Full Text Available Need for a Marine Spatial Data Infrastructure (MSDI as a component of a National Spatial Data Infrastructure (NSDI is widely recognized. An MSDI is relevant not only for hydrographers and government planners, but also for many other sectors which takes interest in marine spatial data, whether they are data users, data providers, or data managers [9]. An MSDI encompasses marine and coastal geographic and business information. For efficient use of Marine Spatial Data, it is necessary to ensure its valid and accessible distribution. A geoportal is a specialized web portal for sharing spatial information at different levels over the Internet. This paper re-examines the implementation of an MSDI and what it means for data custodians and end users. Several geoportals are reviewed (German and Australian to determine their web services functionality, capabilities and the scope to which they support the sharing and reuse of Marine Spatial Data to assist the development of the Croatian MSDI Geoportal. This framework provides a context for better understanding the information bases on spatial data standards and a tool for evaluation of MSDI dissemination - Geoportal.

  13. The Academic SDI—Towards understanding spatial data infrastructures for research and education

    CSIR Research Space (South Africa)

    Coetzee, S

    2017-05-01

    Full Text Available facilitating and coordinating the exchange of geospatial data and services between stakeholders from different levels in the spatial data community. Universities and other research organisations typically have well-established libraries and digital catalogues...

  14. Enabling search services on outsourced private spatial data

    KAUST Repository

    Yiu, Man Lung; Ghinita, Gabriel; Jensen, Christian Sø ndergaard; Kalnis, Panos

    2009-01-01

    Cloud computing services enable organizations and individuals to outsource the management of their data to a service provider in order to save on hardware investments and reduce maintenance costs. Only authorized users are allowed to access the data

  15. Low Cost Aerial and Spatial Data, Transportation Research Synthesis

    Science.gov (United States)

    2018-03-31

    MnDOT Office of Transportation System Management (OTSM) desires to reduce the cycle time for collecting road data updates from county sources and, opportunistically, capture additional data about road and ancillary uses, e.g. bicycle access. Specific...

  16. A Foundation for Spatial Data Warehouses on the Semantic Web


    DEFF Research Database (Denmark)

    Gur, Nurefsan; Pedersen, Torben Bach; Zimaányi, Esteban

    2017-01-01

    Large volumes of geospatial data are being published on the Semantic Web (SW), yielding a need for advanced analysis of such data. However, existing SW technologies only support advanced analytical concepts such as multidimensional (MD) data warehouses and Online Analytical Processing (OLAP) over...

  17. GeoSpatial Data Analysis for DHS Programs

    Energy Technology Data Exchange (ETDEWEB)

    Stephan, Eric G.; Burke, John S.; Carlson, Carrie A.; Gillen, David S.; Joslyn, Cliff A.; Olsen, Bryan K.; Critchlow, Terence J.

    2009-05-10

    The Department of Homeland Security law enforcement faces the continual challenge of analyzing their custom data sources in a geospatial context. From a strategic perspective law enforcement has certain requirements to first broadly characterize a given situation using their custom data sources and then once it is summarily understood, to geospatially analyze their data in detail.

  18. Spatial occupancy models applied to atlas data show Southern Ground Hornbills strongly depend on protected areas.

    Science.gov (United States)

    Broms, Kristin M; Johnson, Devin S; Altwegg, Res; Conquest, Loveday L

    2014-03-01

    Determining the range of a species and exploring species--habitat associations are central questions in ecology and can be answered by analyzing presence--absence data. Often, both the sampling of sites and the desired area of inference involve neighboring sites; thus, positive spatial autocorrelation between these sites is expected. Using survey data for the Southern Ground Hornbill (Bucorvus leadbeateri) from the Southern African Bird Atlas Project, we compared advantages and disadvantages of three increasingly complex models for species occupancy: an occupancy model that accounted for nondetection but assumed all sites were independent, and two spatial occupancy models that accounted for both nondetection and spatial autocorrelation. We modeled the spatial autocorrelation with an intrinsic conditional autoregressive (ICAR) model and with a restricted spatial regression (RSR) model. Both spatial models can readily be applied to any other gridded, presence--absence data set using a newly introduced R package. The RSR model provided the best inference and was able to capture small-scale variation that the other models did not. It showed that ground hornbills are strongly dependent on protected areas in the north of their South African range, but less so further south. The ICAR models did not capture any spatial autocorrelation in the data, and they took an order, of magnitude longer than the RSR models to run. Thus, the RSR occupancy model appears to be an attractive choice for modeling occurrences at large spatial domains, while accounting for imperfect detection and spatial autocorrelation.

  19. Water data in US: a spatial, temporal and sectoral analysis

    Science.gov (United States)

    Josset, L.; Allaire, M.; Rising, J. A.; Thomas, C.; Lall, U.

    2017-12-01

    Water data plays a crucial role in the development and implementation of sustainable water management strategies. Both effective design and assessment hinge on accurate information. This requires environmental, climatic, hydrologic, hydrogeologic, industrial, agricultural, energetic and socio-economic data to accurately characterize and project future supply and demand. In 2001, Vorosmarty et al. painted a stark future for water data, which was qualified as ``a new endangered species". Sixteen years after this publication, we propose a review of the current state of water data in the United States. While considerable progress has been made in data science and model development in the recent years, models are only as good as the data that populate them. After a brief overview of water data aggregated at the national level, we compare datasets from federal agencies with water information collected by individual states. We note in particular the potential gaps in the collected information that would support research beyond water balance accounts to informing regulations, investments, and economic decisions. In addition, we assess the information structures that host and disseminate data as well as data availability and usability (i.e. whether tools are proposed such as metrics, visualization, projections). We conclude our paper with a review of the current technological developments, policies and initiatives that may be transformative and redefine the future of water data. We follow two angles: the progress made in data collection (e.g. remote sensing, datascience, reporting policies) and in data dissemination (frameworks, cyber-infrastructures and standards). We review in particular the current initiatives taking place in US and around the world that promote water data freely available to all.

  20. Spatial Heterogeneity, Scale, Data Character and Sustainable Transport in the Big Data Era

    Science.gov (United States)

    Jiang, Bin

    2018-04-01

    In light of the emergence of big data, I have advocated and argued for a paradigm shift from Tobler's law to scaling law, from Euclidean geometry to fractal geometry, from Gaussian statistics to Paretian statistics, and - more importantly - from Descartes' mechanistic thinking to Alexander's organic thinking. Fractal geometry falls under the third definition of fractal - that is, a set or pattern is fractal if the scaling of far more small things than large ones recurs multiple times (Jiang and Yin 2014) - rather than under the second definition of fractal, which requires a power law between scales and details (Mandelbrot 1982). The new fractal geometry is more towards living geometry that "follows the rules, constraints, and contingent conditions that are, inevitably, encountered in the real world" (Alexander et al. 2012, p. 395), not only for understanding complexity, but also for creating complex or living structure (Alexander 2002-2005). This editorial attempts to clarify why the paradigm shift is essential and to elaborate on several concepts, including spatial heterogeneity (scaling law), scale (or the fourth meaning of scale), data character (in contrast to data quality), and sustainable transport in the big data era.

  1. Fast methods for spatially correlated multilevel functional data

    KAUST Repository

    Staicu, A.-M.; Crainiceanu, C. M.; Carroll, R. J.

    2010-01-01

    -one-out analyses, and nonparametric bootstrap sampling. Our methods are inspired by and applied to data obtained from a state-of-the-art colon carcinogenesis scientific experiment. However, our models are general and will be relevant to many new data sets where

  2. Fast methods for spatially correlated multilevel functional data

    KAUST Repository

    Staicu, A.-M.

    2010-01-19

    We propose a new methodological framework for the analysis of hierarchical functional data when the functions at the lowest level of the hierarchy are correlated. For small data sets, our methodology leads to a computational algorithm that is orders of magnitude more efficient than its closest competitor (seconds versus hours). For large data sets, our algorithm remains fast and has no current competitors. Thus, in contrast to published methods, we can now conduct routine simulations, leave-one-out analyses, and nonparametric bootstrap sampling. Our methods are inspired by and applied to data obtained from a state-of-the-art colon carcinogenesis scientific experiment. However, our models are general and will be relevant to many new data sets where the object of inference are functions or images that remain dependent even after conditioning on the subject on which they are measured. Supplementary materials are available at Biostatistics online.

  3. UUI: Reusable Spatial Data Services in Unified User Interface at NASA GES DISC

    Science.gov (United States)

    Petrenko, Maksym; Hegde, Mahabaleshwa; Bryant, Keith; Pham, Long B.

    2016-01-01

    Unified User Interface (UUI) is a next-generation operational data access tool that has been developed at Goddard Earth Sciences Data and Information Services Center(GES DISC) to provide a simple, unified, and intuitive one-stop shop experience for the key data services available at GES DISC, including subsetting (Simple Subset Wizard -SSW), granule file search (Mirador), plotting (Giovanni), and other legacy spatial data services. UUI has been built based on a flexible infrastructure of reusable web services self-contained building blocks that can easily be plugged into spatial applications, including third-party clients or services, to easily enable new functionality as new datasets and services become available. In this presentation, we will discuss our experience in designing UUI services based on open industry standards. We will also explain how the resulting framework can be used for a rapid development, deployment, and integration of spatial data services, facilitating efficient access and dissemination of spatial data sets.

  4. UUI: Reusable Spatial Data Services in Unified User Interface at NASA GES DISC

    Science.gov (United States)

    Petrenko, M.; Hegde, M.; Bryant, K.; Pham, L.

    2016-12-01

    Unified User Interface (UUI) is a next-generation operational data access tool that has been developed at Goddard Earth Sciences Data and Information Services Center (GES DISC) to provide a simple, unified, and intuitive one-stop shop experience for the key data services available at GES DISC, including subsetting (Simple Subset Wizard - SSW), granule file search (Mirador), plotting (Giovanni), and other legacy spatial data services. UUI has been built based on a flexible infrastructure of reusable web services - self-contained building blocks that can easily be plugged into spatial applications, including third-party clients or services, to easily enable new functionality as new datasets and services become available. In this presentation, we will discuss our experience in designing UUI services based on open industry standards. We will also explain how the resulting framework can be used for a rapid development, deployment, and integration of spatial data services, facilitating efficient access and dissemination of spatial data sets.

  5. Estimating spatial travel times using automatic vehicle identification data

    Science.gov (United States)

    2001-01-01

    Prepared ca. 2001. The paper describes an algorithm that was developed for estimating reliable and accurate average roadway link travel times using Automatic Vehicle Identification (AVI) data. The algorithm presented is unique in two aspects. First, ...

  6. Analysis and integration of spatial data for transportation planning.

    Science.gov (United States)

    2009-06-01

    Transportation planning requires substantial amounts of data and cooperation among transportation planning : agencies. Advances in computer technology and the increasing availability of geographic information : systems (GIS) are giving transportation...

  7. A Spatial Data Infrastructure to Share Earth and Space Science Data

    Science.gov (United States)

    Nativi, S.; Mazzetti, P.; Bigagli, L.; Cuomo, V.

    2006-05-01

    Spatial Data Infrastructure:SDI (also known as Geospatial Data Infrastructure) is fundamentally a mechanism to facilitate the sharing and exchange of geospatial data. SDI is a scheme necessary for the effective collection, management, access, delivery and utilization of geospatial data; it is important for: objective decision making and sound land based policy, support economic development and encourage socially and environmentally sustainable development. As far as data model and semantics are concerned, a valuable and effective SDI should be able to cross the boundaries between the Geographic Information System/Science (GIS) and Earth and Space Science (ESS) communities. Hence, SDI should be able to discover, access and share information and data produced and managed by both GIS and ESS communities, in an integrated way. In other terms, SDI must be built on a conceptual and technological framework which abstracts the nature and structure of shared dataset: feature-based data or Imagery, Gridded and Coverage Data (IGCD). ISO TC211 and the Open Geospatial Consortium provided important artifacts to build up this framework. In particular, the OGC Web Services (OWS) initiatives and several Interoperability Experiment (e.g. the GALEON IE) are extremely useful for this purpose. We present a SDI solution which is able to manage both GIS and ESS datasets. It is based on OWS and other well-accepted or promising technologies, such as: UNIDATA netCDF and CDM, ncML and ncML-GML. Moreover, it uses a specific technology to implement a distributed and federated system of catalogues: the GI-Cat. This technology performs data model mediation and protocol adaptation tasks. It is used to work out a metadata clearinghouse service, implementing a common (federal) catalogue model which is based on the ISO 19115 core metadata for geo-dataset. Nevertheless, other well- accepted or standard catalogue data models can be easily implemented as common view (e.g. OGC CS-W, the next coming

  8. Comparison of different spatial transformations applied to EEG data: A case study of error processing

    NARCIS (Netherlands)

    Cohen, M.X.

    2015-01-01

    The purpose of this paper is to compare the effects of different spatial transformations applied to the same scalp-recorded EEG data. The spatial transformations applied are two referencing schemes (average and linked earlobes), the surface Laplacian, and beamforming (a distributed source

  9. Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

    NARCIS (Netherlands)

    Hoffmann, H.; Zhao, G.; Bussel, van L.G.J.

    2015-01-01

    Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield

  10. a Novel Approach to Veterinary Spatial Epidemiology: Dasymetric Refinement of the Swiss Dog Tumor Registry Data

    Science.gov (United States)

    Boo, G.; Fabrikant, S. I.; Leyk, S.

    2015-08-01

    In spatial epidemiology, disease incidence and demographic data are commonly summarized within larger regions such as administrative units because of privacy concerns. As a consequence, analyses using these aggregated data are subject to the Modifiable Areal Unit Problem (MAUP) as the geographical manifestation of ecological fallacy. In this study, we create small area disease estimates through dasymetric refinement, and investigate the effects on predictive epidemiological models. We perform a binary dasymetric refinement of municipality-aggregated dog tumor incidence counts in Switzerland for the year 2008 using residential land as a limiting ancillary variable. This refinement is expected to improve the quality of spatial data originally aggregated within arbitrary administrative units by deconstructing them into discontinuous subregions that better reflect the underlying population distribution. To shed light on effects of this refinement, we compare a predictive statistical model that uses unrefined administrative units with one that uses dasymetrically refined spatial units. Model diagnostics and spatial distributions of model residuals are assessed to evaluate the model performances in different regions. In particular, we explore changes in the spatial autocorrelation of the model residuals due to spatial refinement of the enumeration units in a selected mountainous region, where the rugged topography induces great shifts of the analytical units i.e., residential land. Such spatial data quality refinement results in a more realistic estimation of the population distribution within administrative units, and thus, in a more accurate modeling of dog tumor incidence patterns. Our results emphasize the benefits of implementing a dasymetric modeling framework in veterinary spatial epidemiology.

  11. Examining heterogeneity and wildfire management expenditures using spatially and temporally descriptive data

    Science.gov (United States)

    Michael S. Hand; Matthew P. Thompson; Dave Calkin

    2016-01-01

    Increasing costs of wildfire management have highlighted the need to better understand suppression expenditures and potential tradeoffs of land management activities that may affect fire risks. Spatially and temporally descriptive data is used to develop a model of wildfire suppression expenditures, providing new insights into the role of spatial and temporal...

  12. Validation of elk resource selection models with spatially independent data

    Science.gov (United States)

    Priscilla K. Coe; Bruce K. Johnson; Michael J. Wisdom; John G. Cook; Marty Vavra; Ryan M. Nielson

    2011-01-01

    Knowledge of how landscape features affect wildlife resource use is essential for informed management. Resource selection functions often are used to make and validate predictions about landscape use; however, resource selection functions are rarely validated with data from landscapes independent of those from which the models were built. This problem has severely...

  13. Applications of spatial statistical network models to stream data

    Science.gov (United States)

    Daniel J. Isaak; Erin E. Peterson; Jay M. Ver Hoef; Seth J. Wenger; Jeffrey A. Falke; Christian E. Torgersen; Colin Sowder; E. Ashley Steel; Marie-Josee Fortin; Chris E. Jordan; Aaron S. Ruesch; Nicholas Som; Pascal. Monestiez

    2014-01-01

    Streams and rivers host a significant portion of Earth's biodiversity and provide important ecosystem services for human populations. Accurate information regarding the status and trends of stream resources is vital for their effective conservation and management. Most statistical techniques applied to data measured on stream networks were developed for...

  14. Spatial variogram estimation from temporally aggregated seabird count data

    NARCIS (Netherlands)

    Perez-Lapena, Blanca; Wijnberg, Kathelijne Mariken; Stein, A.; Hulscher, Suzanne J.M.H.

    2013-01-01

    Seabird abundance is an important indicator for assessing impact of human activities on the marine environment. However, data collection at sea is time consuming and surveys are carried out over several consecutive days for efficiency reasons. This study investigates the validity of aggregating

  15. Mining Co-Location Patterns with Clustering Items from Spatial Data Sets

    Science.gov (United States)

    Zhou, G.; Li, Q.; Deng, G.; Yue, T.; Zhou, X.

    2018-05-01

    The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the spatial data mining. Co-location patterns discovery is an important branch in spatial data mining. Spatial co-locations represent the subsets of features which are frequently located together in geographic space. However, the appearance of a spatial feature C is often not determined by a single spatial feature A or B but by the two spatial features A and B, that is to say where A and B appear together, C often appears. We note that this co-location pattern is different from the traditional co-location pattern. Thus, this paper presents a new concept called clustering terms, and this co-location pattern is called co-location patterns with clustering items. And the traditional algorithm cannot mine this co-location pattern, so we introduce the related concept in detail and propose a novel algorithm. This algorithm is extended by join-based approach proposed by Huang. Finally, we evaluate the performance of this algorithm.

  16. Spatial data analysis and integration for regional-scale geothermal potential mapping, West Java, Indonesia

    Energy Technology Data Exchange (ETDEWEB)

    Carranza, Emmanuel John M.; Barritt, Sally D. [Department of Earth Systems Analysis, International Institute for Geo-information Science and Earth Observation (ITC), Enschede (Netherlands); Wibowo, Hendro; Sumintadireja, Prihadi [Laboratory of Volcanology and Geothermal, Geology Department, Institute of Technology Bandung (ITB), Bandung (Indonesia)

    2008-06-15

    Conceptual modeling and predictive mapping of potential for geothermal resources at the regional-scale in West Java are supported by analysis of the spatial distribution of geothermal prospects and thermal springs, and their spatial associations with geologic features derived from publicly available regional-scale spatial data sets. Fry analysis shows that geothermal occurrences have regional-scale spatial distributions that are related to Quaternary volcanic centers and shallow earthquake epicenters. Spatial frequency distribution analysis shows that geothermal occurrences have strong positive spatial associations with Quaternary volcanic centers, Quaternary volcanic rocks, quasi-gravity lows, and NE-, NNW-, WNW-trending faults. These geological features, with their strong positive spatial associations with geothermal occurrences, constitute spatial recognition criteria of regional-scale geothermal potential in a study area. Application of data-driven evidential belief functions in GIS-based predictive mapping of regional-scale geothermal potential resulted in delineation of high potential zones occupying 25% of West Java, which is a substantial reduction of the search area for further exploration of geothermal resources. The predicted high potential zones delineate about 53-58% of the training geothermal areas and 94% of the validated geothermal occurrences. The results of this study demonstrate the value of regional-scale geothermal potential mapping in: (a) data-poor situations, such as West Java, and (b) regions with geotectonic environments similar to the study area. (author)

  17. Hybrid Spatial Data Model for Indoor Space: Combined Topology and Grid

    Directory of Open Access Journals (Sweden)

    Zhiyong Lin

    2017-11-01

    Full Text Available The construction and application of an indoor spatial data model is an important prerequisite to meet the requirements of diversified indoor spatial location services. The traditional indoor spatial topology model focuses on the construction of topology information. It has high path analysis and query efficiency, but ignores the spatial location information. The grid model retains the plane position information by grid, but increases the data volume and complexity of the model and reduces the efficiency of the model analysis. This paper presents a hybrid model for interior space based on topology and grid. Based on the spatial meshing and spatial division of the interior space, the model retains the position information and topological connectivity information of the interior space by establishing the connection or affiliation between the grid subspace and the topological subspace. The model improves the speed of interior spatial analysis and solves the problem of the topology information and location information updates not being synchronized. In this study, the A* shortest path query efficiency of typical daily indoor activities under the grid model and the hybrid model were compared for the indoor plane of an apartment and a shopping mall. The results obtained show that the hybrid model is 43% higher than the A* algorithm of the grid model as a result of the existence of topology communication information. This paper provides a useful idea for the establishment of a highly efficient and highly available interior spatial data model.

  18. Foundational Data Products for Europa: A Planetary Spatial Data Infrastructure Example

    Science.gov (United States)

    Archinal, B. A.; Laura, J.; Becker, T. L.; Bland, M. T.; Kirk, R. L.

    2017-12-01

    Any Spatial Data Infrastructure (SDI), including a Planetary SDI (PSDI [1]), includes primary components such as "policy, access network, technical standards, people (including partnerships), and data" [2]. Data is largely categorized into critical foundational products and framework data products. Of data themes [3] previously identified for the U. S. National SDI, we identify [4] three types of products that are foundational to a PSDI: geodetic coordinate reference systems, elevation information, and orthomosaics. We previously listed examples of such products for the Moon (ibid.). Here, we list the current state of these three foundational products for Europa, a key destination in the outer Solar System. Geodetic coordinate reference systems for Europa are based on photogrammetric control networks generated from processing of Voyager and Galileo images, the most recent being that created by M. Davies and T. Colvin at The RAND Corporation in the late 1990s. The Voyager and Galileo images provide insufficient stereo coverage to derive a detailed global topographic model, but various global ellipsoidal shape models have been derived using e.g. the RAND network or limb profile data. The best-known global mosaic of Europa is a controlled orthomosaic produced by the U.S. Geological Survey [5], based on the RAND network and triaxial ellipsoid shape model. Near future needs include comparing the resolution and accuracy of these products with estimates for newer products that might supersede them, including released or unreleased regional products (such as digital terrain models or mosaics) and products that could be made by processing of extant data. Understanding these PSDI fundamental needs will also improve assessing and prioritizing products that are planned for by the upcoming NASA Europa Clipper mission. This effort is not only useful for Europa science, but is also a first step toward developing such summaries for all Solar System bodies with relevant data, which

  19. Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Gang Li

    2016-09-01

    Full Text Available The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs. Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection. These two parts are pursued separately in existing works. In this work, the combination of temporal data-driven sleep scheduling (TDSS and spatial data-driven anomaly detection is proposed, where TDSS can reduce data redundancy. The TDSS model is inspired by transmission control protocol (TCP congestion control. Based on long and linear cluster structure in the tunnel monitoring system, cooperative TDSS and spatial data-driven anomaly detection are then proposed. To realize synchronous acquisition in the same ring for analyzing the situation of every ring, TDSS is implemented in a cooperative way in the cluster. To keep the precision of sensor data, spatial data-driven anomaly detection based on the spatial correlation and Kriging method is realized to generate an anomaly indicator. The experiment results show that cooperative TDSS can realize non-uniform sensing effectively to reduce the energy consumption. In addition, spatial data-driven anomaly detection is quite significant for maintaining and improving the precision of sensor data.

  20. Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.

    Directory of Open Access Journals (Sweden)

    David W Redding

    Full Text Available Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species' ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT, to a spatial Bayesian SDM method (fitted using R-INLA, when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1-3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10-12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account

  1. Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.

    Science.gov (United States)

    Redding, David W; Lucas, Tim C D; Blackburn, Tim M; Jones, Kate E

    2017-01-01

    Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species' ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT), to a spatial Bayesian SDM method (fitted using R-INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1-3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10-12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account for spatial

  2. Spatial data management for sustainable land-use

    DEFF Research Database (Denmark)

    Hansen, Henning Sten

    2006-01-01

    as the need for open spaces and green areas, and the protection of sensitive ecosystems. With on average 117.5 people per square kilometre in Europe, it is easy to see why land use planning and management is such an important environmental issue for the EU. SENSOR (http://www.sensor-ip.org/) is funded under...... of its policies on multifunctional and sustainable land-use. Access to reliable and harmonised data across Europe is a fundamental precondition for realisation of the SENSOR project. Interoperability and open architectures are core requirements for state of the art implementations of IT solutions...

  3. Building the IDECi-UIB: the scientific spatial data infrastructure node for the Balearic Islands University

    NARCIS (Netherlands)

    Giner, L.G.; Pérez, M.R.; Stuiver, H.J.

    2011-01-01

    Technical and methodological enhancements in Information Technologies (IT) and Geographical Information Systems (GIS) has permitted the growth in Spatial Data Infrastructures (SDI) performance. In this way, their uses and applications have grown very rapidly. In the scientific and educational

  4. Data on strategically located land and spatially integrated urban human settlements in South Africa.

    Science.gov (United States)

    Musakwa, Walter

    2017-12-01

    In developing countries like South Africa processed geographic information systems (GIS) data on land suitability, is often not available for land use management. Data in this article is based on a published article "The strategically located land index support system for humans settlements land reform in South Africa" (Musakwa et al., 2017) [1]. This article utilities data from Musakwa et al. (2017) [1] and it goes on a step further by presenting the top 25th percentile of areas in the country that are strategically located and suited to develop spatially integrated human settlements. Furthermore the least 25th percentile of the country that are not strategically located and spatially integrated to establish human settlements are also presented. The article also presents the processed spatial datasets that where used to develop the strategically located land index as supplementary material. The data presented is meant to stir debate on spatially integrated human settlements in South Africa.

  5. Spatial and Semantic Validation of Secondary Food Source Data

    DEFF Research Database (Denmark)

    Lyseen, Anders Knørr; Hansen, Henning Sten

    2014-01-01

    Governmental and commercial lists of food retailers are often used to measure food environments and foodscapes for health and nutritional research. Information about the validity of such secondary food source data is relevant to understanding the potential and limitations of its application....... This study assesses the validity of two government lists of food retailer locations and types by comparing them to direct field observations, including an assessment of whether pre-classification of the directories can reduce the need for field observation. Lists of food retailers were obtained from......-classification was measured through the calculation of PPV, sensitivity and negative prediction value (NPV). The application of either CVR or Smiley as a measure of the food environment would result in a misrepresentation. The pre-classification based on the food retailer names was found to be a valid method for identifying...

  6. Spatial-Temporal Similarity Correlation between Public Transit Passengers Using Smart Card Data

    Directory of Open Access Journals (Sweden)

    Hamed Faroqi

    2017-01-01

    Full Text Available The increasing availability of public transit smart card data has enabled several studies to focus on identifying passengers with similar spatial and/or temporal trip characteristics. However, this paper goes one step further by investigating the relationship between passengers’ spatial and temporal characteristics. For the first time, this paper investigates the correlation of the spatial similarity with the temporal similarity between public transit passengers by developing spatial similarity and temporal similarity measures for the public transit network with a novel passenger-based perspective. The perspective considers the passengers as agents who can make multiple trips in the network. The spatial similarity measure takes into account direction as well as the distance between the trips of the passengers. The temporal similarity measure considers both the boarding and alighting time in a continuous linear space. The spatial-temporal similarity correlation between passengers is analysed using histograms, Pearson correlation coefficients, and hexagonal binning. Also, relations between the spatial and temporal similarity values with the trip time and length are examined. The proposed methodology is implemented for four-day smart card data including 80,000 passengers in Brisbane, Australia. The results show a nonlinear spatial-temporal similarity correlation among the passengers.

  7. Portraying Temporal Dynamics of Urban Spatial Divisions with Mobile Phone Positioning Data: A Complex Network Approach

    Directory of Open Access Journals (Sweden)

    Meng Zhou

    2016-12-01

    Full Text Available Spatial structure is a fundamental characteristic of cities that influences the urban functioning to a large extent. While administrative partitioning is generally done in the form of static spatial division, understanding a more temporally dynamic structure of the urban space would benefit urban planning and management immensely. This study makes use of a large-scale mobile phone positioning dataset to characterize the diurnal dynamics of the interaction-based urban spatial structure. To extract the temporally vibrant structure, spatial interaction networks at different times are constructed based on the movement connections of individuals between geographical units. Complex network community detection technique is applied to identify the spatial divisions as well as to quantify their temporal dynamics. Empirical analysis is conducted using data containing all user positions on a typical weekday in Shenzhen, China. Results are compared with official zoning and planned structure and indicate a certain degree of expansion in urban central areas and fragmentation in industrial suburban areas. A high level of variability in spatial divisions at different times of day is detected with some distinct temporal features. Peak and pre-/post-peak hours witness the most prominent fluctuation in spatial division indicating significant change in the characteristics of movements and activities during these periods of time. Findings of this study demonstrate great potential of large-scale mobility data in supporting intelligent spatial decision making and providing valuable knowledge to the urban planning sectors.

  8. Spatial data integration for mineral exploration, resource assessment and environmental studies: A guidebook

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1994-12-01

    The International Atomic Energy Agency has played a significant role over the years in the improvement and use of uranium exploration techniques and data obtained through uranium exploration. Numerous documents on uranium geology and exploration methods have been published. The purpose of this document is to provide an introduction to the new tools and applications of computer based spatial data integration as used by geologists. In order to provide the experts involved in uranium exploration with information on recent developments in computer applications for spatial data integration and image processing, the IAEA convened consultants meetings in November 1991 and November 1992 to produce this guidebook, which contains information on spatially distributed data, data capture, database creation and visualization of data. Vector, and in particular, raster data types and the aspects of integration modelling are discussed. Refs, figs, tabs, 16 plates.

  9. Spatial data integration for mineral exploration, resource assessment and environmental studies: A guidebook

    International Nuclear Information System (INIS)

    1994-12-01

    The International Atomic Energy Agency has played a significant role over the years in the improvement and use of uranium exploration techniques and data obtained through uranium exploration. Numerous documents on uranium geology and exploration methods have been published. The purpose of this document is to provide an introduction to the new tools and applications of computer based spatial data integration as used by geologists. In order to provide the experts involved in uranium exploration with information on recent developments in computer applications for spatial data integration and image processing, the IAEA convened consultants meetings in November 1991 and November 1992 to produce this guidebook, which contains information on spatially distributed data, data capture, database creation and visualization of data. Vector, and in particular, raster data types and the aspects of integration modelling are discussed. Refs, figs, tabs, 16 plates

  10. Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis.

    Science.gov (United States)

    Westerholt, Rene; Steiger, Enrico; Resch, Bernd; Zipf, Alexander

    2016-01-01

    Twitter and related social media feeds have become valuable data sources to many fields of research. Numerous researchers have thereby used social media posts for spatial analysis, since many of them contain explicit geographic locations. However, despite its widespread use within applied research, a thorough understanding of the underlying spatial characteristics of these data is still lacking. In this paper, we investigate how topological outliers influence the outcomes of spatial analyses of social media data. These outliers appear when different users contribute heterogeneous information about different phenomena simultaneously from similar locations. As a consequence, various messages representing different spatial phenomena are captured closely to each other, and are at risk to be falsely related in a spatial analysis. Our results reveal indications for corresponding spurious effects when analyzing Twitter data. Further, we show how the outliers distort the range of outcomes of spatial analysis methods. This has significant influence on the power of spatial inferential techniques, and, more generally, on the validity and interpretability of spatial analysis results. We further investigate how the issues caused by topological outliers are composed in detail. We unveil that multiple disturbing effects are acting simultaneously and that these are related to the geographic scales of the involved overlapping patterns. Our results show that at some scale configurations, the disturbances added through overlap are more severe than at others. Further, their behavior turns into a volatile and almost chaotic fluctuation when the scales of the involved patterns become too different. Overall, our results highlight the critical importance of thoroughly considering the specific characteristics of social media data when analyzing them spatially.

  11. Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics with Level Set Transformation.

    Energy Technology Data Exchange (ETDEWEB)

    Hammond, Glenn Edward; Song, Xuehang; Ye, Ming; Dai, Zhenxue; Zachara, John; Chen, Xingyuan

    2017-03-01

    A new approach is developed to delineate the spatial distribution of discrete facies (geological units that have unique distributions of hydraulic, physical, and/or chemical properties) conditioned not only on direct data (measurements directly related to facies properties, e.g., grain size distribution obtained from borehole samples) but also on indirect data (observations indirectly related to facies distribution, e.g., hydraulic head and tracer concentration). Our method integrates for the first time ensemble data assimilation with traditional transition probability-based geostatistics. The concept of level set is introduced to build shape parameterization that allows transformation between discrete facies indicators and continuous random variables. The spatial structure of different facies is simulated by indicator models using conditioning points selected adaptively during the iterative process of data assimilation. To evaluate the new method, a two-dimensional semi-synthetic example is designed to estimate the spatial distribution and permeability of two distinct facies from transient head data induced by pumping tests. The example demonstrates that our new method adequately captures the spatial pattern of facies distribution by imposing spatial continuity through conditioning points. The new method also reproduces the overall response in hydraulic head field with better accuracy compared to data assimilation with no constraints on spatial continuity on facies.

  12. ESTIMATING GROSS PRIMARY PRODUCTION IN CROPLAND WITH HIGH SPATIAL AND TEMPORAL SCALE REMOTE SENSING DATA

    Directory of Open Access Journals (Sweden)

    S. Lin

    2018-04-01

    Full Text Available Satellite remote sensing data provide spatially continuous and temporally repetitive observations of land surfaces, and they have become increasingly important for monitoring large region of vegetation photosynthetic dynamic. But remote sensing data have their limitation on spatial and temporal scale, for example, higher spatial resolution data as Landsat data have 30-m spatial resolution but 16 days revisit period, while high temporal scale data such as geostationary data have 30-minute imaging period, which has lower spatial resolution (> 1 km. The objective of this study is to investigate whether combining high spatial and temporal resolution remote sensing data can improve the gross primary production (GPP estimation accuracy in cropland. For this analysis we used three years (from 2010 to 2012 Landsat based NDVI data, MOD13 vegetation index product and Geostationary Operational Environmental Satellite (GOES geostationary data as input parameters to estimate GPP in a small region cropland of Nebraska, US. Then we validated the remote sensing based GPP with the in-situ measurement carbon flux data. Results showed that: 1 the overall correlation between GOES visible band and in-situ measurement photosynthesis active radiation (PAR is about 50 % (R2 = 0.52 and the European Center for Medium-Range Weather Forecasts ERA-Interim reanalysis data can explain 64 % of PAR variance (R2 = 0.64; 2 estimating GPP with Landsat 30-m spatial resolution data and ERA daily meteorology data has the highest accuracy(R2 = 0.85, RMSE < 3 gC/m2/day, which has better performance than using MODIS 1-km NDVI/EVI product import; 3 using daily meteorology data as input for GPP estimation in high spatial resolution data would have higher relevance than 8-day and 16-day input. Generally speaking, using the high spatial resolution and high frequency satellite based remote sensing data can improve GPP estimation accuracy in cropland.

  13. Aspects of the incorporation of spatial data into radioecological and restoration analysis

    International Nuclear Information System (INIS)

    Beresford, N.A.; Wright, S.M.; Howard, B.J.; Crout, N.M.J.; Arkhipov, A.; Voigt, G.

    2002-01-01

    In the last decade geographical information systems have been increasingly used to incorporate spatial data into radioecological analysis. This has allowed the development of models with spatially variable outputs. Two main approaches have been adopted in the development of spatial models. Empirical Tag based models applied across a range of spatial scales utilize underlying soil type maps and readily available radioecological data. Soil processes can also be modelled to allow the dynamic prediction of radionuclide soil to plant transfer. We discuss a dynamic semi-mechanistic radiocaesium soil to plant-transfer model, which utilizes readily available spatially variable soil parameters. Both approaches allow the identification of areas that may be vulnerable to radionuclide deposition, therefore enabling the targeting of intervention measures. Improved estimates of radionuclide fluxes and ingestion doses can be achieved by incorporating spatially varying inputs such as agricultural production and dietary habits in to these models. In this paper, aspects of such models, including data requirements, implementation and outputs are discussed and critically evaluated. The relative merits and disadvantages of the two spatial model approaches adopted within radioecology are discussed. We consider the usefulness of such models to aid decision-makers and access the requirements and potential of further application within radiological protection. (author)

  14. Integrative Spatial Data Analytics for Public Health Studies of New York State.

    Science.gov (United States)

    Chen, Xin; Wang, Fusheng

    2016-01-01

    Increased accessibility of health data made available by the government provides unique opportunity for spatial analytics with much higher resolution to discover patterns of diseases, and their correlation with spatial impact indicators. This paper demonstrated our vision of integrative spatial analytics for public health by linking the New York Cancer Mapping Dataset with datasets containing potential spatial impact indicators. We performed spatial based discovery of disease patterns and variations across New York State, and identify potential correlations between diseases and demographic, socio-economic and environmental indicators. Our methods were validated by three correlation studies: the correlation between stomach cancer and Asian race, the correlation between breast cancer and high education population, and the correlation between lung cancer and air toxics. Our work will allow public health researchers, government officials or other practitioners to adequately identify, analyze, and monitor health problems at the community or neighborhood level for New York State.

  15. Spatial-temporal data model and fractal analysis of transportation network in GIS environment

    Science.gov (United States)

    Feng, Yongjiu; Tong, Xiaohua; Li, Yangdong

    2008-10-01

    How to organize transportation data characterized by multi-time, multi-scale, multi-resolution and multi-source is one of the fundamental problems of GIS-T development. A spatial-temporal data model for GIS-T is proposed based on Spatial-temporal- Object Model. Transportation network data is systemically managed using dynamic segmentation technologies. And then a spatial-temporal database is built to integrally store geographical data of multi-time for transportation. Based on the spatial-temporal database, functions of spatial analysis of GIS-T are substantively extended. Fractal module is developed to improve the analyzing in intensity, density, structure and connectivity of transportation network based on the validation and evaluation of topologic relation. Integrated fractal with GIS-T strengthens the functions of spatial analysis and enriches the approaches of data mining and knowledge discovery of transportation network. Finally, the feasibility of the model and methods are tested thorough Guangdong Geographical Information Platform for Highway Project.

  16. Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation

    Directory of Open Access Journals (Sweden)

    Tatem Andrew J

    2012-05-01

    Full Text Available Abstract The use of Global Positioning Systems (GPS and Geographical Information Systems (GIS in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models. Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites. In this paper we discuss the deficiencies of existing

  17. GéoSAS: A modular and interoperable Open Source Spatial Data Infrastructure for research

    Directory of Open Access Journals (Sweden)

    R. Bera

    2015-05-01

    Full Text Available To-date, the commonest way to deal with geographical information and processes still appears to consume local resources, i.e. locally stored data processed on a local desktop or server. The maturity and subsequent growing use of OGC standards to exchange data on the World Wide Web, enhanced in Europe by the INSPIRE Directive, is bound to change the way people (and among them research scientists, especially in environmental sciences make use of, and manage, spatial data. A clever use of OGC standards can help scientists to better store, share and use data, in particular for modelling. We propose a framework for online processing by making an intensive use of OGC standards. We illustrate it using the Spatial Data Infrastructure (SDI GéoSAS which is the SDI set up for researchers’ needs in our department. It is based on the existing open source, modular and interoperable Spatial Data Architecture geOrchestra.

  18. GéoSAS: A modular and interoperable Open Source Spatial Data Infrastructure for research

    Science.gov (United States)

    Bera, R.; Squividant, H.; Le Henaff, G.; Pichelin, P.; Ruiz, L.; Launay, J.; Vanhouteghem, J.; Aurousseau, P.; Cudennec, C.

    2015-05-01

    To-date, the commonest way to deal with geographical information and processes still appears to consume local resources, i.e. locally stored data processed on a local desktop or server. The maturity and subsequent growing use of OGC standards to exchange data on the World Wide Web, enhanced in Europe by the INSPIRE Directive, is bound to change the way people (and among them research scientists, especially in environmental sciences) make use of, and manage, spatial data. A clever use of OGC standards can help scientists to better store, share and use data, in particular for modelling. We propose a framework for online processing by making an intensive use of OGC standards. We illustrate it using the Spatial Data Infrastructure (SDI) GéoSAS which is the SDI set up for researchers' needs in our department. It is based on the existing open source, modular and interoperable Spatial Data Architecture geOrchestra.

  19. Towards Building a High Performance Spatial Query System for Large Scale Medical Imaging Data.

    Science.gov (United States)

    Aji, Ablimit; Wang, Fusheng; Saltz, Joel H

    2012-11-06

    Support of high performance queries on large volumes of scientific spatial data is becoming increasingly important in many applications. This growth is driven by not only geospatial problems in numerous fields, but also emerging scientific applications that are increasingly data- and compute-intensive. For example, digital pathology imaging has become an emerging field during the past decade, where examination of high resolution images of human tissue specimens enables more effective diagnosis, prediction and treatment of diseases. Systematic analysis of large-scale pathology images generates tremendous amounts of spatially derived quantifications of micro-anatomic objects, such as nuclei, blood vessels, and tissue regions. Analytical pathology imaging provides high potential to support image based computer aided diagnosis. One major requirement for this is effective querying of such enormous amount of data with fast response, which is faced with two major challenges: the "big data" challenge and the high computation complexity. In this paper, we present our work towards building a high performance spatial query system for querying massive spatial data on MapReduce. Our framework takes an on demand index building approach for processing spatial queries and a partition-merge approach for building parallel spatial query pipelines, which fits nicely with the computing model of MapReduce. We demonstrate our framework on supporting multi-way spatial joins for algorithm evaluation and nearest neighbor queries for microanatomic objects. To reduce query response time, we propose cost based query optimization to mitigate the effect of data skew. Our experiments show that the framework can efficiently support complex analytical spatial queries on MapReduce.

  20. Bayesian prediction of spatial count data using generalized linear mixed models

    DEFF Research Database (Denmark)

    Christensen, Ole Fredslund; Waagepetersen, Rasmus Plenge

    2002-01-01

    Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, ...

  1. Statistics for Time-Series Spatial Data: Applying Survival Analysis to Study Land-Use Change

    Science.gov (United States)

    Wang, Ninghua Nathan

    2013-01-01

    Traditional spatial analysis and data mining methods fall short of extracting temporal information from data. This inability makes their use difficult to study changes and the associated mechanisms of many geographic phenomena of interest, for example, land-use. On the other hand, the growing availability of land-change data over multiple time…

  2. Anthropogenic heat flux: advisable spatial resolutions when input data are scarce

    Science.gov (United States)

    Gabey, A. M.; Grimmond, C. S. B.; Capel-Timms, I.

    2018-02-01

    Anthropogenic heat flux (QF) may be significant in cities, especially under low solar irradiance and at night. It is of interest to many practitioners including meteorologists, city planners and climatologists. QF estimates at fine temporal and spatial resolution can be derived from models that use varying amounts of empirical data. This study compares simple and detailed models in a European megacity (London) at 500 m spatial resolution. The simple model (LQF) uses spatially resolved population data and national energy statistics. The detailed model (GQF) additionally uses local energy, road network and workday population data. The Fractions Skill Score (FSS) and bias are used to rate the skill with which the simple model reproduces the spatial patterns and magnitudes of QF, and its sub-components, from the detailed model. LQF skill was consistently good across 90% of the city, away from the centre and major roads. The remaining 10% contained elevated emissions and "hot spots" representing 30-40% of the total city-wide energy. This structure was lost because it requires workday population, spatially resolved building energy consumption and/or road network data. Daily total building and traffic energy consumption estimates from national data were within ± 40% of local values. Progressively coarser spatial resolutions to 5 km improved skill for total QF, but important features (hot spots, transport network) were lost at all resolutions when residential population controlled spatial variations. The results demonstrate that simple QF models should be applied with conservative spatial resolution in cities that, like London, exhibit time-varying energy use patterns.

  3. A DATA FIELD METHOD FOR URBAN REMOTELY SENSED IMAGERY CLASSIFICATION CONSIDERING SPATIAL CORRELATION

    Directory of Open Access Journals (Sweden)

    Y. Zhang

    2016-06-01

    Full Text Available Spatial correlation between pixels is important information for remotely sensed imagery classification. Data field method and spatial autocorrelation statistics have been utilized to describe and model spatial information of local pixels. The original data field method can represent the spatial interactions of neighbourhood pixels effectively. However, its focus on measuring the grey level change between the central pixel and the neighbourhood pixels results in exaggerating the contribution of the central pixel to the whole local window. Besides, Geary’s C has also been proven to well characterise and qualify the spatial correlation between each pixel and its neighbourhood pixels. But the extracted object is badly delineated with the distracting salt-and-pepper effect of isolated misclassified pixels. To correct this defect, we introduce the data field method for filtering and noise limitation. Moreover, the original data field method is enhanced by considering each pixel in the window as the central pixel to compute statistical characteristics between it and its neighbourhood pixels. The last step employs a support vector machine (SVM for the classification of multi-features (e.g. the spectral feature and spatial correlation feature. In order to validate the effectiveness of the developed method, experiments are conducted on different remotely sensed images containing multiple complex object classes inside. The results show that the developed method outperforms the traditional method in terms of classification accuracies.

  4. Time asymmetric spacetimes near null and spatial infinity: I. Expansions of developments of conformally flat data

    International Nuclear Information System (INIS)

    Kroon, Juan Antonio Valiente

    2004-01-01

    The conformal Einstein equations and the representation of spatial infinity as a cylinder introduced by Friedrich are used to analyse the behaviour of the gravitational field near null and spatial infinity for the development of data which are asymptotically Euclidean, conformally flat and time asymmetric. Our analysis allows for initial data whose second fundamental form is more general than the one given by the standard Bowen-York ansatz. The conformal Einstein equations imply, upon evaluation on the cylinder at spatial infinity, a hierarchy of transport equations which can be used to calculate asymptotic expansions for the gravitational field in a recursive way. It is found that the solutions to these transport equations develop logarithmic divergences at the critical sets where null infinity meets spatial infinity. Associated with these, there is a series of quantities expressible in terms of the initial data (obstructions), which if zero, preclude the appearance of some of the logarithmic divergences. The obstructions are, in general, time asymmetric. That is, the obstructions at the intersection of future null infinity with spatial infinity are in general different from those obtained at the intersection of past null infinity with spatial infinity. The latter allows for the possibility of having spacetimes where future and past null infinity have different degrees of smoothness. Finally, it is shown that if both sets of obstructions vanish up to a certain order, then the initial data have to be asymptotically Schwarzschildean in a certain sense

  5. Comparison of different spatial transformations applied to EEG data: A case study of error processing.

    Science.gov (United States)

    Cohen, Michael X

    2015-09-01

    The purpose of this paper is to compare the effects of different spatial transformations applied to the same scalp-recorded EEG data. The spatial transformations applied are two referencing schemes (average and linked earlobes), the surface Laplacian, and beamforming (a distributed source localization procedure). EEG data were collected during a speeded reaction time task that provided a comparison of activity between error vs. correct responses. Analyses focused on time-frequency power, frequency band-specific inter-electrode connectivity, and within-subject cross-trial correlations between EEG activity and reaction time. Time-frequency power analyses showed similar patterns of midfrontal delta-theta power for errors compared to correct responses across all spatial transformations. Beamforming additionally revealed error-related anterior and lateral prefrontal beta-band activity. Within-subject brain-behavior correlations showed similar patterns of results across the spatial transformations, with the correlations being the weakest after beamforming. The most striking difference among the spatial transformations was seen in connectivity analyses: linked earlobe reference produced weak inter-site connectivity that was attributable to volume conduction (zero phase lag), while the average reference and Laplacian produced more interpretable connectivity results. Beamforming did not reveal any significant condition modulations of connectivity. Overall, these analyses show that some findings are robust to spatial transformations, while other findings, particularly those involving cross-trial analyses or connectivity, are more sensitive and may depend on the use of appropriate spatial transformations. Copyright © 2014 Elsevier B.V. All rights reserved.

  6. Spatial data efficient transmission in WebGIS based on IPv6

    Science.gov (United States)

    Wang, Zhen-feng; Liu, Ji-ping; Wang, Liang; Tao, Kun-wang

    2008-12-01

    Large-size of spatial data and limited bandwidth of network make it restricted to transmit spatial data in WebGIS. This paper employs IPv6 (Internet Protocol version 6), the successor of IPv4 running now, to transmit spatial data efficiently. As the core of NGN (Next Generation Network), IPv6 brings us many advantages to resolve performance problems in current IPv4 network applications. Multicast, which is mandatory in IPv6 routers, can make one server serve many clients simultaneously efficiently, thus to improve capacity of network applications. The new type of anycast address in IPv6 will make network client applications possible to find the nearest server. This makes data transmission between client and server fastest. The paper introduces how to apply IPv6 multicast and anycast in WebGIS to transmit data efficiently.

  7. The fusion of satellite and UAV data: simulation of high spatial resolution band

    Science.gov (United States)

    Jenerowicz, Agnieszka; Siok, Katarzyna; Woroszkiewicz, Malgorzata; Orych, Agata

    2017-10-01

    Remote sensing techniques used in the precision agriculture and farming that apply imagery data obtained with sensors mounted on UAV platforms became more popular in the last few years due to the availability of low- cost UAV platforms and low- cost sensors. Data obtained from low altitudes with low- cost sensors can be characterised by high spatial and radiometric resolution but quite low spectral resolution, therefore the application of imagery data obtained with such technology is quite limited and can be used only for the basic land cover classification. To enrich the spectral resolution of imagery data acquired with low- cost sensors from low altitudes, the authors proposed the fusion of RGB data obtained with UAV platform with multispectral satellite imagery. The fusion is based on the pansharpening process, that aims to integrate the spatial details of the high-resolution panchromatic image with the spectral information of lower resolution multispectral or hyperspectral imagery to obtain multispectral or hyperspectral images with high spatial resolution. The key of pansharpening is to properly estimate the missing spatial details of multispectral images while preserving their spectral properties. In the research, the authors presented the fusion of RGB images (with high spatial resolution) obtained with sensors mounted on low- cost UAV platforms and multispectral satellite imagery with satellite sensors, i.e. Landsat 8 OLI. To perform the fusion of UAV data with satellite imagery, the simulation of the panchromatic bands from RGB data based on the spectral channels linear combination, was conducted. Next, for simulated bands and multispectral satellite images, the Gram-Schmidt pansharpening method was applied. As a result of the fusion, the authors obtained several multispectral images with very high spatial resolution and then analysed the spatial and spectral accuracies of processed images.

  8. A method to combine non-probability sample data with probability sample data in estimating spatial means of environmental variables

    NARCIS (Netherlands)

    Brus, D.J.; Gruijter, de J.J.

    2003-01-01

    In estimating spatial means of environmental variables of a region from data collected by convenience or purposive sampling, validity of the results can be ensured by collecting additional data through probability sampling. The precision of the pi estimator that uses the probability sample can be

  9. Analysis and Research on Spatial Data Storage Model Based on Cloud Computing Platform

    Science.gov (United States)

    Hu, Yong

    2017-12-01

    In this paper, the data processing and storage characteristics of cloud computing are analyzed and studied. On this basis, a cloud computing data storage model based on BP neural network is proposed. In this data storage model, it can carry out the choice of server cluster according to the different attributes of the data, so as to complete the spatial data storage model with load balancing function, and have certain feasibility and application advantages.

  10. APPLICABILITY OF VARIOUS INTERPOLATION APPROACHES FOR HIGH RESOLUTION SPATIAL MAPPING OF CLIMATE DATA IN KOREA

    Directory of Open Access Journals (Sweden)

    A. Jo

    2018-04-01

    Full Text Available The purpose of this study is to create a new dataset of spatially interpolated monthly climate data for South Korea at high spatial resolution (approximately 30m by performing various spatio-statistical interpolation and comparing with forecast LDAPS gridded climate data provided from Korea Meterological Administration (KMA. Automatic Weather System (AWS and Automated Synoptic Observing System (ASOS data in 2017 obtained from KMA were included for the spatial mapping of temperature and rainfall; instantaneous temperature and 1-hour accumulated precipitation at 09:00 am on 31th March, 21th June, 23th September, and 24th December. Among observation data, 80 percent of the total point (478 and remaining 120 points were used for interpolations and for quantification, respectively. With the training data and digital elevation model (DEM with 30 m resolution, inverse distance weighting (IDW, co-kriging, and kriging were performed by using ArcGIS10.3.1 software and Python 3.6.4. Bias and root mean square were computed to compare prediction performance quantitatively. When statistical analysis was performed for each cluster using 20 % validation data, co kriging was more suitable for spatialization of instantaneous temperature than other interpolation method. On the other hand, IDW technique was appropriate for spatialization of precipitation.

  11. The Impact of Varying Statutory Arrangements on Spatial Data Sharing and Access in Regional NRM Bodies

    Science.gov (United States)

    Paudyal, D. R.; McDougall, K.; Apan, A.

    2014-12-01

    Spatial information plays an important role in many social, environmental and economic decisions and increasingly acknowledged as a national resource essential for wider societal and environmental benefits. Natural Resource Management is one area where spatial information can be used for improved planning and decision making processes. In Australia, state government organisations are the custodians of spatial information necessary for natural resource management and regional NRM bodies are responsible to regional delivery of NRM activities. The access and sharing of spatial information between government agencies and regional NRM bodies is therefore as an important issue for improving natural resource management outcomes. The aim of this paper is to evaluate the current status of spatial information access, sharing and use with varying statutory arrangements and its impacts on spatial data infrastructure (SDI) development in catchment management sector in Australia. Further, it critically examined whether any trends and significant variations exist due to different institutional arrangements (statutory versus non-statutory) or not. A survey method was used to collect primary data from 56 regional natural resource management (NRM) bodies responsible for catchment management in Australia. Descriptive statistics method was used to show the similarities and differences between statutory and non-statutory arrangements. The key factors which influence sharing and access to spatial information are also explored. The results show the current statutory and administrative arrangements and regional focus for natural resource management is reasonable from a spatial information management perspective and provides an opportunity for building SDI at the catchment scale. However, effective institutional arrangements should align catchment SDI development activities with sub-national and national SDI development activities to address catchment management issues. We found minor

  12. Spatial epidemiology of cancer: a review of data sources, methods and risk factors

    Directory of Open Access Journals (Sweden)

    Rita Roquette

    2017-05-01

    Full Text Available Cancer is a major concern among chronic diseases today. Spatial epidemiology plays a relevant role in this matter and we present here a review of this subject, including a discussion of the literature in terms of the level of geographic data aggregation, risk factors and methods used to analyse the spatial distribution of patterns and spatial clusters. For this purpose, we performed a websearch in the Pubmed and Web of Science databases including studies published between 1979 and 2015. We found 180 papers from 63 journals and noted that spatial epidemiology of cancer has been addressed with more emphasis during the last decade with research based on data mostly extracted from cancer registries and official mortality statistics. In general, the research questions present in the reviewed papers can be classified into three different sets: i analysis of spatial distribution of cancer and/or its temporal evolution; ii risk factors; iii development of data analysis methods and/or evaluation of results obtained from application of existing methods. This review is expected to help promote research in this area through the identification of relevant knowledge gaps. Cancer’s spatial epidemiology represents an important concern, mainly for public health policies design aimed to minimise the impact of chronic disease in specific populations.

  13. A full scale approximation of covariance functions for large spatial data sets

    KAUST Repository

    Sang, Huiyan

    2011-10-10

    Gaussian process models have been widely used in spatial statistics but face tremendous computational challenges for very large data sets. The model fitting and spatial prediction of such models typically require O(n 3) operations for a data set of size n. Various approximations of the covariance functions have been introduced to reduce the computational cost. However, most existing approximations cannot simultaneously capture both the large- and the small-scale spatial dependence. A new approximation scheme is developed to provide a high quality approximation to the covariance function at both the large and the small spatial scales. The new approximation is the summation of two parts: a reduced rank covariance and a compactly supported covariance obtained by tapering the covariance of the residual of the reduced rank approximation. Whereas the former part mainly captures the large-scale spatial variation, the latter part captures the small-scale, local variation that is unexplained by the former part. By combining the reduced rank representation and sparse matrix techniques, our approach allows for efficient computation for maximum likelihood estimation, spatial prediction and Bayesian inference. We illustrate the new approach with simulated and real data sets. © 2011 Royal Statistical Society.

  14. Spatial capture-recapture: a promising method for analyzing data collected using artificial cover objects

    Science.gov (United States)

    Sutherland, Chris; Munoz, David; Miller, David A.W.; Grant, Evan H. Campbell

    2016-01-01

    Spatial capture–recapture (SCR) is a relatively recent development in ecological statistics that provides a spatial context for estimating abundance and space use patterns, and improves inference about absolute population density. SCR has been applied to individual encounter data collected noninvasively using methods such as camera traps, hair snares, and scat surveys. Despite the widespread use of capture-based surveys to monitor amphibians and reptiles, there are few applications of SCR in the herpetological literature. We demonstrate the utility of the application of SCR for studies of reptiles and amphibians by analyzing capture–recapture data from Red-Backed Salamanders, Plethodon cinereus, collected using artificial cover boards. Using SCR to analyze spatial encounter histories of marked individuals, we found evidence that density differed little among four sites within the same forest (on average, 1.59 salamanders/m2) and that salamander detection probability peaked in early October (Julian day 278) reflecting expected surface activity patterns of the species. The spatial scale of detectability, a measure of space use, indicates that the home range size for this population of Red-Backed Salamanders in autumn was 16.89 m2. Surveying reptiles and amphibians using artificial cover boards regularly generates spatial encounter history data of known individuals, which can readily be analyzed using SCR methods, providing estimates of absolute density and inference about the spatial scale of habitat use.

  15. A full scale approximation of covariance functions for large spatial data sets

    KAUST Repository

    Sang, Huiyan; Huang, Jianhua Z.

    2011-01-01

    Gaussian process models have been widely used in spatial statistics but face tremendous computational challenges for very large data sets. The model fitting and spatial prediction of such models typically require O(n 3) operations for a data set of size n. Various approximations of the covariance functions have been introduced to reduce the computational cost. However, most existing approximations cannot simultaneously capture both the large- and the small-scale spatial dependence. A new approximation scheme is developed to provide a high quality approximation to the covariance function at both the large and the small spatial scales. The new approximation is the summation of two parts: a reduced rank covariance and a compactly supported covariance obtained by tapering the covariance of the residual of the reduced rank approximation. Whereas the former part mainly captures the large-scale spatial variation, the latter part captures the small-scale, local variation that is unexplained by the former part. By combining the reduced rank representation and sparse matrix techniques, our approach allows for efficient computation for maximum likelihood estimation, spatial prediction and Bayesian inference. We illustrate the new approach with simulated and real data sets. © 2011 Royal Statistical Society.

  16. Comparison of Urban Human Movements Inferring from Multi-Source Spatial-Temporal Data

    Science.gov (United States)

    Cao, Rui; Tu, Wei; Cao, Jinzhou; Li, Qingquan

    2016-06-01

    The quantification of human movements is very hard because of the sparsity of traditional data and the labour intensive of the data collecting process. Recently, much spatial-temporal data give us an opportunity to observe human movement. This research investigates the relationship of city-wide human movements inferring from two types of spatial-temporal data at traffic analysis zone (TAZ) level. The first type of human movement is inferred from long-time smart card transaction data recording the boarding actions. The second type of human movement is extracted from citywide time sequenced mobile phone data with 30 minutes interval. Travel volume, travel distance and travel time are used to measure aggregated human movements in the city. To further examine the relationship between the two types of inferred movements, the linear correlation analysis is conducted on the hourly travel volume. The obtained results show that human movements inferred from smart card data and mobile phone data have a correlation of 0.635. However, there are still some non-ignorable differences in some special areas. This research not only reveals the citywide spatial-temporal human dynamic but also benefits the understanding of the reliability of the inference of human movements with big spatial-temporal data.

  17. COMPARISON OF URBAN HUMAN MOVEMENTS INFERRING FROM MULTI-SOURCE SPATIAL-TEMPORAL DATA

    Directory of Open Access Journals (Sweden)

    R. Cao

    2016-06-01

    Full Text Available The quantification of human movements is very hard because of the sparsity of traditional data and the labour intensive of the data collecting process. Recently, much spatial-temporal data give us an opportunity to observe human movement. This research investigates the relationship of city-wide human movements inferring from two types of spatial-temporal data at traffic analysis zone (TAZ level. The first type of human movement is inferred from long-time smart card transaction data recording the boarding actions. The second type of human movement is extracted from citywide time sequenced mobile phone data with 30 minutes interval. Travel volume, travel distance and travel time are used to measure aggregated human movements in the city. To further examine the relationship between the two types of inferred movements, the linear correlation analysis is conducted on the hourly travel volume. The obtained results show that human movements inferred from smart card data and mobile phone data have a correlation of 0.635. However, there are still some non-ignorable differences in some special areas. This research not only reveals the citywide spatial-temporal human dynamic but also benefits the understanding of the reliability of the inference of human movements with big spatial-temporal data.

  18. Multi-Antenna Data Collector for Smart Metering Networks with Integrated Source Separation by Spatial Filtering

    Science.gov (United States)

    Quednau, Philipp; Trommer, Ralph; Schmidt, Lorenz-Peter

    2016-03-01

    Wireless transmission systems in smart metering networks share the advantage of lower installation costs due to the expandability of separate infrastructure but suffer from transmission problems. In this paper the issue of interference of wireless transmitted smart meter data with third party systems and data from other meters is investigated and an approach for solving the problem is presented. A multi-channel wireless m-bus receiver was developed to separate the desired data from unwanted interferers by spatial filtering. The according algorithms are presented and the influence of different antenna types on the spatial filtering is investigated. The performance of the spatial filtering is evaluated by extensive measurements in a realistic surrounding with several hundreds of active wireless m-bus transponders. These measurements correspond to the future environment for data-collectors as they took place in rural and urban areas with smart gas meters equipped with wireless m-bus transponders installed in almost all surrounding buildings.

  19. Enabling Spatial OLAP Over Environmental and Farming Data with QB4SOLAP

    DEFF Research Database (Denmark)

    Gur, Nurefsan; Hose, Katja; Pedersen, Torben Bach

    2016-01-01

    Governmental organizations and agencies have been making large amounts of spatial data available on the Semantic Web (SW). However, we still lack efficient techniques for analyzing such large amounts of data as we know them from relational database systems, e.g., multidimensional (MD) data...... warehouses and On-line Analytical Processing (OLAP). A basic prerequisite to enable such advanced analytics is a well-defined schema, which can be defined using the QB4SOLAP vocabulary that provides sufficient context for spatial OLAP (SOLAP). In this paper, we address the challenging problem of MD querying...

  20. A MongoDB-Based Management of Planar Spatial Data with a Flattened R-Tree

    Directory of Open Access Journals (Sweden)

    Longgang Xiang

    2016-07-01

    Full Text Available This paper addresses how to manage planar spatial data using MongoDB, a popular NoSQL database characterized as a document-oriented, rich query language and high availability. The core idea is to flatten a hierarchical R-tree structure into a tabular MongoDB collection, during which R-tree nodes are represented as collection documents and R-tree pointers are expressed as document identifiers. By following this strategy, a storage schema to support R-tree-based create, read, update, and delete (CRUD operations is designed and a module to manage planar spatial data by consuming and maintaining flattened R-tree structure is developed. The R-tree module is then seamlessly integrated into MongoDB, so that users could manipulate planar spatial data with existing command interfaces oriented to geodetic spatial data. The experimental evaluation, using real-world datasets with diverse coverage, types, and sizes, shows that planar spatial data can be effectively managed by MongoDB with our flattened R-tree and, therefore, the application extent of MongoDB will be greatly enlarged. Our work resulted in a MongoDB branch with R-tree support, which has been released on GitHub for open access.

  1. A log-Weibull spatial scan statistic for time to event data.

    Science.gov (United States)

    Usman, Iram; Rosychuk, Rhonda J

    2018-06-13

    Spatial scan statistics have been used for the identification of geographic clusters of elevated numbers of cases of a condition such as disease outbreaks. These statistics accompanied by the appropriate distribution can also identify geographic areas with either longer or shorter time to events. Other authors have proposed the spatial scan statistics based on the exponential and Weibull distributions. We propose the log-Weibull as an alternative distribution for the spatial scan statistic for time to events data and compare and contrast the log-Weibull and Weibull distributions through simulation studies. The effect of type I differential censoring and power have been investigated through simulated data. Methods are also illustrated on time to specialist visit data for discharged patients presenting to emergency departments for atrial fibrillation and flutter in Alberta during 2010-2011. We found northern regions of Alberta had longer times to specialist visit than other areas. We proposed the spatial scan statistic for the log-Weibull distribution as a new approach for detecting spatial clusters for time to event data. The simulation studies suggest that the test performs well for log-Weibull data.

  2. Assessing fitness for use: the expected value of spatial data sets

    NARCIS (Netherlands)

    Bruin, de S.; Bregt, A.K.; Ven, van de M.

    2001-01-01

    This paper proposes and illustrates a decision analytical approach to compare the value of alternative spatial data sets. In contrast to other work addressing value of information, its focus is on value of control. This is a useful concept when choosing the best data set for decision making under

  3. Coherent visualization of spatial data adapted to roles, tasks, and hardware

    Science.gov (United States)

    Wagner, Boris; Peinsipp-Byma, Elisabeth

    2012-06-01

    Modern crisis management requires that users with different roles and computer environments have to deal with a high volume of various data from different sources. For this purpose, Fraunhofer IOSB has developed a geographic information system (GIS) which supports the user depending on available data and the task he has to solve. The system provides merging and visualization of spatial data from various civilian and military sources. It supports the most common spatial data standards (OGC, STANAG) as well as some proprietary interfaces, regardless if these are filebased or database-based. To set the visualization rules generic Styled Layer Descriptors (SLDs) are used, which are an Open Geospatial Consortium (OGC) standard. SLDs allow specifying which data are shown, when and how. The defined SLDs consider the users' roles and task requirements. In addition it is possible to use different displays and the visualization also adapts to the individual resolution of the display. Too high or low information density is avoided. Also, our system enables users with different roles to work together simultaneously using the same data base. Every user is provided with the appropriate and coherent spatial data depending on his current task. These so refined spatial data are served via the OGC services Web Map Service (WMS: server-side rendered raster maps), or the Web Map Tile Service - (WMTS: pre-rendered and cached raster maps).

  4. TOOLS FOR PRESENTING SPATIAL AND TEMPORAL PATTERNS OF ENVIRONMENTAL MONITORING DATA

    Science.gov (United States)

    The EPA Health Effects Research Laboratory has developed this data presentation tool for use with a variety of types of data which may contain spatial and temporal patterns of interest. he technology links mainframe computing power to the new generation of "desktop publishing" ha...

  5. Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks.

    Science.gov (United States)

    Zheng, Haifeng; Li, Jiayin; Feng, Xinxin; Guo, Wenzhong; Chen, Zhonghui; Xiong, Neal

    2017-11-08

    Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit the mobility pattern for spatial-temporal data collection and propose a novel mobile data gathering scheme by employing the Metropolis-Hastings algorithm with delayed acceptance, an improved random walk algorithm for a mobile collector to collect data from a sensing field. The proposed scheme exploits Kronecker compressive sensing (KCS) for spatial-temporal correlation of sensory data by allowing the mobile collector to gather temporal compressive measurements from a small subset of randomly selected nodes along a random routing path. More importantly, from the theoretical perspective we prove that the equivalent sensing matrix constructed from the proposed scheme for spatial-temporal compressible signal can satisfy the property of KCS models. The simulation results demonstrate that the proposed scheme can not only significantly reduce communication cost but also improve recovery accuracy for mobile data gathering compared to the other existing schemes. In particular, we also show that the proposed scheme is robust in unreliable wireless environment under various packet losses. All this indicates that the proposed scheme can be an efficient alternative for data gathering application in WSNs .

  6. A contextual ICA stakeholder model approach for the Namibian spatial data infrastructure (NamSDI)

    CSIR Research Space (South Africa)

    Sinvula, KM

    2013-08-01

    Full Text Available In 2011, the Namibian parliament presented and promulgated the Namibian Spatial Data Infrastructure (NamSDI) with the aim of promoting the sharing and improved access and use of geospatial data and services across Namibia. Notable SDI models...

  7. The backbone of a City Information Model (CIM) : Implementing a spatial data model for urban design

    NARCIS (Netherlands)

    Gil, J.A.; Almeida, J.; Duarte, J.P.

    2011-01-01

    We have been witnessing an increased interest in a more holistic approach to urban design practice and education. In this paper we present a spatial data model for urban design that proposes the combination of urban environment feature classes with design process feature classes. This data model is

  8. Spatial regression analysis on 32 years of total column ozone data

    NARCIS (Netherlands)

    Knibbe, J.S.; van der A, J.R.; de Laat, A.T.J.

    2014-01-01

    Multiple-regression analyses have been performed on 32 years of total ozone column data that was spatially gridded with a 1 × 1.5° resolution. The total ozone data consist of the MSR (Multi Sensor Reanalysis; 1979-2008) and 2 years of assimilated SCIAMACHY (SCanning Imaging Absorption spectroMeter

  9. A geo-spatial data management system for potentially active volcanoes—GEOWARN project

    Science.gov (United States)

    Gogu, Radu C.; Dietrich, Volker J.; Jenny, Bernhard; Schwandner, Florian M.; Hurni, Lorenz

    2006-02-01

    Integrated studies of active volcanic systems for the purpose of long-term monitoring and forecast and short-term eruption prediction require large numbers of data-sets from various disciplines. A modern database concept has been developed for managing and analyzing multi-disciplinary volcanological data-sets. The GEOWARN project (choosing the "Kos-Yali-Nisyros-Tilos volcanic field, Greece" and the "Campi Flegrei, Italy" as test sites) is oriented toward potentially active volcanoes situated in regions of high geodynamic unrest. This article describes the volcanological database of the spatial and temporal data acquired within the GEOWARN project. As a first step, a spatial database embedded in a Geographic Information System (GIS) environment was created. Digital data of different spatial resolution, and time-series data collected at different intervals or periods, were unified in a common, four-dimensional representation of space and time. The database scheme comprises various information layers containing geographic data (e.g. seafloor and land digital elevation model, satellite imagery, anthropogenic structures, land-use), geophysical data (e.g. from active and passive seismicity, gravity, tomography, SAR interferometry, thermal imagery, differential GPS), geological data (e.g. lithology, structural geology, oceanography), and geochemical data (e.g. from hydrothermal fluid chemistry and diffuse degassing features). As a second step based on the presented database, spatial data analysis has been performed using custom-programmed interfaces that execute query scripts resulting in a graphical visualization of data. These query tools were designed and compiled following scenarios of known "behavior" patterns of dormant volcanoes and first candidate signs of potential unrest. The spatial database and query approach is intended to facilitate scientific research on volcanic processes and phenomena, and volcanic surveillance.

  10. Calibration of a distributed hydrologic model using observed spatial patterns from MODIS data

    Science.gov (United States)

    Demirel, Mehmet C.; González, Gorka M.; Mai, Juliane; Stisen, Simon

    2016-04-01

    Distributed hydrologic models are typically calibrated against streamflow observations at the outlet of the basin. Along with these observations from gauging stations, satellite based estimates offer independent evaluation data such as remotely sensed actual evapotranspiration (aET) and land surface temperature. The primary objective of the study is to compare model calibrations against traditional downstream discharge measurements with calibrations against simulated spatial patterns and combinations of both types of observations. While the discharge based model calibration typically improves the temporal dynamics of the model, it seems to give rise to minimum improvement of the simulated spatial patterns. In contrast, objective functions specifically targeting the spatial pattern performance could potentially increase the spatial model performance. However, most modeling studies, including the model formulations and parameterization, are not designed to actually change the simulated spatial pattern during calibration. This study investigates the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mesoscale hydrologic model (mHM). This model is selected as it allows for a change in the spatial distribution of key soil parameters through the optimization of pedo-transfer function parameters and includes options for using fully distributed daily Leaf Area Index (LAI) values directly as input. In addition the simulated aET can be estimated at a spatial resolution suitable for comparison to the spatial patterns observed with MODIS data. To increase our control on spatial calibration we introduced three additional parameters to the model. These new parameters are part of an empirical equation to the calculate crop coefficient (Kc) from daily LAI maps and used to update potential evapotranspiration (PET) as model inputs. This is done instead of correcting/updating PET with just a uniform (or aspect driven) factor used in the mHM model

  11. Bayesian Spatial NBDA for Diffusion Data with Home-Base Coordinates.

    Directory of Open Access Journals (Sweden)

    Glenna F Nightingale

    Full Text Available Network-based diffusion analysis (NBDA is a statistical method that allows the researcher to identify and quantify a social influence on the spread of behaviour through a population. Hitherto, NBDA analyses have not directly modelled spatial population structure. Here we present a spatial extension of NBDA, applicable to diffusion data where the spatial locations of individuals in the population, or of their home bases or nest sites, are available. The method is based on the estimation of inter-individual associations (for association matrix construction from the mean inter-point distances as represented on a spatial point pattern of individuals, nests or home bases. We illustrate the method using a simulated dataset, and show how environmental covariates (such as that obtained from a satellite image, or from direct observations in the study area can also be included in the analysis. The analysis is conducted in a Bayesian framework, which has the advantage that prior knowledge of the rate at which the individuals acquire a given task can be incorporated into the analysis. This method is especially valuable for studies for which detailed spatially structured data, but no other association data, is available. Technological advances are making the collection of such data in the wild more feasible: for example, bio-logging facilitates the collection of a wide range of variables from animal populations in the wild. We provide an R package, spatialnbda, which is hosted on the Comprehensive R Archive Network (CRAN. This package facilitates the construction of association matrices with the spatial x and y coordinates as the input arguments, and spatial NBDA analyses.

  12. Elements of spatial data quality as information technology support for sustainable development planning

    Directory of Open Access Journals (Sweden)

    Joksić Dušan

    2004-01-01

    Full Text Available We are witnessing nowadays that the last decade of the past century, as well as the first years of the present one, have brought technology expansion with respect to spatial data gathering and processing which makes a physical basis for management of spatial development. This has resulted in enlargement of the spatial data market. New technologies, presented in computer applications, have greatly expanded the number of users of these products. The philosophy of spatial data collecting has changed; analogue maps and plans printed on paper have been replaced by digital data bases which enable their presentation in a way that is the best for a particular user. Further, digital spatial data bases provide the possibility of their further upgrading by users. The two aspects, with respect to circumstances mentioned above, are very important in the process of data bases production and distribution. Firstly, the users of these data bases should be the ones who decide which of the available bases could satisfy their requirements, or in other words, what is the data quality level necessary for a certain application. On the other hand, the visualization of digital data bases could often mislead, since review of data bases could present data with better accuracy then the actual one. Thus, certain methods that would point to a quality of the selected data in the process of their analysis should be available to users. Specific, already adopted international standards, or specially developed procedures and methodologies, so called de facto standards, could be used in this data processing, enabling the estimation of these data quality. The development of Open GIS concept requires the adoption of widely accepted standards for spatial data quality. It is recommended that ISO standards should be accepted, firstly TC211 standards which are related to geographic information and geomatics. The realization of projects on ISO standards should be finished by 2006, so

  13. Standardized acquisition, storing and provision of 3D enabled spatial data

    Science.gov (United States)

    Wagner, B.; Maier, S.; Peinsipp-Byma, E.

    2017-05-01

    In the area of working with spatial data, in addition to the classic, two-dimensional geometrical data (maps, aerial images, etc.), the needs for three-dimensional spatial data (city models, digital elevation models, etc.) is increasing. Due to this increased demand the acquiring, storing and provision of 3D enabled spatial data in Geographic Information Systems (GIS) is more and more important. Existing proprietary solutions quickly reaches their limits during data exchange and data delivery to other systems. They generate a large workload, which will be very costly. However, it is noticeable that these expenses and costs can generally be significantly reduced using standards. The aim of this research is therefore to develop a concept in the field of three-dimensional spatial data that runs on existing standards whenever possible. In this research, the military image analysts are the preferred user group of the system. To achieve the objective of the widest possible use of standards in spatial 3D data, existing standards, proprietary interfaces and standards under discussion have been analyzed. Since the here used GIS of the Fraunhofer IOSB is already using and supporting OGC (Open Geospatial Consortium) and NATO-STANAG (NATO-Standardization Agreement) standards for the most part of it, a special attention for possible use was laid on their standards. The most promising standard is the OGC standard 3DPS (3D Portrayal Service) with its occurrences W3DS (Web 3D Service) and WVS (Web View Service). A demo system was created, using a standardized workflow from the data acquiring, storing and provision and showing the benefit of our approach.

  14. Importance of the spatial data and the sensor web in the ubiquitous computing area

    Science.gov (United States)

    Akçit, Nuhcan; Tomur, Emrah; Karslıoǧlu, Mahmut O.

    2014-08-01

    Spatial data has become a critical issue in recent years. In the past years, nearly more than three quarters of databases, were related directly or indirectly to locations referring to physical features, which constitute the relevant aspects. Spatial data is necessary to identify or calculate the relationships between spatial objects when using spatial operators in programs or portals. Originally, calculations were conducted using Geographic Information System (GIS) programs on local computers. Subsequently, through the Internet, they formed a geospatial web, which is integrated into a discoverable collection of geographically related web standards and key features, and constitutes a global network of geospatial data that employs the World Wide Web to process textual data. In addition, the geospatial web is used to gather spatial data producers, resources, and users. Standards also constitute a critical dimension in further globalizing the idea of the geospatial web. The sensor web is an example of the real time service that the geospatial web can provide. Sensors around the world collect numerous types of data. The sensor web is a type of sensor network that is used for visualizing, calculating, and analyzing collected sensor data. Today, people use smart devices and systems more frequently because of the evolution of technology and have more than one mobile device. The considerable number of sensors and different types of data that are positioned around the world have driven the production of interoperable and platform-independent sensor web portals. The focus of such production has been on further developing the idea of an interoperable and interdependent sensor web of all devices that share and collect information. The other pivotal idea consists of encouraging people to use and send data voluntarily for numerous purposes with the some level of credibility. The principal goal is to connect mobile and non-mobile device in the sensor web platform together to

  15. An Approach to Indexing and Retrieval of Spatial Data with Reduced R+ Tree and K-NN Query Algorithm

    OpenAIRE

    S. Palaniappan; T.V. Rajinikanth; A. Govardhan

    2015-01-01

    Recently, “spatial data bases have been extensively adopted in the recent decade and various methods have been presented to store, browse, search and retrieve spatial objects”. In this study, a method is plotted for retrieving nearest neighbors from spatial data indexed by R+ tree. The approach uses a reduced R+tree for the purpose of representing the spatial data. Initially the spatial data is selected and R+tree is constructed accordingly. Then a function called joining nodes is applied to ...

  16. Spatial modelling of disease using data- and knowledge-driven approaches.

    Science.gov (United States)

    Stevens, Kim B; Pfeiffer, Dirk U

    2011-09-01

    The purpose of spatial modelling in animal and public health is three-fold: describing existing spatial patterns of risk, attempting to understand the biological mechanisms that lead to disease occurrence and predicting what will happen in the medium to long-term future (temporal prediction) or in different geographical areas (spatial prediction). Traditional methods for temporal and spatial predictions include general and generalized linear models (GLM), generalized additive models (GAM) and Bayesian estimation methods. However, such models require both disease presence and absence data which are not always easy to obtain. Novel spatial modelling methods such as maximum entropy (MAXENT) and the genetic algorithm for rule set production (GARP) require only disease presence data and have been used extensively in the fields of ecology and conservation, to model species distribution and habitat suitability. Other methods, such as multicriteria decision analysis (MCDA), use knowledge of the causal factors of disease occurrence to identify areas potentially suitable for disease. In addition to their less restrictive data requirements, some of these novel methods have been shown to outperform traditional statistical methods in predictive ability (Elith et al., 2006). This review paper provides details of some of these novel methods for mapping disease distribution, highlights their advantages and limitations, and identifies studies which have used the methods to model various aspects of disease distribution. Copyright © 2011. Published by Elsevier Ltd.

  17. a Representation-Driven Ontology for Spatial Data Quality Elements, with Orthoimagery as Running Example

    Science.gov (United States)

    Hangouët, J.-F.

    2015-08-01

    The many facets of what is encompassed by such an expression as "quality of spatial data" can be considered as a specific domain of reality worthy of formal description, i.e. of ontological abstraction. Various ontologies for data quality elements have already been proposed in literature. Today, the system of quality elements is most generally used and discussed according to the configuration exposed in the "data dictionary for data quality" of international standard ISO 19157. Our communication proposes an alternative view. This is founded on a perspective which focuses on the specificity of spatial data as a product: the representation perspective, where data in the computer are meant to show things of the geographic world and to be interpreted as such. The resulting ontology introduces new elements, the usefulness of which will be illustrated by orthoimagery examples.

  18. Harnessing Big Data to Represent 30-meter Spatial Heterogeneity in Earth System Models

    Science.gov (United States)

    Chaney, N.; Shevliakova, E.; Malyshev, S.; Van Huijgevoort, M.; Milly, C.; Sulman, B. N.

    2016-12-01

    Terrestrial land surface processes play a critical role in the Earth system; they have a profound impact on the global climate, food and energy production, freshwater resources, and biodiversity. One of the most fascinating yet challenging aspects of characterizing terrestrial ecosystems is their field-scale (˜30 m) spatial heterogeneity. It has been observed repeatedly that the water, energy, and biogeochemical cycles at multiple temporal and spatial scales have deep ties to an ecosystem's spatial structure. Current Earth system models largely disregard this important relationship leading to an inadequate representation of ecosystem dynamics. In this presentation, we will show how existing global environmental datasets can be harnessed to explicitly represent field-scale spatial heterogeneity in Earth system models. For each macroscale grid cell, these environmental data are clustered according to their field-scale soil and topographic attributes to define unique sub-grid tiles. The state-of-the-art Geophysical Fluid Dynamics Laboratory (GFDL) land model is then used to simulate these tiles and their spatial interactions via the exchange of water, energy, and nutrients along explicit topographic gradients. Using historical simulations over the contiguous United States, we will show how a robust representation of field-scale spatial heterogeneity impacts modeled ecosystem dynamics including the water, energy, and biogeochemical cycles as well as vegetation composition and distribution.

  19. Enhancing spatial detection accuracy for syndromic surveillance with street level incidence data

    Directory of Open Access Journals (Sweden)

    Alemi Farrokh

    2010-01-01

    Full Text Available Abstract Background The Department of Defense Military Health System operates a syndromic surveillance system that monitors medical records at more than 450 non-combat Military Treatment Facilities (MTF worldwide. The Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE uses both temporal and spatial algorithms to detect disease outbreaks. This study focuses on spatial detection and attempts to improve the effectiveness of the ESSENCE implementation of the spatial scan statistic by increasing the spatial resolution of incidence data from zip codes to street address level. Methods Influenza-Like Illness (ILI was used as a test syndrome to develop methods to improve the spatial accuracy of detected alerts. Simulated incident clusters of various sizes were superimposed on real ILI incidents from the 2008/2009 influenza season. Clusters were detected using the spatial scan statistic and their displacement from simulated loci was measured. Detected cluster size distributions were also evaluated for compliance with simulated cluster sizes. Results Relative to the ESSENCE zip code based method, clusters detected using street level incidents were displaced on average 65% less for 2 and 5 mile radius clusters and 31% less for 10 mile radius clusters. Detected cluster size distributions for the street address method were quasi normal and sizes tended to slightly exceed simulated radii. ESSENCE methods yielded fragmented distributions and had high rates of zero radius and oversized clusters. Conclusions Spatial detection accuracy improved notably with regard to both location and size when incidents were geocoded to street addresses rather than zip code centroids. Since street address geocoding success rates were only 73.5%, zip codes were still used for more than one quarter of ILI cases. Thus, further advances in spatial detection accuracy are dependant on systematic improvements in the collection of individual

  20. Bobcat 2013: a hyperspectral data collection supporting the development and evaluation of spatial-spectral algorithms

    Science.gov (United States)

    Kaufman, Jason; Celenk, Mehmet; White, A. K.; Stocker, Alan D.

    2014-06-01

    The amount of hyperspectral imagery (HSI) data currently available is relatively small compared to other imaging modalities, and what is suitable for developing, testing, and evaluating spatial-spectral algorithms is virtually nonexistent. In this work, a significant amount of coincident airborne hyperspectral and high spatial resolution panchromatic imagery that supports the advancement of spatial-spectral feature extraction algorithms was collected to address this need. The imagery was collected in April 2013 for Ohio University by the Civil Air Patrol, with their Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) sensor. The target materials, shapes, and movements throughout the collection area were chosen such that evaluation of change detection algorithms, atmospheric compensation techniques, image fusion methods, and material detection and identification algorithms is possible. This paper describes the collection plan, data acquisition, and initial analysis of the collected imagery.

  1. A Method of Generating Indoor Map Spatial Data Automatically from Architectural Plans

    Directory of Open Access Journals (Sweden)

    SUN Weixin

    2016-06-01

    Full Text Available Taking architectural plans as data source, we proposed a method which can automatically generate indoor map spatial data. Firstly, referring to the spatial data demands of indoor map, we analyzed the basic characteristics of architectural plans, and introduced concepts of wall segment, adjoining node and adjoining wall segment, based on which basic flow of indoor map spatial data automatic generation was further established. Then, according to the adjoining relation between wall lines at the intersection with column, we constructed a repair method for wall connectivity in relation to the column. Utilizing the method of gradual expansibility and graphic reasoning to judge wall symbol local feature type at both sides of door or window, through update the enclosing rectangle of door or window, we developed a repair method for wall connectivity in relation to the door or window and a method for transform door or window into indoor map point feature. Finally, on the basis of geometric relation between adjoining wall segment median lines, a wall center-line extraction algorithm was presented. Taking one exhibition hall's architectural plan as example, we performed experiment and results show that the proposed methods have preferable applicability to deal with various complex situations, and realized indoor map spatial data automatic extraction effectively.

  2. Exploiting spatial degrees of freedom for high data rate ultrasound communication with implantable devices

    Science.gov (United States)

    Wang, Max L.; Arbabian, Amin

    2017-09-01

    We propose and demonstrate an ultrasonic communication link using spatial degrees of freedom to increase data rates for deeply implantable medical devices. Low attenuation and millimeter wavelengths make ultrasound an ideal communication medium for miniaturized low-power implants. While a small spectral bandwidth has drastically limited achievable data rates in conventional ultrasonic implants, a large spatial bandwidth can be exploited by using multiple transducers in a multiple-input/multiple-output system to provide spatial multiplexing gain without additional power, larger bandwidth, or complicated packaging. We experimentally verify the communication link in mineral oil with a transmitter and a receiver 5 cm apart, each housing two custom-designed mm-sized piezoelectric transducers operating at the same frequency. Two streams of data modulated with quadrature phase-shift keying at 125 kbps are simultaneously transmitted and received on both channels, effectively doubling the data rate to 250 kbps with a measured bit error rate below 10-4. We also evaluate the performance and robustness of the channel separation network by testing the communication link after introducing position offsets. These results demonstrate the potential of spatial multiplexing to enable more complex implant applications requiring higher data rates.

  3. Advancing the integration of spatial data to map human and natural drivers on coral reefs

    Science.gov (United States)

    Gove, Jamison M.; Walecka, Hilary R.; Donovan, Mary K.; Williams, Gareth J.; Jouffray, Jean-Baptiste; Crowder, Larry B.; Erickson, Ashley; Falinski, Kim; Friedlander, Alan M.; Kappel, Carrie V.; Kittinger, John N.; McCoy, Kaylyn; Norström, Albert; Nyström, Magnus; Oleson, Kirsten L. L.; Stamoulis, Kostantinos A.; White, Crow; Selkoe, Kimberly A.

    2018-01-01

    A major challenge for coral reef conservation and management is understanding how a wide range of interacting human and natural drivers cumulatively impact and shape these ecosystems. Despite the importance of understanding these interactions, a methodological framework to synthesize spatially explicit data of such drivers is lacking. To fill this gap, we established a transferable data synthesis methodology to integrate spatial data on environmental and anthropogenic drivers of coral reefs, and applied this methodology to a case study location–the Main Hawaiian Islands (MHI). Environmental drivers were derived from time series (2002–2013) of climatological ranges and anomalies of remotely sensed sea surface temperature, chlorophyll-a, irradiance, and wave power. Anthropogenic drivers were characterized using empirically derived and modeled datasets of spatial fisheries catch, sedimentation, nutrient input, new development, habitat modification, and invasive species. Within our case study system, resulting driver maps showed high spatial heterogeneity across the MHI, with anthropogenic drivers generally greatest and most widespread on O‘ahu, where 70% of the state’s population resides, while sedimentation and nutrients were dominant in less populated islands. Together, the spatial integration of environmental and anthropogenic driver data described here provides a first-ever synthetic approach to visualize how the drivers of coral reef state vary in space and demonstrates a methodological framework for implementation of this approach in other regions of the world. By quantifying and synthesizing spatial drivers of change on coral reefs, we provide an avenue for further research to understand how drivers determine reef diversity and resilience, which can ultimately inform policies to protect coral reefs. PMID:29494613

  4. Evaluating spatial and temporal variability in growth and mortality for recreational fisheries with limited catch data

    Science.gov (United States)

    Li, Yan; Wagner, Tyler; Jiao, Yan; Lorantas, Robert M.; Murphy, Cheryl

    2018-01-01

    Understanding the spatial and temporal variability in life-history traits among populations is essential for the management of recreational fisheries. However, valuable freshwater recreational fish species often suffer from a lack of catch information. In this study, we demonstrated the use of an approach to estimate the spatial and temporal variability in growth and mortality in the absence of catch data and apply the method to riverine smallmouth bass (Micropterus dolomieu) populations in Pennsylvania, USA. Our approach included a growth analysis and a length-based analysis that estimates mortality. Using a hierarchical Bayesian approach, we examined spatial variability in growth and mortality by assuming parameters vary spatially but remain constant over time and temporal variability by assuming parameters vary spatially and temporally. The estimated growth and mortality of smallmouth bass showed substantial variability over time and across rivers. We explored the relationships of the estimated growth and mortality with spring water temperature and spring flow. Growth rate was likely to be positively correlated with these two factors, while young mortality was likely to be positively correlated with spring flow. The spatially and temporally varying growth and mortality suggest that smallmouth bass populations across rivers may respond differently to management plans and disturbance such as environmental contamination and land-use change. The analytical approach can be extended to other freshwater recreational species that also lack of catch data. The approach could also be useful in developing population assessments with erroneous catch data or be used as a model sensitivity scenario to verify traditional models even when catch data are available.

  5. A GIS-based disaggregate spatial watershed analysis using RADAR data

    International Nuclear Information System (INIS)

    Al-Hamdan, M.

    2002-01-01

    Hydrology is the study of water in all its forms, origins, and destinations on the earth.This paper develops a novel modeling technique using a geographic information system (GIS) to facilitate watershed hydrological routing using RADAR data. The RADAR rainfall data, segmented to 4 km by 4 km blocks, divides the watershed into several sub basins which are modeled independently. A case study for the GIS-based disaggregate spatial watershed analysis using RADAR data is provided for South Fork Cowikee Creek near Batesville, Alabama. All the data necessary to complete the analysis is maintained in the ArcView GIS software. This paper concludes that the GIS-Based disaggregate spatial watershed analysis using RADAR data is a viable method to calculate hydrological routing for large watersheds. (author)

  6. Delineating Hydrofacies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics

    Energy Technology Data Exchange (ETDEWEB)

    Song, Xuehang [Florida State Univ., Tallahassee, FL (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Chen, Xingyuan [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Ye, Ming [Florida State Univ., Tallahassee, FL (United States); Dai, Zhenxue [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Hammond, Glenn Edward [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-07-01

    This study develops a new framework of facies-based data assimilation for characterizing spatial distribution of hydrofacies and estimating their associated hydraulic properties. This framework couples ensemble data assimilation with transition probability-based geostatistical model via a parameterization based on a level set function. The nature of ensemble data assimilation makes the framework efficient and flexible to be integrated with various types of observation data. The transition probability-based geostatistical model keeps the updated hydrofacies distributions under geological constrains. The framework is illustrated by using a two-dimensional synthetic study that estimates hydrofacies spatial distribution and permeability in each hydrofacies from transient head data. Our results show that the proposed framework can characterize hydrofacies distribution and associated permeability with adequate accuracy even with limited direct measurements of hydrofacies. Our study provides a promising starting point for hydrofacies delineation in complex real problems.

  7. Predictive spatio-temporal model for spatially sparse global solar radiation data

    International Nuclear Information System (INIS)

    André, Maïna; Dabo-Niang, Sophie; Soubdhan, Ted; Ould-Baba, Hanany

    2016-01-01

    This paper introduces a new approach for the forecasting of solar radiation series at a located station for very short time scale. We built a multivariate model in using few stations (3 stations) separated with irregular distances from 26 km to 56 km. The proposed model is a spatio temporal vector autoregressive VAR model specifically designed for the analysis of spatially sparse spatio-temporal data. This model differs from classic linear models in using spatial and temporal parameters where the available predictors are the lagged values at each station. A spatial structure of stations is defined by the sequential introduction of predictors in the model. Moreover, an iterative strategy in the process of our model will select the necessary stations removing the uninteresting predictors and also selecting the optimal p-order. We studied the performance of this model. The metric error, the relative root mean squared error (rRMSE), is presented at different short time scales. Moreover, we compared the results of our model to simple and well known persistence model and those found in literature. - Highlights: • A spatio-temporal VAR forecast model is used for spatially sparse data solar. • Lags and locations are selected by an optimization strategy. • Definition of spatial ordering of predictors influences forecasting results. • The model shows a better performance predictive at 30 min ahead in our context. • Benchmarking study shows a more accurate forecast at 1 h ahead with spatio-temporal VAR.

  8. A Potential Synergy Connecting Educational Leadership, The Geoscience Community, and Spatial Data

    Science.gov (United States)

    Branch, B. D.

    2008-12-01

    The effort to promote more geosciences numbers and greater diversity should reference considerations of federal policy. In congruence, institutions need to include geosciences education in the K-12 curriculum in order to increase the numbers of geoscientists and to increase diversity among geoscientists. For example, No Child Left Behind stated public entities should, ""(1) to carry out programs that prepare prospective teachers to use advanced technology to prepare all students to meet challenging", section 1051 sub section 221. Moreover, Executive Order 12906, the Spatial Data Infrastructure Act, requires all federal agencies to manage their spatial data. Such compliance may influence the job market, education and policy makers to see that spatial thinking transcends the standard course of study. Namely, educational leadership and policy have to be a vital aid to augment the geosciences experience through the K-12 experience and as an inclusion activity in the standard course of study agenda. A simple endorsement by the National Academy of Sciences (2006), in their work titled Learning to think spatially: GIS as a support system in the K-12 curriculum, who stated, "Spatial thinking can be learned, and it can and should be taught at all levels in the education system" (p.3). Such may not be enough to gain the attention and time consideration of educational leadership. Therefore, the challenge for progressive advocates of geosciences is that some may have to consider educational leadership as new frontier to push such policy and research issues.

  9. A Spatial Data Model Desing For The Management Of Agricultural Data (Farmer, Agricultural Land And Agricultural Production)

    Science.gov (United States)

    Taşkanat, Talha; İbrahim İnan, Halil

    2016-04-01

    Since the beginning of the 2000s, it has been conducted many projects such as Agricultural Sector Integrated Management Information System, Agriculture Information System, Agricultural Production Registry System and Farmer Registry System by the Turkish Ministry of Food, Agriculture and Livestock and the Turkish Statistical Institute in order to establish and manage better agricultural policy and produce better agricultural statistics in Turkey. Yet, it has not been carried out any study for the structuring of a system which can meet the requirements of different institutions and organizations that need similar agricultural data. It has been tried to meet required data only within the frame of the legal regulations from present systems. Whereas the developments in GIS (Geographical Information Systems) and standardization, and Turkey National GIS enterprise in this context necessitate to meet the demands of organizations that use the similar data commonly and to act in terms of a data model logic. In this study, 38 institutions or organization which produce and use agricultural data were detected, that and thanks to survey and interviews undertaken, their needs were tried to be determined. In this study which is financially supported by TUBITAK, it was worked out relationship between farmer, agricultural land and agricultural production data and all of the institutions and organizations in Turkey and in this context, it was worked upon the best detailed and effective possible data model. In the model design, UML which provides object-oriented design was used. In the data model, for the management of spatial data, sub-parcel data model was used. Thanks to this data model, declared and undeclared areas can be detected spatially, and thus declarations can be associated to sub-parcels. Within this framework, it will be able to developed agricultural policies as a result of acquiring more extensive, accurate, spatially manageable and easily updatable farmer and

  10. A Bayesian multidimensional scaling procedure for the spatial analysis of revealed choice data

    NARCIS (Netherlands)

    DeSarbo, WS; Kim, Y; Fong, D

    1999-01-01

    We present a new Bayesian formulation of a vector multidimensional scaling procedure for the spatial analysis of binary choice data. The Gibbs sampler is gainfully employed to estimate the posterior distribution of the specified scalar products, bilinear model parameters. The computational procedure

  11. Asymptotic properties of the development of conformally flat data near spatial infinity

    International Nuclear Information System (INIS)

    Valiente Kroon, Juan Antonio

    2007-01-01

    The analysis of the relation between Bondi-type systems (NP-gauge) and a gauge used in the analysis of the structure of spatial infinity (F-gauge) which was carried out by Friedrich and Kannar (2000 J. Math Phys. 41 2195) is retaken and applied to the development of a suitable class of conformally flat initial data sets with non-vanishing second fundamental form. The calculations presented depend on a certain assumption about the existence and regularity of the solutions to the conformal Einstein field equations close to null and spatial infinity. As a result of the calculations the Newman-Penrose constants of both future and past null infinity are calculated in terms of initial data and are shown to be equal. It is also shown that the asymptotic shear goes to zero as one approaches spatial infinity along the generators of null infinity so that it is possible to select, in a canonical fashion, the Poincare group out of the BMS group. An expansion-again in terms of initial data quantities-of the Bondi mass close to spatial infinity is calculated. This expansion shows that if the existence and regularity assumptions hold, the Bondi mass approaches the ADM mass. A discussion of possible conditions on the initial data which would render a peeling development is presented

  12. PHYLOGEOrec: A QGIS plugin for spatial phylogeographic reconstruction from phylogenetic tree and geographical information data

    Science.gov (United States)

    Nashrulloh, Maulana Malik; Kurniawan, Nia; Rahardi, Brian

    2017-11-01

    The increasing availability of genetic sequence data associated with explicit geographic and environment (including biotic and abiotic components) information offers new opportunities to study the processes that shape biodiversity and its patterns. Developing phylogeography reconstruction, by integrating phylogenetic and biogeographic knowledge, provides richer and deeper visualization and information on diversification events than ever before. Geographical information systems such as QGIS provide an environment for spatial modeling, analysis, and dissemination by which phylogenetic models can be explicitly linked with their associated spatial data, and subsequently, they will be integrated with other related georeferenced datasets describing the biotic and abiotic environment. We are introducing PHYLOGEOrec, a QGIS plugin for building spatial phylogeographic reconstructions constructed from phylogenetic tree and geographical information data based on QGIS2threejs. By using PHYLOGEOrec, researchers can integrate existing phylogeny and geographical information data, resulting in three-dimensional geographic visualizations of phylogenetic trees in the Keyhole Markup Language (KML) format. Such formats can be overlaid on a map using QGIS and finally, spatially viewed in QGIS by means of a QGIS2threejs engine for further analysis. KML can also be viewed in reputable geobrowsers with KML-support (i.e., Google Earth).

  13. Comparing spatial regression to random forests for large environmental data sets

    Science.gov (United States)

    Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates, whereas spatial regression, when using reduced rank methods, has a reputatio...

  14. Efficient Implementation of GPR Data Inversion in Case of Spatially Varying Antenna Polarizations

    NARCIS (Netherlands)

    Wang, J.; Aubry, P.J.; Yarovyi, O.

    2018-01-01

    Ground penetrating radar imaging from the data acquired with arbitrarily oriented dipole-like antennas is considered. To take into account variations of antenna orientations resulting in spatial rotation of antenna radiation patterns and polarizations of transmitted fields, the full-wave method

  15. Jackson State University's Center for Spatial Data Research and Applications: New facilities and new paradigms

    Science.gov (United States)

    Davis, Bruce E.; Elliot, Gregory

    1989-01-01

    Jackson State University recently established the Center for Spatial Data Research and Applications, a Geographical Information System (GIS) and remote sensing laboratory. Taking advantage of new technologies and new directions in the spatial (geographic) sciences, JSU is building a Center of Excellence in Spatial Data Management. New opportunities for research, applications, and employment are emerging. GIS requires fundamental shifts and new demands in traditional computer science and geographic training. The Center is not merely another computer lab but is one setting the pace in a new applied frontier. GIS and its associated technologies are discussed. The Center's facilities are described. An ARC/INFO GIS runs on a Vax mainframe, with numerous workstations. Image processing packages include ELAS, LIPS, VICAR, and ERDAS. A host of hardware and software peripheral are used in support. Numerous projects are underway, such as the construction of a Gulf of Mexico environmental data base, development of AI in image processing, a land use dynamics study of metropolitan Jackson, and others. A new academic interdisciplinary program in Spatial Data Management is under development, combining courses in Geography and Computer Science. The broad range of JSU's GIS and remote sensing activities is addressed. The impacts on changing paradigms in the university and in the professional world conclude the discussion.

  16. Spatial data infrastructure and policy development in Europe and the United States

    NARCIS (Netherlands)

    Van Loenen, B.; Kok, B.C.; OTB Research Institute for Housing, Urban and Mobility Studies

    2004-01-01

    Many national governments throughout the world are involved in developing spatial data infrastructures (SDI) to facilitate the availability of information in such a way that the needs of the agencies, organization, citizens, commerce, and society in general are met. This book covers some of the most

  17. Spatial Modeling for Resources Framework (SMRF): A modular framework for developing spatial forcing data for snow modeling in mountain basins

    Science.gov (United States)

    Havens, Scott; Marks, Danny; Kormos, Patrick; Hedrick, Andrew

    2017-12-01

    In the Western US and many mountainous regions of the world, critical water resources and climate conditions are difficult to monitor because the observation network is generally very sparse. The critical resource from the mountain snowpack is water flowing into streams and reservoirs that will provide for irrigation, flood control, power generation, and ecosystem services. Water supply forecasting in a rapidly changing climate has become increasingly difficult because of non-stationary conditions. In response, operational water supply managers have begun to move from statistical techniques towards the use of physically based models. As we begin to transition physically based models from research to operational use, we must address the most difficult and time-consuming aspect of model initiation: the need for robust methods to develop and distribute the input forcing data. In this paper, we present a new open source framework, the Spatial Modeling for Resources Framework (SMRF), which automates and simplifies the common forcing data distribution methods. It is computationally efficient and can be implemented for both research and operational applications. We present an example of how SMRF is able to generate all of the forcing data required to a run physically based snow model at 50-100 m resolution over regions of 1000-7000 km2. The approach has been successfully applied in real time and historical applications for both the Boise River Basin in Idaho, USA and the Tuolumne River Basin in California, USA. These applications use meteorological station measurements and numerical weather prediction model outputs as input. SMRF has significantly streamlined the modeling workflow, decreased model set up time from weeks to days, and made near real-time application of a physically based snow model possible.

  18. A Novel Query Method for Spatial Data in Mobile Cloud Computing Environment

    Directory of Open Access Journals (Sweden)

    Guangsheng Chen

    2018-01-01

    Full Text Available With the development of network communication, a 1000-fold increase in traffic demand from 4G to 5G, it is critical to provide efficient and fast spatial data access interface for applications in mobile environment. In view of the low I/O efficiency and high latency of existing methods, this paper presents a memory-based spatial data query method that uses the distributed memory file system Alluxio to store data and build a two-level index based on the Alluxio key-value structure; moreover, it aims to solve the problem of low efficiency of traditional method; according to the characteristics of Spark computing framework, a data input format for spatial data query is proposed, which can selectively read the file data and reduce the data I/O. The comparative experiments show that the memory-based file system Alluxio has better I/O performance than the disk file system; compared with the traditional distributed query method, the method we proposed reduces the retrieval time greatly.

  19. Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

    Science.gov (United States)

    Hoffmann, Holger; Zhao, Gang; Asseng, Senthold; Bindi, Marco; Biernath, Christian; Constantin, Julie; Coucheney, Elsa; Dechow, Rene; Doro, Luca; Eckersten, Henrik; Gaiser, Thomas; Grosz, Balázs; Heinlein, Florian; Kassie, Belay T.; Kersebaum, Kurt-Christian; Klein, Christian; Kuhnert, Matthias; Lewan, Elisabet; Moriondo, Marco; Nendel, Claas; Priesack, Eckart; Raynal, Helene; Roggero, Pier P.; Rötter, Reimund P.; Siebert, Stefan; Specka, Xenia; Tao, Fulu; Teixeira, Edmar; Trombi, Giacomo; Wallach, Daniel; Weihermüller, Lutz; Yeluripati, Jagadeesh; Ewert, Frank

    2016-01-01

    We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations. PMID:27055028

  20. Multiscale Feature Model for Terrain Data Based on Adaptive Spatial Neighborhood

    Directory of Open Access Journals (Sweden)

    Huijie Zhang

    2013-01-01

    Full Text Available Multiresolution hierarchy based on features (FMRH has been applied in the field of terrain modeling and obtained significant results in real engineering. However, it is difficult to schedule multiresolution data in FMRH from external memory. This paper proposed new multiscale feature model and related strategies to cluster spatial data blocks and solve the scheduling problems of FMRH using spatial neighborhood. In the model, the nodes with similar error in the different layers should be in one cluster. On this basis, a space index algorithm for each cluster guided by Hilbert curve is proposed. It ensures that multi-resolution terrain data can be loaded without traversing the whole FMRH; therefore, the efficiency of data scheduling is improved. Moreover, a spatial closeness theorem of cluster is put forward and is also proved. It guarantees that the union of data blocks composites a whole terrain without any data loss. Finally, experiments have been carried out on many different large scale data sets, and the results demonstrate that the schedule time is shortened and the efficiency of I/O operation is apparently improved, which is important in real engineering.

  1. Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations.

    Science.gov (United States)

    Hoffmann, Holger; Zhao, Gang; Asseng, Senthold; Bindi, Marco; Biernath, Christian; Constantin, Julie; Coucheney, Elsa; Dechow, Rene; Doro, Luca; Eckersten, Henrik; Gaiser, Thomas; Grosz, Balázs; Heinlein, Florian; Kassie, Belay T; Kersebaum, Kurt-Christian; Klein, Christian; Kuhnert, Matthias; Lewan, Elisabet; Moriondo, Marco; Nendel, Claas; Priesack, Eckart; Raynal, Helene; Roggero, Pier P; Rötter, Reimund P; Siebert, Stefan; Specka, Xenia; Tao, Fulu; Teixeira, Edmar; Trombi, Giacomo; Wallach, Daniel; Weihermüller, Lutz; Yeluripati, Jagadeesh; Ewert, Frank

    2016-01-01

    We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

  2. Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations.

    Directory of Open Access Journals (Sweden)

    Holger Hoffmann

    Full Text Available We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

  3. Importance of spatial and spectral data reduction in the detection of internal defects in food products.

    Science.gov (United States)

    Zhang, Xuechen; Nansen, Christian; Aryamanesh, Nader; Yan, Guijun; Boussaid, Farid

    2015-04-01

    Despite the importance of data reduction as part of the processing of reflection-based classifications, this study represents one of the first in which the effects of both spatial and spectral data reductions on classification accuracies are quantified. Furthermore, the effects of approaches to data reduction were quantified for two separate classification methods, linear discriminant analysis (LDA) and support vector machine (SVM). As the model dataset, reflection data were acquired using a hyperspectral camera in 230 spectral channels from 401 to 879 nm (spectral resolution of 2.1 nm) from field pea (Pisum sativum) samples with and without internal pea weevil (Bruchus pisorum) infestation. We deployed five levels of spatial data reduction (binning) and eight levels of spectral data reduction (40 datasets). Forward stepwise LDA was used to select and include only spectral channels contributing the most to the separation of pixels from non-infested and infested field peas. Classification accuracies obtained with LDA and SVM were based on the classification of independent validation datasets. Overall, SVMs had significantly higher classification accuracies than LDAs (P food products with internal defects, and it highlights that spatial and spectral data reductions can (1) improve classification accuracies, (2) vastly decrease computer constraints, and (3) reduce analytical concerns associated with classifications of large and high-dimensional datasets.

  4. Standard Deviation of Spatially-Averaged Surface Cross Section Data from the TRMM Precipitation Radar

    Science.gov (United States)

    Meneghini, Robert; Jones, Jeffrey A.

    2010-01-01

    We investigate the spatial variability of the normalized radar cross section of the surface (NRCS or Sigma(sup 0)) derived from measurements of the TRMM Precipitation Radar (PR) for the period from 1998 to 2009. The purpose of the study is to understand the way in which the sample standard deviation of the Sigma(sup 0) data changes as a function of spatial resolution, incidence angle, and surface type (land/ocean). The results have implications regarding the accuracy by which the path integrated attenuation from precipitation can be inferred by the use of surface scattering properties.

  5. Spatial Statistics and Spatio-Temporal Data Covariance Functions and Directional Properties

    CERN Document Server

    Sherman, Michael

    2010-01-01

    In the spatial or space-time context, specifying the correct covariance function is important to obtain efficient predictions and to understand the underlying physical process of interest. There have been several books in recent years in the general area of spatial statistics. This book focuses on covariance and variogram functions, their role in prediction, and the proper choice of these functions in data applications. Presenting recent methods from 2004-2007 alongside more established methodology of assessing the usual assumptions on such functions such as isotropy, separability and symmetry

  6. SCGICAR: Spatial concatenation based group ICA with reference for fMRI data analysis.

    Science.gov (United States)

    Shi, Yuhu; Zeng, Weiming; Wang, Nizhuan

    2017-09-01

    With the rapid development of big data, the functional magnetic resonance imaging (fMRI) data analysis of multi-subject is becoming more and more important. As a kind of blind source separation technique, group independent component analysis (GICA) has been widely applied for the multi-subject fMRI data analysis. However, spatial concatenated GICA is rarely used compared with temporal concatenated GICA due to its disadvantages. In this paper, in order to overcome these issues and to consider that the ability of GICA for fMRI data analysis can be improved by adding a priori information, we propose a novel spatial concatenation based GICA with reference (SCGICAR) method to take advantage of the priori information extracted from the group subjects, and then the multi-objective optimization strategy is used to implement this method. Finally, the post-processing means of principal component analysis and anti-reconstruction are used to obtain group spatial component and individual temporal component in the group, respectively. The experimental results show that the proposed SCGICAR method has a better performance on both single-subject and multi-subject fMRI data analysis compared with classical methods. It not only can detect more accurate spatial and temporal component for each subject of the group, but also can obtain a better group component on both temporal and spatial domains. These results demonstrate that the proposed SCGICAR method has its own advantages in comparison with classical methods, and it can better reflect the commonness of subjects in the group. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. SMART CITIES INTELLIGENCE SYSTEM (SMACiSYS INTEGRATING SENSOR WEB WITH SPATIAL DATA INFRASTRUCTURES (SENSDI

    Directory of Open Access Journals (Sweden)

    D. Bhattacharya

    2017-09-01

    Full Text Available The paper endeavours to enhance the Sensor Web with crucial geospatial analysis capabilities through integration with Spatial Data Infrastructure. The objective is development of automated smart cities intelligence system (SMACiSYS with sensor-web access (SENSDI utilizing geomatics for sustainable societies. There has been a need to develop automated integrated system to categorize events and issue information that reaches users directly. At present, no web-enabled information system exists which can disseminate messages after events evaluation in real time. Research work formalizes a notion of an integrated, independent, generalized, and automated geo-event analysing system making use of geo-spatial data under popular usage platform. Integrating Sensor Web With Spatial Data Infrastructures (SENSDI aims to extend SDIs with sensor web enablement, converging geospatial and built infrastructure, and implement test cases with sensor data and SDI. The other benefit, conversely, is the expansion of spatial data infrastructure to utilize sensor web, dynamically and in real time for smart applications that smarter cities demand nowadays. Hence, SENSDI augments existing smart cities platforms utilizing sensor web and spatial information achieved by coupling pairs of otherwise disjoint interfaces and APIs formulated by Open Geospatial Consortium (OGC keeping entire platform open access and open source. SENSDI is based on Geonode, QGIS and Java, that bind most of the functionalities of Internet, sensor web and nowadays Internet of Things superseding Internet of Sensors as well. In a nutshell, the project delivers a generalized real-time accessible and analysable platform for sensing the environment and mapping the captured information for optimal decision-making and societal benefit.

  8. Smart Cities Intelligence System (SMACiSYS) Integrating Sensor Web with Spatial Data Infrastructures (sensdi)

    Science.gov (United States)

    Bhattacharya, D.; Painho, M.

    2017-09-01

    The paper endeavours to enhance the Sensor Web with crucial geospatial analysis capabilities through integration with Spatial Data Infrastructure. The objective is development of automated smart cities intelligence system (SMACiSYS) with sensor-web access (SENSDI) utilizing geomatics for sustainable societies. There has been a need to develop automated integrated system to categorize events and issue information that reaches users directly. At present, no web-enabled information system exists which can disseminate messages after events evaluation in real time. Research work formalizes a notion of an integrated, independent, generalized, and automated geo-event analysing system making use of geo-spatial data under popular usage platform. Integrating Sensor Web With Spatial Data Infrastructures (SENSDI) aims to extend SDIs with sensor web enablement, converging geospatial and built infrastructure, and implement test cases with sensor data and SDI. The other benefit, conversely, is the expansion of spatial data infrastructure to utilize sensor web, dynamically and in real time for smart applications that smarter cities demand nowadays. Hence, SENSDI augments existing smart cities platforms utilizing sensor web and spatial information achieved by coupling pairs of otherwise disjoint interfaces and APIs formulated by Open Geospatial Consortium (OGC) keeping entire platform open access and open source. SENSDI is based on Geonode, QGIS and Java, that bind most of the functionalities of Internet, sensor web and nowadays Internet of Things superseding Internet of Sensors as well. In a nutshell, the project delivers a generalized real-time accessible and analysable platform for sensing the environment and mapping the captured information for optimal decision-making and societal benefit.

  9. A novel principal component analysis for spatially misaligned multivariate air pollution data.

    Science.gov (United States)

    Jandarov, Roman A; Sheppard, Lianne A; Sampson, Paul D; Szpiro, Adam A

    2017-01-01

    We propose novel methods for predictive (sparse) PCA with spatially misaligned data. These methods identify principal component loading vectors that explain as much variability in the observed data as possible, while also ensuring the corresponding principal component scores can be predicted accurately by means of spatial statistics at locations where air pollution measurements are not available. This will make it possible to identify important mixtures of air pollutants and to quantify their health effects in cohort studies, where currently available methods cannot be used. We demonstrate the utility of predictive (sparse) PCA in simulated data and apply the approach to annual averages of particulate matter speciation data from national Environmental Protection Agency (EPA) regulatory monitors.

  10. Counting Cats: Spatially Explicit Population Estimates of Cheetah (Acinonyx jubatus Using Unstructured Sampling Data.

    Directory of Open Access Journals (Sweden)

    Femke Broekhuis

    Full Text Available Many ecological theories and species conservation programmes rely on accurate estimates of population density. Accurate density estimation, especially for species facing rapid declines, requires the application of rigorous field and analytical methods. However, obtaining accurate density estimates of carnivores can be challenging as carnivores naturally exist at relatively low densities and are often elusive and wide-ranging. In this study, we employ an unstructured spatial sampling field design along with a Bayesian sex-specific spatially explicit capture-recapture (SECR analysis, to provide the first rigorous population density estimates of cheetahs (Acinonyx jubatus in the Maasai Mara, Kenya. We estimate adult cheetah density to be between 1.28 ± 0.315 and 1.34 ± 0.337 individuals/100km2 across four candidate models specified in our analysis. Our spatially explicit approach revealed 'hotspots' of cheetah density, highlighting that cheetah are distributed heterogeneously across the landscape. The SECR models incorporated a movement range parameter which indicated that male cheetah moved four times as much as females, possibly because female movement was restricted by their reproductive status and/or the spatial distribution of prey. We show that SECR can be used for spatially unstructured data to successfully characterise the spatial distribution of a low density species and also estimate population density when sample size is small. Our sampling and modelling framework will help determine spatial and temporal variation in cheetah densities, providing a foundation for their conservation and management. Based on our results we encourage other researchers to adopt a similar approach in estimating densities of individually recognisable species.

  11. Counting Cats: Spatially Explicit Population Estimates of Cheetah (Acinonyx jubatus) Using Unstructured Sampling Data.

    Science.gov (United States)

    Broekhuis, Femke; Gopalaswamy, Arjun M

    2016-01-01

    Many ecological theories and species conservation programmes rely on accurate estimates of population density. Accurate density estimation, especially for species facing rapid declines, requires the application of rigorous field and analytical methods. However, obtaining accurate density estimates of carnivores can be challenging as carnivores naturally exist at relatively low densities and are often elusive and wide-ranging. In this study, we employ an unstructured spatial sampling field design along with a Bayesian sex-specific spatially explicit capture-recapture (SECR) analysis, to provide the first rigorous population density estimates of cheetahs (Acinonyx jubatus) in the Maasai Mara, Kenya. We estimate adult cheetah density to be between 1.28 ± 0.315 and 1.34 ± 0.337 individuals/100km2 across four candidate models specified in our analysis. Our spatially explicit approach revealed 'hotspots' of cheetah density, highlighting that cheetah are distributed heterogeneously across the landscape. The SECR models incorporated a movement range parameter which indicated that male cheetah moved four times as much as females, possibly because female movement was restricted by their reproductive status and/or the spatial distribution of prey. We show that SECR can be used for spatially unstructured data to successfully characterise the spatial distribution of a low density species and also estimate population density when sample size is small. Our sampling and modelling framework will help determine spatial and temporal variation in cheetah densities, providing a foundation for their conservation and management. Based on our results we encourage other researchers to adopt a similar approach in estimating densities of individually recognisable species.

  12. The spatial data infrastructure for the European Seas Observatory Network (ESONET)

    Science.gov (United States)

    Huber, Robert; Diepenbroek, Michael

    2010-05-01

    ESONET is a Multidisciplinary European Network of Excellence (NoE) in which scientists and engineers from 50 partners and 14 countries cooperate in building the infrastructure for a lasting integration of research and development in deep sea observatories in Europe. This NoE aims to develop strong links between regional nodes of a European network of sub sea observatories and to promote multidiciplinarity and transnationality within each node. Essential for these goals is the provision of an effective data and knowledge infrastructure for both, management and archiving of observatory data as well as knowledge and data sharing among network participants. The ESONET data infrastructure roughly consists of four major components: data policies a common agreement on the data management procedures and prerequisites, data acquisition technologies serve to collect data directly from ESONET observatories, data archives care for long term data management of collected ESONET data and data integration and portal tools which ensure harmonisation of collected data and allow access to the data in a common way. Most critical for ESONET was the development of a spatial data infrastructure (SDI) by using standardised protocols to directly access observatory data in its spatial and temporal context. The ESONET SDI provides means to either access data in quasi real time or harvest locally stored data in order to transfer it to a long term data archive. ESONET SDI largely builds upon the OGC Sensor Web Enablement (SWE) suite of standards. Among those, the Sensor Observation Service (SOS), the Observations & Measurements (O&M), Sensor Markup Language (SensorML) are especially important for the integration of observatory data as well as for the contribution of ESONET data to GEOSS.

  13. Spatial distribution of soil moisture in precision farming using integrated soil scanning and field telemetry data

    Science.gov (United States)

    Kalopesas, Charalampos; Galanis, George; Kalopesa, Eleni; Katsogiannos, Fotis; Kalafatis, Panagiotis; Bilas, George; Patakas, Aggelos; Zalidis, George

    2015-04-01

    Mapping the spatial variation of soil moisture content is a vital parameter for precision agriculture techniques. The aim of this study was to examine the correlation of soil moisture and conductivity (EC) data obtained through scanning techniques with field telemetry data and to spatially separate the field into discrete irrigation management zones. Using the Veris MSP3 model, geo-referenced data for electrical conductivity and organic matter preliminary maps were produced in a pilot kiwifruit field in Chrysoupoli, Kavala. Data from 15 stratified sampling points was used in order to produce the corresponding soil maps. Fusion of the Veris produced maps (OM, pH, ECa) resulted on the delineation of the field into three zones of specific management interest. An appropriate pedotransfer function was used in order to estimate a capacity soil indicator, the saturated volumetric water content (θs) for each zone, while the relationship between ECs and ECa was established for each zone. Validation of the uniformity of the three management zones was achieved by measuring specific electrical conductivity (ECs) along a transect in each zone and corresponding semivariograms for ECs within each zone. Near real-time data produced by a telemetric network consisting of soil moisture and electrical conductivity sensors, were used in order to integrate the temporal component of the specific management zones, enabling the calculation of time specific volumetric water contents on a 10 minute interval, an intensity soil indicator necessary to be incorporated to differentiate spatially the irrigation strategies for each zone. This study emphasizes the benefits yielded by fusing near real time telemetric data with soil scanning data and spatial interpolation techniques, enhancing the precision and validity of the desired results. Furthermore the use of telemetric data in combination with modern database management and geospatial software leads to timely produced operational results

  14. A Quantitative Three-Dimensional Image Analysis Tool for Maximal Acquisition of Spatial Heterogeneity Data.

    Science.gov (United States)

    Allenby, Mark C; Misener, Ruth; Panoskaltsis, Nicki; Mantalaris, Athanasios

    2017-02-01

    Three-dimensional (3D) imaging techniques provide spatial insight into environmental and cellular interactions and are implemented in various fields, including tissue engineering, but have been restricted by limited quantification tools that misrepresent or underutilize the cellular phenomena captured. This study develops image postprocessing algorithms pairing complex Euclidean metrics with Monte Carlo simulations to quantitatively assess cell and microenvironment spatial distributions while utilizing, for the first time, the entire 3D image captured. Although current methods only analyze a central fraction of presented confocal microscopy images, the proposed algorithms can utilize 210% more cells to calculate 3D spatial distributions that can span a 23-fold longer distance. These algorithms seek to leverage the high sample cost of 3D tissue imaging techniques by extracting maximal quantitative data throughout the captured image.

  15. Statistical and Spatial Analysis of Bathymetric Data for the St. Clair River, 1971-2007

    Science.gov (United States)

    Bennion, David

    2009-01-01

    To address questions concerning ongoing geomorphic processes in the St. Clair River, selected bathymetric datasets spanning 36 years were analyzed. Comparisons of recent high-resolution datasets covering the upper river indicate a highly variable, active environment. Although statistical and spatial comparisons of the datasets show that some changes to the channel size and shape have taken place during the study period, uncertainty associated with various survey methods and interpolation processes limit the statistically certain results. The methods used to spatially compare the datasets are sensitive to small variations in position and depth that are within the range of uncertainty associated with the datasets. Characteristics of the data, such as the density of measured points and the range of values surveyed, can also influence the results of spatial comparison. With due consideration of these limitations, apparently active and ongoing areas of elevation change in the river are mapped and discussed.

  16. Visualization of spatial-temporal data based on 3D virtual scene

    Science.gov (United States)

    Wang, Xianghong; Liu, Jiping; Wang, Yong; Bi, Junfang

    2009-10-01

    The main purpose of this paper is to realize the expression of the three-dimensional dynamic visualization of spatialtemporal data based on three-dimensional virtual scene, using three-dimensional visualization technology, and combining with GIS so that the people's abilities of cognizing time and space are enhanced and improved by designing dynamic symbol and interactive expression. Using particle systems, three-dimensional simulation, virtual reality and other visual means, we can simulate the situations produced by changing the spatial location and property information of geographical entities over time, then explore and analyze its movement and transformation rules by changing the interactive manner, and also replay history and forecast of future. In this paper, the main research object is the vehicle track and the typhoon path and spatial-temporal data, through three-dimensional dynamic simulation of its track, and realize its timely monitoring its trends and historical track replaying; according to visualization techniques of spatialtemporal data in Three-dimensional virtual scene, providing us with excellent spatial-temporal information cognitive instrument not only can add clarity to show spatial-temporal information of the changes and developments in the situation, but also be used for future development and changes in the prediction and deduction.

  17. A Space-Frequency Data Compression Method for Spatially Dense Laser Doppler Vibrometer Measurements

    Directory of Open Access Journals (Sweden)

    José Roberto de França Arruda

    1996-01-01

    Full Text Available When spatially dense mobility shapes are measured with scanning laser Doppler vibrometers, it is often impractical to use phase-separation modal parameter estimation methods due to the excessive number of highly coupled modes and to the prohibitive computational cost of processing huge amounts of data. To deal with this problem, a data compression method using Chebychev polynomial approximation in the frequency domain and two-dimensional discrete Fourier series approximation in the spatial domain, is proposed in this article. The proposed space-frequency regressive approach was implemented and verified using a numerical simulation of a free-free-free-free suspended rectangular aluminum plate. To make the simulation more realistic, the mobility shapes were synthesized by modal superposition using mode shapes obtained experimentally with a scanning laser Doppler vibrometer. A reduced and smoothed model, which takes advantage of the sinusoidal spatial pattern of structural mobility shapes and the polynomial frequency-domain pattern of the mobility shapes, is obtained. From the reduced model, smoothed curves with any desired frequency and spatial resolution can he produced whenever necessary. The procedure can he used either to generate nonmodal models or to compress the measured data prior to modal parameter extraction.

  18. Automating an integrated spatial data-mining model for landfill site selection

    Science.gov (United States)

    Abujayyab, Sohaib K. M.; Ahamad, Mohd Sanusi S.; Yahya, Ahmad Shukri; Ahmad, Siti Zubaidah; Aziz, Hamidi Abdul

    2017-10-01

    An integrated programming environment represents a robust approach to building a valid model for landfill site selection. One of the main challenges in the integrated model is the complicated processing and modelling due to the programming stages and several limitations. An automation process helps avoid the limitations and improve the interoperability between integrated programming environments. This work targets the automation of a spatial data-mining model for landfill site selection by integrating between spatial programming environment (Python-ArcGIS) and non-spatial environment (MATLAB). The model was constructed using neural networks and is divided into nine stages distributed between Matlab and Python-ArcGIS. A case study was taken from the north part of Peninsular Malaysia. 22 criteria were selected to utilise as input data and to build the training and testing datasets. The outcomes show a high-performance accuracy percentage of 98.2% in the testing dataset using 10-fold cross validation. The automated spatial data mining model provides a solid platform for decision makers to performing landfill site selection and planning operations on a regional scale.

  19. Urban Spatial Ecological Performance Based on the Data of Remote Sensing of Guyuan

    Science.gov (United States)

    Ren, X.-J.; Chen, X.-J.; Ma, Q.

    2018-04-01

    The evolution analysis of urban landuse and spatial ecological performance are necessary and useful to recognizing the stage of urban development and revealing the regularity and connotation of urban spatial expansion. Moreover, it lies in the core that should be exmined in the urban sustainable development. In this paper, detailed information has been acquired from the high-resolution satellite imageries of Guyuan, China case study. With the support of GIS, the land-use mapping information and the land cover changes are analyzed, and the process of urban spatial ecological performance evolution by the hierarchical methodology is explored. Results demonstrate that in the past 11 years, the urban spatial ecological performance show an improved process with the dramatic landcover change in Guyuan. Firstly, the landuse structure of Guyuan changes significantly and shows an obvious stage characteristic. Secondly, the urban ecological performance of Guyuan continues to be optimized over the 11 years. Thirdly, the findings suggest that a dynamic monitoring mechanism of urban land use based on high-resolution remote sensing data should be established in urban development, and the rational development of urban land use should be guided by the spatial ecological performance as the basic value orientation.

  20. DEVELOPMENT OF A HETEROGENIC DISTRIBUTED ENVIRONMENT FOR SPATIAL DATA PROCESSING USING CLOUD TECHNOLOGIES

    Directory of Open Access Journals (Sweden)

    A. S. Garov

    2016-06-01

    Full Text Available We are developing a unified distributed communication environment for processing of spatial data which integrates web-, desktop- and mobile platforms and combines volunteer computing model and public cloud possibilities. The main idea is to create a flexible working environment for research groups, which may be scaled according to required data volume and computing power, while keeping infrastructure costs at minimum. It is based upon the "single window" principle, which combines data access via geoportal functionality, processing possibilities and communication between researchers. Using an innovative software environment the recently developed planetary information system (http://cartsrv.mexlab.ru/geoportal will be updated. The new system will provide spatial data processing, analysis and 3D-visualization and will be tested based on freely available Earth remote sensing data as well as Solar system planetary images from various missions. Based on this approach it will be possible to organize the research and representation of results on a new technology level, which provides more possibilities for immediate and direct reuse of research materials, including data, algorithms, methodology, and components. The new software environment is targeted at remote scientific teams, and will provide access to existing spatial distributed information for which we suggest implementation of a user interface as an advanced front-end, e.g., for virtual globe system.

  1. Development of a Heterogenic Distributed Environment for Spatial Data Processing Using Cloud Technologies

    Science.gov (United States)

    Garov, A. S.; Karachevtseva, I. P.; Matveev, E. V.; Zubarev, A. E.; Florinsky, I. V.

    2016-06-01

    We are developing a unified distributed communication environment for processing of spatial data which integrates web-, desktop- and mobile platforms and combines volunteer computing model and public cloud possibilities. The main idea is to create a flexible working environment for research groups, which may be scaled according to required data volume and computing power, while keeping infrastructure costs at minimum. It is based upon the "single window" principle, which combines data access via geoportal functionality, processing possibilities and communication between researchers. Using an innovative software environment the recently developed planetary information system (geoportal"target="_blank">http://cartsrv.mexlab.ru/geoportal) will be updated. The new system will provide spatial data processing, analysis and 3D-visualization and will be tested based on freely available Earth remote sensing data as well as Solar system planetary images from various missions. Based on this approach it will be possible to organize the research and representation of results on a new technology level, which provides more possibilities for immediate and direct reuse of research materials, including data, algorithms, methodology, and components. The new software environment is targeted at remote scientific teams, and will provide access to existing spatial distributed information for which we suggest implementation of a user interface as an advanced front-end, e.g., for virtual globe system.

  2. Spatial Aspects of Multi-Sensor Data Fusion: Aerosol Optical Thickness

    Science.gov (United States)

    Leptoukh, Gregory; Zubko, V.; Gopalan, A.

    2007-01-01

    The Goddard Earth Sciences Data and Information Services Center (GES DISC) investigated the applicability and limitations of combining multi-sensor data through data fusion, to increase the usefulness of the multitude of NASA remote sensing data sets, and as part of a larger effort to integrate this capability in the GES-DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni). This initial study focused on merging daily mean Aerosol Optical Thickness (AOT), as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, to increase spatial coverage and produce complete fields to facilitate comparison with models and station data. The fusion algorithm used the maximum likelihood technique to merge the pixel values where available. The algorithm was applied to two regional AOT subsets (with mostly regular and irregular gaps, respectively) and a set of AOT fields that differed only in the size and location of artificially created gaps. The Cumulative Semivariogram (CSV) was found to be sensitive to the spatial distribution of gap areas and, thus, useful for assessing the sensitivity of the fused data to spatial gaps.

  3. A Lightweight I/O Scheme to Facilitate Spatial and Temporal Queries of Scientific Data Analytics

    Science.gov (United States)

    Tian, Yuan; Liu, Zhuo; Klasky, Scott; Wang, Bin; Abbasi, Hasan; Zhou, Shujia; Podhorszki, Norbert; Clune, Tom; Logan, Jeremy; Yu, Weikuan

    2013-01-01

    In the era of petascale computing, more scientific applications are being deployed on leadership scale computing platforms to enhance the scientific productivity. Many I/O techniques have been designed to address the growing I/O bottleneck on large-scale systems by handling massive scientific data in a holistic manner. While such techniques have been leveraged in a wide range of applications, they have not been shown as adequate for many mission critical applications, particularly in data post-processing stage. One of the examples is that some scientific applications generate datasets composed of a vast amount of small data elements that are organized along many spatial and temporal dimensions but require sophisticated data analytics on one or more dimensions. Including such dimensional knowledge into data organization can be beneficial to the efficiency of data post-processing, which is often missing from exiting I/O techniques. In this study, we propose a novel I/O scheme named STAR (Spatial and Temporal AggRegation) to enable high performance data queries for scientific analytics. STAR is able to dive into the massive data, identify the spatial and temporal relationships among data variables, and accordingly organize them into an optimized multi-dimensional data structure before storing to the storage. This technique not only facilitates the common access patterns of data analytics, but also further reduces the application turnaround time. In particular, STAR is able to enable efficient data queries along the time dimension, a practice common in scientific analytics but not yet supported by existing I/O techniques. In our case study with a critical climate modeling application GEOS-5, the experimental results on Jaguar supercomputer demonstrate an improvement up to 73 times for the read performance compared to the original I/O method.

  4. STATE OF THE ART OF THE LANDSCAPE ARCHITECTURE SPATIAL DATA MODEL FROM A GEOSPATIAL PERSPECTIVE

    Directory of Open Access Journals (Sweden)

    A. Kastuari

    2016-10-01

    Full Text Available Spatial data and information had been used for some time in planning or landscape design. For a long time, architects were using spatial data in the form of topographic map for their designs. This method is not efficient, and it is also not more accurate than using spatial analysis by utilizing GIS. Architects are sometimes also only accentuating the aesthetical aspect for their design, but not taking landscape process into account which could cause the design could be not suitable for its use and its purpose. Nowadays, GIS role in landscape architecture has been formalized by the emergence of Geodesign terminology that starts in Representation Model and ends in Decision Model. The development of GIS could be seen in several fields of science that now have the urgency to use 3 dimensional GIS, such as in: 3D urban planning, flood modeling, or landscape planning. In this fields, 3 dimensional GIS is able to support the steps in modeling, analysis, management, and integration from related data, that describe the human activities and geophysics phenomena in more realistic way. Also, by applying 3D GIS and geodesign in landscape design, geomorphology information can be better presented and assessed. In some research, it is mentioned that the development of 3D GIS is not established yet, either in its 3D data structure, or in its spatial analysis function. This study literature will able to accommodate those problems by providing information on existing development of 3D GIS for landscape architecture, data modeling, the data accuracy, representation of data that is needed by landscape architecture purpose, specifically in the river area.

  5. State of the Art of the Landscape Architecture Spatial Data Model from a Geospatial Perspective

    Science.gov (United States)

    Kastuari, A.; Suwardhi, D.; Hanan, H.; Wikantika, K.

    2016-10-01

    Spatial data and information had been used for some time in planning or landscape design. For a long time, architects were using spatial data in the form of topographic map for their designs. This method is not efficient, and it is also not more accurate than using spatial analysis by utilizing GIS. Architects are sometimes also only accentuating the aesthetical aspect for their design, but not taking landscape process into account which could cause the design could be not suitable for its use and its purpose. Nowadays, GIS role in landscape architecture has been formalized by the emergence of Geodesign terminology that starts in Representation Model and ends in Decision Model. The development of GIS could be seen in several fields of science that now have the urgency to use 3 dimensional GIS, such as in: 3D urban planning, flood modeling, or landscape planning. In this fields, 3 dimensional GIS is able to support the steps in modeling, analysis, management, and integration from related data, that describe the human activities and geophysics phenomena in more realistic way. Also, by applying 3D GIS and geodesign in landscape design, geomorphology information can be better presented and assessed. In some research, it is mentioned that the development of 3D GIS is not established yet, either in its 3D data structure, or in its spatial analysis function. This study literature will able to accommodate those problems by providing information on existing development of 3D GIS for landscape architecture, data modeling, the data accuracy, representation of data that is needed by landscape architecture purpose, specifically in the river area.

  6. Spatializing Open Data for the Assessment and the Improvement of Territorial and Social Cohesion

    Science.gov (United States)

    Scorza, F.; Las Casas, G. B.; Murgante, B.

    2016-09-01

    An integrated place-based approach for the improvement of territorial and social cohesion is the new instance for planning disciplines, coming from EU New Cohesion Policies. This paper considers the territorial impact assessment of regional development policies as a precondition in order to develop balanced and effective operative programs at national and regional levels. The contribution of `open data' appears to be mature in order to support this application and in this paper we present a spatial analysis technique for the evaluation of EU funds effects at territorial level, starting from open data by Open Cohesion. The application is focused on internal areas of Basilicata Region: Agri river Valley. A complex contest, where environmental and agricultural traditional vocations conflict with a recent development of oil extraction industries. Conclusions concern further applications and perspectives to improve and support regional development planning considering the exploitation of open data sources and spatial analysis.

  7. Linear mixing model applied to coarse spatial resolution data from multispectral satellite sensors

    Science.gov (United States)

    Holben, Brent N.; Shimabukuro, Yosio E.

    1993-01-01

    A linear mixing model was applied to coarse spatial resolution data from the NOAA Advanced Very High Resolution Radiometer. The reflective component of the 3.55-3.95 micron channel was used with the two reflective channels 0.58-0.68 micron and 0.725-1.1 micron to run a constrained least squares model to generate fraction images for an area in the west central region of Brazil. The fraction images were compared with an unsupervised classification derived from Landsat TM data acquired on the same day. The relationship between the fraction images and normalized difference vegetation index images show the potential of the unmixing techniques when using coarse spatial resolution data for global studies.

  8. AgesGalore-A software program for evaluating spatially resolved luminescence data

    International Nuclear Information System (INIS)

    Greilich, S.; Harney, H.-L.; Woda, C.; Wagner, G.A.

    2006-01-01

    Low-light luminescence is usually recorded by photomultiplier tubes (PMTs) yielding integrated photon-number data. Highly sensitive CCD (charged coupled device) detectors allow for the spatially resolved recording of luminescence. The resulting two-dimensional images require suitable software for data processing. We present a recently developed software program specially designed for equivalent-dose evaluation in the framework of optically stimulated luminescence (OSL) dating. The software is capable of appropriate CCD data handling, parameter estimation using a Bayesian approach, and the pixel-wise fitting of functions for time and dose dependencies to the luminescence signal. The results of the fitting procedure and the equivalent-dose evaluation can be presented and analyzed both as spatial and as frequency distributions

  9. Current data warehousing and OLAP technologies’ status applied to spatial databases

    Directory of Open Access Journals (Sweden)

    Diego Orlando Abril Fradel

    2007-01-01

    Full Text Available Organisations require their information on a timely, dynamic, friendly, centralised and easy-to-access basis for analysing it and taking correct decisions at the right time. Centralisation can be achieved with data warehouse technology. On-line analytical processing (OLAP is used for analysis. Technologies using graphics and maps in data presentation can be exploited for an overall view of a company and helping to take better decisions. Geo- graphic information systems (GIS are useful for spatially locating information and representing it using maps. Data warehouses are generally implemented with a multidimensional data model to make OLAP analysis easier. A fundamental point in this model is the definition of measurements and dimensions; geography lies within such dimensions. Many researchers have concluded that the geographic dimension is another attribute for describing data in current analysis systems but without having an in-depth study of its spatial feature and without locating them on a map, like GIS does. Seen this way, interoperability is necessary between GIS and OLAP (called spatial OLAP or SOLAP and several entities are currently researching this. This document summarises the current status of such research.

  10. 3D hierarchical spatial representation and memory of multimodal sensory data

    Science.gov (United States)

    Khosla, Deepak; Dow, Paul A.; Huber, David J.

    2009-04-01

    This paper describes an efficient method and system for representing, processing and understanding multi-modal sensory data. More specifically, it describes a computational method and system for how to process and remember multiple locations in multimodal sensory space (e.g., visual, auditory, somatosensory, etc.). The multimodal representation and memory is based on a biologically-inspired hierarchy of spatial representations implemented with novel analogues of real representations used in the human brain. The novelty of the work is in the computationally efficient and robust spatial representation of 3D locations in multimodal sensory space as well as an associated working memory for storage and recall of these representations at the desired level for goal-oriented action. We describe (1) A simple and efficient method for human-like hierarchical spatial representations of sensory data and how to associate, integrate and convert between these representations (head-centered coordinate system, body-centered coordinate, etc.); (2) a robust method for training and learning a mapping of points in multimodal sensory space (e.g., camera-visible object positions, location of auditory sources, etc.) to the above hierarchical spatial representations; and (3) a specification and implementation of a hierarchical spatial working memory based on the above for storage and recall at the desired level for goal-oriented action(s). This work is most useful for any machine or human-machine application that requires processing of multimodal sensory inputs, making sense of it from a spatial perspective (e.g., where is the sensory information coming from with respect to the machine and its parts) and then taking some goal-oriented action based on this spatial understanding. A multi-level spatial representation hierarchy means that heterogeneous sensory inputs (e.g., visual, auditory, somatosensory, etc.) can map onto the hierarchy at different levels. When controlling various machine

  11. Functional inverted Wishart for Bayesian multivariate spatial modeling with application to regional climatology model data.

    Science.gov (United States)

    Duan, L L; Szczesniak, R D; Wang, X

    2017-11-01

    Modern environmental and climatological studies produce multiple outcomes at high spatial resolutions. Multivariate spatial modeling is an established means to quantify cross-correlation among outcomes. However, existing models typically suffer from poor computational efficiency and lack the flexibility to simultaneously estimate auto- and cross-covariance structures. In this article, we undertake a novel construction of covariance by utilizing spectral convolution and by imposing an inverted Wishart prior on the cross-correlation structure. The cross-correlation structure with this functional inverted Wishart prior flexibly accommodates not only positive but also weak or negative associations among outcomes while preserving spatial resolution. Furthermore, the proposed model is computationally efficient and produces easily interpretable results, including the individual autocovariances and full cross-correlation matrices, as well as a partial cross-correlation matrix reflecting the outcome correlation after excluding the effects caused by spatial convolution. The model is examined using simulated data sets under different scenarios. It is also applied to the data from the North American Regional Climate Change Assessment Program, examining long-term associations between surface outcomes for air temperature, pressure, humidity, and radiation, on the land area of the North American West Coast. Results and predictive performance are compared with findings from approaches using convolution only or coregionalization.

  12. Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data

    Directory of Open Access Journals (Sweden)

    Jingyi Zhang

    2018-06-01

    Full Text Available This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF method to estimate ground PM2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM2.5 analysis and prediction.

  13. Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data.

    Science.gov (United States)

    Zhang, Jingyi; Li, Bin; Chen, Yumin; Chen, Meijie; Fang, Tao; Liu, Yongfeng

    2018-06-11

    This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM 2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR) models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM 2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM 2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM 2.5 analysis and prediction.

  14. Where do overweight women in Ghana live? Answers from exploratory spatial data analysis

    Directory of Open Access Journals (Sweden)

    Fidelia A.A. Dake

    2012-03-01

    Full Text Available Contextual influence on health outcomes is increasingly becoming an important area of research. Analytical techniques such as spatial analysis help explain the variations and dynamics in health inequalities across different context and among different population groups. This paper explores spatial clustering in body mass index among Ghanaian women by analysing data from the 2008 Ghana Demographic and Health Survey using exploratory spatial data analysis techniques. Overweight was a more common occurrence in urban areas than in rural areas. Close to a quarter of the clusters in Ghana, mostly those in the southern sector contained women who were overweight. Women who lived in clusters where the women were overweight were more likely to live around other clusters where the women were also overweight. The results suggest that the urban environment could be a potential contributing factor to the high levels of obesity in urban areas of Ghana. There is the need for researchers to include a spatial dimension to obesity research in Ghana paying particular attention the urban environment.

  15. Functional inverted Wishart for Bayesian multivariate spatial modeling with application to regional climatology model data

    Science.gov (United States)

    Duan, L. L.; Szczesniak, R. D.; Wang, X.

    2018-01-01

    Modern environmental and climatological studies produce multiple outcomes at high spatial resolutions. Multivariate spatial modeling is an established means to quantify cross-correlation among outcomes. However, existing models typically suffer from poor computational efficiency and lack the flexibility to simultaneously estimate auto- and cross-covariance structures. In this article, we undertake a novel construction of covariance by utilizing spectral convolution and by imposing an inverted Wishart prior on the cross-correlation structure. The cross-correlation structure with this functional inverted Wishart prior flexibly accommodates not only positive but also weak or negative associations among outcomes while preserving spatial resolution. Furthermore, the proposed model is computationally efficient and produces easily interpretable results, including the individual autocovariances and full cross-correlation matrices, as well as a partial cross-correlation matrix reflecting the outcome correlation after excluding the effects caused by spatial convolution. The model is examined using simulated data sets under different scenarios. It is also applied to the data from the North American Regional Climate Change Assessment Program, examining long-term associations between surface outcomes for air temperature, pressure, humidity, and radiation, on the land area of the North American West Coast. Results and predictive performance are compared with findings from approaches using convolution only or coregionalization. PMID:29576735

  16. GraphTeams: a method for discovering spatial gene clusters in Hi-C sequencing data.

    Science.gov (United States)

    Schulz, Tizian; Stoye, Jens; Doerr, Daniel

    2018-05-08

    Hi-C sequencing offers novel, cost-effective means to study the spatial conformation of chromosomes. We use data obtained from Hi-C experiments to provide new evidence for the existence of spatial gene clusters. These are sets of genes with associated functionality that exhibit close proximity to each other in the spatial conformation of chromosomes across several related species. We present the first gene cluster model capable of handling spatial data. Our model generalizes a popular computational model for gene cluster prediction, called δ-teams, from sequences to graphs. Following previous lines of research, we subsequently extend our model to allow for several vertices being associated with the same label. The model, called δ-teams with families, is particular suitable for our application as it enables handling of gene duplicates. We develop algorithmic solutions for both models. We implemented the algorithm for discovering δ-teams with families and integrated it into a fully automated workflow for discovering gene clusters in Hi-C data, called GraphTeams. We applied it to human and mouse data to find intra- and interchromosomal gene cluster candidates. The results include intrachromosomal clusters that seem to exhibit a closer proximity in space than on their chromosomal DNA sequence. We further discovered interchromosomal gene clusters that contain genes from different chromosomes within the human genome, but are located on a single chromosome in mouse. By identifying δ-teams with families, we provide a flexible model to discover gene cluster candidates in Hi-C data. Our analysis of Hi-C data from human and mouse reveals several known gene clusters (thus validating our approach), but also few sparsely studied or possibly unknown gene cluster candidates that could be the source of further experimental investigations.

  17. Mapping and simulating systematics due to spatially varying observing conditions in DES science verification data

    International Nuclear Information System (INIS)

    Leistedt, B.; Peiris, H. V.; Elsner, F.; Benoit-Lévy, A.; Amara, A.

    2016-01-01

    Spatially varying depth and the characteristics of observing conditions, such as seeing, airmass, or sky background, are major sources of systematic uncertainties in modern galaxy survey analyses, particularly in deep multi-epoch surveys. We present a framework to extract and project these sources of systematics onto the sky, and apply it to the Dark Energy Survey (DES) to map the observing conditions of the Science Verification (SV) data. The resulting distributions and maps of sources of systematics are used in several analyses of DES–SV to perform detailed null tests with the data, and also to incorporate systematics in survey simulations. We illustrate the complementary nature of these two approaches by comparing the SV data with BCC-UFig, a synthetic sky catalog generated by forward-modeling of the DES–SV images. We analyze the BCC-UFig simulation to construct galaxy samples mimicking those used in SV galaxy clustering studies. We show that the spatially varying survey depth imprinted in the observed galaxy densities and the redshift distributions of the SV data are successfully reproduced by the simulation and are well-captured by the maps of observing conditions. The combined use of the maps, the SV data, and the BCC-UFig simulation allows us to quantify the impact of spatial systematics on N(z), the redshift distributions inferred using photometric redshifts. We conclude that spatial systematics in the SV data are mainly due to seeing fluctuations and are under control in current clustering and weak-lensing analyses. However, they will need to be carefully characterized in upcoming phases of DES in order to avoid biasing the inferred cosmological results. Finally, the framework presented here is relevant to all multi-epoch surveys and will be essential for exploiting future surveys such as the Large Synoptic Survey Telescope, which will require detailed null tests and realistic end-to-end image simulations to correctly interpret the deep, high

  18. CONCEPTS, MODELS AND IMPLEMENTATION OF THE MARINE SPATIAL DATA INFRASTRUCTURE IN GERMANY (MDI-DE

    Directory of Open Access Journals (Sweden)

    C. Rüh

    2012-07-01

    Full Text Available In Germany currently the development of a marine data infrastructure takes place with the aim of merging information concerning the fields coastal engineering, hydrography and surveying, protection of the marine environment, maritime conservation, regional planning and coastal research. This undertaking is embedded in a series of regulations and developments on many administrative levels from which specifications and courses of action derive. To set up a conceptual framework for the marine data infrastructure (MDI-DE scientists at the Professorship for Geodesy and Geoinformatics at Rostock University are building a reference model, evaluating meta-information systems and developing models to support common workflows in marine applications. The reference model for the marine spatial data infrastructure of Germany (MDI-DE is the guideline for all developments inside this infrastructure. Because the undertaking is embedded in a series of regulations and developments this paper illustrates an approach on modelling a scenario for the Marine Strategy Framework Directive (MSFD using the Unified Modelling Language (UML. Evaluating how other countries built their marine spatial infrastructures is of main importance, to learn where obstacles are and errors are likely to occur. To be able to look at other initiatives from a neutral point of view it is necessary to construct a framework for evaluation of marine spatial data infrastructures. Spatial data infrastructure assessment approaches were used as bases and were expanded to meet the requirements of the marine domain. As an international case-study this paper will look at Canada's Marine Geospatial Data Infrastructure (MGDI, COINAtlantic and GeoPortal.

  19. Concepts, Models and Implementation of the Marine Spatial Data Infrastructure in Germany Mdi-De

    Science.gov (United States)

    Rüh, C.; Bill, R.

    2012-07-01

    In Germany currently the development of a marine data infrastructure takes place with the aim of merging information concerning the fields coastal engineering, hydrography and surveying, protection of the marine environment, maritime conservation, regional planning and coastal research. This undertaking is embedded in a series of regulations and developments on many administrative levels from which specifications and courses of action derive. To set up a conceptual framework for the marine data infrastructure (MDI-DE) scientists at the Professorship for Geodesy and Geoinformatics at Rostock University are building a reference model, evaluating meta-information systems and developing models to support common workflows in marine applications. The reference model for the marine spatial data infrastructure of Germany (MDI-DE) is the guideline for all developments inside this infrastructure. Because the undertaking is embedded in a series of regulations and developments this paper illustrates an approach on modelling a scenario for the Marine Strategy Framework Directive (MSFD) using the Unified Modelling Language (UML). Evaluating how other countries built their marine spatial infrastructures is of main importance, to learn where obstacles are and errors are likely to occur. To be able to look at other initiatives from a neutral point of view it is necessary to construct a framework for evaluation of marine spatial data infrastructures. Spatial data infrastructure assessment approaches were used as bases and were expanded to meet the requirements of the marine domain. As an international case-study this paper will look at Canada's Marine Geospatial Data Infrastructure (MGDI), COINAtlantic and GeoPortal.

  20. Analysis of Regularly and Irregularly Sampled Spatial, Multivariate, and Multi-temporal Data

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    1994-01-01

    This thesis describes different methods that are useful in the analysis of multivariate data. Some methods focus on spatial data (sampled regularly or irregularly), others focus on multitemporal data or data from multiple sources. The thesis covers selected and not all aspects of relevant data......-variograms are described. As a new way of setting up a well-balanced kriging support the Delaunay triangulation is suggested. Two case studies show the usefulness of 2-D semivariograms of geochemical data from areas in central Spain (with a geologist's comment) and South Greenland, and kriging/cokriging of an undersampled...... are considered as repetitions. Three case studies show the strength of the methods; one uses SPOT High Resolution Visible (HRV) multispectral (XS) data covering economically important pineapple and coffee plantations near Thika, Kiambu District, Kenya, the other two use Landsat Thematic Mapper (TM) data covering...

  1. Technology Advancements in the Next Generation of Domain Agnostic Spatial Data Infrastructures

    Science.gov (United States)

    Golodoniuc, Pavel; Rankine, Terry; Box, Paul; Atkinson, Rob; Kostanski, Laura

    2013-04-01

    Spatial Data Infrastructures (SDI) are typically composed of a suite of products focused on improving spatial information discovery and access. Proliferation of SDI initiatives has caused the "Yet Another Portal" (YAP) syndrome to emerge with each initiative providing a new mechanism for cataloguing and enabling users to search for spatial information resources. Often coarse-grained and incomplete metadata information available via these SDIs renders them to being analogous with an antiquated library catalogue. We posit that the successful use of SDI resources requires attention to be focused on various semantic aspects of the information contained within - particularly the information models and vocabularies. Currently it is common for understanding of these models and vocabularies to be built into portals. This does not enhance interoperability between SDIs, nor does this provide a means for referencing or searching for a specific feature (e.g., the City of Sydney) without first knowing the location of the information source for the feature and the form in which it is represented. SDI interfaces, such as OGC WFS, provide data from a spatial representation perspective, but do not provide identifiers that can easily be cited or used across system boundaries. The lack of mechanisms to provide stable identifiers of a feature renders it permanently scoped to a particular dataset. The other three important aspects that are commonly lacking in SDIs are the inadequate handling of feature level metadata that is commonly not sufficient enough for more than the most basic data discovery; features delivered through SDI are not well integrated with information systems that deliver statistical information about those features; and, importantly there are inadequate mechanisms to reconcile and associate multiple identities and representations of the same real world feature. In this paper we present an extended view of an SDI architecture with integrated support for information

  2. A ubiquitous method for street scale spatial data collection and analysis in challenging urban environments: mapping health risks using spatial video in Haiti.

    Science.gov (United States)

    Curtis, Andrew; Blackburn, Jason K; Widmer, Jocelyn M; Morris, J Glenn

    2013-04-15

    Fine-scale and longitudinal geospatial analysis of health risks in challenging urban areas is often limited by the lack of other spatial layers even if case data are available. Underlying population counts, residential context, and associated causative factors such as standing water or trash locations are often missing unless collected through logistically difficult, and often expensive, surveys. The lack of spatial context also hinders the interpretation of results and designing intervention strategies structured around analytical insights. This paper offers a ubiquitous spatial data collection approach using a spatial video that can be used to improve analysis and involve participatory collaborations. A case study will be used to illustrate this approach with three health risks mapped at the street scale for a coastal community in Haiti. Spatial video was used to collect street and building scale information, including standing water, trash accumulation, presence of dogs, cohort specific population characteristics, and other cultural phenomena. These data were digitized into Google Earth and then coded and analyzed in a GIS using kernel density and spatial filtering approaches. The concentrations of these risks around area schools which are sometimes sources of diarrheal disease infection because of the high concentration of children and variable sanitary practices will show the utility of the method. In addition schools offer potential locations for cholera education interventions. Previously unavailable fine scale health risk data vary in concentration across the town, with some schools being proximate to greater concentrations of the mapped risks. The spatial video is also used to validate coded data and location specific risks within these "hotspots". Spatial video is a tool that can be used in any environment to improve local area health analysis and intervention. The process is rapid and can be repeated in study sites through time to track spatio

  3. An integrated photogrammetric and spatial database management system for producing fully structured data using aerial and remote sensing images.

    Science.gov (United States)

    Ahmadi, Farshid Farnood; Ebadi, Hamid

    2009-01-01

    3D spatial data acquired from aerial and remote sensing images by photogrammetric techniques is one of the most accurate and economic data sources for GIS, map production, and spatial data updating. However, there are still many problems concerning storage, structuring and appropriate management of spatial data obtained using these techniques. According to the capabilities of spatial database management systems (SDBMSs); direct integration of photogrammetric and spatial database management systems can save time and cost of producing and updating digital maps. This integration is accomplished by replacing digital maps with a single spatial database. Applying spatial databases overcomes the problem of managing spatial and attributes data in a coupled approach. This management approach is one of the main problems in GISs for using map products of photogrammetric workstations. Also by the means of these integrated systems, providing structured spatial data, based on OGC (Open GIS Consortium) standards and topological relations between different feature classes, is possible at the time of feature digitizing process. In this paper, the integration of photogrammetric systems and SDBMSs is evaluated. Then, different levels of integration are described. Finally design, implementation and test of a software package called Integrated Photogrammetric and Oracle Spatial Systems (IPOSS) is presented.

  4. An Integrated Photogrammetric and Spatial Database Management System for Producing Fully Structured Data Using Aerial and Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Farshid Farnood Ahmadi

    2009-03-01

    Full Text Available 3D spatial data acquired from aerial and remote sensing images by photogrammetric techniques is one of the most accurate and economic data sources for GIS, map production, and spatial data updating. However, there are still many problems concerning storage, structuring and appropriate management of spatial data obtained using these techniques. According to the capabilities of spatial database management systems (SDBMSs; direct integration of photogrammetric and spatial database management systems can save time and cost of producing and updating digital maps. This integration is accomplished by replacing digital maps with a single spatial database. Applying spatial databases overcomes the problem of managing spatial and attributes data in a coupled approach. This management approach is one of the main problems in GISs for using map products of photogrammetric workstations. Also by the means of these integrated systems, providing structured spatial data, based on OGC (Open GIS Consortium standards and topological relations between different feature classes, is possible at the time of feature digitizing process. In this paper, the integration of photogrammetric systems and SDBMSs is evaluated. Then, different levels of integration are described. Finally design, implementation and test of a software package called Integrated Photogrammetric and Oracle Spatial Systems (IPOSS is presented.

  5. Spatial data mining of pipeline data provides new wave of O and M capital cost optimization opportunities

    Energy Technology Data Exchange (ETDEWEB)

    Richardson, D. [QM4 Engineering Ltd., Calgary, AB (Canada)

    2010-07-01

    This paper discussed the cost optimization benefits of spatial data mining in upstream oil and gas pipeline operations. The data mining method was used to enhance the characterization and management of internal corrosion risk and to optimize pipeline corrosion inhibition, as well as to identify pipeline network hydraulic bottlenecks. The data mining method formed part of a quality-based pipeline integrity management program. Results of the data mining study highlighted trends in well operational data and historical pipeline failure events. Use of the methodology resulted in significant savings. It was demonstrated that the key to a successful pipeline management model is a complete inventory characterization and determination of failure susceptibility profiles through the application of rigorous data standards. 4 tabs., 8 figs.

  6. Exploring prediction uncertainty of spatial data in geostatistical and machine learning Approaches

    Science.gov (United States)

    Klump, J. F.; Fouedjio, F.

    2017-12-01

    Geostatistical methods such as kriging with external drift as well as machine learning techniques such as quantile regression forest have been intensively used for modelling spatial data. In addition to providing predictions for target variables, both approaches are able to deliver a quantification of the uncertainty associated with the prediction at a target location. Geostatistical approaches are, by essence, adequate for providing such prediction uncertainties and their behaviour is well understood. However, they often require significant data pre-processing and rely on assumptions that are rarely met in practice. Machine learning algorithms such as random forest regression, on the other hand, require less data pre-processing and are non-parametric. This makes the application of machine learning algorithms to geostatistical problems an attractive proposition. The objective of this study is to compare kriging with external drift and quantile regression forest with respect to their ability to deliver reliable prediction uncertainties of spatial data. In our comparison we use both simulated and real world datasets. Apart from classical performance indicators, comparisons make use of accuracy plots, probability interval width plots, and the visual examinations of the uncertainty maps provided by the two approaches. By comparing random forest regression to kriging we found that both methods produced comparable maps of estimated values for our variables of interest. However, the measure of uncertainty provided by random forest seems to be quite different to the measure of uncertainty provided by kriging. In particular, the lack of spatial context can give misleading results in areas without ground truth data. These preliminary results raise questions about assessing the risks associated with decisions based on the predictions from geostatistical and machine learning algorithms in a spatial context, e.g. mineral exploration.

  7. Revealing Spatial Variation and Correlation of Urban Travels from Big Trajectory Data

    Science.gov (United States)

    Li, X.; Tu, W.; Shen, S.; Yue, Y.; Luo, N.; Li, Q.

    2017-09-01

    With the development of information and communication technology, spatial-temporal data that contain rich human mobility information are growing rapidly. However, the consistency of multi-mode human travel behind multi-source spatial-temporal data is not clear. To this aim, we utilized a week of taxies' and buses' GPS trajectory data and smart card data in Shenzhen, China to extract city-wide travel information of taxi, bus and metro and tested the correlation of multi-mode travel characteristics. Both the global correlation and local correlation of typical travel indicator were examined. The results show that: (1) Significant differences exist in of urban multi-mode travels. The correlation between bus travels and taxi travels, metro travel and taxi travels are globally low but locally high. (2) There are spatial differences of the correlation relationship between bus, metro and taxi travel. These findings help us understanding urban travels deeply therefore facilitate both the transport policy making and human-space interaction research.

  8. Guide for the spatial analysis of compositional data; Guia para el analisis espacial de datos composicionales

    Energy Technology Data Exchange (ETDEWEB)

    Tolosana-Delgado, R.

    2011-07-01

    Dealing with spatially-dependent compositional databases (including proportions, data in percentages, concentrations etc) should pay heed to the mathematical properties of these kinds of data: a valid composition must have positive components whose sum is at most a constant (1, 100% etc.). Generally speaking this is easily done by working on a set of log-ratios of components rather than using the raw data. To study the spatial variability of these databases it is best to estimate and model the ir-variograms, i.e. the set of variogram of all possible pairwise log-ratios of components in the composition. Such lr-variogram contain all the information necessary to deal with intrinsic stationary compositions and may be modelled with standard geostatistical tools such as the linear model of coregionalization. Moreover, the properties of the model can be studied and relationships inferred between components and possible processes linked to a given spatial scale. Finally, component-by-component interpolation and mapping is straightforward with existing kriging and simulation techniques: these tools and concepts should be applied to any set of invertible component log-ratios, i.e. log-ratio transformations, in such a way that the original composition can be recovered from the transformed data and vice versa. (Author) 17 refs.

  9. REVEALING SPATIAL VARIATION AND CORRELATION OF URBAN TRAVELS FROM BIG TRAJECTORY DATA

    Directory of Open Access Journals (Sweden)

    X. Li

    2017-09-01

    Full Text Available With the development of information and communication technology, spatial-temporal data that contain rich human mobility information are growing rapidly. However, the consistency of multi-mode human travel behind multi-source spatial-temporal data is not clear. To this aim, we utilized a week of taxies’ and buses’ GPS trajectory data and smart card data in Shenzhen, China to extract city-wide travel information of taxi, bus and metro and tested the correlation of multi-mode travel characteristics. Both the global correlation and local correlation of typical travel indicator were examined. The results show that: (1 Significant differences exist in of urban multi-mode travels. The correlation between bus travels and taxi travels, metro travel and taxi travels are globally low but locally high. (2 There are spatial differences of the correlation relationship between bus, metro and taxi travel. These findings help us understanding urban travels deeply therefore facilitate both the transport policy making and human-space interaction research.

  10. Spatial data on energy, environmental, and socioeconomic themes at Oak Ridge National Laboratory

    Energy Technology Data Exchange (ETDEWEB)

    Olson, R. J.; Watts, J. A.; Shonka, D. B.; Leobe, A. S.; Johnson, M. L.; Ogle, M. C.; Malthouse, N. S.; Madewell, D. G.; Hull, J. F.

    1977-02-01

    Spatial data files covering energy, environmental, and socioeconomic themes at Oak Ridge National Laboratory (ORNL) are described. The textual descriptions are maintained by the Regional and Urban Studies Information Center (RUSTIC) within the Data Management and Analysis Group, Energy Division, as part of the Oak Ridge Computerized Hierarchical Information System (ORCHIS) and are available for online retrieval using the ORLOOK program. Descriptions provide abstracts, geographic coverage, original data source, availability limitations, and contact person. Most of the files described in this document are available on a cost-recovery basis.

  11. Laying Bare the Landscape: commercial archaeology and the potential of digital spatial data

    Directory of Open Access Journals (Sweden)

    Wendy Morrison

    2014-08-01

    Full Text Available This article summarises the methodology we have applied to an intensively investigated part of the Upper Thames Valley. We discuss the potential of digital spatially referenced data to help bridge the gaps between the various commercial units who work side by side in the landscape, as well as between the various planning authorities. This article will be of interest to anyone working with digital data or with diverse datasets to understand wider landscapes, as well as anyone working with various funders, developers, and consultancies to plan for the best use of such 'big data' to improve heritage management and archaeological enquiry.

  12. A spatial scan statistic for survival data based on Weibull distribution.

    Science.gov (United States)

    Bhatt, Vijaya; Tiwari, Neeraj

    2014-05-20

    The spatial scan statistic has been developed as a geographical cluster detection analysis tool for different types of data sets such as Bernoulli, Poisson, ordinal, normal and exponential. We propose a scan statistic for survival data based on Weibull distribution. It may also be used for other survival distributions, such as exponential, gamma, and log normal. The proposed method is applied on the survival data of tuberculosis patients for the years 2004-2005 in Nainital district of Uttarakhand, India. Simulation studies reveal that the proposed method performs well for different survival distribution functions. Copyright © 2013 John Wiley & Sons, Ltd.

  13. Spatially disaggregated population estimates in the absence of national population and housing census data

    Science.gov (United States)

    Wardrop, N. A.; Jochem, W. C.; Bird, T. J.; Chamberlain, H. R.; Clarke, D.; Kerr, D.; Bengtsson, L.; Juran, S.; Seaman, V.; Tatem, A. J.

    2018-01-01

    Population numbers at local levels are fundamental data for many applications, including the delivery and planning of services, election preparation, and response to disasters. In resource-poor settings, recent and reliable demographic data at subnational scales can often be lacking. National population and housing census data can be outdated, inaccurate, or missing key groups or areas, while registry data are generally lacking or incomplete. Moreover, at local scales accurate boundary data are often limited, and high rates of migration and urban growth make existing data quickly outdated. Here we review past and ongoing work aimed at producing spatially disaggregated local-scale population estimates, and discuss how new technologies are now enabling robust and cost-effective solutions. Recent advances in the availability of detailed satellite imagery, geopositioning tools for field surveys, statistical methods, and computational power are enabling the development and application of approaches that can estimate population distributions at fine spatial scales across entire countries in the absence of census data. We outline the potential of such approaches as well as their limitations, emphasizing the political and operational hurdles for acceptance and sustainable implementation of new approaches, and the continued importance of traditional sources of national statistical data. PMID:29555739

  14. Long-Term Spatial Data Preservation and Archiving: What Are the Issues?

    International Nuclear Information System (INIS)

    BLEAKLY, DENISE R.

    2002-01-01

    The Department of Energy (DOE) is moving towards Long-Term Stewardship (LTS) of many environmental restoration sites that cannot be released for unrestricted use. One aspect of information management for LTS is geospatial data archiving. This report discusses the challenges facing the DOE LTS program concerning the data management and archiving of geospatial data. It discusses challenges in using electronic media for archiving, overcoming technological obsolescence, data refreshing, data migration, and emulation. It gives an overview of existing guidance and policy and discusses what the United States Geological Service (USGS), National Oceanic and Atmospheric Administration (NOAA) and the Federal Emergency Management Agency (FEMA) are doing to archive the geospatial data that their agencies are responsible for. In the conclusion, this report provides issues for further discussion around long-term spatial data archiving

  15. Spatially Explicit Estimation of Optimal Light Use Efficiency for Improved Satellite Data Driven Ecosystem Productivity Modeling

    Science.gov (United States)

    Madani, N.; Kimball, J. S.; Running, S. W.

    2014-12-01

    Remote sensing based light use efficiency (LUE) models, including the MODIS (MODerate resolution Imaging Spectroradiometer) MOD17 algorithm are commonly used for regional estimation and monitoring of vegetation gross primary production (GPP) and photosynthetic carbon (CO2) uptake. A common model assumption is that plants in a biome matrix operate at their photosynthetic capacity under optimal climatic conditions. A prescribed biome maximum light use efficiency parameter defines the maximum photosynthetic carbon conversion rate under prevailing climate conditions and is a large source of model uncertainty. Here, we used tower (FLUXNET) eddy covariance measurement based carbon flux data for estimating optimal LUE (LUEopt) over a North American domain. LUEopt was first estimated using tower observed daily carbon fluxes, meteorology and satellite (MODIS) observed fraction of photosynthetically active radiation (FPAR). LUEopt was then spatially interpolated over the domain using empirical models derived from independent geospatial data including global plant traits, surface soil moisture, terrain aspect, land cover type and percent tree cover. The derived LUEopt maps were then used as primary inputs to the MOD17 LUE algorithm for regional GPP estimation; these results were evaluated against tower observations and alternate MOD17 GPP estimates determined using Biome-specific LUEopt constants. Estimated LUEopt shows large spatial variability within and among different land cover classes indicated from a sparse North American tower network. Leaf nitrogen content and soil moisture are two important factors explaining LUEopt spatial variability. GPP estimated from spatially explicit LUEopt inputs shows significantly improved model accuracy against independent tower observations (R2 = 0.76; Mean RMSE plant trait information can explain spatial heterogeneity in LUEopt, leading to improved GPP estimates from satellite based LUE models.

  16. The stock-flow model of spatial data infrastructure development refined by fuzzy logic.

    Science.gov (United States)

    Abdolmajidi, Ehsan; Harrie, Lars; Mansourian, Ali

    2016-01-01

    The system dynamics technique has been demonstrated to be a proper method by which to model and simulate the development of spatial data infrastructures (SDI). An SDI is a collaborative effort to manage and share spatial data at different political and administrative levels. It is comprised of various dynamically interacting quantitative and qualitative (linguistic) variables. To incorporate linguistic variables and their joint effects in an SDI-development model more effectively, we suggest employing fuzzy logic. Not all fuzzy models are able to model the dynamic behavior of SDIs properly. Therefore, this paper aims to investigate different fuzzy models and their suitability for modeling SDIs. To that end, two inference and two defuzzification methods were used for the fuzzification of the joint effect of two variables in an existing SDI model. The results show that the Average-Average inference and Center of Area defuzzification can better model the dynamics of SDI development.

  17. Lowering the barriers for accessing distributed geospatial big data to advance spatial data science: the PolarHub solution

    Science.gov (United States)

    Li, W.

    2017-12-01

    Data is the crux of science. The widespread availability of big data today is of particular importance for fostering new forms of geospatial innovation. This paper reports a state-of-the-art solution that addresses a key cyberinfrastructure research problem—providing ready access to big, distributed geospatial data resources on the Web. We first formulate this data-access problem and introduce its indispensable elements, including identifying the cyber-location, space and time coverage, theme, and quality of the dataset. We then propose strategies to tackle each data-access issue and make the data more discoverable and usable for geospatial data users and decision makers. Among these strategies is large-scale web crawling as a key technique to support automatic collection of online geospatial data that are highly distributed, intrinsically heterogeneous, and known to be dynamic. To better understand the content and scientific meanings of the data, methods including space-time filtering, ontology-based thematic classification, and service quality evaluation are incorporated. To serve a broad scientific user community, these techniques are integrated into an operational data crawling system, PolarHub, which is also an important cyberinfrastructure building block to support effective data discovery. A series of experiments were conducted to demonstrate the outstanding performance of the PolarHub system. We expect this work to contribute significantly in building the theoretical and methodological foundation for data-driven geography and the emerging spatial data science.

  18. Multilevel discretized random field models with 'spin' correlations for the simulation of environmental spatial data

    International Nuclear Information System (INIS)

    Žukovič, Milan; Hristopulos, Dionissios T

    2009-01-01

    A current problem of practical significance is how to analyze large, spatially distributed, environmental data sets. The problem is more challenging for variables that follow non-Gaussian distributions. We show by means of numerical simulations that the spatial correlations between variables can be captured by interactions between 'spins'. The spins represent multilevel discretizations of environmental variables with respect to a number of pre-defined thresholds. The spatial dependence between the 'spins' is imposed by means of short-range interactions. We present two approaches, inspired by the Ising and Potts models, that generate conditional simulations of spatially distributed variables from samples with missing data. Currently, the sampling and simulation points are assumed to be at the nodes of a regular grid. The conditional simulations of the 'spin system' are forced to respect locally the sample values and the system statistics globally. The second constraint is enforced by minimizing a cost function representing the deviation between normalized correlation energies of the simulated and the sample distributions. In the approach based on the N c -state Potts model, each point is assigned to one of N c classes. The interactions involve all the points simultaneously. In the Ising model approach, a sequential simulation scheme is used: the discretization at each simulation level is binomial (i.e., ± 1). Information propagates from lower to higher levels as the simulation proceeds. We compare the two approaches in terms of their ability to reproduce the target statistics (e.g., the histogram and the variogram of the sample distribution), to predict data at unsampled locations, as well as in terms of their computational complexity. The comparison is based on a non-Gaussian data set (derived from a digital elevation model of the Walker Lake area, Nevada, USA). We discuss the impact of relevant simulation parameters, such as the domain size, the number of

  19. Suitability of the RGB Channels for a Pixel Manipulation in a Spatial Domain Data Hiding Techniques

    Directory of Open Access Journals (Sweden)

    Ante Poljicak

    2010-06-01

    Full Text Available The aim of this research was to determine which channel in rgb color space is the most suitable (regarding perceptibility for a pixel manipulation in a spatial domain data  hiding techniques. For this purpose three custom test targets were generated. The research also shows the behavior of two closely related colors in the ps (Print-Scan process. The results are interpreted using both a quantitative method (statistical comparison and a qualitative method (visual comparison.

  20. SMART CITIES INTELLIGENCE SYSTEM (SMACiSYS) INTEGRATING SENSOR WEB WITH SPATIAL DATA INFRASTRUCTURES (SENSDI)

    OpenAIRE

    D. Bhattacharya; M. Painho

    2017-01-01

    The paper endeavours to enhance the Sensor Web with crucial geospatial analysis capabilities through integration with Spatial Data Infrastructure. The objective is development of automated smart cities intelligence system (SMACiSYS) with sensor-web access (SENSDI) utilizing geomatics for sustainable societies. There has been a need to develop automated integrated system to categorize events and issue information that reaches users directly. At present, no web-enabled information system exists...

  1. Multilevel discretized random field models with 'spin' correlations for the simulation of environmental spatial data

    Science.gov (United States)

    Žukovič, Milan; Hristopulos, Dionissios T.

    2009-02-01

    A current problem of practical significance is how to analyze large, spatially distributed, environmental data sets. The problem is more challenging for variables that follow non-Gaussian distributions. We show by means of numerical simulations that the spatial correlations between variables can be captured by interactions between 'spins'. The spins represent multilevel discretizations of environmental variables with respect to a number of pre-defined thresholds. The spatial dependence between the 'spins' is imposed by means of short-range interactions. We present two approaches, inspired by the Ising and Potts models, that generate conditional simulations of spatially distributed variables from samples with missing data. Currently, the sampling and simulation points are assumed to be at the nodes of a regular grid. The conditional simulations of the 'spin system' are forced to respect locally the sample values and the system statistics globally. The second constraint is enforced by minimizing a cost function representing the deviation between normalized correlation energies of the simulated and the sample distributions. In the approach based on the Nc-state Potts model, each point is assigned to one of Nc classes. The interactions involve all the points simultaneously. In the Ising model approach, a sequential simulation scheme is used: the discretization at each simulation level is binomial (i.e., ± 1). Information propagates from lower to higher levels as the simulation proceeds. We compare the two approaches in terms of their ability to reproduce the target statistics (e.g., the histogram and the variogram of the sample distribution), to predict data at unsampled locations, as well as in terms of their computational complexity. The comparison is based on a non-Gaussian data set (derived from a digital elevation model of the Walker Lake area, Nevada, USA). We discuss the impact of relevant simulation parameters, such as the domain size, the number of

  2. High Resolution Mesoscale Weather Data Improvement to Spatial Effects for Dose-Rate Contour Plot Predictions

    Science.gov (United States)

    2007-03-01

    radiation with diurnal cycle and interaction with clouds, shallow convection, an interactive surface hydrology , and horizontal and vertical diffusion...file (New Surface.txt) which was created ! by saving an Excel File as a tab- delimited text file. The data for this file comes from Google ! Earth, and...Mississippi River Basin to the Spatial Distribution of Initial Soil Moisture,” Journal of Geophysical Research, Vol. 108, NO. D22. (31 OCT 2002) 15

  3. STARS: An ArcGIS Toolset Used to Calculate the Spatial Information Needed to Fit Spatial Statistical Models to Stream Network Data

    Directory of Open Access Journals (Sweden)

    Erin Peterson

    2014-01-01

    Full Text Available This paper describes the STARS ArcGIS geoprocessing toolset, which is used to calcu- late the spatial information needed to fit spatial statistical models to stream network data using the SSN package. The STARS toolset is designed for use with a landscape network (LSN, which is a topological data model produced by the FLoWS ArcGIS geoprocessing toolset. An overview of the FLoWS LSN structure and a few particularly useful tools is also provided so that users will have a clear understanding of the underlying data struc- ture that the STARS toolset depends on. This document may be used as an introduction to new users. The methods used to calculate the spatial information and format the final .ssn object are also explicitly described so that users may create their own .ssn object using other data models and software.

  4. Farmer data sourcing. The case study of the spatial soil information maps in South Tyrol.

    Science.gov (United States)

    Della Chiesa, Stefano; Niedrist, Georg; Thalheimer, Martin; Hafner, Hansjörg; La Cecilia, Daniele

    2017-04-01

    Nord-Italian region South Tyrol is Europe's largest apple growing area exporting ca. 15% in Europe and 2% worldwide. Vineyards represent ca. 1% of Italian production. In order to deliver high quality food, most of the farmers in South Tyrol follow sustainable farming practices. One of the key practice is the sustainable soil management, where farmers collect regularly (each 5 years) soil samples and send for analyses to improve cultivation management, yield and finally profitability. However, such data generally remain inaccessible. On this regard, in South Tyrol, private interests and the public administration have established a long tradition of collaboration with the local farming industry. This has granted to the collection of large spatial and temporal database of soil analyses along all the cultivated areas. Thanks to this best practice, information on soil properties are centralized and geocoded. The large dataset consist mainly in soil information of texture, humus content, pH and microelements availability such as, K, Mg, Bor, Mn, Cu Zn. This data was finally spatialized by mean of geostatistical methods and several high-resolution digital maps were created. In this contribution, we present the best practice where farmers data source soil information in South Tyrol. Show the capability of a large spatial-temporal geocoded soil dataset to reproduce detailed digital soil property maps and to assess long-term changes in soil properties. Finally, implication and potential application are discussed.

  5. Microtheories for Spatial Data Infrastructures - Accounting for Diversity of Local Conceptualizations at a Global Level

    Science.gov (United States)

    Duce, Stephanie; Janowicz, Krzysztof

    The categorization of our environment into feature types is an essential prerequisite for cartography, geographic information retrieval, routing applications, spatial decision support systems, and data sharing in general. However, there is no a priori conceptualization of the world and the creation of features and types is an act of cognition. Humans conceptualize their environment based on multiple criteria such as their cultural background, knowledge, motivation, and particularly by space and time. Sharing and making these conceptualizations explicit in a formal, unambiguous way is at the core of semantic interoperability. One way to cope with semantic heterogeneities is by standardization, i.e., by agreeing on a shared conceptualization. This bears the danger of losing local diversity. In contrast, this work proposes the use of microtheories for Spatial Data Infrastructures, such as INSPIRE, to account for the diversity of local conceptualizations while maintaining their semantic interoperability at a global level. We introduce a novel methodology to structure ontologies by spatial and temporal aspects, in our case administrative boundaries, which reflect variations in feature conceptualization. A local, bottom-up approach, based on non-standard inference, is used to compute global feature definitions which are neither too broad nor too specific. Using different conceptualizations of rivers and other geographic feature types, we demonstrate how the present approach can improve the INSPIRE data model and ease its adoption by European member states.

  6. Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data.

    Science.gov (United States)

    Kim, Sehwi; Jung, Inkyung

    2017-01-01

    The spatial scan statistic is an important tool for spatial cluster detection. There have been numerous studies on scanning window shapes. However, little research has been done on the maximum scanning window size or maximum reported cluster size. Recently, Han et al. proposed to use the Gini coefficient to optimize the maximum reported cluster size. However, the method has been developed and evaluated only for the Poisson model. We adopt the Gini coefficient to be applicable to the spatial scan statistic for ordinal data to determine the optimal maximum reported cluster size. Through a simulation study and application to a real data example, we evaluate the performance of the proposed approach. With some sophisticated modification, the Gini coefficient can be effectively employed for the ordinal model. The Gini coefficient most often picked the optimal maximum reported cluster sizes that were the same as or smaller than the true cluster sizes with very high accuracy. It seems that we can obtain a more refined collection of clusters by using the Gini coefficient. The Gini coefficient developed specifically for the ordinal model can be useful for optimizing the maximum reported cluster size for ordinal data and helpful for properly and informatively discovering cluster patterns.

  7. Spatial regression test for ensuring temperature data quality in southern Spain

    Science.gov (United States)

    Estévez, J.; Gavilán, P.; García-Marín, A. P.

    2018-01-01

    Quality assurance of meteorological data is crucial for ensuring the reliability of applications and models that use such data as input variables, especially in the field of environmental sciences. Spatial validation of meteorological data is based on the application of quality control procedures using data from neighbouring stations to assess the validity of data from a candidate station (the station of interest). These kinds of tests, which are referred to in the literature as spatial consistency tests, take data from neighbouring stations in order to estimate the corresponding measurement at the candidate station. These estimations can be made by weighting values according to the distance between the stations or to the coefficient of correlation, among other methods. The test applied in this study relies on statistical decision-making and uses a weighting based on the standard error of the estimate. This paper summarizes the results of the application of this test to maximum, minimum and mean temperature data from the Agroclimatic Information Network of Andalusia (southern Spain). This quality control procedure includes a decision based on a factor f, the fraction of potential outliers for each station across the region. Using GIS techniques, the geographic distribution of the errors detected has been also analysed. Finally, the performance of the test was assessed by evaluating its effectiveness in detecting known errors.

  8. Development of Framework for Aggregation and Visualization of Three-Dimensional (3D Spatial Data

    Directory of Open Access Journals (Sweden)

    Mihal Miu

    2018-03-01

    Full Text Available Geospatial information plays an important role in environmental modelling, resource management, business operations, and government policy. However, very little or no commonality between formats of various geospatial data has led to difficulties in utilizing the available geospatial information. These disparate data sources must be aggregated before further extraction and analysis may be performed. The objective of this paper is to develop a framework called PlaniSphere, which aggregates various geospatial datasets, synthesizes raw data, and allows for third party customizations of the software. PlaniSphere uses NASA World Wind to access remote data and map servers using Web Map Service (WMS as the underlying protocol that supports service-oriented architecture (SOA. The results show that PlaniSphere can aggregate and parses files that reside in local storage and conforms to the following formats: GeoTIFF, ESRI shape files, and KML. Spatial data retrieved using WMS from the Internet can create geospatial data sets (map data from multiple sources, regardless of who the data providers are. The plug-in function of this framework can be expanded for wider uses, such as aggregating and fusing geospatial data from different data sources, by providing customizations to serve future uses, which the capacity of the commercial ESRI ArcGIS software is limited to add libraries and tools due to its closed-source architectures and proprietary data structures. Analysis and increasing availability of geo-referenced data may provide an effective way to manage spatial information by using large-scale storage, multidimensional data management, and Online Analytical Processing (OLAP capabilities in one system.

  9. A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis

    Science.gov (United States)

    Wang, Jun; Wang, Yang; Zeng, Hui

    2016-01-01

    A key issue to address in synthesizing spatial data with variable-support in spatial analysis and modeling is the change-of-support problem. We present an approach for solving the change-of-support and variable-support data fusion problems. This approach is based on geostatistical inverse modeling that explicitly accounts for differences in spatial support. The inverse model is applied here to produce both the best predictions of a target support and prediction uncertainties, based on one or more measurements, while honoring measurements. Spatial data covering large geographic areas often exhibit spatial nonstationarity and can lead to computational challenge due to the large data size. We developed a local-window geostatistical inverse modeling approach to accommodate these issues of spatial nonstationarity and alleviate computational burden. We conducted experiments using synthetic and real-world raster data. Synthetic data were generated and aggregated to multiple supports and downscaled back to the original support to analyze the accuracy of spatial predictions and the correctness of prediction uncertainties. Similar experiments were conducted for real-world raster data. Real-world data with variable-support were statistically fused to produce single-support predictions and associated uncertainties. The modeling results demonstrate that geostatistical inverse modeling can produce accurate predictions and associated prediction uncertainties. It is shown that the local-window geostatistical inverse modeling approach suggested offers a practical way to solve the well-known change-of-support problem and variable-support data fusion problem in spatial analysis and modeling.

  10. Spatial Field Variability Mapping of Rice Crop using Clustering Technique from Space Borne Hyperspectral Data

    Science.gov (United States)

    Moharana, S.; Dutta, S.

    2015-12-01

    Precision farming refers to field-specific management of an agricultural crop at a spatial scale with an aim to get the highest achievable yield and to achieve this spatial information on field variability is essential. The difficulty in mapping of spatial variability occurring within an agriculture field can be revealed by employing spectral techniques in hyperspectral imagery rather than multispectral imagery. However an advanced algorithm needs to be developed to fully make use of the rich information content in hyperspectral data. In the present study, potential of hyperspectral data acquired from space platform was examined to map the field variation of paddy crop and its species discrimination. This high dimensional data comprising 242 spectral narrow bands with 30m ground resolution Hyperion L1R product acquired for Assam, India (30th Sept and 3rd Oct, 2014) were allowed for necessary pre-processing steps followed by geometric correction using Hyperion L1GST product. Finally an atmospherically corrected and spatially deduced image consisting of 112 band was obtained. By employing an advanced clustering algorithm, 12 different clusters of spectral waveforms of the crop were generated from six paddy fields for each images. The findings showed that, some clusters were well discriminated representing specific rice genotypes and some clusters were mixed treating as a single rice genotype. As vegetation index (VI) is the best indicator of vegetation mapping, three ratio based VI maps were also generated and unsupervised classification was performed for it. The so obtained 12 clusters of paddy crop were mapped spatially to the derived VI maps. From these findings, the existence of heterogeneity was clearly captured in one of the 6 rice plots (rice plot no. 1) while heterogeneity was observed in rest of the 5 rice plots. The degree of heterogeneous was found more in rice plot no.6 as compared to other plots. Subsequently, spatial variability of paddy field was

  11. Time-varying spatial data integration and visualization: 4 Dimensions Environmental Observations Platform (4-DEOS)

    Science.gov (United States)

    Paciello, Rossana; Coviello, Irina; Filizzola, Carolina; Genzano, Nicola; Lisi, Mariano; Mazzeo, Giuseppe; Pergola, Nicola; Sileo, Giancanio; Tramutoli, Valerio

    2014-05-01

    In environmental studies the integration of heterogeneous and time-varying data, is a very common requirement for investigating and possibly visualize correlations among physical parameters underlying the dynamics of complex phenomena. Datasets used in such kind of applications has often different spatial and temporal resolutions. In some case superimposition of asynchronous layers is required. Traditionally the platforms used to perform spatio-temporal visual data analyses allow to overlay spatial data, managing the time using 'snapshot' data model, each stack of layers being labeled with different time. But this kind of architecture does not incorporate the temporal indexing neither the third spatial dimension which is usually given as an independent additional layer. Conversely, the full representation of a generic environmental parameter P(x,y,z,t) in the 4D space-time domain could allow to handle asynchronous datasets as well as less traditional data-products (e.g. vertical sections, punctual time-series, etc.) . In this paper we present the 4 Dimensions Environmental Observation Platform (4-DEOS), a system based on a web services architecture Client-Broker-Server. This platform is a new open source solution for both a timely access and an easy integration and visualization of heterogeneous (maps, vertical profiles or sections, punctual time series, etc.) asynchronous, geospatial products. The innovative aspect of the 4-DEOS system is that users can analyze data/products individually moving through time, having also the possibility to stop the display of some data/products and focus on other parameters for better studying their temporal evolution. This platform gives the opportunity to choose between two distinct display modes for time interval or for single instant. Users can choose to visualize data/products in two ways: i) showing each parameter in a dedicated window or ii) visualize all parameters overlapped in a single window. A sliding time bar, allows

  12. Reevaluation of Stratospheric Ozone Trends From SAGE II Data Using a Simultaneous Temporal and Spatial Analysis

    Science.gov (United States)

    Damadeo, R. P.; Zawodny, J. M.; Thomason, L. W.

    2014-01-01

    This paper details a new method of regression for sparsely sampled data sets for use with time-series analysis, in particular the Stratospheric Aerosol and Gas Experiment (SAGE) II ozone data set. Non-uniform spatial, temporal, and diurnal sampling present in the data set result in biased values for the long-term trend if not accounted for. This new method is performed close to the native resolution of measurements and is a simultaneous temporal and spatial analysis that accounts for potential diurnal ozone variation. Results show biases, introduced by the way data is prepared for use with traditional methods, can be as high as 10%. Derived long-term changes show declines in ozone similar to other studies but very different trends in the presumed recovery period, with differences up to 2% per decade. The regression model allows for a variable turnaround time and reveals a hemispheric asymmetry in derived trends in the middle to upper stratosphere. Similar methodology is also applied to SAGE II aerosol optical depth data to create a new volcanic proxy that covers the SAGE II mission period. Ultimately this technique may be extensible towards the inclusion of multiple data sets without the need for homogenization.

  13. Using Participatory Approach to Improve Availability of Spatial Data for Local Government

    Science.gov (United States)

    Kliment, T.; Cetl, V.; Tomič, H.; Lisiak, J.; Kliment, M.

    2016-09-01

    Nowadays, the availability of authoritative geospatial features of various data themes is becoming wider on global, regional and national levels. The reason is existence of legislative frameworks for public sector information and related spatial data infrastructure implementations, emergence of support for initiatives as open data, big data ensuring that online geospatial information are made available to digital single market, entrepreneurs and public bodies on both national and local level. However, the availability of authoritative reference spatial data linking the geographic representation of the properties and their owners are still missing in an appropriate quantity and quality level, even though this data represent fundamental input for local governments regarding the register of buildings used for property tax calculations, identification of illegal buildings, etc. We propose a methodology to improve this situation by applying the principles of participatory GIS and VGI used to collect observations, update authoritative datasets and verify the newly developed datasets of areas of buildings used to calculate property tax rates issued to their owners. The case study was performed within the district of the City of Požega in eastern Croatia in the summer 2015 and resulted in a total number of 16072 updated and newly identified objects made available online for quality verification by citizens using open source geospatial technologies.

  14. Contouring a guide to the analysis and display of spatial data

    CERN Document Server

    Watson, Debbie

    2013-01-01

    This unique book is the key to computer contouring, exploring in detail the practice and principles using a personal computer. Contouring allows a three dimensional view in two dimensions and is a fundamental technique to represent spatial data. All aspects of this type of representation are covered including data preparation, selecting contour intervals, interpolation and griding, computing volumes and output and display. Formulated for both the novice and the experienced user, this book initially conducts the reader through a step by step explanation of PC software and its application to per

  15. Applying spatial reasoning to topographical data with a grounded geographical ontology

    OpenAIRE

    Mallenby, D.; Bennett, B.

    2007-01-01

    Grounding an ontology upon geographical data has been pro-\\ud posed as a method of handling the vagueness in the domain more effectively. In order to do this, we require methods of reasoning about the spatial relations between the regions within the data. This stage can be computationally expensive, as we require information on the location of\\ud points in relation to each other. This paper illustrates how using knowledge about regions allows us to reduce the computation required in an effici...

  16. 3D STREAMING PROTOCOLS FOR SPATIAL DATA INFRASTRUCTURE: A BRIEF REVIEW

    Directory of Open Access Journals (Sweden)

    C. B. Siew

    2016-09-01

    Full Text Available Web services utilizations in Spatial Data Infrastructure (SDI have been well established and standardized by Open Geospatial 3D graphics rendering has been a topic of interest among scientific domain from both computer science and geospatial science. Different methods were proposed and discussed in these researches for different domains and applications. Each method provides advantages and trade-offs. Some methods proposed image based rendering for 3D graphics and ultimately. This paper attempts to discuss several techniques from past researches and attempts to propose another method inspired from these techniques, customized for 3D SDI its data workflow use cases.

  17. Generalized information fusion and visualization using spatial voting and data modeling

    Science.gov (United States)

    Jaenisch, Holger M.; Handley, James W.

    2013-05-01

    We present a novel and innovative information fusion and visualization framework for multi-source intelligence (multiINT) data using Spatial Voting (SV) and Data Modeling. We describe how different sources of information can be converted into numerical form for further processing downstream, followed by a short description of how this information can be fused using the SV grid. As an illustrative example, we show the modeling of cyberspace as cyber layers for the purpose of tracking cyber personas. Finally we describe a path ahead for creating interactive agile networks through defender customized Cyber-cubes for network configuration and attack visualization.

  18. Developing Spatial Data Infrastructure in Croatia – Incorporating National and Regional Approach

    Directory of Open Access Journals (Sweden)

    Željko Bačić

    2010-12-01

    Full Text Available Although still not a member State of the European Union, Croatia has recognized in the spatial data infrastructure a concept that can significantly incite the modernization and effectiveness of the State administration, and create preconditions for the accelerated economic growth. Given this fact, Croatia has defined, after preparations which lasted several years, the legal framework for the national spatial data infrastructure establishment by adopting the Law on State Survey and Real Property Cadastre in 2007. During the adoption of this Law, great attention was paid to it being in line with the EU INSPIRE Directive (European Union, 2007 that was being adopted at the time, so the adopted provisions were fully compatible with the INSPIRE provisions. Regarding the model that Croatia has chosen in the establishment of its National Spatial Data Infrastructure (NSDI, the role of the State Geodetic Administration (SGA, the Croatian National Mapping and Cadastre Agency (NMCA, is significant. The SGA acts as a coordination body for the NSDI establishment, giving technical support to the NSDI bodies. One of the obligations is the establishment of a metadata catalogue through the national geoportal. Significant activities have been undertaken in the field of raising the awareness. The most important studies describing the manner of the NSDI establishment and current national as well as European situation have been translated into Croatian language and distributed to more than 1,000 NSDI stakeholders. Several workshops have been organised in order to transfer the best practices from the countries that have achieved big progress in this field. In parallel with the national activities, Croatia, or rather the SGA, has recognized that the spatial data infrastructure (SDI development cannot be based on isolated national activities connected exclusively to the INSPIRE Directive but that the Croatian spatial data infrastructure development activities must be

  19. Spectral-spatial classification of hyperspectral data with mutual information based segmented stacked autoencoder approach

    Science.gov (United States)

    Paul, Subir; Nagesh Kumar, D.

    2018-04-01

    Hyperspectral (HS) data comprises of continuous spectral responses of hundreds of narrow spectral bands with very fine spectral resolution or bandwidth, which offer feature identification and classification with high accuracy. In the present study, Mutual Information (MI) based Segmented Stacked Autoencoder (S-SAE) approach for spectral-spatial classification of the HS data is proposed to reduce the complexity and computational time compared to Stacked Autoencoder (SAE) based feature extraction. A non-parametric dependency measure (MI) based spectral segmentation is proposed instead of linear and parametric dependency measure to take care of both linear and nonlinear inter-band dependency for spectral segmentation of the HS bands. Then morphological profiles are created corresponding to segmented spectral features to assimilate the spatial information in the spectral-spatial classification approach. Two non-parametric classifiers, Support Vector Machine (SVM) with Gaussian kernel and Random Forest (RF) are used for classification of the three most popularly used HS datasets. Results of the numerical experiments carried out in this study have shown that SVM with a Gaussian kernel is providing better results for the Pavia University and Botswana datasets whereas RF is performing better for Indian Pines dataset. The experiments performed with the proposed methodology provide encouraging results compared to numerous existing approaches.

  20. Practices to Develop Spatial Data Infrastructures: Exploring the Contribution to E-Government

    Science.gov (United States)

    Crompvoets, Joep; Vancauwenberghe, Glenn; Bouckaert, Geert; Vandenbroucke, Danny

    The main objectives of this chapter are to introduce Spatial Data Infrastructures (SDIs), and to explore their potential contribution to good e-government. In order to understand the possible strengths of SDIs for good e-government, the concept, components, governance, and the cost-benefit analyses regarding the implementation of these infrastructures are first explained and presented followed by a short presentation of four existing SDIs in practice (Europe, Catalonia, Flanders, and Leiedal). These practices clearly show the dynamic, integrated, and multiple natures of SDIs. The main reason to invest in SDIs is that they facilitate the sharing of spatial data in a way that the management and use of these spatial resources happens more efficiently and effectively. This concept of sharing resources from multiple sources is not common practice in e-government research and implementation. However, it is very likely that ICTs will play a key role in improving the sharing of public resources in order to have a more efficient and effective management and use of these resources. Therefore, the lessons learnt from the existing SDI-practices and understanding of the nature of SDIs could be useful support in developing good e-governments.

  1. Restoring method for missing data of spatial structural stress monitoring based on correlation

    Science.gov (United States)

    Zhang, Zeyu; Luo, Yaozhi

    2017-07-01

    Long-term monitoring of spatial structures is of great importance for the full understanding of their performance and safety. The missing part of the monitoring data link will affect the data analysis and safety assessment of the structure. Based on the long-term monitoring data of the steel structure of the Hangzhou Olympic Center Stadium, the correlation between the stress change of the measuring points is studied, and an interpolation method of the missing stress data is proposed. Stress data of correlated measuring points are selected in the 3 months of the season when missing data is required for fitting correlation. Data of daytime and nighttime are fitted separately for interpolation. For a simple linear regression when single point's correlation coefficient is 0.9 or more, the average error of interpolation is about 5%. For multiple linear regression, the interpolation accuracy is not significantly increased after the number of correlated points is more than 6. Stress baseline value of construction step should be calculated before interpolating missing data in the construction stage, and the average error is within 10%. The interpolation error of continuous missing data is slightly larger than that of the discrete missing data. The data missing rate of this method should better not exceed 30%. Finally, a measuring point's missing monitoring data is restored to verify the validity of the method.

  2. Effects of errors and gaps in spatial data sets on assessment of conservation progress.

    Science.gov (United States)

    Visconti, P; Di Marco, M; Álvarez-Romero, J G; Januchowski-Hartley, S R; Pressey, R L; Weeks, R; Rondinini, C

    2013-10-01

    Data on the location and extent of protected areas, ecosystems, and species' distributions are essential for determining gaps in biodiversity protection and identifying future conservation priorities. However, these data sets always come with errors in the maps and associated metadata. Errors are often overlooked in conservation studies, despite their potential negative effects on the reported extent of protection of species and ecosystems. We used 3 case studies to illustrate the implications of 3 sources of errors in reporting progress toward conservation objectives: protected areas with unknown boundaries that are replaced by buffered centroids, propagation of multiple errors in spatial data, and incomplete protected-area data sets. As of 2010, the frequency of protected areas with unknown boundaries in the World Database on Protected Areas (WDPA) caused the estimated extent of protection of 37.1% of the terrestrial Neotropical mammals to be overestimated by an average 402.8% and of 62.6% of species to be underestimated by an average 10.9%. Estimated level of protection of the world's coral reefs was 25% higher when using recent finer-resolution data on coral reefs as opposed to globally available coarse-resolution data. Accounting for additional data sets not yet incorporated into WDPA contributed up to 6.7% of additional protection to marine ecosystems in the Philippines. We suggest ways for data providers to reduce the errors in spatial and ancillary data and ways for data users to mitigate the effects of these errors on biodiversity assessments. © 2013 Society for Conservation Biology.

  3. Developing an open source-based spatial data infrastructure for integrated monitoring of mining areas

    Science.gov (United States)

    Lahn, Florian; Knoth, Christian; Prinz, Torsten; Pebesma, Edzer

    2014-05-01

    In all phases of mining campaigns, comprehensive spatial information is an essential requirement in order to ensure economically efficient but also safe mining activities as well as to reduce environmental impacts. Earth observation data acquired from various sources like remote sensing or ground measurements is important e.g. for the exploration of mineral deposits, the monitoring of mining induced impacts on vegetation or the detection of ground subsidence. The GMES4Mining project aims at exploring new remote sensing techniques and developing analysis methods on various types of sensor data to provide comprehensive spatial information during mining campaigns (BENECKE et al. 2013). One important task in this project is the integration of the data gathered (e.g. hyperspectral images, spaceborne radar data and ground measurements) as well as results of the developed analysis methods within a web-accessible data source based on open source software. The main challenges here are to provide various types and formats of data from different sensors and to enable access to analysis and processing techniques without particular software or licensing requirements for users. Furthermore the high volume of the involved data (especially hyperspectral remote sensing images) makes data transfer a major issue in this use case. To engage these problems a spatial data infrastructure (SDI) including a web portal as user frontend is being developed which allows users to access not only the data but also several analysis methods. The Geoserver software is used for publishing the data, which is then accessed and visualized in a JavaScript-based web portal. In order to perform descriptive statistics and some straightforward image processing techniques on the raster data (e.g. band arithmetic or principal component analysis) the statistics software R is implemented on a server and connected via Rserve. The analysis is controlled and executed directly by the user through the web portal and

  4. The deegree framework - Spatial Data Infrastructure solution for end-users and developers

    Science.gov (United States)

    Kiehle, Christian; Poth, Andreas

    2010-05-01

    The open source software framework deegree is a comprehensive implementa­tion of standards as defined by ISO and Open Geospatial Consortium (OGC). It has been developed with two goals in mind: provide a uniform framework for implementing Spatial Data Infrastructures (SDI) and adhering to standards as strictly as possible. Although being open source software (Lesser GNU Public Li­cense, LGPL), deegree has been developed with a business model in mind: providing the general building blocks of SDIs without license fees and offer cus­tomization, consulting and tailoring by specialized companies. The core of deegree is a comprehensive Java Application Programming Inter­face (API) offering access to spatial features, analysis, metadata and coordinate reference systems. As a library, deegree can and has been integrated as a core module inside spatial information systems. It is reference implementation for several OGC standards and based on an ISO 19107 geometry model. For end users, deegree is shipped as a web application providing easy-to-set-up components for web mapping and spatial analysis. Since 2000, deegree has been the backbone of many productive SDIs, first and foremost for governmental stakeholders (e.g. Federal Agency for Cartography and Geodesy in Germany, the Ministry of Housing, Spatial Planning and the En­vironment in the Netherlands, etc.) as well as for research and development projects as an early adoption of standards, drafts and discussion papers. Be­sides mature standards like Web Map Service, Web Feature Service and Cata­logue Services, deegree also implements rather new standards like the Sensor Observation Service, the Web Processing Service and the Web Coordinate Transformation Service (WCTS). While a robust background in standardization (knowledge and implementation) is a must for consultancy, standard-compliant services and encodings alone do not provide solutions for customers. The added value is comprised by a sophistic­ated set of

  5. Dimensionality-varied convolutional neural network for spectral-spatial classification of hyperspectral data

    Science.gov (United States)

    Liu, Wanjun; Liang, Xuejian; Qu, Haicheng

    2017-11-01

    Hyperspectral image (HSI) classification is one of the most popular topics in remote sensing community. Traditional and deep learning-based classification methods were proposed constantly in recent years. In order to improve the classification accuracy and robustness, a dimensionality-varied convolutional neural network (DVCNN) was proposed in this paper. DVCNN was a novel deep architecture based on convolutional neural network (CNN). The input of DVCNN was a set of 3D patches selected from HSI which contained spectral-spatial joint information. In the following feature extraction process, each patch was transformed into some different 1D vectors by 3D convolution kernels, which were able to extract features from spectral-spatial data. The rest of DVCNN was about the same as general CNN and processed 2D matrix which was constituted by by all 1D data. So that the DVCNN could not only extract more accurate and rich features than CNN, but also fused spectral-spatial information to improve classification accuracy. Moreover, the robustness of network on water-absorption bands was enhanced in the process of spectral-spatial fusion by 3D convolution, and the calculation was simplified by dimensionality varied convolution. Experiments were performed on both Indian Pines and Pavia University scene datasets, and the results showed that the classification accuracy of DVCNN improved by 32.87% on Indian Pines and 19.63% on Pavia University scene than spectral-only CNN. The maximum accuracy improvement of DVCNN achievement was 13.72% compared with other state-of-the-art HSI classification methods, and the robustness of DVCNN on water-absorption bands noise was demonstrated.

  6. Assessing modelled spatial distributions of ice water path using satellite data

    Science.gov (United States)

    Eliasson, S.; Buehler, S. A.; Milz, M.; Eriksson, P.; John, V. O.

    2010-05-01

    The climate models used in the IPCC AR4 show large differences in monthly mean cloud ice. The most valuable source of information that can be used to potentially constrain the models is global satellite data. For this, the data sets must be long enough to capture the inter-annual variability of Ice Water Path (IWP). PATMOS-x was used together with ISCCP for the annual cycle evaluation in Fig. 7 while ECHAM-5 was used for the correlation with other models in Table 3. A clear distinction between ice categories in satellite retrievals, as desired from a model point of view, is currently impossible. However, long-term satellite data sets may still be used to indicate the climatology of IWP spatial distribution. We evaluated satellite data sets from CloudSat, PATMOS-x, ISCCP, MODIS and MSPPS in terms of monthly mean IWP, to determine which data sets can be used to evaluate the climate models. IWP data from CloudSat cloud profiling radar provides the most advanced data set on clouds. As CloudSat data are too short to evaluate the model data directly, it was mainly used here to evaluate IWP from the other satellite data sets. ISCCP and MSPPS were shown to have comparatively low IWP values. ISCCP shows particularly low values in the tropics, while MSPPS has particularly low values outside the tropics. MODIS and PATMOS-x were in closest agreement with CloudSat in terms of magnitude and spatial distribution, with MODIS being the best of the two. As PATMOS-x extends over more than 25 years and is in fairly close agreement with CloudSat, it was chosen as the reference data set for the model evaluation. In general there are large discrepancies between the individual climate models, and all of the models show problems in reproducing the observed spatial distribution of cloud-ice. Comparisons consistently showed that ECHAM-5 is the GCM from IPCC AR4 closest to satellite observations.

  7. High Spatial Resolution Airborne Multispectral Thermal Infrared Remote Sensing Data for Analysis of Urban Landscape Characteristics

    Science.gov (United States)

    Quattrochi, Dale A.; Luvall, Jeffrey C.; Estes, Maurice G., Jr.; Arnold, James E. (Technical Monitor)

    2000-01-01

    We have used airborne multispectral thermal infrared (TIR) remote sensing data collected at a high spatial resolution (i.e., 10m) over several cities in the United States to study thermal energy characteristics of the urban landscape. These TIR data provide a unique opportunity to quantify thermal responses from discrete surfaces typical of the urban landscape and to identify both the spatial arrangement and patterns of thermal processes across the city. The information obtained from these data is critical to understanding how urban surfaces drive or force development of the Urban Heat Island (UHI) effect, which exists as a dome of elevated air temperatures that presides over cities in contrast to surrounding non-urbanized areas. The UHI is most pronounced in the summertime where urban surfaces, such as rooftops and pavement, store solar radiation throughout the day, and release this stored energy slowly after sunset creating air temperatures over the city that are in excess of 2-4'C warmer in contrast with non-urban or rural air temperatures. The UHI can also exist as a daytime phenomenon with surface temperatures in downtown areas of cities exceeding 38'C. The implications of the UHI are significant, particularly as an additive source of thermal energy input that exacerbates the overall production of ground level ozone over cities. We have used the Airborne Thermal and Land Applications Sensor (ATLAS), flown onboard a Lear 23 jet aircraft from the NASA Stennis Space Center, to acquire high spatial resolution multispectral TIR data (i.e., 6 bandwidths between 8.2-12.2 (um) over Huntsville, Alabama, Atlanta, Georgia, Baton Rouge, Louisiana, Salt Lake City, Utah, and Sacramento, California. These TIR data have been used to produce maps and other products, showing the spatial distribution of heating and cooling patterns over these cities to better understand how the morphology of the urban landscape affects development of the UHI. In turn, these data have been used

  8. Spatial Data Mining for Estimating Cover Management Factor of Universal Soil Loss Equation

    Science.gov (United States)

    Tsai, F.; Lin, T. C.; Chiang, S. H.; Chen, W. W.

    2016-12-01

    Universal Soil Loss Equation (USLE) is a widely used mathematical model that describes long-term soil erosion processes. Among the six different soil erosion risk factors of USLE, the cover-management factor (C-factor) is related to land-cover/land-use. The value of C-factor ranges from 0.001 to 1, so it alone might cause a thousandfold difference in a soil erosion analysis using USLE. The traditional methods for the estimation of USLE C-factor include in situ experiments, soil physical parameter models, USLE look-up tables with land use maps, and regression models between vegetation indices and C-factors. However, these methods are either difficult or too expensive to implement in large areas. In addition, the values of C-factor obtained using these methods can not be updated frequently, either. To address this issue, this research developed a spatial data mining approach to estimate the values of C-factor with assorted spatial datasets for a multi-temporal (2004 to 2008) annual soil loss analysis of a reservoir watershed in northern Taiwan. The idea is to establish the relationship between the USLE C-factor and spatial data consisting of vegetation indices and texture features extracted from satellite images, soil and geology attributes, digital elevation model, road and river distribution etc. A decision tree classifier was used to rank influential conditional attributes in the preliminary data mining. Then, factor simplification and separation were considered to optimize the model and the random forest classifier was used to analyze 9 simplified factor groups. Experimental results indicate that the overall accuracy of the data mining model is about 79% with a kappa value of 0.76. The estimated soil erosion amounts in 2004-2008 according to the data mining results are about 50.39 - 74.57 ton/ha-year after applying the sediment delivery ratio and correction coefficient. Comparing with estimations calculated with C-factors from look-up tables, the soil erosion

  9. Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data.

    Directory of Open Access Journals (Sweden)

    Yu Liu

    Full Text Available The article revisits spatial interaction and distance decay from the perspective of human mobility patterns and spatially-embedded networks based on an empirical data set. We extract nationwide inter-urban movements in China from a check-in data set that covers half a million individuals within 370 cities to analyze the underlying patterns of trips and spatial interactions. By fitting the gravity model, we find that the observed spatial interactions are governed by a power law distance decay effect. The obtained gravity model also closely reproduces the exponential trip displacement distribution. The movement of an individual, however, may not obey the same distance decay effect, leading to an ecological fallacy. We also construct a spatial network where the edge weights denote the interaction strengths. The communities detected from the network are spatially cohesive and roughly consistent with province boundaries. We attribute this pattern to different distance decay parameters between intra-province and inter-province trips.

  10. Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data.

    Science.gov (United States)

    Liu, Yu; Sui, Zhengwei; Kang, Chaogui; Gao, Yong

    2014-01-01

    The article revisits spatial interaction and distance decay from the perspective of human mobility patterns and spatially-embedded networks based on an empirical data set. We extract nationwide inter-urban movements in China from a check-in data set that covers half a million individuals within 370 cities to analyze the underlying patterns of trips and spatial interactions. By fitting the gravity model, we find that the observed spatial interactions are governed by a power law distance decay effect. The obtained gravity model also closely reproduces the exponential trip displacement distribution. The movement of an individual, however, may not obey the same distance decay effect, leading to an ecological fallacy. We also construct a spatial network where the edge weights denote the interaction strengths. The communities detected from the network are spatially cohesive and roughly consistent with province boundaries. We attribute this pattern to different distance decay parameters between intra-province and inter-province trips.

  11. Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data

    Science.gov (United States)

    Liu, Yu; Sui, Zhengwei; Kang, Chaogui; Gao, Yong

    2014-01-01

    The article revisits spatial interaction and distance decay from the perspective of human mobility patterns and spatially-embedded networks based on an empirical data set. We extract nationwide inter-urban movements in China from a check-in data set that covers half a million individuals within 370 cities to analyze the underlying patterns of trips and spatial interactions. By fitting the gravity model, we find that the observed spatial interactions are governed by a power law distance decay effect. The obtained gravity model also closely reproduces the exponential trip displacement distribution. The movement of an individual, however, may not obey the same distance decay effect, leading to an ecological fallacy. We also construct a spatial network where the edge weights denote the interaction strengths. The communities detected from the network are spatially cohesive and roughly consistent with province boundaries. We attribute this pattern to different distance decay parameters between intra-province and inter-province trips. PMID:24465849

  12. Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region

    Directory of Open Access Journals (Sweden)

    Xihua Yang

    2015-01-01

    Full Text Available This paper presents spatial interpolation techniques to produce finer-scale daily rainfall data from regional climate modeling. Four common interpolation techniques (ANUDEM, Spline, IDW, and Kriging were compared and assessed against station rainfall data and modeled rainfall. The performance was assessed by the mean absolute error (MAE, mean relative error (MRE, root mean squared error (RMSE, and the spatial and temporal distributions. The results indicate that Inverse Distance Weighting (IDW method is slightly better than the other three methods and it is also easy to implement in a geographic information system (GIS. The IDW method was then used to produce forty-year (1990–2009 and 2040–2059 time series rainfall data at daily, monthly, and annual time scales at a ground resolution of 100 m for the Greater Sydney Region (GSR. The downscaled daily rainfall data have been further utilized to predict rainfall erosivity and soil erosion risk and their future changes in GSR to support assessments and planning of climate change impact and adaptation in local scale.

  13. Disparities in Spatial Prevalence of Feline Retroviruses due to Data Aggregation: A Case of the Modifiable Areal Unit Problem

    Directory of Open Access Journals (Sweden)

    Bimal K. Chhetri

    2014-01-01

    Full Text Available The knowledge of the spatial distribution feline immunodeficiency virus and feline leukemia virus infections, which are untreatable, can inform on their risk factors and high-risk areas to enhance control. However, when spatial analysis involves aggregated spatial data, results may be influenced by the spatial scale of aggregation, an effect known as the modifiable areal unit problem (MAUP. In this study, area level risk factors for both infections in 28,914 cats tested with ELISA were investigated by multivariable spatial Poisson regression models along with MAUP effect on spatial clustering and cluster detection (for postal codes, counties, and states by Moran’s I test and spatial scan test, respectively. The study results indicate that the significance and magnitude of the association of risk factors with both infections varied with aggregation scale. Further more, Moran’s I test only identified spatial clustering at postal code and county levels of aggregation. Similarly, the spatial scan test indicated that the number, size, and location of clusters varied over aggregation scales. In conclusion, the association between infection and area was influenced by the choice of spatial scale and indicates the importance of study design and data analysis with respect to specific research questions.

  14. Remote sensing and GIS integration: Towards intelligent imagery within a spatial data infrastructure

    Science.gov (United States)

    Abdelrahim, Mohamed Mahmoud Hosny

    2001-11-01

    In this research, an "Intelligent Imagery System Prototype" (IISP) was developed. IISP is an integration tool that facilitates the environment for active, direct, and on-the-fly usage of high resolution imagery, internally linked to hidden GIS vector layers, to query the real world phenomena and, consequently, to perform exploratory types of spatial analysis based on a clear/undisturbed image scene. The IISP was designed and implemented using the software components approach to verify the hypothesis that a fully rectified, partially rectified, or even unrectified digital image can be internally linked to a variety of different hidden vector databases/layers covering the end user area of interest, and consequently may be reliably used directly as a base for "on-the-fly" querying of real-world phenomena and for performing exploratory types of spatial analysis. Within IISP, differentially rectified, partially rectified (namely, IKONOS GEOCARTERRA(TM)), and unrectified imagery (namely, scanned aerial photographs and captured video frames) were investigated. The system was designed to handle four types of spatial functions, namely, pointing query, polygon/line-based image query, database query, and buffering. The system was developed using ESRI MapObjects 2.0a as the core spatial component within Visual Basic 6.0. When used to perform the pre-defined spatial queries using different combinations of image and vector data, the IISP provided the same results as those obtained by querying pre-processed vector layers even when the image used was not orthorectified and the vector layers had different parameters. In addition, the real-time pixel location orthorectification technique developed and presented within the IKONOS GEOCARTERRA(TM) case provided a horizontal accuracy (RMSE) of +/- 2.75 metres. This accuracy is very close to the accuracy level obtained when purchasing the orthorectified IKONOS PRECISION products (RMSE of +/- 1.9 metre). The latter cost approximately four

  15. A Spatial Panel Data Analysis of Economic Growth, Urbanization, and NOx Emissions in China

    Science.gov (United States)

    Ge, Xiangyu; Zhou, Yanli; Liu, Songlin

    2018-01-01

    Is nitrogen oxides emissions spatially correlated in a Chinese context? What is the relationship between nitrogen oxides emission levels and fast-growing economy/urbanization? More importantly, what environmental preservation and economic developing policies should China’s central and local governments take to mitigate the overall nitrogen oxides emissions and prevent severe air pollution at the provincial level in specific locations and their neighboring areas? The present study aims to tackle these issues. This is the first research that simultaneously studies the nexus between nitrogen oxides emissions and economic development/urbanization, with the application of a spatial panel data technique. Our empirical findings suggest that spatial dependence of nitrogen oxides emissions distribution exists at the provincial level. Through the investigation of the existence of an environmental Kuznets curve (EKC) embedded within the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework, we conclude something interesting: an inverse N-shaped EKC describes both the income-nitrogen oxides nexus and the urbanization-nitrogen oxides nexus. Some well-directed policy advice is provided to reduce nitrogen oxides in the future. Moreover, these results contribute to the literature on development and pollution. PMID:29641500

  16. Mapping plastic greenhouse with medium spatial resolution satellite data: Development of a new spectral index

    Science.gov (United States)

    Yang, Dedi; Chen, Jin; Zhou, Yuan; Chen, Xiang; Chen, Xuehong; Cao, Xin

    2017-06-01

    Plastic greenhouses (PGs) are an important agriculture development technique to protect and control the growing environment for food crops. The extensive use of PGs can change the agriculture landscape and affects the local environment. Accurately mapping and estimating the coverage of PGs is a necessity to the strategic planning of modern agriculture. Unfortunately, PG mapping over large areas is methodologically challenging, as the medium spatial resolution satellite imagery (such as Landsat data) used for analysis lacks spatial details and spectral variations. To fill the gap, the paper proposes a new plastic greenhouse index (PGI) based on the spectral, sensitivity, and separability analysis of PGs using medium spatial resolution images. In the context of the Landsat Enhanced Thematic Mapper Plus (ETM+) imagery, the paper examines the effectiveness and capability of the proposed PGI. The results indicate that PGs in Landsat ETM+ image can be successfully detected by the PGI if the PG fraction is greater than 12% in a mixed pixel. A kappa coefficient of 0.83 and overall accuracy of 91.2% were achieved when applying the proposed PGI in the case of Weifang District, Shandong, China. These results show that the proposed index can be applied to identifying transparent PGs in atmospheric corrected Landsat image and has the potential for the digital mapping of plastic greenhouse coverage over a large area.

  17. Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors

    KAUST Repository

    Sang, Huiyan

    2011-12-01

    This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models. Our method allows for a nonseparable and nonstationary cross-covariance structure. We also present a covariance approximation approach to facilitate the computation in the modeling and analysis of very large multivariate spatial data sets. The covariance approximation consists of two parts: a reduced-rank part to capture the large-scale spatial dependence, and a sparse covariance matrix to correct the small-scale dependence error induced by the reduced rank approximation. We pay special attention to the case that the second part of the approximation has a block-diagonal structure. Simulation results of model fitting and prediction show substantial improvement of the proposed approximation over the predictive process approximation and the independent blocks analysis. We then apply our computational approach to the joint statistical modeling of multiple climate model errors. © 2012 Institute of Mathematical Statistics.

  18. A Spatial Panel Data Analysis of Economic Growth, Urbanization, and NOx Emissions in China.

    Science.gov (United States)

    Ge, Xiangyu; Zhou, Zhimin; Zhou, Yanli; Ye, Xinyue; Liu, Songlin

    2018-04-11

    Abstract : Is nitrogen oxides emissions spatially correlated in a Chinese context? What is the relationship between nitrogen oxides emission levels and fast-growing economy/urbanization? More importantly, what environmental preservation and economic developing policies should China's central and local governments take to mitigate the overall nitrogen oxides emissions and prevent severe air pollution at the provincial level in specific locations and their neighboring areas? The present study aims to tackle these issues. This is the first research that simultaneously studies the nexus between nitrogen oxides emissions and economic development/urbanization, with the application of a spatial panel data technique. Our empirical findings suggest that spatial dependence of nitrogen oxides emissions distribution exists at the provincial level. Through the investigation of the existence of an environmental Kuznets curve (EKC) embedded within the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework, we conclude something interesting: an inverse N-shaped EKC describes both the income-nitrogen oxides nexus and the urbanization-nitrogen oxides nexus. Some well-directed policy advice is provided to reduce nitrogen oxides in the future. Moreover, these results contribute to the literature on development and pollution.

  19. Dimensionality-varied deep convolutional neural network for spectral-spatial classification of hyperspectral data

    Science.gov (United States)

    Qu, Haicheng; Liang, Xuejian; Liang, Shichao; Liu, Wanjun

    2018-01-01

    Many methods of hyperspectral image classification have been proposed recently, and the convolutional neural network (CNN) achieves outstanding performance. However, spectral-spatial classification of CNN requires an excessively large model, tremendous computations, and complex network, and CNN is generally unable to use the noisy bands caused by water-vapor absorption. A dimensionality-varied CNN (DV-CNN) is proposed to address these issues. There are four stages in DV-CNN and the dimensionalities of spectral-spatial feature maps vary with the stages. DV-CNN can reduce the computation and simplify the structure of the network. All feature maps are processed by more kernels in higher stages to extract more precise features. DV-CNN also improves the classification accuracy and enhances the robustness to water-vapor absorption bands. The experiments are performed on data sets of Indian Pines and Pavia University scene. The classification performance of DV-CNN is compared with state-of-the-art methods, which contain the variations of CNN, traditional, and other deep learning methods. The experiment of performance analysis about DV-CNN itself is also carried out. The experimental results demonstrate that DV-CNN outperforms state-of-the-art methods for spectral-spatial classification and it is also robust to water-vapor absorption bands. Moreover, reasonable parameters selection is effective to improve classification accuracy.

  20. A Spatial Panel Data Analysis of Economic Growth, Urbanization, and NOx Emissions in China

    Directory of Open Access Journals (Sweden)

    Xiangyu Ge

    2018-04-01

    Full Text Available Is nitrogen oxides emissions spatially correlated in a Chinese context? What is the relationship between nitrogen oxides emission levels and fast-growing economy/urbanization? More importantly, what environmental preservation and economic developing policies should China’s central and local governments take to mitigate the overall nitrogen oxides emissions and prevent severe air pollution at the provincial level in specific locations and their neighboring areas? The present study aims to tackle these issues. This is the first research that simultaneously studies the nexus between nitrogen oxides emissions and economic development/urbanization, with the application of a spatial panel data technique. Our empirical findings suggest that spatial dependence of nitrogen oxides emissions distribution exists at the provincial level. Through the investigation of the existence of an environmental Kuznets curve (EKC embedded within the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT framework, we conclude something interesting: an inverse N-shaped EKC describes both the income-nitrogen oxides nexus and the urbanization-nitrogen oxides nexus. Some well-directed policy advice is provided to reduce nitrogen oxides in the future. Moreover, these results contribute to the literature on development and pollution.

  1. Cost Analysis of Spatial Data Production as Part of Business Intelligence Within the Mapping Department

    Science.gov (United States)

    Kisa, A.; Erkek, B.; Çolak, S.

    2012-07-01

    Business intelligence is becoming an important strategic tool for business management. Companies have invested significant resources in applications for customer relationship management (CRM), supply chain management (SCM), enterprise resource planning (ERP), e-commerce, among others, which collect vast amounts of data. Today, these same companies are realizing that no matter how robust their application feature sets are, without an equally robust BI mechanism to make use of the collected data, these applications are ultimately coming up short. They do not provide actionable information to end users nor can they give a global understanding among all the organization's information from the various databases for accounting, CRM, and so on. General Directorate of Land Registry and Cadastre (GDLRC) is the leader organizations in Turkey on the field of mapping-land registry-cadastre. GDLRC has executed spatial based projects on the way National Spatial Data Infrastructure especially from the beginnings of 2000s. such as; Continuously Operating GPS Reference Stations (TUSAGA-Aktif), Geo-Metadata Portal (HBB), Orthophoto-Base Map Production and web services, Completion of Initial Cadastre, Cadastral Renovation Project (TKMP), Land Registry and Cadastre Information System (TAKBIS), Turkish National Spatial Data Infrastructure Project (TNSDI), Ottoman Land Registry Archive Information System (TARBIS). Most of this project has been completed. Some software has been developed within the mentioned project, especially reporting for management level to take decision. In the year of 2010 a new law launched and forced to reorganization of General Directorate of Land Registry and Cadastre. The new structural changes effected to whole organization, management understanding, carrier understanding so on. Even in mapping department which is spatial data producer, now there is no technician, there is no section; there are new carrier as experts. Because of that, all procedures and

  2. Spatial data analytics on heterogeneous multi- and many-core parallel architectures using python

    Science.gov (United States)

    Laura, Jason R.; Rey, Sergio J.

    2017-01-01

    Parallel vector spatial analysis concerns the application of parallel computational methods to facilitate vector-based spatial analysis. The history of parallel computation in spatial analysis is reviewed, and this work is placed into the broader context of high-performance computing (HPC) and parallelization research. The rise of cyber infrastructure and its manifestation in spatial analysis as CyberGIScience is seen as a main driver of renewed interest in parallel computation in the spatial sciences. Key problems in spatial analysis that have been the focus of parallel computing are covered. Chief among these are spatial optimization problems, computational geometric problems including polygonization and spatial contiguity detection, the use of Monte Carlo Markov chain simulation in spatial statistics, and parallel implementations of spatial econometric methods. Future directions for research on parallelization in computational spatial analysis are outlined.

  3. Utilizing NASA DISCOVER-AQ Data to Examine Spatial Gradients in Complex Emission Environments

    Science.gov (United States)

    Buzanowicz, M. E.; Moore, W.; Crawford, J. H.; Schroeder, J.

    2017-12-01

    Although many regulations have been enacted with the goal of improving air quality, many parts of the US are still classified as `non-attainment areas' because they frequently violate federal air quality standards. Adequately monitoring the spatial distribution of pollutants both within and outside of non-attainment areas has been an ongoing challenge for regulators. Observations of near-surface pollution from space-based platforms would provide an unprecedented view of the spatial distribution of pollution, but this goal has not yet been realized due to fundamental limitations of satellites, specifically because the footprint size of satellite measurements may not be sufficiently small enough to capture true gradients in pollution, and rather represents an average over a large area. NASA's DISCOVER-AQ was a multi-year field campaign aimed at improving our understanding of the role that remote sensing, including satellite-based remote sensing, could play in air quality monitoring systems. DISCOVER-AQ data will be utilized to create a metric to examine spatial gradients and how satellites can capture those gradients in areas with complex emission environments. Examining horizontal variability within a vertical column is critical to understanding mixing within the atmosphere. Aircraft spirals conducted during DISCOVER-AQ were divided into octants, and averages of a given a species were calculated, with certain points receiving a flag. These flags were determined by calculating gradients between subsequent octants. Initial calculations have shown that over areas with large point source emissions, such as Platteville and Denver-La Casa in Colorado, and Essex, Maryland, satellite retrievals may not adequately capture spatial variability in the atmosphere, thus complicating satellite inversion techniques and limiting our ability to understand human exposure on sub-grid scales. Further calculations at other locations and for other trace gases are necessary to determine

  4. Snapshot science: new research possibilities facilitated by spatially dense data sets in limnology

    Science.gov (United States)

    Stanley, E. H.; Loken, L. C.; Crawford, J.; Butitta, V.; Schramm, P.

    2017-12-01

    The recent increase in availability of high frequency sensors is transforming the study of inland aquatic ecosystems, allowing the detection of rare or difficult-to-capture events, revealing previously unappreciated temporal dynamics, and providing rich data sets that can be used to calibrate or inform process-based models in ways that have not previously been possible. Yet sensor deployment is typically a 1-D practice, so insights are tempered by device placement. Limnologists have long known that there can be substantial spatial variability in physical, chemical, and biological features within water bodies, but in most cases, logistical difficulties limit our ability to quantify this heterogeneity. Recent improvements in remote sensing are helping to overcome this deficit for a subset of variables. Alternatively, devices such as the Fast Limnology Automated Measurement platform that deploy sensors on watercraft can be used to quickly generate spatially-rich data sets. This expanded capacity leads to new questions about what can be seen and learned about underlying processes. Surveys of multiple Wisconsin lakes reveal both homogeneity and heterogeneity among sites and variables, indicating that the limnological tradition of sampling at a single fixed point is unlikely to represent the entire lake area. Initial inferences drawn from surface water maps include identification of biogeochemical hotspots or areas of elevated loading. At a more sophisticated level, evaluation of changes in spatial structure among sites or dates is commonly used to infer process by landscape ecologists, and these same practices can now be applied to lakes and rivers. For example, a recent study documented significant changes in spatial variance and the magnitude of spatial autocorrelation of phycocyanin prior to the onset of a cyanobacterial bloom. This may provide information on population growth dynamics of cyanobacteria, and be used as early warnings of impending algal blooms. As the

  5. Counterstatement to Article Entitled "A Framework for Evaluation of Marine Spatial Data Geoportals Using Case Studies"

    Directory of Open Access Journals (Sweden)

    Seip Christian

    2015-12-01

    Full Text Available In December 2014 in volume 60 issue 4 a paper was published entitled “A Framework for Evaluation of Marine Spatial Data Geoportals Using Case Studies” by Marina Tavra, Vlado Cetl and Tea Duplancic Leder which is suspected to constitute academic misconduct. This comment reasons that the core of the paper was taken from another source and thus does not offer new and original scientific work and therefore does not add knowledge to the body of science. Furthermore it argues that apart from the plagiarism the paper shows major weaknesses and thus should have not been published even it was not plagiarized.

  6. A description of spatial data infrastructure stakeholders in Ghana using the ICA model

    CSIR Research Space (South Africa)

    Owusu-Banahene, W

    2013-11-01

    Full Text Available Service, the Electoral Commission and the Ghana Meteorological Services Department. Some private companies, such as Rudan Engineering and GeoTech, were involved in NAFGIM as contractors or agents who worked for the Survey Department. As shown in Table...://www.gisdevelopment.net/proceedings/gisdeco/2004/keynote/ezipf.ht m [Accessed 8 April 2013]. Georgiadou, Y., Puri, S.K. and Sahay, S., (2005), Towards a Potential Research Agenda to Guide the Implementation of Spatial Data Infrastructures ʹ A Case Study from India. International Journal...

  7. GEOINFORMATIONAL TECHNOLOGY IN THE SYSTEM ‘BANK OF SPATIAL DATA KRASNOYARSK REGION’

    Directory of Open Access Journals (Sweden)

    A. A. Kadochnikov

    2015-01-01

    Full Text Available Formation and effective use of geospatial data is today one of the most pressing problems facing the scientific community and public authorities. Are posed the task of technological and organizational support geographically distributed systems for collecting, processing, storing and providing spatial data and metadata. These systems must provide its users with remote access to digital geographic information, provide them with information interaction. Consider the stages and features of the creation of the state information system, the ‘Bank of spatial data’ for interagency cooperation and integration projects of Krasnoyarsk region along line cataloging, storage, analytical processing and publishing of geospatial data. Considerable attention is given to web services, software interfaces and generally accepted standards. In developing the software many different software libraries and components were used. Web mapping user interface was created using a number of open source libraries. To create a server-side web application author used GIS platforms MapGuide Open Source and Minnesota MapServer. GeoWebCache was another essential component of distributed web mapping environmental monitoring applications.

  8. Understanding Urban Spatial Structure of Shanghai Central City Based on Mobile Phone Data

    Institute of Scientific and Technical Information of China (English)

    Niu; Xinyi; Ding; Liang; Song; Xiaodong; Zhang; Qingfei

    2015-01-01

    Taking Shanghai Central City as its case study, this paper presents an approach to exploring the urban spatial structure through mobile phone positioning data. Firstly, based on base station location data and mobile phone signaling data, the paper analyses the number of users connecting to each base station, and further generates the maps of mobile phone user density through kernel density analysis. We move on to calculate the multi-day average user density based on a time frame of 10:00 and 23:00 at workdays and 15:00 and 23:00 at weekends for Shanghai Central City. Then, through spatial aggregation and density classifi cation on the density maps of 10:00 at workdays and 15:00 at weekends, we identify the ranks and functions of public centers within Shanghai Central City. Lastly, we identify residential areas, business off ice areas, and leisure areas in Shanghai Central City and measure the degree of functional mix by comparing the ratio of day and night user density as well as the user density at nighttime of workdays and weekends.

  9. An efficient communication strategy for mobile agent based distributed spatial data mining application

    Science.gov (United States)

    Han, Guodong; Wang, Jiazhen

    2005-11-01

    An efficient communication strategy is proposed in this paper, which aims to improve the response time and availability of mobile agent based distributed spatial data mining applications. When dealing with decomposed complex data mining tasks or On-Line Analytical Processing (OLAP), mobile agents authorized by the specified user need to coordinate and cooperate with each other by employing given communication method to fulfill the subtasks delegated to them. Agent interactive behavior, e.g. messages passing, intermediate results exchanging and final results merging, must happen after the specified path is determined by executing given routing selection algorithm. Most of algorithms exploited currently run in time that grows approximately quadratic with the size of the input nodes where mobile agents migrate between. In order to gain enhanced communication performance by reducing the execution time of the decision algorithm, we propose an approach to reduce the number of nodes involved in the computation. In practice, hosts in the system are reorganized into groups in terms of the bandwidth between adjacent nodes. Then, we find an optimal node for each group with high bandwidth and powerful computing resources, which is managed by an agent dispatched by agent home node. With that, the communication pattern can be implemented at a higher level of abstraction and contribute to improving the overall performance of mobile agent based distributed spatial data mining applications.

  10. Architecture of the local spatial data infrastructure for regional climate change research

    Science.gov (United States)

    Titov, Alexander; Gordov, Evgeny

    2013-04-01

    Georeferenced datasets (meteorological databases, modeling and reanalysis results, etc.) are actively used in modeling and analysis of climate change for various spatial and temporal scales. Due to inherent heterogeneity of environmental datasets as well as their size which might constitute up to tens terabytes for a single dataset studies in the area of climate and environmental change require a special software support based on SDI approach. A dedicated architecture of the local spatial data infrastructure aiming at regional climate change analysis using modern web mapping technologies is presented. Geoportal is a key element of any SDI, allowing searching of geoinformation resources (datasets and services) using metadata catalogs, producing geospatial data selections by their parameters (data access functionality) as well as managing services and applications of cartographical visualization. It should be noted that due to objective reasons such as big dataset volume, complexity of data models used, syntactic and semantic differences of various datasets, the development of environmental geodata access, processing and visualization services turns out to be quite a complex task. Those circumstances were taken into account while developing architecture of the local spatial data infrastructure as a universal framework providing geodata services. So that, the architecture presented includes: 1. Effective in terms of search, access, retrieval and subsequent statistical processing, model of storing big sets of regional georeferenced data, allowing in particular to store frequently used values (like monthly and annual climate change indices, etc.), thus providing different temporal views of the datasets 2. General architecture of the corresponding software components handling geospatial datasets within the storage model 3. Metadata catalog describing in detail using ISO 19115 and CF-convention standards datasets used in climate researches as a basic element of the

  11. Multi-scale application of spatial metrics for quantifying forest spatial structure and diversity from Corine Land Cover and FMERS-WiFS raster data

    DEFF Research Database (Denmark)

    Nielsen, Niels Christian; Blackburn, Alan

    2005-01-01

    In this paper, the moving-windows approach to calculation and analysis of spatial metrics is tested with particular focus on forest mapping. The influence of window size on average metrics values, agreement between values from different EO-based data sources and local variance of metrics values i...

  12. Possibilities Of Using Innovative Sources Of Information On Real Estate In The Spatial Data Collection Process

    Directory of Open Access Journals (Sweden)

    Kwartnik-Pruc Anita

    2015-03-01

    Full Text Available The register of land and buildings is an essential source of information on real estate. The use of cadastral data in land management is manifold, starting from spatial planning, through the calculation of taxes, the designation of real estate in land and mortgage registers, and finally, in real estate management. The accuracy of information from public records, i.e. the register of land and buildings, obtained by entities managing property has a direct impact on the correctness of the conducted processes of land management. Data necessary to determine the position of boundaries of cadastral parcels are taken from surveying documentation accepted into the national geodetic and cartographic resource database. If there is no such documentation, or the data contained therein are not reliable, information on the boundaries of cadastral parcels are obtained by means of field or photogrammetric surveys, preceded by the determination of the course of these boundaries.

  13. GeoXp : An R Package for Exploratory Spatial Data Analysis

    Directory of Open Access Journals (Sweden)

    Thibault Laurent

    2012-04-01

    Full Text Available We present GeoXp, an R package implementing interactive graphics for exploratory spatial data analysis. We use a data set concerning public schools of the French MidiPyrenees region to illustrate the use of these exploratory techniques based on the coupling between a statistical graph and a map. Besides elementary plots like boxplots,histograms or simple scatterplots, GeoXp also couples maps with Moran scatterplots, variogram clouds, Lorenz curves and other graphical tools. In order to make the most of the multidimensionality of the data, GeoXp includes dimension reduction techniques such as principal components analysis and cluster analysis whose results are also linked to the map.

  14. Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

    Directory of Open Access Journals (Sweden)

    M. C. Demirel

    2018-02-01

    Full Text Available Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the

  15. Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

    Science.gov (United States)

    Demirel, Mehmet C.; Mai, Juliane; Mendiguren, Gorka; Koch, Julian; Samaniego, Luis; Stisen, Simon

    2018-02-01

    Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex

  16. The sensitivity of ecosystem service models to choices of input data and spatial resolution

    Science.gov (United States)

    Bagstad, Kenneth J.; Cohen, Erika; Ancona, Zachary H.; McNulty, Steven; Sun, Ge

    2018-01-01

    Although ecosystem service (ES) modeling has progressed rapidly in the last 10–15 years, comparative studies on data and model selection effects have become more common only recently. Such studies have drawn mixed conclusions about whether different data and model choices yield divergent results. In this study, we compared the results of different models to address these questions at national, provincial, and subwatershed scales in Rwanda. We compared results for carbon, water, and sediment as modeled using InVEST and WaSSI using (1) land cover data at 30 and 300 m resolution and (2) three different input land cover datasets. WaSSI and simpler InVEST models (carbon storage and annual water yield) were relatively insensitive to the choice of spatial resolution, but more complex InVEST models (seasonal water yield and sediment regulation) produced large differences when applied at differing resolution. Six out of nine ES metrics (InVEST annual and seasonal water yield and WaSSI) gave similar predictions for at least two different input land cover datasets. Despite differences in mean values when using different data sources and resolution, we found significant and highly correlated results when using Spearman's rank correlation, indicating consistent spatial patterns of high and low values. Our results confirm and extend conclusions of past studies, showing that in certain cases (e.g., simpler models and national-scale analyses), results can be robust to data and modeling choices. For more complex models, those with different output metrics, and subnational to site-based analyses in heterogeneous environments, data and model choices may strongly influence study findings.

  17. Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America.

    Science.gov (United States)

    Wang, Tongli; Hamann, Andreas; Spittlehouse, Dave; Carroll, Carlos

    2016-01-01

    Large volumes of gridded climate data have become available in recent years including interpolated historical data from weather stations and future predictions from general circulation models. These datasets, however, are at various spatial resolutions that need to be converted to scales meaningful for applications such as climate change risk and impact assessments or sample-based ecological research. Extracting climate data for specific locations from large datasets is not a trivial task and typically requires advanced GIS and data management skills. In this study, we developed a software package, ClimateNA, that facilitates this task and provides a user-friendly interface suitable for resource managers and decision makers as well as scientists. The software locally downscales historical and future monthly climate data layers into scale-free point estimates of climate values for the entire North American continent. The software also calculates a large number of biologically relevant climate variables that are usually derived from daily weather data. ClimateNA covers 1) 104 years of historical data (1901-2014) in monthly, annual, decadal and 30-year time steps; 2) three paleoclimatic periods (Last Glacial Maximum, Mid Holocene and Last Millennium); 3) three future periods (2020s, 2050s and 2080s); and 4) annual time-series of model projections for 2011-2100. Multiple general circulation models (GCMs) were included for both paleo and future periods, and two representative concentration pathways (RCP4.5 and 8.5) were chosen for future climate data.

  18. Excess under-5 female mortality across India: a spatial analysis using 2011 census data

    Directory of Open Access Journals (Sweden)

    Christophe Z Guilmoto, PhD

    2018-06-01

    Full Text Available Summary: Background: Excess female mortality causes half of the missing women (estimated deficit of women in countries with suspiciously low proportion of females in their population today. Globally, most of these avoidable deaths of women occur during childhood in China and India. We aimed to estimate excess female under-5 mortality rate (U5MR for India's 35 states and union territories and 640 districts. Methods: Using the summary birth history method (or Brass method, we derived district-level estimates of U5MR by sex from 2011 census data. We used data from 46 countries with no evidence of gender bias for mortality to estimate the effects and intensity of excess female mortality at district level. We used a detailed spatial and statistical analysis to highlight the correlates of excess mortality at district level. Findings: Excess female U5MR was 18·5 per 1000 livebirths (95% CI 13·1–22·6 in India 2000–2005, which corresponds to an estimated 239 000 excess deaths (169 000–293 000 per year. More than 90% of districts had excess female mortality, but the four largest states in northern India (Uttar Pradesh, Bihar, Rajasthan, and Madhya Pradesh accounted for two-thirds of India's total number. Low economic development, gender inequity, and high fertility were the main predictors of excess female mortality. Spatial analysis confirmed the strong spatial clustering of postnatal discrimination against girls in India. Interpretation: The considerable effect of gender bias on mortality in India highlights the need for more proactive engagement with the issue of postnatal sex discrimination and a focus on the northern districts. Notably, these regions are not the same as those most affected by skewed sex ratio at birth. Funding: None.

  19. Definition of Management Zones for Enhancing Cultivated Land Conservation Using Combined Spatial Data

    Science.gov (United States)

    Li, Yan; Shi, Zhou; Wu, Hao-Xiang; Li, Feng; Li, Hong-Yi

    2013-10-01

    The loss of cultivated land has increasingly become an issue of regional and national concern in China. Definition of management zones is an important measure to protect limited cultivated land resource. In this study, combined spatial data were applied to define management zones in Fuyang city, China. The yield of cultivated land was first calculated and evaluated and the spatial distribution pattern mapped; the limiting factors affecting the yield were then explored; and their maps of the spatial variability were presented using geostatistics analysis. Data were jointly analyzed for management zone definition using a combination of principal component analysis with a fuzzy clustering method, two cluster validity functions were used to determine the optimal number of cluster. Finally one-way variance analysis was performed on 3,620 soil sampling points to assess how well the defined management zones reflected the soil properties and productivity level. It was shown that there existed great potential for increasing grain production, and the amount of cultivated land played a key role in maintaining security in grain production. Organic matter, total nitrogen, available phosphorus, elevation, thickness of the plow layer, and probability of irrigation guarantee were the main limiting factors affecting the yield. The optimal number of management zones was three, and there existed significantly statistical differences between the crop yield and field parameters in each defined management zone. Management zone I presented the highest potential crop yield, fertility level, and best agricultural production condition, whereas management zone III lowest. The study showed that the procedures used may be effective in automatically defining management zones; by the development of different management zones, different strategies of cultivated land management and practice in each zone could be determined, which is of great importance to enhance cultivated land conservation

  20. Excess under-5 female mortality across India: a spatial analysis using 2011 census data.

    Science.gov (United States)

    Guilmoto, Christophe Z; Saikia, Nandita; Tamrakar, Vandana; Bora, Jayanta Kumar

    2018-06-01

    Excess female mortality causes half of the missing women (estimated deficit of women in countries with suspiciously low proportion of females in their population) today. Globally, most of these avoidable deaths of women occur during childhood in China and India. We aimed to estimate excess female under-5 mortality rate (U5MR) for India's 35 states and union territories and 640 districts. Using the summary birth history method (or Brass method), we derived district-level estimates of U5MR by sex from 2011 census data. We used data from 46 countries with no evidence of gender bias for mortality to estimate the effects and intensity of excess female mortality at district level. We used a detailed spatial and statistical analysis to highlight the correlates of excess mortality at district level. Excess female U5MR was 18·5 per 1000 livebirths (95% CI 13·1-22·6) in India 2000-2005, which corresponds to an estimated 239 000 excess deaths (169 000-293 000) per year. More than 90% of districts had excess female mortality, but the four largest states in northern India (Uttar Pradesh, Bihar, Rajasthan, and Madhya Pradesh) accounted for two-thirds of India's total number. Low economic development, gender inequity, and high fertility were the main predictors of excess female mortality. Spatial analysis confirmed the strong spatial clustering of postnatal discrimination against girls in India. The considerable effect of gender bias on mortality in India highlights the need for more proactive engagement with the issue of postnatal sex discrimination and a focus on the northern districts. Notably, these regions are not the same as those most affected by skewed sex ratio at birth. None. Copyright © 2018 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.

  1. Applications of seismic spatial wavefield gradient and rotation data in exploration seismology

    Science.gov (United States)

    Schmelzbach, C.; Van Renterghem, C.; Sollberger, D.; Häusler, M.; Robertsson, J. O. A.

    2017-12-01

    Seismic spatial wavefield gradient and rotation data have the potential to open up new ways to address long-standing problems in land-seismic exploration such as identifying and separating P-, S-, and surface waves. Gradient-based acquisition and processing techniques could enable replacing large arrays of densely spaced receivers by sparse spatially-compact receiver layouts or even one single multicomponent station with dedicated instruments (e.g., rotational seismometers). Such approaches to maximize the information content of single-station recordings are also of significant interest for seismic measurements at sites with limited access such as boreholes, the sea bottom, and extraterrestrial seismology. Arrays of conventional three-component (3C) geophones enable measuring not only the particle velocity in three dimensions but also estimating their spatial gradients. Because the free-surface condition allows to express vertical derivatives in terms of horizontal derivatives, the full gradient tensor and, hence, curl and divergence of the wavefield can be computed. In total, three particle velocity components, three rotational components, and divergence, result seven-component (7C) seismic data. Combined particle velocity and gradient data can be used to isolate the incident P- or S-waves at the land surface or the sea bottom using filtering techniques based on the elastodynamic representation theorem. Alternatively, as only S-waves exhibit rotational motion, rotational measurements can directly be used to identify S-waves. We discuss the derivations of the gradient-based filters as well as their application to synthetic and field data, demonstrating that rotational data can be of particular interest to S-wave reflection and P-to-S-wave conversion imaging. The concept of array-derived gradient estimation can be extended to source arrays as well. Therefore, source arrays allow us to emulate rotational (curl) and dilatational (divergence) sources. Combined with 7C

  2. Spatial Data Infrastructures (SDIs): Improving the Scientific Environmental Data Management and Visualization with ArcGIS Platform

    Science.gov (United States)

    Shrestha, S. R.; Hogeweg, M.; Rose, B.; Turner, A.

    2017-12-01

    The requirement for quality, authoritatively sourced data can often be challenging when working with scientific data. In addition, the lack of standard mechanism to discover, access, and use such data can be cumbersome. This results in slow research, poor dissemination and missed opportunities for research to positively impact policy and knowledge. There is widespread recognition that authoritative datasets are maintained by multiple organizations following various standards, and addressing these challenges will involve multiple stakeholders as well. The bottom line is that organizations need a mechanism to efficiently create, share, catalog, and discover data, and the ability to apply these to create an authoritative information products and applications is powerful and provides value. In real-world applications, individual organizations develop, modify, finalize, and support foundational data for distributed users across the system and thus require an efficient method of data management. For this, the SDI (Spatial Data Infrastructure) framework can be applied for GIS users to facilitate efficient and powerful decision making based on strong visualization and analytics. Working with research institutions, governments, and organizations across the world, we have developed a Hub framework for data and analysis sharing that is driven by outcome-centric goals which apply common methodologies and standards. SDI are an operational capability that should be equitably accessible to policy-makers, analysts, departments and public communities. These SDI need to align with operational workflows and support social communications and collaboration. The Hub framework integrates data across agencies, projects and organizations to support interoperability and drive coordination. We will present and share how Esri has been supporting the development of local, state, and national SDIs for many years and show some use cases for applications of planetary SDI. We will also share what

  3. Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China.

    Science.gov (United States)

    Wu, Chao; Ye, Xinyue; Ren, Fu; Wan, You; Ning, Pengfei; Du, Qingyun

    2016-01-01

    Housing is among the most pressing issues in urban China and has received considerable scholarly attention. Researchers have primarily concentrated on identifying the factors that influence residential property prices and how such mechanisms function. However, few studies have examined the potential factors that influence housing prices from a big data perspective. In this article, we use a big data perspective to determine the willingness of buyers to pay for various factors. The opinions and geographical preferences of individuals for places can be represented by visit frequencies given different motivations. Check-in data from the social media platform Sina Visitor System is used in this article. Here, we use kernel density estimation (KDE) to analyse the spatial patterns of check-in spots (or places of interest, POIs) and employ the Getis-Ord [Formula: see text] method to identify the hot spots for different types of POIs in Shenzhen, China. New indexes are then proposed based on the hot-spot results as measured by check-in data to analyse the effects of these locations on housing prices. This modelling is performed using the hedonic price method (HPM) and the geographically weighted regression (GWR) method. The results show that the degree of clustering of POIs has a significant influence on housing values. Meanwhile, the GWR method has a better interpretive capacity than does the HPM because of the former method's ability to capture spatial heterogeneity. This article integrates big social media data to expand the scope (new study content) and depth (study scale) of housing price research to an unprecedented degree.

  4. Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China.

    Directory of Open Access Journals (Sweden)

    Chao Wu

    Full Text Available Housing is among the most pressing issues in urban China and has received considerable scholarly attention. Researchers have primarily concentrated on identifying the factors that influence residential property prices and how such mechanisms function. However, few studies have examined the potential factors that influence housing prices from a big data perspective. In this article, we use a big data perspective to determine the willingness of buyers to pay for various factors. The opinions and geographical preferences of individuals for places can be represented by visit frequencies given different motivations. Check-in data from the social media platform Sina Visitor System is used in this article. Here, we use kernel density estimation (KDE to analyse the spatial patterns of check-in spots (or places of interest, POIs and employ the Getis-Ord [Formula: see text] method to identify the hot spots for different types of POIs in Shenzhen, China. New indexes are then proposed based on the hot-spot results as measured by check-in data to analyse the effects of these locations on housing prices. This modelling is performed using the hedonic price method (HPM and the geographically weighted regression (GWR method. The results show that the degree of clustering of POIs has a significant influence on housing values. Meanwhile, the GWR method has a better interpretive capacity than does the HPM because of the former method's ability to capture spatial heterogeneity. This article integrates big social media data to expand the scope (new study content and depth (study scale of housing price research to an unprecedented degree.

  5. Spatial assessment of land degradation through key ecosystem services: The role of globally available data.

    Science.gov (United States)

    Cerretelli, Stefania; Poggio, Laura; Gimona, Alessandro; Yakob, Getahun; Boke, Shiferaw; Habte, Mulugeta; Coull, Malcolm; Peressotti, Alessandro; Black, Helaina

    2018-07-01

    Land degradation is a serious issue especially in dry and developing countries leading to ecosystem services (ESS) degradation due to soil functions' depletion. Reliably mapping land degradation spatial distribution is therefore important for policy decisions. The main objectives of this paper were to infer land degradation through ESS assessment and compare the modelling results obtained using different sets of data. We modelled important physical processes (sediment erosion and nutrient export) and the equivalent ecosystem services (sediment and nutrient retention) to infer land degradation in an area in the Ethiopian Great Rift Valley. To model soil erosion/retention capability, and nitrogen export/retention capability, two datasets were used: a 'global' dataset derived from existing global-coverage data and a hybrid dataset where global data were integrated with data from local surveys. The results showed that ESS assessments can be used to infer land degradation and identify priority areas for interventions. The comparison between the modelling results of the two different input datasets showed that caution is necessary if only global-coverage data are used at a local scale. In remote and data-poor areas, an approach that integrates global data with targeted local sampling campaigns might be a good compromise to use ecosystem services in decision-making. Copyright © 2018. Published by Elsevier B.V.

  6. A vorticity transport model to restore spatial gaps in velocity data

    Science.gov (United States)

    Ameli, Siavash; Shadden, Shawn

    2017-11-01

    Often measurements of velocity data do not have full spatial coverage in the probed domain or near boundaries. These gaps can be due to missing measurements or masked regions of corrupted data. These gaps confound interpretation, and are problematic when the data is used to compute Lagrangian or trajectory-based analyses. Various techniques have been proposed to overcome coverage limitations in velocity data such as unweighted least square fitting, empirical orthogonal function analysis, variational interpolation as well as boundary modal analysis. In this talk, we present a vorticity transport PDE to reconstruct regions of missing velocity vectors. The transport model involves both nonlinear anisotropic diffusion and advection. This approach is shown to preserve the main features of the flow even in cases of large gaps, and the reconstructed regions are continuous up to second order. We illustrate results for high-frequency radar (HFR) measurements of the ocean surface currents as this is a common application of limited coverage. We demonstrate that the error of the method is on the same order of the error of the original velocity data. In addition, we have developed a web-based gateway for data restoration, and we will demonstrate a practical application using available data. This work is supported by the NSF Grant No. 1520825.

  7. Spatial access method for urban geospatial database management: An efficient approach of 3D vector data clustering technique

    DEFF Research Database (Denmark)

    Azri, Suhaibah; Ujang, Uznir; Rahman, Alias Abdul

    2014-01-01

    In the last few years, 3D urban data and its information are rapidly increased due to the growth of urban area and urbanization phenomenon. These datasets are then maintain and manage in 3D spatial database system. However, performance deterioration is likely to happen due to the massiveness of 3D...... datasets. As a solution, 3D spatial index structure is used as a booster to increase the performance of data retrieval. In commercial database, commonly and widely used index structure for 3D spatial database is 3D R-Tree. This is due to its simplicity and promising method in handling spatial data. However......D geospatial data clustering to be used in the construction of 3D R-Tree and respectively could reduce the overlapping among nodes. The proposed method is tested on 3D urban dataset for the application of urban infill development. By using several cases of data updating operations such as building...

  8. Multidimensional Analysis and Location Intelligence Application for Spatial Data Warehouse Hotspot in Indonesia using SpagoBI

    Science.gov (United States)

    Uswatun Hasanah, Gamma; Trisminingsih, Rina

    2016-01-01

    Spatial data warehouse refers to data warehouse which has a spatial component that represents the geographic location of the position or an object on the Earth's surface. Spatial data warehouse can be visualized in the form of a crosstab tables, graphs, and maps. Spatial data warehouse of hotspot in Indonesia has been constructed by researchers from FIRM NASA 2006-2015. This research develops multidimensional analysis module and location intelligence module using SpagoBI. The multidimensional analysis module is able to visualize online analytical processing (OLAP). The location intelligence module creates dynamic map visualization in map zone and map point. Map zone can display the different colors based on the number of hotspot in each region and map point can display different sizes of the point to represent the number of hotspots in each region. This research is expected to facilitate users in the presentation of hotspot data as needed.

  9. A Spatial Data Infrastructure Integrating Multisource Heterogeneous Geospatial Data and Time Series: A Study Case in Agriculture

    Directory of Open Access Journals (Sweden)

    Gloria Bordogna

    2016-05-01

    Full Text Available Currently, the best practice to support land planning calls for the development of Spatial Data Infrastructures (SDI capable of integrating both geospatial datasets and time series information from multiple sources, e.g., multitemporal satellite data and Volunteered Geographic Information (VGI. This paper describes an original OGC standard interoperable SDI architecture and a geospatial data and metadata workflow for creating and managing multisource heterogeneous geospatial datasets and time series, and discusses it in the framework of the Space4Agri project study case developed to support the agricultural sector in Lombardy region, Northern Italy. The main novel contributions go beyond the application domain for which the SDI has been developed and are the following: the ingestion within an a-centric SDI, potentially distributed in several nodes on the Internet to support scalability, of products derived by processing remote sensing images, authoritative data, georeferenced in-situ measurements and voluntary information (VGI created by farmers and agronomists using an original Smart App; the workflow automation for publishing sets and time series of heterogeneous multisource geospatial data and relative web services; and, finally, the project geoportal, that can ease the analysis of the geospatial datasets and time series by providing complex intelligent spatio-temporal query and answering facilities.

  10. Macroscopic Spatial Complexity of the Game of Life Cellular Automaton: A Simple Data Analysis

    Science.gov (United States)

    Hernández-Montoya, A. R.; Coronel-Brizio, H. F.; Rodríguez-Achach, M. E.

    In this chapter we present a simple data analysis of an ensemble of 20 time series, generated by averaging the spatial positions of the living cells for each state of the Game of Life Cellular Automaton (GoL). We show that at the macroscopic level described by these time series, complexity properties of GoL are also presented and the following emergent properties, typical of data extracted complex systems such as financial or economical come out: variations of the generated time series following an asymptotic power law distribution, large fluctuations tending to be followed by large fluctuations, and small fluctuations tending to be followed by small ones, and fast decay of linear correlations, however, the correlations associated to their absolute variations exhibit a long range memory. Finally, a Detrended Fluctuation Analysis (DFA) of the generated time series, indicates that the GoL spatial macro states described by the time series are not either completely ordered or random, in a measurable and very interesting way.

  11. Spatial Data Envelopment Analysis Method for the Evaluation of Regional Infrastructure Disparities

    Directory of Open Access Journals (Sweden)

    Birutė Galinienė

    2012-12-01

    Full Text Available Purpose—to achieve a more detailed assessment of regional differences, exploring regional infrastructure and human capital usage efficiency and to display analysis capabilities of spatial data efficient frontier method.Design/methodology/approach—the data envelopment analysis (DEA is applied to find the efficient frontier, which extends the application of production function of the regions. This method of mathematical programming optimization allows assessing the effectiveness of the regional spatial aspects presented. In recent studies this method is applied for evaluating the European Union regional policy issues.Findings—the application of DEA reveals its feasibility for regional input and output studies to evaluate more detailed and more reasonable fund allocation between Lithuanian regions. This analysis shows that in the comparatively efficient Lithuanian regions, such as Vilnius and Klaipėda, “the bottleneck” of usage of transport infrastructure and regional specific human capital is reached. It is stated that decision-making units could enhance region attractiveness for private investors by improving indirect factors in these regions. For practical significance of the study the results are compared with German regional analysis, conducted by Schaffer and other researchers (2011.Practical implications—the practical value of this work is based on giving more accurate planning tools for fund allocation decisions in Lithuanian regions while planning infrastructure and human capital development. The regional indicators were analyzed for 2010.Research type—case study.

  12. Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data

    Directory of Open Access Journals (Sweden)

    Huiyu Chen

    2017-01-01

    Full Text Available Understanding travel patterns of vehicle can support the planning and design of better services. In addition, vehicle clustering can improve management efficiency through more targeted access to groups of interest and facilitate planning by more specific survey design. This paper clustered 854,712 vehicles in a week using K-means clustering algorithm based on license plate recognition (LPR data obtained in Shenzhen, China. Firstly, several travel characteristics related to temporal and spatial variability and activity patterns are used to identify homogeneous clusters. Then, Davies-Bouldin index (DBI and Silhouette Coefficient (SC are applied to capture the optimal number of groups and, consequently, six groups are classified in weekdays and three groups are sorted in weekends, including commuting vehicles and some other occasional leisure travel vehicles. Moreover, a detailed analysis of the characteristics of each group in terms of spatial travel patterns and temporal changes are presented. This study highlights the possibility of applying LPR data for discovering the underlying factor in vehicle travel patterns and examining the characteristic of some groups specifically.

  13. a Simple Spatially Weighted Measure of Temporal Stability for Data with Limited Temporal Observations

    Science.gov (United States)

    Piburn, J.; Stewart, R.; Morton, A.

    2017-10-01

    Identifying erratic or unstable time-series is an area of interest to many fields. Recently, there have been successful developments towards this goal. These new developed methodologies however come from domains where it is typical to have several thousand or more temporal observations. This creates a challenge when attempting to apply these methodologies to time-series with much fewer temporal observations such as for socio-cultural understanding, a domain where a typical time series of interest might only consist of 20-30 annual observations. Most existing methodologies simply cannot say anything interesting with so few data points, yet researchers are still tasked to work within in the confines of the data. Recently a method for characterizing instability in a time series with limitedtemporal observations was published. This method, Attribute Stability Index (ASI), uses an approximate entropy based method tocharacterize a time series' instability. In this paper we propose an explicitly spatially weighted extension of the Attribute StabilityIndex. By including a mechanism to account for spatial autocorrelation, this work represents a novel approach for the characterizationof space-time instability. As a case study we explore national youth male unemployment across the world from 1991-2014.

  14. The Spatial Mechanism and Drive Mechanism Study of Chinese Urban Efficiency - Based on the Spatial Panel Data Model

    Directory of Open Access Journals (Sweden)

    Yuan Xiaoling

    2016-08-01

    Full Text Available In this article, the urban efficiency factors of 285 Chinese prefecture-level cities in the period from 2003 to 2012 are analyzed by using the spatial econometric model. The result shows that the development of urban efficiency between the cities positively correlates with space. And we conclude that the Industrial Structure, Openness and the Infrastructure can promote the development of such urban efficiency. The Urban Agglomeration Scale, Government Control, Fixed Asset Investment and other factors can inhibit the development of urban efficiency to a certain degree. Therefore, we come to a conclusion that, in the new urbanization construction process, the cities need to achieve cross-regional coordination from the perspective of urban agglomerations and metropolitan development. The efficiency of the city together with the scientific and rational flow of the factors should also be improved.

  15. The sensitivity of catchment runoff models to rainfall data at different spatial scales

    Directory of Open Access Journals (Sweden)

    V. A. Bell

    2000-01-01

    Full Text Available The sensitivity of catchment runoff models to rainfall is investigated at a variety of spatial scales using data from a dense raingauge network and weather radar. These data form part of the HYREX (HYdrological Radar EXperiment dataset. They encompass records from 49 raingauges over the 135 km2 Brue catchment in south-west England together with 2 and 5 km grid-square radar data. Separate rainfall time-series for the radar and raingauge data are constructed on 2, 5 and 10 km grids, and as catchment average values, at a 15 minute time-step. The sensitivity of the catchment runoff models to these grid scales of input data is evaluated on selected convective and stratiform rainfall events. Each rainfall time-series is used to produce an ensemble of modelled hydrographs in order to investigate this sensitivity. The distributed model is shown to be sensitive to the locations of the raingauges within the catchment and hence to the spatial variability of rainfall over the catchment. Runoff sensitivity is strongest during convective rainfall when a broader spread of modelled hydrographs results, with twice the variability of that arising from stratiform rain. Sensitivity to rainfall data and model resolution is explored and, surprisingly, best performance is obtained using a lower resolution of rainfall data and model. Results from the distributed catchment model, the Simple Grid Model, are compared with those obtained from a lumped model, the PDM. Performance from the distributed model is found to be only marginally better during stratiform rain (R2 of 0.922 compared to 0.911 but significantly better during convective rain (R2 of 0.953 compared to 0.909. The improved performance from the distributed model can, in part, be accredited to the excellence of the dense raingauge network which would not be the norm for operational flood warning systems. In the final part of the paper, the effect of rainfall resolution on the performance of the 2 km distributed

  16. COMICS: Cartoon Visualization of Omics Data in Spatial Context Using Anatomical Ontologies.

    Science.gov (United States)

    Travin, Dmitrii; Popov, Iaroslav; Guler, Arzu Tugce; Medvedev, Dmitry; van der Plas-Duivesteijn, Suzanne; Varela, Monica; Kolder, Iris C R M; Meijer, Annemarie H; Spaink, Herman P; Palmblad, Magnus

    2018-01-05

    COMICS is an interactive and open-access web platform for integration and visualization of molecular expression data in anatomograms of zebrafish, carp, and mouse model systems. Anatomical ontologies are used to map omics data across experiments and between an experiment and a particular visualization in a data-dependent manner. COMICS is built on top of several existing resources. Zebrafish and mouse anatomical ontologies with their controlled vocabulary (CV) and defined hierarchy are used with the ontoCAT R package to aggregate data for comparison and visualization. Libraries from the QGIS geographical information system are used with the R packages "maps" and "maptools" to visualize and interact with molecular expression data in anatomical drawings of the model systems. COMICS allows users to upload their own data from omics experiments, using any gene or protein nomenclature they wish, as long as CV terms are used to define anatomical regions or developmental stages. Common nomenclatures such as the ZFIN gene names and UniProt accessions are provided additional support. COMICS can be used to generate publication-quality visualizations of gene and protein expression across experiments. Unlike previous tools that have used anatomical ontologies to interpret imaging data in several animal models, including zebrafish, COMICS is designed to take spatially resolved data generated by dissection or fractionation and display this data in visually clear anatomical representations rather than large data tables. COMICS is optimized for ease-of-use, with a minimalistic web interface and automatic selection of the appropriate visual representation depending on the input data.

  17. Cluster: A New Application for Spatial Analysis of Pixelated Data for Epiphytotics.

    Science.gov (United States)

    Nelson, Scot C; Corcoja, Iulian; Pethybridge, Sarah J

    2017-12-01

    Spatial analysis of epiphytotics is essential to develop and test hypotheses about pathogen ecology, disease dynamics, and to optimize plant disease management strategies. Data collection for spatial analysis requires substantial investment in time to depict patterns in various frames and hierarchies. We developed a new approach for spatial analysis of pixelated data in digital imagery and incorporated the method in a stand-alone desktop application called Cluster. The user isolates target entities (clusters) by designating up to 24 pixel colors as nontargets and moves a threshold slider to visualize the targets. The app calculates the percent area occupied by targeted pixels, identifies the centroids of targeted clusters, and computes the relative compass angle of orientation for each cluster. Users can deselect anomalous clusters manually and/or automatically by specifying a size threshold value to exclude smaller targets from the analysis. Up to 1,000 stochastic simulations randomly place the centroids of each cluster in ranked order of size (largest to smallest) within each matrix while preserving their calculated angles of orientation for the long axes. A two-tailed probability t test compares the mean inter-cluster distances for the observed versus the values derived from randomly simulated maps. This is the basis for statistical testing of the null hypothesis that the clusters are randomly distributed within the frame of interest. These frames can assume any shape, from natural (e.g., leaf) to arbitrary (e.g., a rectangular or polygonal field). Cluster summarizes normalized attributes of clusters, including pixel number, axis length, axis width, compass orientation, and the length/width ratio, available to the user as a downloadable spreadsheet. Each simulated map may be saved as an image and inspected. Provided examples demonstrate the utility of Cluster to analyze patterns at various spatial scales in plant pathology and ecology and highlight the

  18. Investigating “Locality” of Intra-Urban Spatial Interactions in New York City Using Foursquare Data

    Directory of Open Access Journals (Sweden)

    Yeran Sun

    2016-03-01

    Full Text Available Thanks to the increasing popularity of location-based social networks, a large amount of user-generated geo-referenced check-in data is now available, and such check-in data is becoming a new data source in the study of mobility and travel. Conventionally, spatial interactions between places were measured based on the trips made between them. This paper empirically investigates the use of social media data (i.e., Foursquare data to study the “locality” of such intra-urban spatial interactions in New York City, and specifically: (i the level of “locality” of spatial interactions; (ii the impacts of personal characteristics on “locality” of spatial interaction and finally; (iii the heterogeneity in spatial distribution of “local” interactions. The results of this study indicate that: (1 spatial interactions show a high degree of locality; (2 gender does not have a considerable impact on the locality of spatial interactions and finally; (3 “local” interactions likely cluster in some places within the research city.

  19. Understanding spatial organizations of chromosomes via statistical analysis of Hi-C data

    Science.gov (United States)

    Hu, Ming; Deng, Ke; Qin, Zhaohui; Liu, Jun S.

    2015-01-01

    Understanding how chromosomes fold provides insights into the transcription regulation, hence, the functional state of the cell. Using the next generation sequencing technology, the recently developed Hi-C approach enables a global view of spatial chromatin organization in the nucleus, which substantially expands our knowledge about genome organization and function. However, due to multiple layers of biases, noises and uncertainties buried in the protocol of Hi-C experiments, analyzing and interpreting Hi-C data poses great challenges, and requires novel statistical methods to be developed. This article provides an overview of recent Hi-C studies and their impacts on biomedical research, describes major challenges in statistical analysis of Hi-C data, and discusses some perspectives for future research. PMID:26124977

  20. Investigation of Spatial Data with Open Source Social Network Analysis and Geographic Information Systems Applications

    Science.gov (United States)

    Sabah, L.; Şimşek, M.

    2017-11-01

    Social networks are the real social experience of individuals in the online environment. In this environment, people use symbolic gestures and mimics, sharing thoughts and content. Social network analysis is the visualization of complex and large quantities of data to ensure that the overall picture appears. It is the understanding, development, quantitative and qualitative analysis of the relations in the social networks of Graph theory. Social networks are expressed in the form of nodes and edges. Nodes are people/organizations, and edges are relationships between nodes. Relations are directional, non-directional, weighted, and weightless. The purpose of this study is to examine the effects of social networks on the evaluation of person data with spatial coordinates. For this, the cluster size and the effect on the geographical area of the circle where the placements of the individual are influenced by the frequently used placeholder feature in the social networks have been studied.

  1. Using Satellite Remote Sensing Data in a Spatially Explicit Price Model

    Science.gov (United States)

    Brown, Molly E.; Pinzon, Jorge E.; Prince, Stephen D.

    2007-01-01

    Famine early warning organizations use data from multiple disciplines to assess food insecurity of communities and regions in less-developed parts of the World. In this paper we integrate several indicators that are available to enhance the information for preparation for and responses to food security emergencies. The assessment uses a price model based on the relationship between the suitability of the growing season and market prices for coarse grain. The model is then used to create spatially continuous maps of millet prices. The model is applied to the dry central and northern areas of West Africa, using satellite-derived vegetation indices for the entire region. By coupling the model with vegetation data estimated for one to four months into the future, maps are created of a leading indicator of potential price movements. It is anticipated that these maps can be used to enable early warning of famine and for planning appropriate responses.

  2. Spatially coupled low-density parity-check error correction for holographic data storage

    Science.gov (United States)

    Ishii, Norihiko; Katano, Yutaro; Muroi, Tetsuhiko; Kinoshita, Nobuhiro

    2017-09-01

    The spatially coupled low-density parity-check (SC-LDPC) was considered for holographic data storage. The superiority of SC-LDPC was studied by simulation. The simulations show that the performance of SC-LDPC depends on the lifting number, and when the lifting number is over 100, SC-LDPC shows better error correctability compared with irregular LDPC. SC-LDPC is applied to the 5:9 modulation code, which is one of the differential codes. The error-free point is near 2.8 dB and over 10-1 can be corrected in simulation. From these simulation results, this error correction code can be applied to actual holographic data storage test equipment. Results showed that 8 × 10-2 can be corrected, furthermore it works effectively and shows good error correctability.

  3. SPATIAL DATA MINING TOOLBOX FOR MAPPING SUITABILITY OF LANDFILL SITES USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    S. K. M. Abujayyab

    2016-09-01

    Full Text Available Mapping the suitability of landfill sites is a complex field and is involved with multidiscipline. The purpose of this research is to create an ArcGIS spatial data mining toolbox for mapping the suitability of landfill sites at a regional scale using neural networks. The toolbox is constructed from six sub-tools to prepare, train, and process data. The employment of the toolbox is straightforward. The multilayer perceptron (MLP neural networks structure with a backpropagation learning algorithm is used. The dataset is mined from the north states in Malaysia. A total of 14 criteria are utilized to build the training dataset. The toolbox provides a platform for decision makers to implement neural networks for mapping the suitability of landfill sites in the ArcGIS environment. The result shows the ability of the toolbox to produce suitability maps for landfill sites.

  4. Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data

    Directory of Open Access Journals (Sweden)

    Xiaolu Zhou

    2017-03-01

    Full Text Available Social media data provide a great opportunity to investigate event flow in cities. Despite the advantages of social media data in these investigations, the data heterogeneity and big data size pose challenges to researchers seeking to identify useful information about events from the raw data. In addition, few studies have used social media posts to capture how events develop in space and time. This paper demonstrates an efficient approach based on machine learning and geovisualization to identify events and trace the development of these events in real-time. We conducted an empirical study to delineate the temporal and spatial evolution of a natural event (heavy precipitation and a social event (Pope Francis’ visit to the US in the New York City—Washington, DC regions. By investigating multiple features of Twitter data (message, author, time, and geographic location information, this paper demonstrates how voluntary local knowledge from tweets can be used to depict city dynamics, discover spatiotemporal characteristics of events, and convey real-time information.

  5. Direct prediction of spatially and temporally varying physical properties from time-lapse electrical resistance data

    Science.gov (United States)

    Hermans, Thomas; Oware, Erasmus; Caers, Jef

    2016-09-01

    Time-lapse applications of electrical methods have grown significantly over the last decade. However, the quantitative interpretation of tomograms in terms of physical properties, such as salinity, temperature or saturation, remains difficult. In many applications, geophysical models are transformed into hydrological models, but this transformation suffers from spatially and temporally varying resolution resulting from the regularization used by the deterministic inversion. In this study, we investigate a prediction-focused approach (PFA) to directly estimate subsurface physical properties with electrical resistance data, circumventing the need for classic tomographic inversions. First, we generate a prior set of resistance data and physical property forecast through hydrogeological and geophysical simulations mimicking the field experiment. We reduce the dimension of both the data and the forecast through principal component analysis in order to keep the most informative part of both sets in a reduced dimension space. Then, we apply canonical correlation analysis to explore the relationship between the data and the forecast in their reduced dimension space. If a linear relationship can be established, the posterior distribution of the forecast can be directly sampled using a Gaussian process regression where the field data scores are the conditioning data. In this paper, we demonstrate PFA for various physical property distributions. We also develop a framework to propagate the estimated noise level in the reduced dimension space. We validate the results by a Monte Carlo study on the posterior distribution and demonstrate that PFA yields accurate uncertainty for the cases studied.

  6. Architecture of a spatial data service system for statistical analysis and visualization of regional climate changes

    Science.gov (United States)

    Titov, A. G.; Okladnikov, I. G.; Gordov, E. P.

    2017-11-01

    The use of large geospatial datasets in climate change studies requires the development of a set of Spatial Data Infrastructure (SDI) elements, including geoprocessing and cartographical visualization web services. This paper presents the architecture of a geospatial OGC web service system as an integral part of a virtual research environment (VRE) general architecture for statistical processing and visualization of meteorological and climatic data. The architecture is a set of interconnected standalone SDI nodes with corresponding data storage systems. Each node runs a specialized software, such as a geoportal, cartographical web services (WMS/WFS), a metadata catalog, and a MySQL database of technical metadata describing geospatial datasets available for the node. It also contains geospatial data processing services (WPS) based on a modular computing backend realizing statistical processing functionality and, thus, providing analysis of large datasets with the results of visualization and export into files of standard formats (XML, binary, etc.). Some cartographical web services have been developed in a system’s prototype to provide capabilities to work with raster and vector geospatial data based on OGC web services. The distributed architecture presented allows easy addition of new nodes, computing and data storage systems, and provides a solid computational infrastructure for regional climate change studies based on modern Web and GIS technologies.

  7. Integrating spatial and temporal oxygen data to improve the quantification of in situ petroleum biodegradation rates.

    Science.gov (United States)

    Davis, Gregory B; Laslett, Dean; Patterson, Bradley M; Johnston, Colin D

    2013-03-15

    Accurate estimation of biodegradation rates during remediation of petroleum impacted soil and groundwater is critical to avoid excessive costs and to ensure remedial effectiveness. Oxygen depth profiles or oxygen consumption over time are often used separately to estimate the magnitude and timeframe for biodegradation of petroleum hydrocarbons in soil and subsurface environments. Each method has limitations. Here we integrate spatial and temporal oxygen concentration data from a field experiment to develop better estimates and more reliably quantify biodegradation rates. During a nine-month bioremediation trial, 84 sets of respiration rate data (where aeration was halted and oxygen consumption was measured over time) were collected from in situ oxygen sensors at multiple locations and depths across a diesel non-aqueous phase liquid (NAPL) contaminated subsurface. Additionally, detailed vertical soil moisture (air-filled porosity) and NAPL content profiles were determined. The spatial and temporal oxygen concentration (respiration) data were modeled assuming one-dimensional diffusion of oxygen through the soil profile which was open to the atmosphere. Point and vertically averaged biodegradation rates were determined, and compared to modeled data from a previous field trial. Point estimates of biodegradation rates assuming no diffusion ranged up to 58 mg kg(-1) day(-1) while rates accounting for diffusion ranged up to 87 mg kg(-1) day(-1). Typically, accounting for diffusion increased point biodegradation rate estimates by 15-75% and vertically averaged rates by 60-80% depending on the averaging method adopted. Importantly, ignoring diffusion led to overestimation of biodegradation rates where the location of measurement was outside the zone of NAPL contamination. Over or underestimation of biodegradation rate estimates leads to cost implications for successful remediation of petroleum impacted sites. Crown Copyright © 2013. Published by Elsevier Ltd. All rights

  8. Estimating spatial and temporal components of variation in count data using negative binomial mixed models

    Science.gov (United States)

    Irwin, Brian J.; Wagner, Tyler; Bence, James R.; Kepler, Megan V.; Liu, Weihai; Hayes, Daniel B.

    2013-01-01

    Partitioning total variability into its component temporal and spatial sources is a powerful way to better understand time series and elucidate trends. The data available for such analyses of fish and other populations are usually nonnegative integer counts of the number of organisms, often dominated by many low values with few observations of relatively high abundance. These characteristics are not well approximated by the Gaussian distribution. We present a detailed description of a negative binomial mixed-model framework that can be used to model count data and quantify temporal and spatial variability. We applied these models to data from four fishery-independent surveys of Walleyes Sander vitreus across the Great Lakes basin. Specifically, we fitted models to gill-net catches from Wisconsin waters of Lake Superior; Oneida Lake, New York; Saginaw Bay in Lake Huron, Michigan; and Ohio waters of Lake Erie. These long-term monitoring surveys varied in overall sampling intensity, the total catch of Walleyes, and the proportion of zero catches. Parameter estimation included the negative binomial scaling parameter, and we quantified the random effects as the variations among gill-net sampling sites, the variations among sampled years, and site × year interactions. This framework (i.e., the application of a mixed model appropriate for count data in a variance-partitioning context) represents a flexible approach that has implications for monitoring programs (e.g., trend detection) and for examining the potential of individual variance components to serve as response metrics to large-scale anthropogenic perturbations or ecological changes.

  9. Lunar archive panoramas: modern image processing and access to the historic data based on spatial context

    Science.gov (United States)

    Kozlova, Natalia; Kokhanov, Alexander; Oberst, Jürgen; Zubarev, Anatoliy; Nadezhdina, Irina; Patraty, Vyacheslav; Karachevtseva, Irina; Garov, Andrey; Matveev, Evgeniy

    The objectives of our work are to fully exploit the historic Soviet Lunokhod data and use the results for scientific and public purposes. Unfortunately, many of the relevant operational parameters of the Lunokhods missions are lost. Modern photogrammetry is a key to solving these issues, providing analysis techniques, not available at the time of the early lunar missions. For this purpose we use special developed software, GIS tools and high-resolution LRO data [1]. Results of new image processing of historic data are part of PRoViDE project (Planetary Robotics Vision Data Exploitation) which aims to assemble a major portion of the imaging data gathered from different vehicles and probes on planetary surfaces into a unique database, bringing them into a spatial context and providing access to a complete set of 3D vision products (http://www.provide-space.eu/). Our technology of archive panorama processing allows us to recover lost information of Soviet lunar missions and study lunar landing site imagery by state-of-the-art photogrammetric techniques. Our main task is to perform lunar panoramas in measurement form based on photogrammetry and geoanalyses methods, and then involve them in more detailed morphometric analyses [2] and 3D-modeling of lunar surface based on LROC NAC image processing [3]. The results of our work are various types of new products: panoramas in different projections, updated metadata with recovering parameters, and ortho-panoramas, which can be used for quantitative geomorphology assessment based on spatial tools [4]. All data products obtained as a result of the study are to be placed into Planetary data storage which is developing as Geodesy and Cartography Node [5]. Access to archive lunar data will be organized via Geo-portal (http://cartsrv.mexlab.ru/geoportal/) using authorization service, which provided data security and user control. Planetary spatial information system can integrate various types of data for planets and their

  10. Urban Landscape Spatial Pattern Estimation of Cities in Shandong Province Using Nighttime Luminosity Data

    Science.gov (United States)

    Fan, J.; He, H.; Hu, T.; Li, G.; Gao, H.; Zhao, X.

    2017-09-01

    China's cities have been undergoing rapid and intense urbanization processes in the past few decades. Shandong is a coastal province which is located in East China with big economy and population scales, and which also plays an important role in the rapid process of China's modernization. The DMSP/OLS dataset has been widely used for the urban development assessments in long time-series and large spatial scales situations. In this paper, we used a time series of nighttime light data to estimate the landscape spatial pattern changes of cities in Shandong province from 1994 to 2012. Nine landscape metrics were calculated and analyzed to figure out the spatial patterns of urban area developments of the cities in Shandong province. The landscape metrics include the number of patches (NP), the landscape total area (TA), the aggregation index (AI), the largest patch index (LPI), the mean patch area (AREA_MN), the landscape shape index (LSI), the total edge length (TE), the edge density (ED), and the mean radius of gyration (GYRATE_MN). The experimental results reveal that, in 1994-2012, the total urban area of cities in Shandong province expanded for 1.17 times, the average urban area increased by about 93.00%, the average annual growth rate of the TE metric is 2.67 %, while the ED metric decreased about 1.44 % annually. Bigger cities in this area show relative slower urbanization development processes, such as Jinan and Qingdao. Coastal cities represented much more rapid expansion velocities than inland cities. In the middle area of Shandong province, the connectivity between developed urban areas was constantly increased.

  11. Influence of Elevation Data Resolution on Spatial Prediction of Colluvial Soils in a Luvisol Region.

    Directory of Open Access Journals (Sweden)

    Vít Penížek

    Full Text Available The development of a soil cover is a dynamic process. Soil cover can be altered within a few decades, which requires updating of the legacy soil maps. Soil erosion is one of the most important processes quickly altering soil cover on agriculture land. Colluvial soils develop in concave parts of the landscape as a consequence of sedimentation of eroded material. Colluvial soils are recognised as important soil units because they are a vast sink of soil organic carbon. Terrain derivatives became an important tool in digital soil mapping and are among the most popular auxiliary data used for quantitative spatial prediction. Prediction success rates are often directly dependent on raster resolution. In our study, we tested how raster resolution (1, 2, 3, 5, 10, 20 and 30 meters influences spatial prediction of colluvial soils. Terrain derivatives (altitude, slope, plane curvature, topographic position index, LS factor and convergence index were calculated for the given raster resolutions. Four models were applied (boosted tree, neural network, random forest and Classification/Regression Tree to spatially predict the soil cover over a 77 ha large study plot. Models training and validation was based on 111 soil profiles surveyed on a regular sampling grid. Moreover, the predicted real extent and shape of the colluvial soil area was examined. In general, no clear trend in the accuracy prediction was found without the given raster resolution range. Higher maximum prediction accuracy for colluvial soil, compared to prediction accuracy of total soil cover of the study plot, can be explained by the choice of terrain derivatives that were best for Colluvial soils differentiation from other soil units. Regarding the character of the predicted Colluvial soils area, maps of 2 to 10 m resolution provided reasonable delineation of the colluvial soil as part of the cover over the study area.

  12. URBAN LANDSCAPE SPATIAL PATTERN ESTIMATION OF CITIES IN SHANDONG PROVINCE USING NIGHTTIME LUMINOSITY DATA

    Directory of Open Access Journals (Sweden)

    J. Fan

    2017-09-01

    Full Text Available China’s cities have been undergoing rapid and intense urbanization processes in the past few decades. Shandong is a coastal province which is located in East China with big economy and population scales, and which also plays an important role in the rapid process of China’s modernization. The DMSP/OLS dataset has been widely used for the urban development assessments in long time-series and large spatial scales situations. In this paper, we used a time series of nighttime light data to estimate the landscape spatial pattern changes of cities in Shandong province from 1994 to 2012. Nine landscape metrics were calculated and analyzed to figure out the spatial patterns of urban area developments of the cities in Shandong province. The landscape metrics include the number of patches (NP, the landscape total area (TA, the aggregation index (AI, the largest patch index (LPI, the mean patch area (AREA_MN, the landscape shape index (LSI, the total edge length (TE, the edge density (ED, and the mean radius of gyration (GYRATE_MN. The experimental results reveal that, in 1994–2012, the total urban area of cities in Shandong province expanded for 1.17 times, the average urban area increased by about 93.00%, the average annual growth rate of the TE metric is 2.67 %, while the ED metric decreased about 1.44 % annually. Bigger cities in this area show relative slower urbanization development processes, such as Jinan and Qingdao. Coastal cities represented much more rapid expansion velocities than inland cities. In the middle area of Shandong province, the connectivity between developed urban areas was constantly increased.

  13. Influence of Elevation Data Resolution on Spatial Prediction of Colluvial Soils in a Luvisol Region

    Science.gov (United States)

    Penížek, Vít; Zádorová, Tereza; Kodešová, Radka; Vaněk, Aleš

    2016-01-01

    The development of a soil cover is a dynamic process. Soil cover can be altered within a few decades, which requires updating of the legacy soil maps. Soil erosion is one of the most important processes quickly altering soil cover on agriculture land. Colluvial soils develop in concave parts of the landscape as a consequence of sedimentation of eroded material. Colluvial soils are recognised as important soil units because they are a vast sink of soil organic carbon. Terrain derivatives became an important tool in digital soil mapping and are among the most popular auxiliary data used for quantitative spatial prediction. Prediction success rates are often directly dependent on raster resolution. In our study, we tested how raster resolution (1, 2, 3, 5, 10, 20 and 30 meters) influences spatial prediction of colluvial soils. Terrain derivatives (altitude, slope, plane curvature, topographic position index, LS factor and convergence index) were calculated for the given raster resolutions. Four models were applied (boosted tree, neural network, random forest and Classification/Regression Tree) to spatially predict the soil cover over a 77 ha large study plot. Models training and validation was based on 111 soil profiles surveyed on a regular sampling grid. Moreover, the predicted real extent and shape of the colluvial soil area was examined. In general, no clear trend in the accuracy prediction was found without the given raster resolution range. Higher maximum prediction accuracy for colluvial soil, compared to prediction accuracy of total soil cover of the study plot, can be explained by the choice of terrain derivatives that were best for Colluvial soils differentiation from other soil units. Regarding the character of the predicted Colluvial soils area, maps of 2 to 10 m resolution provided reasonable delineation of the colluvial soil as part of the cover over the study area. PMID:27846230

  14. Sub-hour solar data for power system modeling from static spatial variability analysis

    Energy Technology Data Exchange (ETDEWEB)

    Hummon, Marissa R.; Ibanez, Eduardo; Brinkman, Gregory; Lew, Debra [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2012-07-01

    High penetration renewable integration studies need high quality solar power data with spatial-temporal correlations that are representative of a real system. For instance, as additional solar power sites are added, the relative amount of variability should decrease due to spatial averaging of localized irradiance fluctuations. This presentation will summarize the research relating sequential point-source sub-hour global horizontal irradiance (GHI) values to static, spatially distributed GHI values. This research led to the development of an algorithm for generating coherent sub-hour datasets that span distances ranging from 10 km to 4,000 km. The algorithm, in brief, generates synthetic GHI values at an interval of one minute, for a specific location, using SUNY/Clean Power Research, satellite-derived, hourly irradiance values for the nearest grid cell to that location and grid cells within 40 km. During each hour, the observed GHI value for the grid cell of interest and the surrounding grid cells is related, via probability distributions, to one of live temporal cloud coverage classifications (class I, II, III, IV, V). Synthesis algorithms are used to select one-minute time step GHI values based on the classification of the grid cell of interest in a particular hour. Three primary statistical measures of the dataset are demonstrated: reduction in ramps as a function of aggregation; coherence of GHI values across sites ranging from 6 to 400 km apart over time scales from one minute to three hours; and ramp magnitude and duration distributions as a function of time of day and day of year. (orig.)

  15. Spatial and temporal analysis of a 17-year lightning climatology over Bangladesh with LIS data

    Science.gov (United States)

    Dewan, Ashraf; Ongee, Emmanuel T.; Rahman, Md. Masudur; Mahmood, Rezaul; Yamane, Yusuke

    2017-10-01

    Using NASA's TRMM Lightning Imaging Sensor (LIS) data from 1998 to 2014, this paper presents a 17-year lightning climatology of Bangladesh, at 0.5° × 0.5° spatial resolution. Diurnal, seasonal, monthly and annual variations in the occurrence of lightning flashes were explored. The diurnal regime of lightning is dominated by afternoon/evening events. Overall, peak lightning activity occurs in the early morning (0200 LST) and evening (1900 LST). The distribution of lightning flash counts by season over Bangladesh landmass is as follows: pre-monsoon (69.2%), monsoon (24.1%), post-monsoon (4.6%) and winter (2.1%). Flash rate density (FRD) hotspots were primarily located in the north and north-eastern parts of Bangladesh, with a maximum of 72 fl km-2 year-1. Spatially, the distribution of FRD increases from the Bay of Bengal in the south to relatively higher elevations (of the Himalayan foothills) in the north. A spatial shift in FRD hotspots occurs with change in season. For example, in monsoon season, hotspots of lightning activity move in a south-westerly direction from their pre-monsoon location (i.e. north-eastern Bangladesh) towards West Bengal in India. South and south-eastern parts of Bangladesh experience high lightning activity during post-monsoon season due to regional orographic lifting and low-pressure systems (i.e. cyclone) in the Bay of Bengal. To the best of our knowledge, this is the first study focused on LIS-based lightning climatology over Bangladesh. This baseline study, therefore, is an essential first step towards effective management of lightning-related hazards in Bangladesh.

  16. A Bayesian spatial model for neuroimaging data based on biologically informed basis functions.

    Science.gov (United States)

    Huertas, Ismael; Oldehinkel, Marianne; van Oort, Erik S B; Garcia-Solis, David; Mir, Pablo; Beckmann, Christian F; Marquand, Andre F

    2017-11-01

    The dominant approach to neuroimaging data analysis employs the voxel as the unit of computation. While convenient, voxels lack biological meaning and their size is arbitrarily determined by the resolution of the image. Here, we propose a multivariate spatial model in which neuroimaging data are characterised as a linearly weighted combination of multiscale basis functions which map onto underlying brain nuclei or networks or nuclei. In this model, the elementary building blocks are derived to reflect the functional anatomy of the brain during the resting state. This model is estimated using a Bayesian framework which accurately quantifies uncertainty and automatically finds the most accurate and parsimonious combination of basis functions describing the data. We demonstrate the utility of this framework by predicting quantitative SPECT images of striatal dopamine function and we compare a variety of basis sets including generic isotropic functions, anatomical representations of the striatum derived from structural MRI, and two different soft functional parcellations of the striatum derived from resting-state fMRI (rfMRI). We found that a combination of ∼50 multiscale functional basis functions accurately represented the striatal dopamine activity, and that functional basis functions derived from an advanced parcellation technique known as Instantaneous Connectivity Parcellation (ICP) provided the most parsimonious models of dopamine function. Importantly, functional basis functions derived from resting fMRI were more accurate than both structural and generic basis sets in representing dopamine function in the striatum for a fixed model order. We demonstrate the translational validity of our framework by constructing classification models for discriminating parkinsonian disorders and their subtypes. Here, we show that ICP approach is the only basis set that performs well across all comparisons and performs better overall than the classical voxel-based approach

  17. Constraints on spatially oscillating sub-mm forces from the Stanford Optically Levitated Microsphere Experiment data

    Science.gov (United States)

    Antoniou, I.; Perivolaropoulos, L.

    2017-11-01

    A recent analysis by one of the authors [L. Perivolaropoulos, Phys. Rev. D 95, 084050 (2017), 10.1103/PhysRevD.95.084050] has indicated the presence of a 2 σ signal of spatially oscillating new force residuals in the torsion balance data of the Washington experiment. We extend that study and analyze the data of the Stanford Optically Levitated Microsphere Experiment (SOLME) [A. D. Rider et al., Phys. Rev. Lett. 117, 101101 (2016), 10.1103/PhysRevLett.117.101101] (kindly provided by A. D. Rider et al.) searching for sub-mm spatially oscillating new force signals. We find a statistically significant oscillating signal for a force residual of the form F (z )=α cos (2/π λ z +c ) where z is the distance between the macroscopic interacting masses (levitated microsphere and cantilever). The best fit parameter values are α =(1.1 ±0.4 )×10-17N , λ =(35.2 ±0.6 ) μ m . Monte Carlo simulation of the SOLME data under the assumption of zero force residuals has indicated that the statistical significance of this signal is at about 2 σ level. The improvement of the χ2 fit compared to the null hypothesis (zero residual force) corresponds to Δ χ2=13.1 . There are indications that this previously unnoticed signal is indeed in the data but is most probably induced by a systematic effect caused by diffraction of non-Gaussian tails of the laser beam. Thus the amplitude of this detected signal can only be useful as an upper bound to the amplitude of new spatially oscillating forces on sub-mm scales. In the context of gravitational origin of the signal emerging from a fundamental modification of the Newtonian potential of the form Veff(r )=-G M/r (1 +αOcos (2/π λ r +θ ))≡VN(r )+Vosc(r ) , we evaluate the source integral of the oscillating macroscopically induced force. If the origin of the SOLME oscillating signal is systematic, the parameter αO is bounded as αOchameleon oscillating potentials etc.).

  18. The Evolution of Spatial Representation During Complex Visual Data Analysis: Knowing When and How to be Exact

    National Research Council Canada - National Science Library

    Schunn, Christian D; Saner, Lelyn D; Trafton, J. G; Trickett, Susan B; Kirschenbaum, Susan K; Knepp, Michael; Shoup, Melanie

    2005-01-01

    ... (weather forecasting, submarine target motion analysis, and fMRI data analysis). Internal spatial representations are coded from spontaneous gestures made during cued-recall summaries of problem solving activities...

  19. Comparison of tests for spatial heterogeneity on data with global clustering patterns and outliers

    Directory of Open Access Journals (Sweden)

    Hachey Mark

    2009-10-01

    Full Text Available Abstract Background The ability to evaluate geographic heterogeneity of cancer incidence and mortality is important in cancer surveillance. Many statistical methods for evaluating global clustering and local cluster patterns are developed and have been examined by many simulation studies. However, the performance of these methods on two extreme cases (global clustering evaluation and local anomaly (outlier detection has not been thoroughly investigated. Methods We compare methods for global clustering evaluation including Tango's Index, Moran's I, and Oden's I*pop; and cluster detection methods such as local Moran's I and SaTScan elliptic version on simulated count data that mimic global clustering patterns and outliers for cancer cases in the continental United States. We examine the power and precision of the selected methods in the purely spatial analysis. We illustrate Tango's MEET and SaTScan elliptic version on a 1987-2004 HIV and a 1950-1969 lung cancer mortality data in the United States. Results For simulated data with outlier patterns, Tango's MEET, Moran's I and I*pop had powers less than 0.2, and SaTScan had powers around 0.97. For simulated data with global clustering patterns, Tango's MEET and I*pop (with 50% of total population as the maximum search window had powers close to 1. SaTScan had powers around 0.7-0.8 and Moran's I has powers around 0.2-0.3. In the real data example, Tango's MEET indicated the existence of global clustering patterns in both the HIV and lung cancer mortality data. SaTScan found a large cluster for HIV mortality rates, which is consistent with the finding from Tango's MEET. SaTScan also found clusters and outliers in the lung cancer mortality data. Conclusion SaTScan elliptic version is more efficient for outlier detection compared with the other methods evaluated in this article. Tango's MEET and Oden's I*pop perform best in global clustering scenarios among the selected methods. The use of SaTScan for

  20. Real-Time Spatial Monitoring of Vehicle Vibration Data as a Model for TeleGeoMonitoring Systems

    OpenAIRE

    Robidoux, Jeff

    2005-01-01

    This research presents the development and proof of concept of a TeleGeoMonitoring (TGM) system for spatially monitoring and analyzing, in real-time, data derived from vehicle-mounted sensors. In response to the concern for vibration related injuries experienced by equipment operators in surface mining and construction operations, the prototype TGM system focuses on spatially monitoring vehicle vibration in real-time. The TGM vibration system consists of 3 components: (1) Data Acquisition ...

  1. Effects of the spatial resolution of urban drainage data on nonpoint source pollution prediction.

    Science.gov (United States)

    Dai, Ying; Chen, Lei; Hou, Xiaoshu; Shen, Zhenyao

    2018-03-14

    Detailed urban drainage data are important for urban nonpoint source (NPS) pollution prediction. However, the difficulties in collecting complete pipeline data usually interfere with urban NPS pollution studies, especially in large-scale study areas. In this study, NPS pollution models were constructed for a typical urban catchment using the SWMM, based on five drainage datasets with different resolution levels. The influence of the data resolution on the simulation results was examined. The calibration and validation results of the higher-resolution (HR) model indicated a satisfactory model performance with relatively detailed drainage data. However, the performances of the parameter-regionalized lower-resolution (LR) models were still affected by the drainage data scale. This scale effect was due not only to the pipe routing process but also to changes in the effective impervious area, which could be limited by a scale threshold. The runoff flow and NPS pollution responded differently to changes in scale, primarily because of the difference between buildup and washoff and the more significant decrease in pollutant infiltration loss and the much greater increase of pollutant flooding loss while scaling up. Additionally, scale effects were also affected by the rainfall type. Sub-area routing between impervious and pervious areas could improve the LR model performances to an extent, and this approach is recommended to offset the influence of spatial resolution deterioration.

  2. Using mobile phone data to predict the spatial spread of cholera.

    Science.gov (United States)

    Bengtsson, Linus; Gaudart, Jean; Lu, Xin; Moore, Sandra; Wetter, Erik; Sallah, Kankoe; Rebaudet, Stanislas; Piarroux, Renaud

    2015-03-09

    Effective response to infectious disease epidemics requires focused control measures in areas predicted to be at high risk of new outbreaks. We aimed to test whether mobile operator data could predict the early spatial evolution of the 2010 Haiti cholera epidemic. Daily case data were analysed for 78 study areas from October 16 to December 16, 2010. Movements of 2.9 million anonymous mobile phone SIM cards were used to create a national mobility network. Two gravity models of population mobility were implemented for comparison. Both were optimized based on the complete retrospective epidemic data, available only after the end of the epidemic spread. Risk of an area experiencing an outbreak within seven days showed strong dose-response relationship with the mobile phone-based infectious pressure estimates. The mobile phone-based model performed better (AUC 0.79) than the retrospectively optimized gravity models (AUC 0.66 and 0.74, respectively). Infectious pressure at outbreak onset was significantly correlated with reported cholera cases during the first ten days of the epidemic (p Mobile operator data is a highly promising data source for improving preparedness and response efforts during cholera outbreaks. Findings may be particularly important for containment efforts of emerging infectious diseases, including high-mortality influenza strains.

  3. Reactor for in situ measurements of spatially resolved kinetic data in heterogeneous catalysis

    Science.gov (United States)

    Horn, R.; Korup, O.; Geske, M.; Zavyalova, U.; Oprea, I.; Schlögl, R.

    2010-06-01

    The present work describes a reactor that allows in situ measurements of spatially resolved kinetic data in heterogeneous catalysis. The reactor design allows measurements up to temperatures of 1300 °C and 45 bar pressure, i.e., conditions of industrial relevance. The reactor involves reactants flowing through a solid catalyst bed containing a sampling capillary with a side sampling orifice through which a small fraction of the reacting fluid (gas or liquid) is transferred into an analytical device (e.g., mass spectrometer, gas chromatograph, high pressure liquid chromatograph) for quantitative analysis. The sampling capillary can be moved with μm resolution in or against flow direction to measure species profiles through the catalyst bed. Rotation of the sampling capillary allows averaging over several scan lines. The position of the sampling orifice is such that the capillary channel through the catalyst bed remains always occupied by the capillary preventing flow disturbance and fluid bypassing. The second function of the sampling capillary is to provide a well which can accommodate temperature probes such as a thermocouple or a pyrometer fiber. If a thermocouple is inserted in the sampling capillary and aligned with the sampling orifice fluid temperature profiles can be measured. A pyrometer fiber can be used to measure the temperature profile of the solid catalyst bed. Spatial profile measurements are demonstrated for methane oxidation on Pt and methane oxidative coupling on Li/MgO, both catalysts supported on reticulated α -Al2O3 foam supports.

  4. Redistribution population data across a regular spatial grid according to buildings characteristics

    Science.gov (United States)

    Calka, Beata; Bielecka, Elzbieta; Zdunkiewicz, Katarzyna

    2016-12-01

    Population data are generally provided by state census organisations at the predefined census enumeration units. However, these datasets very are often required at userdefined spatial units that differ from the census output levels. A number of population estimation techniques have been developed to address these problems. This article is one of those attempts aimed at improving county level population estimates by using spatial disaggregation models with support of buildings characteristic, derived from national topographic database, and average area of a flat. The experimental gridded population surface was created for Opatów county, sparsely populated rural region located in Central Poland. The method relies on geolocation of population counts in buildings, taking into account the building volume and structural building type and then aggregation the people total in 1 km quadrilateral grid. The overall quality of population distribution surface expressed by the mean of RMSE equals 9 persons, and the MAE equals 0.01. We also discovered that nearly 20% of total county area is unpopulated and 80% of people lived on 33% of the county territory.

  5. Normative data for cutaneous threshold and spatial discrimination in the feet.

    Science.gov (United States)

    Rinkel, Willem D; Aziz, M Hosein; Van Deelen, Meike J M; Willemsen, Sten P; Castro Cabezas, Manuel; Van Neck, Johan W; Coert, J Henk

    2017-09-01

    No data are available for normative values of cutaneous threshold and spatial discrimination in the feet. We developed clinically applicable reference values in relation to the nerve distributions of the feet. We determined foot sensation in 196 healthy individuals. Cutaneous threshold (1-point static discrimination, S1PD) was tested with monofilaments (0.008 to 300 gram) and spatial discrimination (2-point static [S2PD] and moving [M2PD] discrimination) on five locations per foot. There was a significant age-dependent increase in S1PD, S2PD, and M2PD values (P < 0.05). No significant differences were found between both feet. S1PD values differed up to 0.8 g between genders. There were no significant differences between genders for S2PD and M2PD measurements. M2PD values were generally lower than S2PD values. This study provides age-related normative values for foot sensation to help clinicians assess sensory deficits in relation to aging and identify patients with underlying nerve problems. Muscle Nerve 56: 399-407, 2017. © 2016 Wiley Periodicals, Inc.

  6. Spatial interaction models from Irish commuting data: variations in trip length by occupation and gender

    Science.gov (United States)

    O'Kelly, Morton E.; Niedzielski, Michael A.; Gleeson, Justin

    2012-10-01

    Core and peripheral contrasts in journey-to-work trip length can be interpreted as imputing the relative value of origin and destination accessibility (yielding theoretical proxies for rent and wages). Because the main variables are shown to be critically dependent on spatial structure, they may be interpreted as showing the shadow prices due to comparative location. There is also a unifying connection between these results and the existing literature on many dimensions: rent gradients, accessibility, and emissivity. In an empirical example, the advantages of a panoramic view of national commuting statistics are shown, using an Irish data set. Variations in the rates of participation in trip making by location, occupation, and gender are examined. Places that emit more trips than would be expected from their relative location are identified. Further, examining ways in which such emissivity is sensitive to a change in trip length highlights the regions where trips could possibly be adjusted to produce a shorter average trip length or which might be especially sensitive to reduction in employment. A careful reinterpretation of one of the key outputs from a calibrated spatial interaction model is shown to be consistent with the declining rent gradient expected from Alonso's theory of land use.

  7. Socioeconomic inequality of cancer mortality in the United States: a spatial data mining approach

    Directory of Open Access Journals (Sweden)

    Lam Nina SN

    2006-02-01

    Full Text Available Abstract Background The objective of this study was to demonstrate the use of an association rule mining approach to discover associations between selected socioeconomic variables and the four most leading causes of cancer mortality in the United States. An association rule mining algorithm was applied to extract associations between the 1988–1992 cancer mortality rates for colorectal, lung, breast, and prostate cancers defined at the Health Service Area level and selected socioeconomic variables from the 1990 United States census. Geographic information system technology was used to integrate these data which were defined at different spatial resolutions, and to visualize and analyze the results from the association rule mining process. Results Health Service Areas with high rates of low education, high unemployment, and low paying jobs were found to associate with higher rates of cancer mortality. Conclusion Association rule mining with geographic information technology helps reveal the spatial patterns of socioeconomic inequality in cancer mortality in the United States and identify regions that need further attention.

  8. Plan4all – A European Approach to Spatial Data Used on Territorial Planning

    Directory of Open Access Journals (Sweden)

    GERGELY TÖRÖK

    2011-01-01

    Full Text Available In order to ensure the increased efficiency of European policies and the implementation of Structural Funds in a more balanced manner in the different territories of the country, a number of eight development regions have been established in Romania, corresponding to NUTS II level. In between these regions, as well as within each of them, a major concern is the diminution of economic and social disparities. Moreover, these disparities can manifest themselves in a cross-border context, jumping the administrative boundaries of regions and even neighbouring countries. In this sense, development plans provide certain measures in order to stimulate a more balanced development of the territories, using spatial planning as the actual support in identifying factors that lead to the emergence and intensifying of regional disparities. The present paper focuses on these aspects related to spatial planning data in the context of the recent European initiatives, through the activities of the Plan4all project that is being implemented by a European consortium. Romania is partner in this project and is represented by the North-West Regional Development Agency (North-West RDA. The paper presents the achievements of the running Plan4all project until the present day, as well as the involvement and the contributions of the North-West RDA so far.

  9. Geospatial environmental data modelling applications using remote sensing, GIS and spatial statistics

    Energy Technology Data Exchange (ETDEWEB)

    Siljander, M.

    2010-07-01

    This thesis presents novel modelling applications for environmental geospatial data using remote sensing, GIS and statistical modelling techniques. The studied themes can be classified into four main themes: (i) to develop advanced geospatial databases. Paper (I) demonstrates the creation of a geospatial database for the Glanville fritillary butterfly (Melitaea cinxia) in the Aaland Islands, south-western Finland; (ii) to analyse species diversity and distribution using GIS techniques. Paper (II) presents a diversity and geographical distribution analysis for Scopulini moths at a world-wide scale; (iii) to study spatiotemporal forest cover change. Paper (III) presents a study of exotic and indigenous tree cover change detection in Taita Hills Kenya using airborne imagery and GIS analysis techniques; (iv) to explore predictive modelling techniques using geospatial data. In Paper (IV) human population occurrence and abundance in the Taita Hills highlands was predicted using the generalized additive modelling (GAM) technique. Paper (V) presents techniques to enhance fire prediction and burned area estimation at a regional scale in East Caprivi Namibia. Paper (VI) compares eight state-of-the-art predictive modelling methods to improve fire prediction, burned area estimation and fire risk mapping in East Caprivi Namibia. The results in Paper (I) showed that geospatial data can be managed effectively using advanced relational database management systems. Metapopulation data for Melitaea cinxia butterfly was successfully combined with GPS-delimited habitat patch information and climatic data. Using the geospatial database, spatial analyses were successfully conducted at habitat patch level or at more coarse analysis scales. Moreover, this study showed it appears evident that at a large-scale spatially correlated weather conditions are one of the primary causes of spatially correlated changes in Melitaea cinxia population sizes. In Paper (II) spatiotemporal characteristics

  10. Embracing the Open-Source Movement for the Management of Spatial Data: A Case Study of African Trypanosomiasis in Kenya.

    Science.gov (United States)

    Langley, Shaun A; Messina, Joseph P

    2011-01-01

    The past decade has seen an explosion in the availability of spatial data not only for researchers, but the public alike. As the quantity of data increases, the ability to effectively navigate and understand the data becomes more challenging. Here we detail a conceptual model for a spatially explicit database management system that addresses the issues raised with the growing data management problem. We demonstrate utility with a case study in disease ecology: to develop a multi-scale predictive model of African Trypanosomiasis in Kenya. International collaborations and varying technical expertise necessitate a modular open-source software solution. Finally, we address three recurring problems with data management: scalability, reliability, and security.

  11. Implications of spatial data variations for protected areas management: an example from East Africa.

    Science.gov (United States)

    Dowhaniuk, Nicholas; Hartter, Joel; Ryan, Sadie J

    2014-09-01

    Geographic information systems and remote sensing technologies have become an important tool for visualizing conservation management and developing solutions to problems associated with conservation. When multiple organizations separately develop spatial data representations of protected areas, implicit error arises due to variation between data sets. We used boundary data produced by three conservation organizations (International Union for the Conservation of Nature, World Resource Institute, and Uganda Wildlife Authority), for seven Ugandan parks, to study variation in the size represented and the location of boundaries. We found variation in the extent of overlapping total area encompassed by the three data sources, ranging from miniscule (0.4 %) differences to quite large ones (9.0 %). To underscore how protected area boundary discrepancies may have implications to protected area management, we used a landcover classification, defining crop, shrub, forest, savanna, and grassland. The total area in the different landcover classes varied most in smaller protected areas (those less than 329 km(2)), with forest and cropland area estimates varying up to 65 %. The discrepancies introduced by boundary errors could, in this hypothetical case, generate erroneous findings and could have a significant impact on conservation, such as local-scale management for encroachment and larger-scale assessments of deforestation.

  12. Research and implementation of geography service bus in spatial data sharing platform

    Science.gov (United States)

    Zou, Zhiqiang; Nan, Jiang; Lin, Tao; Bai, Mingbai; He, Xingfu

    2006-10-01

    Geographic Information Systems, GIS, software has wide applications in business; however, implementation of the interoperability among the GIS has also become a challenge. This paper presents a solution based on Geography Service Bus that uses web services to achieve the interoperability among these heterogeneous GIS to allow users share the Geosciences data as well as access service. Referring to the abstract specification of OWS (OGC Web Services), the proposed solution adopts the SOA (Service-Oriented Architecture) when implementing SDSP (Spatial Data Sharing Platform). To accomplish this, a new abstract layer, GSB (Geography Service Bus), is created to provide standard interface. GSB extends ESB (Enterprise Service Bus) proposed by IBM and SUN, and combines the application in geography. GSB inherits the general features of ESB, such as interoperability, heterogeneity and service-oriented while offering unique functions like the high volume geo-data access and better management in geographic services. GSB includes the following JAVA implemented components: the management component of the geography registry service, the route component of the geography request service and the geographical business process component, etc. GSB plays an important role in SDSP and has been developed and successfully applied in the Data Center for Resources & Environmental Sciences in East China as a key project of Chinese Academy of Sciences. It has been observed that the introduction of GSB has tremendously improved both performance and interoperability of SDSP among heterogeneous GIS than traditional methods.

  13. Newspaper archives + text mining = rich sources of historical geo-spatial data

    Science.gov (United States)

    Yzaguirre, A.; Smit, M.; Warren, R.

    2016-04-01

    Newspaper archives are rich sources of cultural, social, and historical information. These archives, even when digitized, are typically unstructured and organized by date rather than by subject or location, and require substantial manual effort to analyze. The effort of journalists to be accurate and precise means that there is often rich geo-spatial data embedded in the text, alongside text describing events that editors considered to be of sufficient importance to the region or the world to merit column inches. A regional newspaper can add over 100,000 articles to its database each year, and extracting information from this data for even a single country would pose a substantial Big Data challenge. In this paper, we describe a pilot study on the construction of a database of historical flood events (location(s), date, cause, magnitude) to be used in flood assessment projects, for example to calibrate models, estimate frequency, establish high water marks, or plan for future events in contexts ranging from urban planning to climate change adaptation. We then present a vision for extracting and using the rich geospatial data available in unstructured text archives, and suggest future avenues of research.

  14. Integrated use of spatial and semantic relationships for extracting road networks from floating car data

    Science.gov (United States)

    Li, Jun; Qin, Qiming; Xie, Chao; Zhao, Yue

    2012-10-01

    The update frequency of digital road maps influences the quality of road-dependent services. However, digital road maps surveyed by probe vehicles or extracted from remotely sensed images still have a long updating circle and their cost remain high. With GPS technology and wireless communication technology maturing and their cost decreasing, floating car technology has been used in traffic monitoring and management, and the dynamic positioning data from floating cars become a new data source for updating road maps. In this paper, we aim to update digital road maps using the floating car data from China's National Commercial Vehicle Monitoring Platform, and present an incremental road network extraction method suitable for the platform's GPS data whose sampling frequency is low and which cover a large area. Based on both spatial and semantic relationships between a trajectory point and its associated road segment, the method classifies each trajectory point, and then merges every trajectory point into the candidate road network through the adding or modifying process according to its type. The road network is gradually updated until all trajectories have been processed. Finally, this method is applied in the updating process of major roads in North China and the experimental results reveal that it can accurately derive geometric information of roads under various scenes. This paper provides a highly-efficient, low-cost approach to update digital road maps.

  15. Modelling Spatial Compositional Data: Reconstructions of past land cover and uncertainties

    DEFF Research Database (Denmark)

    Pirzamanbein, Behnaz; Lindström, Johan; Poska, Anneli

    2018-01-01

    In this paper, we construct a hierarchical model for spatial compositional data, which is used to reconstruct past land-cover compositions (in terms of coniferous forest, broadleaved forest, and unforested/open land) for five time periods during the past $6\\,000$ years over Europe. The model...... to a fast MCMC algorithm. Reconstructions are obtained by combining pollen-based estimates of vegetation cover at a limited number of locations with scenarios of past deforestation and output from a dynamic vegetation model. To evaluate uncertainties in the predictions a novel way of constructing joint...... confidence regions for the entire composition at each prediction location is proposed. The hierarchical model's ability to reconstruct past land cover is evaluated through cross validation for all time periods, and by comparing reconstructions for the recent past to a present day European forest map...

  16. A System Architecture for a Transnational Data Infrastructure supporting Maritime Spatial Planning

    DEFF Research Database (Denmark)

    Hansen, Henning Sten; Reiter, Ida Maria; Schrøder, Anne Lise

    2017-01-01

    The use of the seas and oceans is overall regulated by the United Nations through the UN Convention on the Law of the Sea, which defines the rights and responsibilities. However, with the rapidly increasing use of the sea and oceans it is inevitable that conflicts may arise. Accordingly, there has...... been an increasing international recognition of the need to manage human activities that influence the marine environment and its ecosystems in an integrated, cross-sectoral manner. Recently, Maritime Spatial Planning (MSP) has gained significant attention as a new paradigm aiming at minimising...... the conflicts among different sea uses through involving various stakeholders and sectors while aiming for sustainable growth. The aim of this research is to build a conceptual model for a Data Infrastructure to support marine space in a transnational context addressing the challenges related to the increasing...