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.

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.

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

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

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

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.

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.

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...

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

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

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.

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.

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

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

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...

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,

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.

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.

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...

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.

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.

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 ...

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;

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.

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.

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.

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.

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.

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.

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

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

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.

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.

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.

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

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

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.

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.

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.

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

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.

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...

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.

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

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.

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.

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...

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

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.

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....

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.

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.

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.

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

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...

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.

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

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.

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.

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.

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.

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.

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.

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.

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...

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.

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...

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

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

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....

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.

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.

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.

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 ...

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 .

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).

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.

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.

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.

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.

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.

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

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.

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)

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)

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.)

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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

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.

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.

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.

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.

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...

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.

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.

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.

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

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.

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 ...

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

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.

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

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.

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...

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.

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.

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

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.

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”.

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.

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.

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.

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...

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.

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

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.

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.

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.

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.

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...

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

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.

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.

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)

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, ...

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

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

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...

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.

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

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.

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...

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.

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.

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.

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.

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).

Error propagation in spatial modeling of public health data: a simulation approach using pediatric blood lead level data for Syracuse, New York.

Science.gov (United States)

Lee, Monghyeon; Chun, Yongwan; Griffith, Daniel A

2018-04-01

Lead poisoning produces serious health problems, which are worse when a victim is younger. The US government and society have tried to prevent lead poisoning, especially since the 1970s; however, lead exposure remains prevalent. Lead poisoning analyses frequently use georeferenced blood lead level data. Like other types of data, these spatial data may contain uncertainties, such as location and attribute measurement errors, which can propagate to analysis results. For this paper, simulation experiments are employed to investigate how selected uncertainties impact regression analyses of blood lead level data in Syracuse, New York. In these simulations, location error and attribute measurement error, as well as a combination of these two errors, are embedded into the original data, and then these data are aggregated into census block group and census tract polygons. These aggregated data are analyzed with regression techniques, and comparisons are reported between the regression coefficients and their standard errors for the error added simulation results and the original results. To account for spatial autocorrelation, the eigenvector spatial filtering method and spatial autoregressive specifications are utilized with linear and generalized linear models. Our findings confirm that location error has more of an impact on the differences than does attribute measurement error, and show that the combined error leads to the greatest deviations. Location error simulation results show that smaller administrative units experience more of a location error impact, and, interestingly, coefficients and standard errors deviate more from their true values for a variable with a low level of spatial autocorrelation. These results imply that uncertainty, especially location error, has a considerable impact on the reliability of spatial analysis results for public health data, and that the level of spatial autocorrelation in a variable also has an impact on modeling results.

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.

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 ...

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....

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

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...

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

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.

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...

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

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.

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.

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

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

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.

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.

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

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.

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.

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

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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

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...

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

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...

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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

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.

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

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.

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

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.

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.

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.

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

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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.

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.

Spatial features register: toward standardization of spatial features

Science.gov (United States)

Cascio, Janette

1994-01-01

As the need to share spatial data increases, more than agreement on a common format is needed to ensure that the data is meaningful to both the importer and the exporter. Effective data transfer also requires common definitions of spatial features. To achieve this, part 2 of the Spatial Data Transfer Standard (SDTS) provides a model for a spatial features data content specification and a glossary of features and attributes that fit this model. The model provides a foundation for standardizing spatial features. The glossary now contains only a limited subset of hydrographic and topographic features. For it to be useful, terms and definitions must be included for other categories, such as base cartographic, bathymetric, cadastral, cultural and demographic, geodetic, geologic, ground transportation, international boundaries, soils, vegetation, water, and wetlands, and the set of hydrographic and topographic features must be expanded. This paper will review the philosophy of the SDTS part 2 and the current plans for creating a national spatial features register as one mechanism for maintaining part 2.

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...

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

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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.

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.

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.

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.

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

Web-Scale Multidimensional Visualization of Big Spatial Data to Support Earth Sciences—A Case Study with Visualizing Climate Simulation Data

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Sizhe Wang

2017-06-01

Full Text Available The world is undergoing rapid changes in its climate, environment, and ecosystems due to increasing population growth, urbanization, and industrialization. Numerical simulation is becoming an important vehicle to enhance the understanding of these changes and their impacts, with regional and global simulation models producing vast amounts of data. Comprehending these multidimensional data and fostering collaborative scientific discovery requires the development of new visualization techniques. In this paper, we present a cyberinfrastructure solution—PolarGlobe—that enables comprehensive analysis and collaboration. PolarGlobe is implemented upon an emerging web graphics library, WebGL, and an open source virtual globe system Cesium, which has the ability to map spatial data onto a virtual Earth. We have also integrated volume rendering techniques, value and spatial filters, and vertical profile visualization to improve rendered images and support a comprehensive exploration of multi-dimensional spatial data. In this study, the climate simulation dataset produced by the extended polar version of the well-known Weather Research and Forecasting Model (WRF is used to test the proposed techniques. PolarGlobe is also easily extendable to enable data visualization for other Earth Science domains, such as oceanography, weather, or geology.

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.

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...

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.

DESIGN FOR CONNECTING SPATIAL DATA INFRASTRUCTURES WITH SENSOR WEB (SENSDI

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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.

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

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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.

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.

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...

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

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).

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 ...

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

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

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

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.

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

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.

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.

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.

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

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.

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.

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.

A Comparison of Data Sets Varying in Spatial Accuracy Used to Predict the Occurrence of Wildlife-Vehicle Collisions

Science.gov (United States)

Gunson, Kari E.; Clevenger, Anthony P.; Ford, Adam T.; Bissonette, John A.; Hardy, Amanda

2009-08-01

Wildlife-vehicle collisions (WVCs) pose a significant safety and conservation concern in areas where high-traffic roads are situated adjacent to wildlife habitat. Improving transportation safety, accurately planning highway mitigation, and identifying key habitat linkage areas may all depend on the quality of WVC data collection. Two common approaches to describe the location of WVCs are spatially accurate data derived from global positioning systems (GPS) or vehicle odometer measurements and less accurate road-marker data derived from reference points (e.g., mile-markers or landmarks) along the roadside. In addition, there are two common variable types used to predict WVC locations: (1) field-derived, site-specific measurements and (2) geographic information system (GIS)-derived information. It is unclear whether these different approaches produce similar results when attempting to identify and explain the location of WVCs. Our first objective was to determine and compare the spatial error found in road-marker data (in our case the closest mile-marker) and landmark-referenced data. Our second objective was to evaluate the performance of models explaining high- and low-probability WVC locations, using congruent, spatially accurate (GIS-derived explanatory variables. Our WVC data sets were comprised of ungulate collisions and were located along five major roads in the central Canadian Rocky Mountains. We found that spatial error (mean ± SD) was higher for WVC data referenced to nearby landmarks (516 ± 808 m) than for data referenced to the closest mile-marker data (401 ± 219 m). The top-performing model using the spatially accurate WVC locations contained all explanatory variable types, whereas GIS-derived variables were only influential in the best road-marker model and the spatially accurate reduced model. Our study showed that spatial error and sample size, using road-marker data for ungulate species, are important to consider for model output interpretation

Challenges of Replacing NAD 83, NAVD 88, and IGLD 85: Exploiting the Characteristics of 3-D Digital Spatial Data

Science.gov (United States)

Burkholder, E. F.

2016-12-01

One way to address challenges of replacing NAD 83, NGVD 88 and IGLD 85 is to exploit the characteristics of 3-D digital spatial data. This presentation describes the 3-D global spatial data model (GSDM) which accommodates rigorous scientific endeavors while simultaneously supporting a local flat-earth view of the world. The GSDM is based upon the assumption of a single origin for 3-D spatial data and uses rules of solid geometry for manipulating spatial data components. This approach exploits the characteristics of 3-D digital spatial data and preserves the quality of geodetic measurements while providing spatial data users the option of working with rectangular flat-earth components and computational procedures for local applications. This flexibility is provided by using a bidirectional rotation matrix that allows any 3-D vector to be used in a geodetic reference frame for high-end applications and/or the local frame for flat-earth users. The GSDM is viewed as compatible with the datum products being developed by NGS and provides for unambiguous exchange of 3-D spatial data between disciplines and users worldwide. Three geometrical models will be summarized - geodetic, map projection, and 3-D. Geodetic computations are performed on an ellipsoid and are without equal in providing rigorous coordinate values for latitude, longitude, and ellipsoid height. Members of the user community have, for generations, sought ways to "flatten the world" to accommodate a flat-earth view and to avoid the complexity of working on an ellipsoid. Map projections have been defined for a wide variety of applications and remain very useful for visualizing spatial data. But, the GSDM supports computations based on 3-D components that have not been distorted in a 2-D map projection. The GSDM does not invalidate either geodesy or cartographic computational processes but provides a geometrically correct view of any point cloud from any point selected by the user. As a bonus, the GSDM also

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

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.

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

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.

Concept of a spatial data infrastructure for web-mapping, processing and service provision for geo-hazards

Science.gov (United States)

Weinke, Elisabeth; Hölbling, Daniel; Albrecht, Florian; Friedl, Barbara

2017-04-01

Geo-hazards and their effects are distributed geographically over wide regions. The effective mapping and monitoring is essential for hazard assessment and mitigation. It is often best achieved using satellite imagery and new object-based image analysis approaches to identify and delineate geo-hazard objects (landslides, floods, forest fires, storm damages, etc.). At the moment, several local/national databases and platforms provide and publish data of different types of geo-hazards as well as web-based risk maps and decision support systems. Also, the European commission implemented the Copernicus Emergency Management Service (EMS) in 2015 that publishes information about natural and man-made disasters and risks. Currently, no platform for landslides or geo-hazards as such exists that enables the integration of the user in the mapping and monitoring process. In this study we introduce the concept of a spatial data infrastructure for object delineation, web-processing and service provision of landslide information with the focus on user interaction in all processes. A first prototype for the processing and mapping of landslides in Austria and Italy has been developed within the project Land@Slide, funded by the Austrian Research Promotion Agency FFG in the Austrian Space Applications Program ASAP. The spatial data infrastructure and its services for the mapping, processing and analysis of landslides can be extended to other regions and to all types of geo-hazards for analysis and delineation based on Earth Observation (EO) data. The architecture of the first prototypical spatial data infrastructure includes four main areas of technical components. The data tier consists of a file storage system and the spatial data catalogue for the management of EO-data, other geospatial data on geo-hazards, as well as descriptions and protocols for the data processing and analysis. An interface to extend the data integration from external sources (e.g. Sentinel-2 data) is planned

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.

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.

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.

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

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.

Accounting for the measurement error of spectroscopically inferred soil carbon data for improved precision of spatial predictions.

Science.gov (United States)

Somarathna, P D S N; Minasny, Budiman; Malone, Brendan P; Stockmann, Uta; McBratney, Alex B

2018-08-01

Spatial modelling of environmental data commonly only considers spatial variability as the single source of uncertainty. In reality however, the measurement errors should also be accounted for. In recent years, infrared spectroscopy has been shown to offer low cost, yet invaluable information needed for digital soil mapping at meaningful spatial scales for land management. However, spectrally inferred soil carbon data are known to be less accurate compared to laboratory analysed measurements. This study establishes a methodology to filter out the measurement error variability by incorporating the measurement error variance in the spatial covariance structure of the model. The study was carried out in the Lower Hunter Valley, New South Wales, Australia where a combination of laboratory measured, and vis-NIR and MIR inferred topsoil and subsoil soil carbon data are available. We investigated the applicability of residual maximum likelihood (REML) and Markov Chain Monte Carlo (MCMC) simulation methods to generate parameters of the Matérn covariance function directly from the data in the presence of measurement error. The results revealed that the measurement error can be effectively filtered-out through the proposed technique. When the measurement error was filtered from the data, the prediction variance almost halved, which ultimately yielded a greater certainty in spatial predictions of soil carbon. Further, the MCMC technique was successfully used to define the posterior distribution of measurement error. This is an important outcome, as the MCMC technique can be used to estimate the measurement error if it is not explicitly quantified. Although this study dealt with soil carbon data, this method is amenable for filtering the measurement error of any kind of continuous spatial environmental data. Copyright © 2018 Elsevier B.V. All rights reserved.

Handbook of Spatial Statistics

CERN Document Server

Gelfand, Alan E

2010-01-01

Offers an introduction detailing the evolution of the field of spatial statistics. This title focuses on the three main branches of spatial statistics: continuous spatial variation (point referenced data); discrete spatial variation, including lattice and areal unit data; and, spatial point patterns.

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.

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.

Spatial econometrics using microdata

CERN Document Server

Dubé, Jean

2014-01-01

This book provides an introduction to spatial analyses concerning disaggregated (or micro) spatial data.Particular emphasis is put on spatial data compilation and the structuring of the connections between the observations. Descriptive analysis methods of spatial data are presented in order to identify and measure the spatial, global and local dependency.The authors then focus on autoregressive spatial models, to control the problem of spatial dependency between the residues of a basic linear statistical model, thereby contravening one of the basic hypotheses of the ordinary least squares appr

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

Investigating Public Facility Characteristics from a Spatial Interaction Perspective: A Case Study of Beijing Hospitals Using Taxi Data

Directory of Open Access Journals (Sweden)

Xiaoqing Kong

2017-02-01

Full Text Available Services provided by public facilities are essential to people’s lives and are closely associated with human mobility. Traditionally, public facility access characteristics, such as accessibility, equity issues and service areas, are investigated mainly based on static data (census data, travel surveys and particular records, such as medical records. Currently, the advent of big data offers an unprecedented opportunity to obtain large-scale human mobility data, which can be used to study the characteristics of public facilities from the spatial interaction perspective. Intuitively, spatial interaction characteristics and service areas of different types and sizes of public facilities are different, but how different remains an open question, so we, in turn, examine this question. Based on spatial interaction, we classify public facilities and explore the differences in facilities. In the research, based on spatial interaction extracted from taxi data, we introduce an unsupervised classification method to classify 78 hospitals in 6 districts of Beijing, and the results better reflect the type of hospital. The findings are of great significance for optimizing the spatial configuration of medical facilities or other types of public facilities, allocating public resources reasonably and relieving traffic pressure.

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.

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...

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

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.

A Bayesian method to mine spatial data sets to evaluate the vulnerability of human beings to catastrophic risk.

Science.gov (United States)

Li, Lianfa; Wang, Jinfeng; Leung, Hareton; Zhao, Sisi

2012-06-01

Vulnerability of human beings exposed to a catastrophic disaster is affected by multiple factors that include hazard intensity, environment, and individual characteristics. The traditional approach to vulnerability assessment, based on the aggregate-area method and unsupervised learning, cannot incorporate spatial information; thus, vulnerability can be only roughly assessed. In this article, we propose Bayesian network (BN) and spatial analysis techniques to mine spatial data sets to evaluate the vulnerability of human beings. In our approach, spatial analysis is leveraged to preprocess the data; for example, kernel density analysis (KDA) and accumulative road cost surface modeling (ARCSM) are employed to quantify the influence of geofeatures on vulnerability and relate such influence to spatial distance. The knowledge- and data-based BN provides a consistent platform to integrate a variety of factors, including those extracted by KDA and ARCSM to model vulnerability uncertainty. We also consider the model's uncertainty and use the Bayesian model average and Occam's Window to average the multiple models obtained by our approach to robust prediction of the risk and vulnerability. We compare our approach with other probabilistic models in the case study of seismic risk and conclude that our approach is a good means to mining spatial data sets for evaluating vulnerability. © 2012 Society for Risk Analysis.

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.

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

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.

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.

Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing

Science.gov (United States)

Gao, Shengguo; Zhu, Zhongli; Liu, Shaomin; Jin, Rui; Yang, Guangchao; Tan, Lei

2014-10-01

Soil moisture (SM) plays a fundamental role in the land-atmosphere exchange process. Spatial estimation based on multi in situ (network) data is a critical way to understand the spatial structure and variation of land surface soil moisture. Theoretically, integrating densely sampled auxiliary data spatially correlated with soil moisture into the procedure of spatial estimation can improve its accuracy. In this study, we present a novel approach to estimate the spatial pattern of soil moisture by using the BME method based on wireless sensor network data and auxiliary information from ASTER (Terra) land surface temperature measurements. For comparison, three traditional geostatistic methods were also applied: ordinary kriging (OK), which used the wireless sensor network data only, regression kriging (RK) and ordinary co-kriging (Co-OK) which both integrated the ASTER land surface temperature as a covariate. In Co-OK, LST was linearly contained in the estimator, in RK, estimator is expressed as the sum of the regression estimate and the kriged estimate of the spatially correlated residual, but in BME, the ASTER land surface temperature was first retrieved as soil moisture based on the linear regression, then, the t-distributed prediction interval (PI) of soil moisture was estimated and used as soft data in probability form. The results indicate that all three methods provide reasonable estimations. Co-OK, RK and BME can provide a more accurate spatial estimation by integrating the auxiliary information Compared to OK. RK and BME shows more obvious improvement compared to Co-OK, and even BME can perform slightly better than RK. The inherent issue of spatial estimation (overestimation in the range of low values and underestimation in the range of high values) can also be further improved in both RK and BME. We can conclude that integrating auxiliary data into spatial estimation can indeed improve the accuracy, BME and RK take better advantage of the auxiliary

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...

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

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.

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

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.

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...

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.

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

Simplified methods for spatial sampling: application to first-phase data of Italian National Forest Inventory (INFC in Sicily

Directory of Open Access Journals (Sweden)

Cullotta S

2006-01-01

Full Text Available Methodological approaches able to integrate data from sample plots with cartographic processes are widely applied. Based on mathematic-statistical techniques, the spatial analysis allows the exploration and spatialization of geographic data. Starting from the punctual information on land use types obtained from the dataset of the first phase of the ongoing new Italian NFI (INFC, a spatialization of land cover classes was carried out using the Inverse Distance Weighting (IDW method. In order to validate the obtained results, an overlay with other vectorial land use data was carried out. In particular, the overlay compared data at different scales, evaluating differences in terms of degree of correspondence between the interpolated and reference land cover.

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 ...

Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey

International Nuclear Information System (INIS)

Keser, Saniye; Duzgun, Sebnem; Aksoy, Aysegul

2012-01-01

Highlights: ► Spatial autocorrelation exists in municipal solid waste generation rates for different provinces in Turkey. ► Traditional non-spatial regression models may not provide sufficient information for better solid waste management. ► Unemployment rate is a global variable that significantly impacts the waste generation rates in Turkey. ► Significances of global parameters may diminish at local scale for some provinces. ► GWR model can be used to create clusters of cities for solid waste management. - Abstract: In studies focusing on the factors that impact solid waste generation habits and rates, the potential spatial dependency in solid waste generation data is not considered in relating the waste generation rates to its determinants. In this study, spatial dependency is taken into account in determination of the significant socio-economic and climatic factors that may be of importance for the municipal solid waste (MSW) generation rates in different provinces of Turkey. Simultaneous spatial autoregression (SAR) and geographically weighted regression (GWR) models are used for the spatial data analyses. Similar to ordinary least squares regression (OLSR), regression coefficients are global in SAR model. In other words, the effect of a given independent variable on a dependent variable is valid for the whole country. Unlike OLSR or SAR, GWR reveals the local impact of a given factor (or independent variable) on the waste generation rates of different provinces. Results show that provinces within closer neighborhoods have similar MSW generation rates. On the other hand, this spatial autocorrelation is not very high for the exploratory variables considered in the study. OLSR and SAR models have similar regression coefficients. GWR is useful to indicate the local determinants of MSW generation rates. GWR model can be utilized to plan waste management activities at local scale including waste minimization, collection, treatment, and disposal. At global

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…

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.

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.

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

Onondaga Lake Watershed – A Geographic Information System Project Phase I – Needs assessment and spatial data framework

Science.gov (United States)

Freehafer, Douglas A.; Pierson, Oliver

2004-01-01

In the fall of 2002, the Onondaga Lake Partnership (OLP) formed a Geographic Information System (GIS) Planning Committee to begin the process of developing a comprehensive watershed geographic information system for Onondaga Lake. The goal of the Onondaga Lake Partnership geographic information system is to integrate the various types of spatial data used for scientific investigations, resource management, and planning and design of improvement projects in the Onondaga Lake Watershed. A needs-assessment survey was conducted and a spatial data framework developed to support the Onondaga Lake Partnership use of geographic information system technology. The design focused on the collection, management, and distribution of spatial data, maps, and internet mapping applications. A geographic information system library of over 100 spatial datasets and metadata links was assembled on the basis of the results of the needs assessment survey. Implementation options were presented, and the Geographic Information System Planning Committee offered recommendations for the management and distribution of spatial data belonging to Onondaga Lake Partnership members. The Onondaga Lake Partnership now has a strong foundation for building a comprehensive geographic information system for the Onondaga Lake watershed. The successful implementation of a geographic information system depends on the Onondaga Lake Partnership’s determination of: (1) the design and plan for a geographic information system, including the applications and spatial data that will be provided and to whom, (2) the level of geographic information system technology to be utilized and funded, and (3) the institutional issues of operation and maintenance of the system.

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...

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.

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

Data management with a landslide inventory of the Franconian Alb (Germany) using a spatial database and GIS tools

Science.gov (United States)

Bemm, Stefan; Sandmeier, Christine; Wilde, Martina; Jaeger, Daniel; Schwindt, Daniel; Terhorst, Birgit

2014-05-01

The area of the Swabian-Franconian cuesta landscape (Southern Germany) is highly prone to landslides. This was apparent in the late spring of 2013, when numerous landslides occurred as a consequence of heavy and long-lasting rainfalls. The specific climatic situation caused numerous damages with serious impact on settlements and infrastructure. Knowledge on spatial distribution of landslides, processes and characteristics are important to evaluate the potential risk that can occur from mass movements in those areas. In the frame of two projects about 400 landslides were mapped and detailed data sets were compiled during years 2011 to 2014 at the Franconian Alb. The studies are related to the project "Slope stability and hazard zones in the northern Bavarian cuesta" (DFG, German Research Foundation) as well as to the LfU (The Bavarian Environment Agency) within the project "Georisks and climate change - hazard indication map Jura". The central goal of the present study is to create a spatial database for landslides. The database should contain all fundamental parameters to characterize the mass movements and should provide the potential for secure data storage and data management, as well as statistical evaluations. The spatial database was created with PostgreSQL, an object-relational database management system and PostGIS, a spatial database extender for PostgreSQL, which provides the possibility to store spatial and geographic objects and to connect to several GIS applications, like GRASS GIS, SAGA GIS, QGIS and GDAL, a geospatial library (Obe et al. 2011). Database access for querying, importing, and exporting spatial and non-spatial data is ensured by using GUI or non-GUI connections. The database allows the use of procedural languages for writing advanced functions in the R, Python or Perl programming languages. It is possible to work directly with the (spatial) data entirety of the database in R. The inventory of the database includes (amongst others

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.

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.

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

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.

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...

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

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.

Automation method to identify the geological structure of seabed using spatial statistic analysis of echo sounding data

Science.gov (United States)

Kwon, O.; Kim, W.; Kim, J.

2017-12-01

Recently construction of subsea tunnel has been increased globally. For safe construction of subsea tunnel, identifying the geological structure including fault at design and construction stage is more than important. Then unlike the tunnel in land, it's very difficult to obtain the data on geological structure because of the limit in geological survey. This study is intended to challenge such difficulties in a way of developing the technology to identify the geological structure of seabed automatically by using echo sounding data. When investigation a potential site for a deep subsea tunnel, there is the technical and economical limit with borehole of geophysical investigation. On the contrary, echo sounding data is easily obtainable while information reliability is higher comparing to above approaches. This study is aimed at developing the algorithm that identifies the large scale of geological structure of seabed using geostatic approach. This study is based on theory of structural geology that topographic features indicate geological structure. Basic concept of algorithm is outlined as follows; (1) convert the seabed topography to the grid data using echo sounding data, (2) apply the moving window in optimal size to the grid data, (3) estimate the spatial statistics of the grid data in the window area, (4) set the percentile standard of spatial statistics, (5) display the values satisfying the standard on the map, (6) visualize the geological structure on the map. The important elements in this study include optimal size of moving window, kinds of optimal spatial statistics and determination of optimal percentile standard. To determine such optimal elements, a numerous simulations were implemented. Eventually, user program based on R was developed using optimal analysis algorithm. The user program was designed to identify the variations of various spatial statistics. It leads to easy analysis of geological structure depending on variation of spatial statistics

Geospatial data sharing, online spatial analysis and processing of Indian Biodiversity data in Internet GIS domain - A case study for raster based online geo-processing

Science.gov (United States)

Karnatak, H.; Pandey, K.; Oberai, K.; Roy, A.; Joshi, D.; Singh, H.; Raju, P. L. N.; Krishna Murthy, Y. V. N.

2014-11-01

National Biodiversity Characterization at Landscape Level, a project jointly sponsored by Department of Biotechnology and Department of Space, was implemented to identify and map the potential biodiversity rich areas in India. This project has generated spatial information at three levels viz. Satellite based primary information (Vegetation Type map, spatial locations of road & village, Fire occurrence); geospatially derived or modelled information (Disturbance Index, Fragmentation, Biological Richness) and geospatially referenced field samples plots. The study provides information of high disturbance and high biological richness areas suggesting future management strategies and formulating action plans. The study has generated for the first time baseline database in India which will be a valuable input towards climate change study in the Indian Subcontinent. The spatial data generated during the study is organized as central data repository in Geo-RDBMS environment using PostgreSQL and POSTGIS. The raster and vector data is published as OGC WMS and WFS standard for development of web base geoinformation system using Service Oriented Architecture (SOA). The WMS and WFS based system allows geo-visualization, online query and map outputs generation based on user request and response. This is a typical mashup architecture based geo-information system which allows access to remote web services like ISRO Bhuvan, Openstreet map, Google map etc., with overlay on Biodiversity data for effective study on Bio-resources. The spatial queries and analysis with vector data is achieved through SQL queries on POSTGIS and WFS-T operations. But the most important challenge is to develop a system for online raster based geo-spatial analysis and processing based on user defined Area of Interest (AOI) for large raster data sets. The map data of this study contains approximately 20 GB of size for each data layer which are five in number. An attempt has been to develop system using

Delineating Spatial Patterns in Human Settlements Using VIIRS Nighttime Light Data: A Watershed-Based Partition Approach

Directory of Open Access Journals (Sweden)

Ting Ma

2018-03-01

Full Text Available As an informative proxy measure for a range of urbanization and socioeconomic variables, satellite-derived nighttime light data have been widely used to investigate diverse anthropogenic activities in human settlements over time and space from the regional to the national scale. With a higher spatial resolution and fewer over-glow and saturation effects, nighttime light data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS instrument with day/night band (DNB, which is on the Suomi National Polar-Orbiting Partnership satellite (Suomi-NPP, may further improve our understanding of spatiotemporal dynamics and socioeconomic activities, particularly at the local scale. Capturing and identifying spatial patterns in human settlements from VIIRS images, however, is still challenging due to the lack of spatially explicit texture characteristics, which are usually crucial for general image classification methods. In this study, we propose a watershed-based partition approach by combining a second order exponential decay model for the spatial delineation of human settlements with VIIRS-derived nighttime light images. Our method spatially partitions the human settlement into five different types of sub-regions: high, medium-high, medium, medium-low and low lighting areas with different degrees of human activity. This is primarily based on the local coverage of locally maximum radiance signals (watershed-based and the rank and magnitude of the nocturnal radiance signal across the whole region, as well as remotely sensed building density data and social media-derived human activity information. The comparison results for the relationship between sub-regions with various density nighttime brightness levels and human activities, as well as the densities of different types of interest points (POIs, show that our method can distinctly identify various degrees of human activity based on artificial nighttime radiance and ancillary data. Furthermore

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.

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.

Spatial downscaling algorithm of TRMM precipitation based on multiple high-resolution satellite data for Inner Mongolia, China

Science.gov (United States)

Duan, Limin; Fan, Keke; Li, Wei; Liu, Tingxi

2017-12-01

Daily precipitation data from 42 stations in Inner Mongolia, China for the 10 years period from 1 January 2001 to 31 December 2010 was utilized along with downscaled data from the Tropical Rainfall Measuring Mission (TRMM) with a spatial resolution of 0.25° × 0.25° for the same period based on the statistical relationships between the normalized difference vegetation index (NDVI), meteorological variables, and digital elevation models (https://en.wikipedia.org/wiki/Digital_elevation_model) (DEM) using the leave-one-out (LOO) cross validation method and multivariate step regression. The results indicate that (1) TRMM data can indeed be used to estimate annual precipitation in Inner Mongolia and there is a linear relationship between annual TRMM and observed precipitation; (2) there is a significant relationship between TRMM-based precipitation and predicted precipitation, with a spatial resolution of 0.50° × 0.50°; (3) NDVI and temperature are important factors influencing the downscaling of TRMM precipitation data for DEM and the slope is not the most significant factor affecting the downscaled TRMM data; and (4) the downscaled TRMM data reflects spatial patterns in annual precipitation reasonably well, showing less precipitation falling in west Inner Mongolia and more in the south and southeast. The new approach proposed here provides a useful alternative for evaluating spatial patterns in precipitation and can thus be applied to generate a more accurate precipitation dataset to support both irrigation management and the conservation of this fragile grassland ecosystem.

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;

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.

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.

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.

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.

Development of an Asset Value Map for Disaster Risk Assessment in China by Spatial Disaggregation Using Ancillary Remote Sensing Data.

Science.gov (United States)

Wu, Jidong; Li, Ying; Li, Ning; Shi, Peijun

2018-01-01

The extent of economic losses due to a natural hazard and disaster depends largely on the spatial distribution of asset values in relation to the hazard intensity distribution within the affected area. Given that statistical data on asset value are collected by administrative units in China, generating spatially explicit asset exposure maps remains a key challenge for rapid postdisaster economic loss assessment. The goal of this study is to introduce a top-down (or downscaling) approach to disaggregate administrative-unit level asset value to grid-cell level. To do so, finding the highly correlated "surrogate" indicators is the key. A combination of three data sets-nighttime light grid, LandScan population grid, and road density grid, is used as ancillary asset density distribution information for spatializing the asset value. As a result, a high spatial resolution asset value map of China for 2015 is generated. The spatial data set contains aggregated economic value at risk at 30 arc-second spatial resolution. Accuracy of the spatial disaggregation reflects redistribution errors introduced by the disaggregation process as well as errors from the original ancillary data sets. The overall accuracy of the results proves to be promising. The example of using the developed disaggregated asset value map in exposure assessment of watersheds demonstrates that the data set offers immense analytical flexibility for overlay analysis according to the hazard extent. This product will help current efforts to analyze spatial characteristics of exposure and to uncover the contributions of both physical and social drivers of natural hazard and disaster across space and time. © 2017 Society for Risk Analysis.

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

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

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

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.

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.

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

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

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.

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

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.

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

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.

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.

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.

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.

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

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 ...

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

The grain of spatially referenced economic cost and biodiversity benefit data and the effectiveness of a cost targeting strategy.

Science.gov (United States)

Sutton, N J; Armsworth, P R

2014-12-01

Facing tight resource constraints, conservation organizations must allocate funds available for habitat protection as effectively as possible. Often, they combine spatially referenced economic and biodiversity data to prioritize land for protection. We tested how sensitive these prioritizations could be to differences in the spatial grain of these data by demonstrating how the conclusion of a classic debate in conservation planning between cost and benefit targeting was altered based on the available information. As a case study, we determined parcel-level acquisition costs and biodiversity benefits of land transactions recently undertaken by a nonprofit conservation organization that seeks to protect forests in the eastern United States. Then, we used hypothetical conservation plans to simulate the types of ex ante priorities that an organization could use to prioritize areas for protection. We found the apparent effectiveness of cost and benefit targeting depended on the spatial grain of the data used when prioritizing parcels based on local species richness. However, when accounting for complementarity, benefit targeting consistently was more efficient than a cost targeting strategy regardless of the spatial grain of the data involved. More pertinently for other studies, we found that combining data collected over different spatial grains inflated the apparent effectiveness of a cost targeting strategy and led to overestimation of the efficiency gain offered by adopting a more integrative return-on-investment approach. © 2014 Society for Conservation Biology.

GeoCREV: veterinary geographical information system and the development of a practical sub-national spatial data infrastructure

Directory of Open Access Journals (Sweden)

Nicola Ferrè

2011-05-01

Full Text Available This paper illustrates and discusses the key issues of the geographical information system (GIS developed by the Unit of Veterinary Epidemiology of the Veneto region (CREV, defined according to user needs, spatial data (availability, accessibility and applicability, development, technical aspects, inter-institutional relationships, constraints and policies. GeoCREV, the support system for decision-making, was designed to integrate geographic information and veterinary laboratory data with the main aim to develop a sub-national, spatial data infrastructure (SDI for the veterinary services of the Veneto region in north-eastern Italy. Its implementation required (i collection of data and information; (ii building a geodatabase; and (iii development of a WebGIS application. Tools for the management, collection, validation and dissemination of the results (public access and limited access were developed. The modular concept facilitates the updating and development of the system according to user needs and data availability. The GIS management practices that were followed to develop the system are outlined, followed by a detailed discussion of the key elements of the GIS implementation process (data model, technical aspects, inter-institutional relationship, user dimension and institutional framework. Problems encountered in organising the non-spatial data and the future work directions are also described.

GeoCREV: veterinary geographical information system and the development of a practical sub-national spatial data infrastructure.

Science.gov (United States)

Ferrè, Nicola; Mulatti, Paolo; Mazzucato, Matteo; Lorenzetto, Monica; Trolese, Matteo; Pandolfo, Dario; Vio, Piero; Sitta, Guido; Marangon, Stefano

2011-05-01

This paper illustrates and discusses the key issues of the geographical information system (GIS) developed by the Unit of Veterinary Epidemiology of the Veneto region (CREV), defined according to user needs, spatial data (availability, accessibility and applicability), development, technical aspects, inter-institutional relationships, constraints and policies. GeoCREV, the support system for decision-making, was designed to integrate geographic information and veterinary laboratory data with the main aim to develop a sub-national, spatial data infrastructure (SDI) for the veterinary services of the Veneto region in north-eastern Italy. Its implementation required (i) collection of data and information; (ii) building a geodatabase; and (iii) development of a WebGIS application. Tools for the management, collection, validation and dissemination of the results (public access and limited access) were developed. The modular concept facilitates the updating and development of the system according to user needs and data availability. The GIS management practices that were followed to develop the system are outlined, followed by a detailed discussion of the key elements of the GIS implementation process (data model, technical aspects, inter-institutional relationship, user dimension and institutional framework). Problems encountered in organising the non-spatial data and the future work directions are also described.

About the use of spatial interpolation methods to denoising Moroccan resistivity data phosphate “disturbances” map

Directory of Open Access Journals (Sweden)

Mahacine Amrani

2008-06-01

Full Text Available Several methods are currently used to optimize edges and contours of geophysical data maps. A resistivity map was expectedto allow the electrical resistivity signal to be imaged in 2D in Moroccan resistivity survey in the phosphate mining domain. Anomalouszones of phosphate deposit “disturbances” correspond to resistivity anomalies. The resistivity measurements were taken at 5151discrete locations. Much of the geophysical spatial analysis requires a continuous data set and this study is designed to create that surface. This paper identifies the best spatial interpolation method to use for the creation of continuous data for Moroccan resistivity data of phosphate “disturbances” zones. The effectiveness of our approach for successfully reducing noise has been used much successin the analysis of stationary geophysical data as resistivity data. The interpolation filtering approach methods applied to modelingsurface phosphate “disturbances” was found to be consistently useful.

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

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

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...

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.

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.

Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang; Dai, Xiaoxiao; Gao, David Wenzhong; Zhang, Jun Jason; Zhang, Yingchen; Muljadi, Eduard

2016-09-01

An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized in the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.

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