Continuous-Time Modeling with Spatial Dependence
Oud, J.H.L.; Folmer, H.; Patuelli, R.; Nijkamp, P.
2012-01-01
(Spatial) panel data are routinely modeled in discrete time (DT). However, compelling arguments exist for continuous-time (CT) modeling of (spatial) panel data. Particularly, most social processes evolve in CT, so that statistical analysis in DT is an oversimplification, gives an incomplete
Continuous-Time Modeling with Spatial Dependence
Oud, J.; Folmer, H.; Patuelli, R.; Nijkamp, P.
(Spatial) panel data are routinely modeled in discrete time (DT). However, compelling arguments exist for continuous-time (CT) modeling of (spatial) panel data. Particularly, most social processes evolve in CT, so that statistical analysis in DT is an oversimplification, gives an incomplete
Capturing Multivariate Spatial Dependence: Model, Estimate and then Predict
Cressie, Noel; Burden, Sandy; Davis, Walter; Krivitsky, Pavel N.; Mokhtarian, Payam; Suesse, Thomas; Zammit-Mangion, Andrew
2015-01-01
Physical processes rarely occur in isolation, rather they influence and interact with one another. Thus, there is great benefit in modeling potential dependence between both spatial locations and different processes. It is the interaction between these two dependencies that is the focus of Genton and Kleiber's paper under discussion. We see the problem of ensuring that any multivariate spatial covariance matrix is nonnegative definite as important, but we also see it as a means to an end. Tha...
A Structural Equation Approach to Models with Spatial Dependence
Oud, J.H.L.; Folmer, H.
2008-01-01
We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it poss
A structural equation approach to models with spatial dependence
Oud, J.H.L.; Folmer, H.
2008-01-01
We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it poss
Panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable
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
Spatially dependent polya tree modeling for survival data.
Zhao, Luping; Hanson, Timothy E
2011-06-01
With the proliferation of spatially oriented time-to-event data, spatial modeling in the survival context has received increased recent attention. A traditional way to capture a spatial pattern is to introduce frailty terms in the linear predictor of a semiparametric model, such as proportional hazards or accelerated failure time. We propose a new methodology to capture the spatial pattern by assuming a prior based on a mixture of spatially dependent Polya trees for the baseline survival in the proportional hazards model. Thanks to modern Markov chain Monte Carlo (MCMC) methods, this approach remains computationally feasible in a fully hierarchical Bayesian framework. We compare the spatially dependent mixture of Polya trees (MPT) approach to the traditional spatial frailty approach, and illustrate the usefulness of this method with an analysis of Iowan breast cancer survival data from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute. Our method provides better goodness of fit over the traditional alternatives as measured by log pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and full sample score (FSS) statistics. © 2010, The International Biometric Society.
Spatial scale dependency of the modelled climatic response to deforestation
Longobardi, P.; Montenegro, A.; H. Beltrami; M. Eby
2012-01-01
Deforestation is associated with increased atmospheric CO2 and alterations to the surface energy and mass balances that can lead to local and global climate changes. Previous modelling studies show that the global surface air temperature (SAT) response to deforestation depends on latitude, with most simulations showing that high latitude deforestation results in cooling, low latitude deforestation causes warming and that the mid latitude response is mixed. T...
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Ming He
2015-11-01
Full Text Available We propose a random effects panel data model with both spatially correlated error components and spatially lagged dependent variables. We focus on diagnostic testing procedures and derive Lagrange multiplier (LM test statistics for a variety of hypotheses within this model. We first construct the joint LM test for both the individual random effects and the two spatial effects (spatial error correlation and spatial lag dependence. We then provide LM tests for the individual random effects and for the two spatial effects separately. In addition, in order to guard against local model misspecification, we derive locally adjusted (robust LM tests based on the Bera and Yoon principle (Bera and Yoon, 1993. We conduct a small Monte Carlo simulation to show the good finite sample performances of these LM test statistics and revisit the cigarette demand example in Baltagi and Levin (1992 to illustrate our testing procedures.
Spatial dependence of entanglement renormalization in XY model
Usman, M.; Ilyas, Asif; Khan, Khalid
2017-09-01
In this article, a comparative study of the renormalization of entanglement in one-, two- and three-dimensional space and its relation with quantum phase transition (QPT) near the critical point is presented by implementing the quantum renormalization group (QRG) method using numerical techniques. Adopting the Kadanoff's block approach, numerical results for the concurrence are obtained for the spin {-}1/2 XY model in all the spatial dimensions. The results show similar qualitative behavior as we move from the lower to the higher dimensions in space, but the number of iterations reduces for achieving the QPT in the thermodynamic limit. We find that in the two-dimensional and three-dimensional spin {-}1/2 XY model, maximum value of the concurrence reduce by the factor of 1 / n (n=2,3) with reference to the maximum value of one-dimensional case. Moreover, we study the scaling behavior and the entanglement exponent. We compare the results for one-, two- and three-dimensional cases and illustrate how the system evolves near the critical point.
Liu, Quan-Xing; Jin, Zhen
2006-01-01
Results are reported concerning the formation of spatial patterns in the two-species ratio-dependent predator-prey model driven by spatial colored-noise. The results show that there is a critical value with respect to the intensity of spatial noise for this system when the parameters are in the Turing space, above which the regular spatial patterns appear in two dimensions, but under which there are not regular spatial patterns produced. In particular, we investigate in two-dimensional space ...
Spatial scale dependency of the modelled climatic response to deforestation
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P. Longobardi
2012-10-01
Full Text Available Deforestation is associated with increased atmospheric CO_{2} and alterations to the surface energy and mass balances that can lead to local and global climate changes. Previous modelling studies show that the global surface air temperature (SAT response to deforestation depends on latitude, with most simulations showing that high latitude deforestation results in cooling, low latitude deforestation causes warming and that the mid latitude response is mixed. These earlier conclusions are based on simulated large scale land cover change, with complete removal of trees from whole latitude bands. Using a global climate model we determine effects of removing fractions of 5% to 100% of forested areas in the high, mid and low latitudes. All high latitude deforestation scenarios reduce mean global SAT, the opposite occurring for low latitude deforestation, although a decrease in SAT is registered over low latitude deforested areas. Mid latitude SAT response is mixed. For all simulations deforested areas tend to become drier and have lower surface air temperature, although soil temperatures increase over deforested mid and low latitude grid cells. For high latitude deforestation fractions of 45% and above, larger net primary productivity, in conjunction with colder and drier conditions after deforestation, cause an increase in soil carbon large enough to generate a previously not reported net drawdown of CO_{2} from the atmosphere. Our results support previous indications of the importance of changes in cloud cover in the modelled temperature response to deforestation at low latitudes. They also show the complex interaction between soil carbon dynamics and climate and the role this plays on the climatic response to land cover change.
The importance of spatial models for estimating the strength of density dependence
DEFF Research Database (Denmark)
Thorson, James T.; Skaug, Hans J.; Kristensen, Kasper;
2014-01-01
Identifying the existence and magnitude of density dependence is one of the oldest concerns in ecology. Ecologists have aimed to estimate density dependence in population and community data by fitting a simple autoregressive (Gompertz) model for density dependence to time series of abundance...... for an entire population. However, it is increasingly recognized that spatial heterogeneity in population densities has implications for population and community dynamics. We therefore adapt the Gompertz model to approximate local densities over continuous space instead of population-wide abundance......, and to allow productivity to vary spatially. Using simulated data generated from a spatial model, we show that the conventional (nonspatial) Gompertz model will result in biased estimates of density dependence, e.g., identifying oscillatory dynamics when not present. By contrast, the spatial Gompertz model...
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B. Li
2012-09-01
Full Text Available Past studies on soil moisture spatial variability have been mainly conducted in catchment scales where soil moisture is often sampled over a short time period. Because of limited climate and weather conditions, the observed soil moisture often exhibited smaller dynamic ranges which prevented the complete revelation of soil moisture spatial variability as a function of mean soil moisture. In this study, spatial statistics (mean, spatial variability and skewness of in situ soil moisture measurements (from a continuously monitored network across the US, modeled and satellite retrieved soil moisture obtained in a warm season (198 days were examined at large extent scales (>100 km over three different climate regions. The investigation on in situ measurements revealed that their spatial moments strongly depend on climates, with distinct mean, spatial variability and skewness observed in each climate zone. In addition, an upward convex shape, which was revealed in several smaller scale studies, was observed for the relationship between spatial variability of in situ soil moisture and its spatial mean across dry, intermediate, and wet climates. These climate specific features were vaguely or partially observable in modeled and satellite retrieved soil moisture estimates, which is attributed to the fact that these two data sets do not have climate specific and seasonal sensitive mean soil moisture values, in addition to lack of dynamic ranges. From the point measurements to satellite retrievals, soil moisture spatial variability decreased in each climate region. The three data sources all followed the power law in the scale dependency of spatial variability, with coarser resolution data showing stronger scale dependency than finer ones. The main findings from this study are: (1 the statistical distribution of soil moisture depends on spatial mean soil moisture values and thus need to be derived locally within any given area; (2 the boundedness of soil
Spatial dependence of extreme rainfall
Radi, Noor Fadhilah Ahmad; Zakaria, Roslinazairimah; Satari, Siti Zanariah; Azman, Muhammad Az-zuhri
2017-05-01
This study aims to model the spatial extreme daily rainfall process using the max-stable model. The max-stable model is used to capture the dependence structure of spatial properties of extreme rainfall. Three models from max-stable are considered namely Smith, Schlather and Brown-Resnick models. The methods are applied on 12 selected rainfall stations in Kelantan, Malaysia. Most of the extreme rainfall data occur during wet season from October to December of 1971 to 2012. This period is chosen to assure the available data is enough to satisfy the assumption of stationarity. The dependence parameters including the range and smoothness, are estimated using composite likelihood approach. Then, the bootstrap approach is applied to generate synthetic extreme rainfall data for all models using the estimated dependence parameters. The goodness of fit between the observed extreme rainfall and the synthetic data is assessed using the composite likelihood information criterion (CLIC). Results show that Schlather model is the best followed by Brown-Resnick and Smith models based on the smallest CLIC's value. Thus, the max-stable model is suitable to be used to model extreme rainfall in Kelantan. The study on spatial dependence in extreme rainfall modelling is important to reduce the uncertainties of the point estimates for the tail index. If the spatial dependency is estimated individually, the uncertainties will be large. Furthermore, in the case of joint return level is of interest, taking into accounts the spatial dependence properties will improve the estimation process.
Time-dependent solutions of the spatially implicit neutral model of biodiversity.
Chisholm, Ryan A
2011-09-01
Previous research into the neutral theory of biodiversity has focused mainly on equilibrium solutions rather than time-dependent solutions. Understanding the time-dependent solutions is essential for applying neutral theory to ecosystems in which time-dependent processes, such as succession and invasion, are driving the dynamics. Time-dependent solutions also facilitate tests against data that are stronger than those based on static equilibrium patterns. Here I investigate the time-dependent solutions of the classic spatially implicit neutral model, in which a small local community is coupled to a much larger metacommunity through immigration. I present explicit general formulas for the eigenvalues, left eigenvectors and right eigenvectors of the models's transition matrix. The time-dependent solutions can then be expressed in terms of these eigenvalues and eigenvectors. Some of these results are translated directly from existing results for the classic Moran model of population genetics (the Moran model is equivalent to the spatially implicit neutral model after a reparameterization); others of the results are new. I demonstrate that the asymptotic time-dependent solution corresponding to just these first two eigenvectors can be a good approximation to the full time-dependent solution. I also demonstrate the feasibility of a partial eigendecomposition of the transition matrix, which facilitates direct application of the results to a biologically relevant example in which a newly invading species is initially present in the metacommunity but absent from the local community.
Gowrishankar, T R; Stewart, Donald A; Martin, Gregory T; Weaver, James C
2004-11-17
Investigation of bioheat transfer problems requires the evaluation of temporal and spatial distributions of temperature. This class of problems has been traditionally addressed using the Pennes bioheat equation. Transport of heat by conduction, and by temperature-dependent, spatially heterogeneous blood perfusion is modeled here using a transport lattice approach. We represent heat transport processes by using a lattice that represents the Pennes bioheat equation in perfused tissues, and diffusion in nonperfused regions. The three layer skin model has a nonperfused viable epidermis, and deeper regions of dermis and subcutaneous tissue with perfusion that is constant or temperature-dependent. Two cases are considered: (1) surface contact heating and (2) spatially distributed heating. The model is relevant to the prediction of the transient and steady state temperature rise for different methods of power deposition within the skin. Accumulated thermal damage is estimated by using an Arrhenius type rate equation at locations where viable tissue temperature exceeds 42 degrees C. Prediction of spatial temperature distributions is also illustrated with a two-dimensional model of skin created from a histological image. The transport lattice approach was validated by comparison with an analytical solution for a slab with homogeneous thermal properties and spatially distributed uniform sink held at constant temperatures at the ends. For typical transcutaneous blood gas sensing conditions the estimated damage is small, even with prolonged skin contact to a 45 degrees C surface. Spatial heterogeneity in skin thermal properties leads to a non-uniform temperature distribution during a 10 GHz electromagnetic field exposure. A realistic two-dimensional model of the skin shows that tissue heterogeneity does not lead to a significant local temperature increase when heated by a hot wire tip. The heat transport system model of the skin was solved by exploiting the mathematical
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Martin Gregory T
2004-11-01
Full Text Available Abstract Background Investigation of bioheat transfer problems requires the evaluation of temporal and spatial distributions of temperature. This class of problems has been traditionally addressed using the Pennes bioheat equation. Transport of heat by conduction, and by temperature-dependent, spatially heterogeneous blood perfusion is modeled here using a transport lattice approach. Methods We represent heat transport processes by using a lattice that represents the Pennes bioheat equation in perfused tissues, and diffusion in nonperfused regions. The three layer skin model has a nonperfused viable epidermis, and deeper regions of dermis and subcutaneous tissue with perfusion that is constant or temperature-dependent. Two cases are considered: (1 surface contact heating and (2 spatially distributed heating. The model is relevant to the prediction of the transient and steady state temperature rise for different methods of power deposition within the skin. Accumulated thermal damage is estimated by using an Arrhenius type rate equation at locations where viable tissue temperature exceeds 42°C. Prediction of spatial temperature distributions is also illustrated with a two-dimensional model of skin created from a histological image. Results The transport lattice approach was validated by comparison with an analytical solution for a slab with homogeneous thermal properties and spatially distributed uniform sink held at constant temperatures at the ends. For typical transcutaneous blood gas sensing conditions the estimated damage is small, even with prolonged skin contact to a 45°C surface. Spatial heterogeneity in skin thermal properties leads to a non-uniform temperature distribution during a 10 GHz electromagnetic field exposure. A realistic two-dimensional model of the skin shows that tissue heterogeneity does not lead to a significant local temperature increase when heated by a hot wire tip. Conclusions The heat transport system model of the
A Self-consistent and Spatially Dependent Model of the Multiband Emission of Pulsar Wind Nebulae
Lu, Fang-Wu; Gao, Quan-Gui; Zhang, Li
2017-01-01
A self-consistent and spatially dependent model is presented to investigate the multiband emission of pulsar wind nebulae (PWNe). In this model, a spherically symmetric system is assumed and the dynamical evolution of the PWN is included. The processes of convection, diffusion, adiabatic loss, radiative loss, and photon–photon pair production are taken into account in the electron’s evolution equation, and the processes of synchrotron radiation, inverse Compton scattering, synchrotron self-absorption, and pair production are included for the photon’s evolution equation. Both coupled equations are simultaneously solved. The model is applied to explain observed results of the PWN in MSH 15–52. Our results show that the spectral energy distributions (SEDs) of both electrons and photons are all a function of distance. The observed photon SED of MSH 15–52 can be well reproduced in this model. With the parameters obtained by fitting the observed SED, the spatial variations of photon index and surface brightness observed in the X-ray band can also be well reproduced. Moreover, it can be derived that the present-day diffusion coefficient of MSH 15–52 at the termination shock is {κ }0=6.6× {10}24 {{cm}}2 {{{s}}}-1, the spatial average has a value of \\bar{κ }=1.4× {10}25 {{cm}}2 {{{s}}}-1, and the present-day magnetic field at the termination shock has a value of {B}0=26.6 μ {{G}} and the spatial averaged magnetic field is \\bar{B}=14.9 μ {{G}}. The spatial changes of the spectral index and surface brightness at different bands are predicted.
Modeling spatial-temporal operations with context-dependent associative memories.
Mizraji, Eduardo; Lin, Juan
2015-10-01
We organize our behavior and store structured information with many procedures that require the coding of spatial and temporal order in specific neural modules. In the simplest cases, spatial and temporal relations are condensed in prepositions like "below" and "above", "behind" and "in front of", or "before" and "after", etc. Neural operators lie beneath these words, sharing some similarities with logical gates that compute spatial and temporal asymmetric relations. We show how these operators can be modeled by means of neural matrix memories acting on Kronecker tensor products of vectors. The complexity of these memories is further enhanced by their ability to store episodes unfolding in space and time. How does the brain scale up from the raw plasticity of contingent episodic memories to the apparent stable connectivity of large neural networks? We clarify this transition by analyzing a model that flexibly codes episodic spatial and temporal structures into contextual markers capable of linking different memory modules.
Smerdon, Jason E.; Coats, Sloan; Ault, Toby R.
2016-03-01
The spatial skill of four climate field reconstruction (CFR) methods is investigated using pseudoproxy experiments (PPEs) based on five last millennium and historical simulations from the Coupled and Paleo Model Intercomparison Projects Phases 5 and 3 (CMIP5/PMIP3) data archives. These simulations are used for the first time in a PPE context, the frameworks of which are constructed to test a recently assembled multiproxy network and multiple CFR techniques. The experiments confirm earlier findings demonstrating consistent methodological performance across the employed methods and spatially dependent reconstruction errors in all of the derived CFRs. Spectral biases in the reconstructed fields demonstrate that CFR methods can alone alter the ratio of spectral power at all locations in the field, independent of whether there are any spectral biases inherent in the underlying pseudoproxy series. The patterns of spectral biases are model dependent and indicate the potential for regions in the derived CFRs to be biased by changes in either low or high-frequency spectral power. CFR methods are also shown to alter the pattern of mean differences in the tropical Pacific during the Medieval Climate Anomaly and the Little Ice Age, with some model experiments indicating that CFR methodologies enhance the statistical likelihood of achieving larger mean differences between independent 300-year periods in the region. All of the characteristics of CFR performance are model dependent, indicating that CFR methods must be evaluated across multiple models and that conclusions from PPEs should be carefully connected to the spatial statistics of real-world climatic fields.
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Łaszkiewicz Edyta
2014-12-01
Full Text Available As is well known, ignoring spatial heterogeneity leads to biased parameter estimates, while omitting the spatial lag of a dependent variable results in biasness and inconsistency (Anselin, 1988. However, the common approach to analysing households’ expenditures is to ignore the potential spatial effects and social dependence. In light of this, the aim of this paper is to examine the consequences of omitting the spatial effects as well as social dependence in households’ expenditures.
A spatial interpretation of the density dependence model in industrial demography
van Wissen, L
2004-01-01
In this paper the density dependence model, which was developed in organizational ecology, is compared to the economic-geographical notion of agglomeration economies. There is a basic resemblance: both involve some form of positive feedback between size of the population and growth. The paper explor
A spatial interpretation of the density dependence model in industrial demography
van Wissen, L
2004-01-01
In this paper the density dependence model, which was developed in organizational ecology, is compared to the economic-geographical notion of agglomeration economies. There is a basic resemblance: both involve some form of positive feedback between size of the population and growth. The paper explor
A spatial interpretation of the density dependence model in industrial demography
van Wissen, L
2004-01-01
In this paper the density dependence model, which was developed in organizational ecology, is compared to the economic-geographical notion of agglomeration economies. There is a basic resemblance: both involve some form of positive feedback between size of the population and growth. The paper
Scale-dependent approaches to modeling spatial epidemiology of chronic wasting disease.
Conner, Mary M.; Gross, John E.; Cross, Paul C.; Michael R, Ebinger; Gillies, Robert; Samuel, Michael D.; Miller, Michael W.
2007-01-01
This e-book is the product of a second workshop that was funded and promoted by the United States Geological Survey to enhance cooperation between states for the management of chronic wasting disease (CWD). The first workshop addressed issues surrounding the statistical design and collection of surveillance data for CWD. The second workshop, from which this document arose, followed logically from the first workshop and focused on appropriate methods for analysis, interpretation, and use of CWD surveillance and related epidemiology data. Consequently, the emphasis of this e-book is on modeling approaches to describe and gain insight of the spatial epidemiology of CWD. We designed this e-book for wildlife managers and biologists who are responsible for the surveillance of CWD in their state or agency. We chose spatial methods that are popular or common in the spatial epidemiology literature and evaluated them for their relevance to modeling CWD. Our opinion of the usefulness and relevance of each method was based on the type of field data commonly collected as part of CWD surveillance programs and what we know about CWD biology, ecology, and epidemiology. Specifically, we expected the field data to consist primarily of the infection status of a harvested or culled sample along with its date of collection (not date of infection), location, and demographic status. We evaluated methods in light of the fact that CWD does not appear to spread rapidly through wild populations, relative to more highly contagious viruses, and can be spread directly from animal to animal or indirectly through environmental contamination.
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Hamed Hooshangnejad
2016-06-01
Full Text Available The main purpose of this study is to build a Computational model based on ModelFest dataset which is able to predict contrast sensitivity while it benefits from simplicity, efficiency and accuracy, which makes it suitable for hardware implementation, practical uses, online tests, real-time processes, an improved Standard Observer and retina prostheses. It encompasses several components, and in particular, frequency dependent aperture effect (FDAE which is used for the first time on this dataset, which made the model more accurate and closer to reality. Shortcomings of previous models and the necessity of existence of FDAE for more accuracy led us to develop a new model based on Wavelet Transform that gives us the advantage of speed and the capability to process each frequency channels output. Considering our goal for building an efficient model, we introduce a new formula for modeling contrast sensitivity function, which generates lower RMS error and better timing performance. Eventually, this new model leads to having as yet lowest RMS error and solving the problem of long execution time of prior models and reduces them by almost a factor of twenty.
One-dimensional spatially dependent solute transport in semi ...
African Journals Online (AJOL)
One-dimensional spatially dependent solute transport in semi-infinite porous media: an analytical solution. ... Journal Home > Vol 9, No 4 (2017) > ... In this mathematical model the dispersion coefficient is considered spatially dependent while ...
Modeling for spatial multilevel structural data
Min, Suqin; He, Xiaoqun
2013-03-01
The traditional multilevel model assumed independence between groups. However, the datasets grouped by geographical units often has spatial dependence. The individual is influenced not only by its region but also by the adjacent regions, and level-2 residual distribution assumption of traditional multilevel model is violated. In order to deal with such spatial multilevel data, we introduce spatial statistics and spatial econometric models into multilevel model, and apply spatial parameters and adjacency matrix in traditional level-2 model to reflect the spatial autocorrelation. Spatial lag model express spatial effects. We build spatial multilevel model which consider both multilevel thinking and spatial correlation.
A Classification for a Geostatistical Index of Spatial Dependence
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Enio Júnior Seidel
Full Text Available ABSTRACT: In geostatistical studies, spatial dependence can generally be described by means of the semivariogram or, in complementary form, with a single index followed by its categorization to classify the degree of such dependence. The objective of this study was to construct a categorization for the spatial dependence index (SDI proposed by Seidel and Oliveira (2014 in order to classify spatial variability in terms of weak, moderate, and strong dependence. Theoretical values were constructed from different degrees of spatial dependence, which served as a basis for calculation of the SDI. In view of the form of distribution and SDI descriptive measures, we developed a categorization for posterior classification of spatial dependence, specific to each semivariogram model. The SDI categorization was based on its median and 3rd quartile, allowing us to classify spatial dependence as weak, moderate, or strong. We established that for the spherical semivariogram: SDISpherical (% ≤ 7 % (weak spatial dependence, 7 % 15 % (strong spatial dependence; for the exponential semivariogram: SDIExponential (% ≤ 6 % (weak spatial dependence, 6 % 13 % (strong spatial dependence; and for the Gaussian semivariogram: SDIGaussian (% ≤ 9 % (weak spatial dependence, 9 % 20 % (strong spatial dependence. The proposed categorization allows the user to transform the numerical values calculated for SDI into categories of variability of spatial dependence, with adequate power for explanation and comparison.
Lawson, Andrew B
2002-01-01
Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal ...
Spatially dependent electromagnetically induced transparency
Radwell, Neal; Piccirillo, Bruno; Barnett, Stephen M; Franke-Arnold, Sonja
2014-01-01
Recent years have seen vast progress in the generation and detection of structured light, with potential applications in high capacity optical data storage and continuous variable quantum technologies. Here we measure the transmission of structured light through cold rubidium atoms and observe regions of electromagnetically induced transparency (EIT). We use q-plates to generate a probe beam with azimuthally varying phase and polarisation structure, and its right and left circular polarisation components provide the probe and control of an EIT transition. We observe an azimuthal modulation of the absorption profile that is dictated by the phase and polarisation structure of the probe laser. Conventional EIT systems do not exhibit phase sensitivity. We show, however, that a weak transverse magnetic field closes the EIT transitions, thereby generating phase dependent dark states which in turn lead to phase dependent transparency, in agreement with our measurements.
Are topological relations dependent on the shape of spatial objects?
Institute of Scientific and Technical Information of China (English)
SHI Wenzhong; LIU Kimfung
2005-01-01
Topology is normally considered as independent of shape of spatial objects. This may not necessarily be true in describing relations between spatial objects in GIS. In this paper, we present proof that the topological relations between spatial objects are dependent on the shape of spatial objects. That is, that the topological relations of non-convex sets cannot be deformed to the topological relations of convex sets. The significant theoretical value of this paper is on its findings that topology of spatial objects is shape dependent.This indicates that when we want to describe topological relations between spatial objects in GIS, both topology and the shape of objects need to be considered. As a result, spatial data modeling, query and analysis based on the existing understanding of topology of spatial objects may need re-assessed.
Spatially dependent Kondo effect in Quantum Corrals
Rossi, Enrico; Morr, Dirk K.
2007-03-01
We study the Kondo screening of a single magnetic impurity placed inside a quantum corral consisting of non-magnetic impurities on the surface of a metallic host system. We show that the spatial structure of the corral's eigenmodes leads to a spatially dependent Kondo effect whose signatures are experimentally measurable spatial variations of the Kondo temperature, TK, and of the critical Kondo coupling, Jcr. Moreover we find that the screening of the magnetic impurity is accompanied by the formation of multiple Kondo resonances with characteristic spatial patterns that provide further experimental signatures of the spatially dependent Kondo effect. Our results demonstrate that quantum corrals provide new possibilities to manipulate and explore the Kondo effect.
Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel
2016-07-01
A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of field and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.
Dynamic spatial panels : models, methods, and inferences
Elhorst, J. Paul
This paper provides a survey of the existing literature on the specification and estimation of dynamic spatial panel data models, a collection of models for spatial panels extended to include one or more of the following variables and/or error terms: a dependent variable lagged in time, a dependent
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Marion Inostroza
Full Text Available Cognitive impairment is a major concern in temporal lobe epilepsy (TLE. While different experimental models have been used to characterize TLE-related cognitive deficits, little is known on whether a particular deficit is more associated with the underlying brain injuries than with the epileptic condition per se. Here, we look at the relationship between the pattern of brain damage and spatial memory deficits in two chronic models of TLE (lithium-pilocarpine, LIP and kainic acid, KA from two different rat strains (Wistar and Sprague-Dawley using the Morris water maze and the elevated plus maze in combination with MRI imaging and post-morten neuronal immunostaining. We found fundamental differences between LIP- and KA-treated epileptic rats regarding spatial memory deficits and anxiety. LIP-treated animals from both strains showed significant impairment in the acquisition and retention of spatial memory, and were unable to learn a cued version of the task. In contrast, KA-treated rats were differently affected. Sprague-Dawley KA-treated rats learned less efficiently than Wistar KA-treated animals, which performed similar to control rats in the acquisition and in a probe trial testing for spatial memory. Different anxiety levels and the extension of brain lesions affecting the hippocampus and the amydgala concur with spatial memory deficits observed in epileptic rats. Hence, our results suggest that hippocampal-dependent spatial memory is not necessarily affected in TLE and that comorbidity between spatial deficits and anxiety is more related with the underlying brain lesions than with the epileptic condition per se.
Li, Cheng; Kauffmann, Guinevere; Boerner, Gerhard; Kang, Xi; Wang, Lan; 10.1111/j.1365-2966.2007.11518.x
2008-01-01
By comparing semi-analytic galaxy catalogues with data from the Sloan Digital Sky Survey (SDSS), we show that current galaxy formation models reproduce qualitatively the dependence of galaxy clustering and pairwise peculiar velocities on luminosity, but some subtle discrepancies with the data still remain. The comparisons are carried out by constructing a large set of mock galaxy redshift surveys that have the same selection function as the SDSS Data Release Four (DR4). The mock surveys are based on two sets of semi-analytic catalogues presented by Croton et al. and Kang et al. From the mock catalogues, we measure the redshift-space projected two-point correlation function, the power spectrum, and the pairwise velocity dispersion (PVD) in Fourier space and in configuration space, for galaxies in different luminosity intervals. We then compare these theoretical predictions with the measurements derived from the SDSS DR4. On large scales and for galaxies brighter than L*, both sets of mock catalogues agree well...
Eco-evolutionary population simulation models are powerful new forecasting tools for exploring management strategies for climate change and other dynamic disturbance regimes. Additionally, eco-evo individual-based models (IBMs) are useful for investigating theoretical feedbacks ...
Eco-evolutionary population simulation models are powerful new forecasting tools for exploring management strategies for climate change and other dynamic disturbance regimes. Additionally, eco-evo individual-based models (IBMs) are useful for investigating theoretical feedbacks ...
Balanced Networks of Spiking Neurons with Spatially Dependent Recurrent Connections
Rosenbaum, Robert; Doiron, Brent
2014-04-01
Networks of model neurons with balanced recurrent excitation and inhibition capture the irregular and asynchronous spiking activity reported in cortex. While mean-field theories of spatially homogeneous balanced networks are well understood, a mean-field analysis of spatially heterogeneous balanced networks has not been fully developed. We extend the analysis of balanced networks to include a connection probability that depends on the spatial separation between neurons. In the continuum limit, we derive that stable, balanced firing rate solutions require that the spatial spread of external inputs be broader than that of recurrent excitation, which in turn must be broader than or equal to that of recurrent inhibition. Notably, this implies that network models with broad recurrent inhibition are inconsistent with the balanced state. For finite size networks, we investigate the pattern-forming dynamics arising when balanced conditions are not satisfied. Our study highlights the new challenges that balanced networks pose for the spatiotemporal dynamics of complex systems.
Non-Stationary Dependence Structures for Spatial Extremes
Huser, Raphaël
2016-03-03
Max-stable processes are natural models for spatial extremes because they provide suitable asymptotic approximations to the distribution of maxima of random fields. In the recent past, several parametric families of stationary max-stable models have been developed, and fitted to various types of data. However, a recurrent problem is the modeling of non-stationarity. In this paper, we develop non-stationary max-stable dependence structures in which covariates can be easily incorporated. Inference is performed using pairwise likelihoods, and its performance is assessed by an extensive simulation study based on a non-stationary locally isotropic extremal t model. Evidence that unknown parameters are well estimated is provided, and estimation of spatial return level curves is discussed. The methodology is demonstrated with temperature maxima recorded over a complex topography. Models are shown to satisfactorily capture extremal dependence.
Building dynamic spatial environmental models
Karssenberg, D.J.
2003-01-01
An environmental model is a representation or imitation of complex natural phenomena that can be discerned by human cognitive processes. This thesis deals with the type of environmental models referred to as dynamic spatial environmental models. The word spatial refers to the geographic domain whi
Continuous Spatial Process Models for Spatial Extreme Values
Sang, Huiyan
2010-01-28
We propose a hierarchical modeling approach for explaining a collection of point-referenced extreme values. In particular, annual maxima over space and time are assumed to follow generalized extreme value (GEV) distributions, with parameters μ, σ, and ξ specified in the latent stage to reflect underlying spatio-temporal structure. The novelty here is that we relax the conditionally independence assumption in the first stage of the hierarchial model, an assumption which has been adopted in previous work. This assumption implies that realizations of the the surface of spatial maxima will be everywhere discontinuous. For many phenomena including, e. g., temperature and precipitation, this behavior is inappropriate. Instead, we offer a spatial process model for extreme values that provides mean square continuous realizations, where the behavior of the surface is driven by the spatial dependence which is unexplained under the latent spatio-temporal specification for the GEV parameters. In this sense, the first stage smoothing is viewed as fine scale or short range smoothing while the larger scale smoothing will be captured in the second stage of the modeling. In addition, as would be desired, we are able to implement spatial interpolation for extreme values based on this model. A simulation study and a study on actual annual maximum rainfall for a region in South Africa are used to illustrate the performance of the model. © 2009 International Biometric Society.
Regulation mechanisms in spatial stochastic development models
Finkelshtein, Dmitri
2008-01-01
The aim of this paper is to analyze different regulation mechanisms in spatial continuous stochastic development models. We describe the density behavior for models with global mortality and local establishment rates. We prove that the local self-regulation via a competition mechanism (density dependent mortality) may suppress a unbounded growth of the averaged density if the competition kernel is superstable.
Thermodynamic Model of Spatial Memory
Kaufman, Miron; Allen, P.
1998-03-01
We develop and test a thermodynamic model of spatial memory. Our model is an application of statistical thermodynamics to cognitive science. It is related to applications of the statistical mechanics framework in parallel distributed processes research. Our macroscopic model allows us to evaluate an entropy associated with spatial memory tasks. We find that older adults exhibit higher levels of entropy than younger adults. Thurstone's Law of Categorical Judgment, according to which the discriminal processes along the psychological continuum produced by presentations of a single stimulus are normally distributed, is explained by using a Hooke spring model of spatial memory. We have also analyzed a nonlinear modification of the ideal spring model of spatial memory. This work is supported by NIH/NIA grant AG09282-06.
Directory of Open Access Journals (Sweden)
Chun-Sing Lam
Full Text Available Chronic intermittent hypoxia (CIH is a hallmark of obstructive sleep apnea (OSA, which induces hippocampal injuries mediated by oxidative stress. This study aims to examine the neuroprotective mechanism of Lycium barbarum polysaccharides (LBP against CIH-induced spatial memory deficits. Adult Sprague-Dawley rats were exposed to hypoxic treatment resembling a severe OSA condition for a week. The animals were orally fed with LBP solution (1 mg/kg daily 2 hours prior to hypoxia or in air for the control. The effect of LBP on the spatial memory and levels of oxidative stress, inflammation, endoplasmic reticulum (ER stress, apoptosis and neurogenesis in the hippocampus was examined. There was a significant deficit in the spatial memory and an elevated level of malondialdehyde with a decreased expression of antioxidant enzymes (SOD, GPx-1 in the hypoxic group when compared with the normoxic control. In addition, redox-sensitive nuclear factor kappa B (NFКB canonical pathway was activated with a translocation of NFКB members (p65, p50 and increased expression levels of NFКB-dependent inflammatory cytokines and mediator (TNFα, IL-1β, COX-2; also, a significantly elevated level of ER stress (GRP78/Bip, PERK, CHOP and autophagic flux in the hypoxic group, leading to neuronal apoptosis in hippocampal subfields (DG, CA1, CA3. Remarkably, LBP administration normalized the elevated level of oxidative stress, neuroinflammation, ER stress, autophagic flux and apoptosis induced by hypoxia. Moreover, LBP significantly mitigated both the caspase-dependent intrinsic (Bax, Bcl2, cytochrome C, cleaved caspase-3 and extrinsic (FADD, cleaved caspase-8, Bid signaling apoptotic cascades. Furthermore, LBP administration prevented the spatial memory deficit and enhanced the hippocampal neurogenesis induced by hypoxia. Our results suggest that LBP is neuroprotective against CIH-induced hippocampal-dependent spatial memory deficits by promoting hippocampal neurogenesis
Experience-dependent spatial expectations in mouse visual cortex
DEFF Research Database (Denmark)
Fiser, Aris; Mahringer, David; Oyibo, Hassana K.
2016-01-01
In generative models of brain function, internal representations are used to generate predictions of sensory input, yet little is known about how internal models influence sensory processing. Here we show that, with experience in a virtual environment, the activity of neurons in layer 2/3 of mouse...... primary visual cortex (V1) becomes increasingly informative of spatial location. We found that a subset of V1 neurons exhibited responses that were predictive of the upcoming visual stimulus in a spatially dependent manner and that the omission of an expected stimulus drove strong responses in V1....... Stimulus-predictive responses also emerged in V1-projecting anterior cingulate cortex axons, suggesting that anterior cingulate cortex serves as a source of predictions of visual input to V1. These findings are consistent with the hypothesis that visual cortex forms an internal representation of the visual...
Spatial Dependence of Crime in Monterrey, Mexico
Directory of Open Access Journals (Sweden)
Ernesto Aguayo Téllez
2014-06-01
Full Text Available This paper studies the impact that the characteristics of the environment have on crime using neighborhood aggregate data of the Monterrey Metropolitan Area for the year 2010. Data spatial autocorrelation is corroborated, i.e. neighborhoods with high crime rates have a positive impact on the crime rates of its surrounding neighborhoods. Once it was controlled through the bias caused by spatial autocorrelation and data censoring, it is evidenced that the likelihood of being a crime victim and the probability of becoming an offender is positively related to variables such as unemployment, the percentage of young men and the existence of schools, hospitals or markets in the neighborhood.
Performance of Information Criteria for Spatial Models.
Lee, Hyeyoung; Ghosh, Sujit K
2009-01-01
Model choice is one of the most crucial aspect in any statistical data analysis. It is well known that most models are just an approximation to the true data generating process but among such model approximations it is our goal to select the "best" one. Researchers typically consider a finite number of plausible models in statistical applications and the related statistical inference depends on the chosen model. Hence model comparison is required to identify the "best" model among several such candidate models. This article considers the problem of model selection for spatial data. The issue of model selection for spatial models has been addressed in the literature by the use of traditional information criteria based methods, even though such criteria have been developed based on the assumption of independent observations. We evaluate the performance of some of the popular model selection critera via Monte Carlo simulation experiments using small to moderate samples. In particular, we compare the performance of some of the most popular information criteria such as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Corrected AIC (AICc) in selecting the true model. The ability of these criteria to select the correct model is evaluated under several scenarios. This comparison is made using various spatial covariance models ranging from stationary isotropic to nonstationary models.
Competition in spatial location models
Webers, H.M.
1996-01-01
Models of spatial competition are designed and analyzed to describe the fact that space, by its very nature, is a source of market power. This field of research, lying at the interface of game theory and economics, has attracted much interest because location problems are related to many aspects of
Competition in spatial location models
Webers, H.M.
1996-01-01
Models of spatial competition are designed and analyzed to describe the fact that space, by its very nature, is a source of market power. This field of research, lying at the interface of game theory and economics, has attracted much interest because location problems are related to many aspects of
Kernel parameter dependence in spatial factor analysis
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg
2010-01-01
feature space via the kernel function and then performing a linear analysis in that space. In this paper we shall apply a kernel version of maximum autocorrelation factor (MAF) [7, 8] analysis to irregularly sampled stream sediment geochemistry data from South Greenland and illustrate the dependence...
Spatially explicit non-Mendelian diploid model
Lanchier, N; 10.1214/09-AAP598
2009-01-01
We introduce a spatially explicit model for the competition between type $a$ and type $b$ alleles. Each vertex of the $d$-dimensional integer lattice is occupied by a diploid individual, which is in one of three possible states or genotypes: $aa$, $ab$ or $bb$. We are interested in the long-term behavior of the gene frequencies when Mendel's law of segregation does not hold. This results in a voter type model depending on four parameters; each of these parameters measures the strength of competition between genes during meiosis. We prove that with or without a spatial structure, type $a$ and type $b$ alleles coexist at equilibrium when homozygotes are poor competitors. The inclusion of a spatial structure, however, reduces the parameter region where coexistence occurs.
Towards a taxonomy of spatial scale-dependence
DEFF Research Database (Denmark)
Sandel, Brody Steven
2015-01-01
Spatial scale-dependence is a ubiquitous feature of ecological systems. This presents a challenge for ecologists who seek to discern general principles. A solution is to search for generalities in patterns of scale-dependence – that is, what kinds of things are scale-dependent, in what ways, and ...
Factor Copula Models for Replicated Spatial Data
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.
Energy Technology Data Exchange (ETDEWEB)
Qin, Zhangcai; Dunn, Jennifer B.; Kwon, Ho-young; Mueller, Steffen; Wander, Michelle M.
2016-01-01
Converting land to biofuel feedstock production incurs changes in soil organic carbon (SOC) that can influence biofuel life-cycle greenhouse gas (GHG) emissions. Estimates of these land use change (LUC) and life-cycle GHG emissions affect biofuels’ attractiveness and eligibility under a number of renewable fuel policies in the U.S. and abroad. Modeling was used to refine the spatial resolution and depth-extent of domestic estimates of SOC change for land (cropland, cropland pasture, grasslands, and forests) conversion scenarios to biofuel crops (corn, corn stover, switchgrass, Miscanthus, poplar, and willow). In most regions, conversions from cropland and cropland pasture to biofuel crops led to neutral or small levels of SOC sequestration, while conversion of grassland and forest generally caused net SOC loss. Results of SOC change were incorporated into the Greenhouse Gases, Regulated Emissions, and Energy use in Transportation (GREET) model to assess their influence on life-cycle GHG emissions for the biofuels considered. Total LUC GHG emissions (g CO2eq MJ-1) were 2.1–9.3 for corn, -0.7 for corn stover, -3.4–12.9 for switchgrass, and -20.1–-6.2 for Miscanthus; these varied with SOC modeling assumptions applied. Extending soil depth from 30 to 100cm affected spatially-explicit SOC change and overall LUC GHG emissions; however the influence on LUC GHG emissions estimates were less significant in corn and corn stover than cellulosic feedstocks. Total life-cycle GHG emissions (g CO2eq MJ-1, 100cm) were estimated to be 59–66 for corn ethanol, 14 for stover ethanol, 18-26 for switchgrass ethanol, and -0.6–-7 for Miscanthus ethanol.
Liu, Lin; Park, Jonghyun; Lin, Xianke; Sastry, Ann Marie; Lu, Wei
2014-12-01
The formation of a SEI layer and its growth cause internal resistance increase and capacity loss, leading to performance degradation of lithium-ion batteries. In order to comprehensively investigate the effects of SEI growth on battery performance, a one-dimensional thermal-electrochemical model was developed. This model is equipped with a growth mechanism of the SEI layer coupled with thermal evolution, based on the diffusional process of the solvent through the SEI layer and the kinetic process at the interface between the solid and liquid phases. The model is able to reveal the effects of diffusivity, reaction kinetics and temperature on SEI layer growth and cell capacity fade. We show that depending on the SEI thickness, the growth can be kinetics-limited or diffusion-limited. With the layer becoming thicker, its growth rate slows down gradually due to increased diffusion resistance. The SEI layer grows faster during charge than discharge due to the difference in the electron flux through the SEI layer and the temperature change during cycling. Temperature rise due to reaction and joule heating accelerates the SEI layer growth, leading to more capacity loss. Our model can provide insights on position-dependent SEI growth rate and be used to guide the strategic monitoring location.
Felder, Stefan; Tauchmann, Harald
2013-02-01
Due to regional competition and patient migration, the efficiency of healthcare provision at the regional level is subject to spatial dependence. We address this issue by applying a spatial autoregressive model to longitudinal data from Germany at the district ('Kreis') level. The empirical model is specified to explain efficiency scores, which we derive through non-parametric order-m efficiency analysis of regional health production. The focus is on the role of health policy of federal states ('Bundesländer') for district efficiency. Regression results reveal significant spatial spillover effects. Notably, accounting for spatial dependence does not decrease but increases the estimated effect of federal states on district efficiency. It appears that genuinely more efficient states are less affected by positive efficiency spillovers, so that taking into account spatial dependence clarifies the importance of health policy at the state level.
Selection of spatial reference frames depends on task's demands
Directory of Open Access Journals (Sweden)
Greeshma Sharma
2016-12-01
Full Text Available Spatial reference frames (SRF are the means of representing spatial relations or locations either in an egocentric coordinate system (centred on navigator or in an allocentric coordinate system (Centred on object. It is necessary to understand when and how spatial representation switches between allocentric and egocentric reference frames in context to spatial tasks. The objective of this study was to explore if the elementary spatial representation does exist, whether it would remain consistent or change under the influence of a task's demand. Also, we explored how the SRF would assist if the environment is enriched with landmarks, having multiple routes for wayfinding. The results showed that the switching of SRF depends not only on the default representation but also on a task's demand. They also demonstrated that participants who were using allocentric representation performed better in the presence of landmarks.
Emergence of Strange Spatial Pattern in a Spatial Epidemic Model
Institute of Scientific and Technical Information of China (English)
SUN Gui-Quan; JIN Zhen; LIU Quan-Xing; LI Li
2008-01-01
Pattern formation of a spatial epidemic model with nonlinear incidence rate kI2 S/ (1 + αI2) is investigated. Our results show that strange spatial dynamics, i.e., filament-like pattern, can be obtained by both mathematical analysis and numerical simulation, which are different from the previous results in the spatial epidemic model such as stripe-like or spotted or coexistence of both pattern and so on. The obtained results well extend the finding of pattern formation in the epidemic model and may well explain the distribution of the infected of some epidemic.
SPATIAL DEPENDENCE STUDY OF Eucalyptus grandis DENDROMETRIC CHARACTERISTICS
Directory of Open Access Journals (Sweden)
José Marcio de Mello
2005-06-01
Full Text Available The knowledge of spatial continuity structure of dendrometric characteristics is crucial in forest inventory, managementand planning. The inclusion of spatial continuity effect in inventory analysis provides stable and safe results that can be used in forestmanagement and planning. This paper evaluated the structure of spatial continuity of four dendrometric characteristics obtainedby two sampling procedures. Data were collected in 987 hectares of Eucalyptus grandis, located in the south of São Paulo State.Two sampling procedures were used, systematic grading and unencumbered systematic. For each plot from the two samples, thefollowing dendrometric characteristics were measured: volume, quadratic average diameter, basal area and average height of thedominant trees. For each one of these characteristics, anisotropic and isotropic semi variogrammes were built, in order toevaluate the spatial continuity structure. The semi variogrammes were built using the moment estimator method. The mainauthorized functions were adjusted to the experimental semi variogrammes, by The Minimum Square Method. The behavior of thespatial continuity was evaluated through the degree of spatial dependence and of the assigned semi variogrammes for the fourcharacteristics, in the appraised sampling methods, respectively. All appraised characteristics presented spatially structured,independently of the appraised sampling procedure. The continuity structure of the four characteristics was isotropic, i.e., thevariance among pairs of points depends on the separation vector h. Therefore, unidirectional semi variogrammes can be built forall appraised characteristics. The results suggest that, in forest inventory, the spatial component should be considered, i.e., plotsshould not be treated separately.
Spatially explicit modeling in ecology: A review
DeAngelis, Donald L.; Yurek, Simeon
2017-01-01
The use of spatially explicit models (SEMs) in ecology has grown enormously in the past two decades. One major advancement has been that fine-scale details of landscapes, and of spatially dependent biological processes, such as dispersal and invasion, can now be simulated with great precision, due to improvements in computer technology. Many areas of modeling have shifted toward a focus on capturing these fine-scale details, to improve mechanistic understanding of ecosystems. However, spatially implicit models (SIMs) have played a dominant role in ecology, and arguments have been made that SIMs, which account for the effects of space without specifying spatial positions, have an advantage of being simpler and more broadly applicable, perhaps contributing more to understanding. We address this debate by comparing SEMs and SIMs in examples from the past few decades of modeling research. We argue that, although SIMs have been the dominant approach in the incorporation of space in theoretical ecology, SEMs have unique advantages for addressing pragmatic questions concerning species populations or communities in specific places, because local conditions, such as spatial heterogeneities, organism behaviors, and other contingencies, produce dynamics and patterns that usually cannot be incorporated into simpler SIMs. SEMs are also able to describe mechanisms at the local scale that can create amplifying positive feedbacks at that scale, creating emergent patterns at larger scales, and therefore are important to basic ecological theory. We review the use of SEMs at the level of populations, interacting populations, food webs, and ecosystems and argue that SEMs are not only essential in pragmatic issues, but must play a role in the understanding of causal relationships on landscapes.
Modeling the spatial reach of the LFP.
Lindén, Henrik; Tetzlaff, Tom; Potjans, Tobias C; Pettersen, Klas H; Grün, Sonja; Diesmann, Markus; Einevoll, Gaute T
2011-12-08
The local field potential (LFP) reflects activity of many neurons in the vicinity of the recording electrode and is therefore useful for studying local network dynamics. Much of the nature of the LFP is, however, still unknown. There are, for instance, contradicting reports on the spatial extent of the region generating the LFP. Here, we use a detailed biophysical modeling approach to investigate the size of the contributing region by simulating the LFP from a large number of neurons around the electrode. We find that the size of the generating region depends on the neuron morphology, the synapse distribution, and the correlation in synaptic activity. For uncorrelated activity, the LFP represents cells in a small region (within a radius of a few hundred micrometers). If the LFP contributions from different cells are correlated, the size of the generating region is determined by the spatial extent of the correlated activity.
Spatial dependencies between large-scale brain networks.
Directory of Open Access Journals (Sweden)
Robert Leech
Full Text Available Functional neuroimaging reveals both increases (task-positive and decreases (task-negative in neural activation with many tasks. Many studies show a temporal relationship between task positive and task negative networks that is important for efficient cognitive functioning. Here we provide evidence for a spatial relationship between task positive and negative networks. There are strong spatial similarities between many reported task negative brain networks, termed the default mode network, which is typically assumed to be a spatially fixed network. However, this is not the case. The spatial structure of the DMN varies depending on what specific task is being performed. We test whether there is a fundamental spatial relationship between task positive and negative networks. Specifically, we hypothesize that the distance between task positive and negative voxels is consistent despite different spatial patterns of activation and deactivation evoked by different cognitive tasks. We show significantly reduced variability in the distance between within-condition task positive and task negative voxels than across-condition distances for four different sensory, motor and cognitive tasks--implying that deactivation patterns are spatially dependent on activation patterns (and vice versa, and that both are modulated by specific task demands. We also show a similar relationship between positively and negatively correlated networks from a third 'rest' dataset, in the absence of a specific task. We propose that this spatial relationship may be the macroscopic analogue of microscopic neuronal organization reported in sensory cortical systems, and that this organization may reflect homeostatic plasticity necessary for efficient brain function.
Yokogawa, D.
2016-09-01
Theoretical approach to design bright bio-imaging molecules is one of the most progressing ones. However, because of the system size and computational accuracy, the number of theoretical studies is limited to our knowledge. To overcome the difficulties, we developed a new method based on reference interaction site model self-consistent field explicitly including spatial electron density distribution and time-dependent density functional theory. We applied it to the calculation of indole and 5-cyanoindole at ground and excited states in gas and solution phases. The changes in the optimized geometries were clearly explained with resonance structures and the Stokes shift was correctly reproduced.
Hippocampus-dependent place learning enables spatial flexibility in mice
Directory of Open Access Journals (Sweden)
Karl R. Kleinknecht
2012-12-01
Full Text Available Spatial navigation is a fundamental capability necessary in everyday life to locate food, social partners and shelter. It results from two very different strategies: (i place learning which enables for flexible way finding and (ii response learning that leads to a more rigid ‘route following’. Despite the importance of knockout techniques that are only available in mice, little is known about mice’ flexibility in spatial navigation tasks.Here we demonstrate for C57BL6/N mice in a water-cross maze that only place learning enables spatial flexibility and relearning of a platform position, whereas response learning does not. This capability depends on an intact hippocampal formation, since hippocampus lesions by ibotenic acid disrupted relearning. In vivo manganese-enhanced magnetic resonance imaging revealed a volume loss of ≥ 60% of the hippocampus as a critical threshold for relearning impairments. In particular the changes in the left ventral hippocampus were indicative of relearning deficits.In summary, our findings establish the importance of hippocampus-dependent place learning for spatial flexibility and provide a first systematic analysis on spatial flexibility in mice.
Spatial-frequency dependent binocular imbalance in amblyopia
Kwon, MiYoung; Wiecek, Emily; Dakin, Steven C.; Bex, Peter J.
2015-01-01
While amblyopia involves both binocular imbalance and deficits in processing high spatial frequency information, little is known about the spatial-frequency dependence of binocular imbalance. Here we examined binocular imbalance as a function of spatial frequency in amblyopia using a novel computer-based method. Binocular imbalance at four spatial frequencies was measured with a novel dichoptic letter chart in individuals with amblyopia, or normal vision. Our dichoptic letter chart was composed of band-pass filtered letters arranged in a layout similar to the ETDRS acuity chart. A different chart was presented to each eye of the observer via stereo-shutter glasses. The relative contrast of the corresponding letter in each eye was adjusted by a computer staircase to determine a binocular Balance Point at which the observer reports the letter presented to either eye with equal probability. Amblyopes showed pronounced binocular imbalance across all spatial frequencies, with greater imbalance at high compared to low spatial frequencies (an average increase of 19%, p amblyopia and as an outcome measure for recovery of binocular vision following therapy. PMID:26603125
Integrated spatial sampling modeling of geospatial data
Institute of Scientific and Technical Information of China (English)
LI Lianfa; WANG Jinfeng
2004-01-01
Spatial sampling is a necessary and important method for extracting geospatial data and its methodology directly affects the geo-analysis results. Counter to the deficiency of separate models of spatial sampling, this article analyzes three crucial elements of spatial sampling (frame, correlation and decision diagram) and induces its general integrated model. The program of Spatial Sampling Integration (SSI) has been developed with Component Object Model (COM) to realize the general integrated model. In two practical applications, i.e. design of the monitoring network of natural disasters and sampling survey of the areas of non-cultivated land, SSI has produced accurate results at less cost, better realizing the cost-effective goal of sampling toward the geo-objects with spatial correlation. The two cases exemplify expanded application and convenient implementation of the general integrated model with inset components in an integrated environment, which can also be extended to other modeling of spatial analysis.
On a time-dependent extra spatial dimension
Kuhfittig, P K F
2006-01-01
In the usual brane-world scenario matter fields are confined to the four-dimensional spacetime, called a 3-brane, embedded in a higher-dimensional space, usually referred to as the bulk spacetime. In this paper we assume that the 3-brane a de Sitter space; there is only one extra spatial dimension, assumed to be time dependent. By using the form of the brane-world energy-momentum tensor suggested by Shiromizu et al. in the five-dimensional Einstein equations, it is shown that whenever the bulk cosmological constant \\Lambda is negative, the extra spatial dimension rapidly shrinks during the inflation of the brane. When \\Lambda>0, on the other hand, the extra spatial dimension either completely follows the cosmological expansion of the brane or completely ignores it. This behavior resembles the all-or-nothing behavior of ordinary systems in an expanding universe, as recently demonstrated by R.H. Price.
Modelling evolution in a spatial continuum
Barton, N. H.; Etheridge, A. M.; Véber, A.
2013-01-01
We survey a class of models for spatially structured populations which we have called spatial Λ-Fleming-Viot processes. They arise from a flexible framework for modelling in which the key innovation is that random genetic drift is driven by a Poisson point process of spatial 'events'. We demonstrate how this overcomes some of the obstructions to modelling populations which evolve in two-(and higher-) dimensional spatial continua, how its predictions match phenomena observed in data and how it fits with classical models. Finally we outline some directions for future research.
Liu, Chang; Li, Feng-Ri; Zhen, Zhen
2014-10-01
Abstract: Based on the data from Chinese National Forest Inventory (CNFI) and Key Ecological Benefit Forest Monitoring plots (5075 in total) in Heilongjiang Province in 2010 and concurrent meteorological data coming from 59 meteorological stations located in Heilongjiang, Jilin and Inner Mongolia, this paper established a spatial error model (SEM) by GeoDA using carbon storage as dependent variable and several independent variables, including diameter of living trees (DBH), number of trees per hectare (TPH), elevation (Elev), slope (Slope), and product of precipitation and temperature (Rain_Temp). Global Moran's I was computed for describing overall spatial autocorrelations of model results at different spatial scales. Local Moran's I was calculated at the optimal bandwidth (25 km) to present spatial distribution residuals. Intra-block spatial variances were computed to explain spatial heterogeneity of residuals. Finally, a spatial distribution map of carbon storage in Heilongjiang was visualized based on predictions. The results showed that the distribution of forest carbon storage in Heilongjiang had spatial effect and was significantly influenced by stand, topographic and meteorological factors, especially average DBH. SEM could solve the spatial autocorrelation and heterogeneity well. There were significant spatial differences in distribution of forest carbon storage. The carbon storage was mainly distributed in Zhangguangcai Mountain, Xiao Xing'an Mountain and Da Xing'an Mountain where dense, forests existed, rarely distributed in Songnen Plains, while Wanda Mountain had moderate-level carbon storage.
Local models for spatial analysis
Lloyd, Christopher D
2010-01-01
Focusing on solutions, this second edition provides guidance to a wide variety of real-world problems. The text presents a complete introduction to key concepts and a clear mapping of the methods discussed. It also explores connections between methods. New chapters address spatial patterning in single variables and spatial relations. In addition, every chapter now includes links to key related studies. The author clearly distinguishes between local and global methods and provides more detailed coverage of geographical weighting, image texture measures, local spatial autocorrelation, and multic
Determining Scale-dependent Patterns in Spatial and Temporal Datasets
Roy, A.; Perfect, E.; Mukerji, T.; Sylvester, L.
2016-12-01
Spatial and temporal datasets of interest to Earth scientists often contain plots of one variable against another, e.g., rainfall magnitude vs. time or fracture aperture vs. spacing. Such data, comprised of distributions of events along a transect / timeline along with their magnitudes, can display persistent or antipersistent trends, as well as random behavior, that may contain signatures of underlying physical processes. Lacunarity is a technique that was originally developed for multiscale analysis of data. In a recent study we showed that lacunarity can be used for revealing changes in scale-dependent patterns in fracture spacing data. Here we present a further improvement in our technique, with lacunarity applied to various non-binary datasets comprised of event spacings and magnitudes. We test our technique on a set of four synthetic datasets, three of which are based on an autoregressive model and have magnitudes at every point along the "timeline" thus representing antipersistent, persistent, and random trends. The fourth dataset is made up of five clusters of events, each containing a set of random magnitudes. The concept of lacunarity ratio, LR, is introduced; this is the lacunarity of a given dataset normalized to the lacunarity of its random counterpart. It is demonstrated that LR can successfully delineate scale-dependent changes in terms of antipersistence and persistence in the synthetic datasets. This technique is then applied to three different types of data: a hundred-year rainfall record from Knoxville, TN, USA, a set of varved sediments from Marca Shale, and a set of fracture aperture and spacing data from NE Mexico. While the rainfall data and varved sediments both appear to be persistent at small scales, at larger scales they both become random. On the other hand, the fracture data shows antipersistence at small scale (within cluster) and random behavior at large scales. Such differences in behavior with respect to scale-dependent changes in
KING GEORGE ISLAND SPATIAL DATA MODEL
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Distribution,interoperability,interactivity,component are four main features of distributed GIS.Based on the principle of hypermap,hypermedia and distributed database,the paper comes up with a kind of distributed spatial data model which is in accordance with those features of distributed GIS.The model takes catalog service as the outline of spatial information globalization,and defines data structure of hypermap node in different level.Based on the model,it is feasible to manage and process distributed spatial information,and integrate multi_source,heterogeneous spatial data into a framework.Traditionally,to retrieve and access spatial data via Internet is only by theme or map name.With the concept of the model,it is possible to retrieve,load,and link spatial data by vector_based graphics on the Internet.
An Improved Direction Relation Detection Model for Spatial Objects
Institute of Scientific and Technical Information of China (English)
FENG Yucai; YI Baolin
2004-01-01
Direction is a common spatial concept that is used in our daily life. It is frequently used as a selection condition in spatial queries. As a result, it is important for spatial databases to provide a mechanism for modeling and processing direction queries and reasoning. Depending on the direction relation matrix, an inverted direction relation matrix and the concept of direction pre- dominance are proposed to improve the detection of direction relation between objects. Direction predicates of spatial systems are also extended. These techniques can improve the veracity of direction queries and reasoning. Experiments show excellent efficiency and performance in view of direction queries.
Bayesian Spatial Modelling with R-INLA
Directory of Open Access Journals (Sweden)
Finn Lindgren
2015-02-01
Full Text Available The principles behind the interface to continuous domain spatial models in the R- INLA software package for R are described. The integrated nested Laplace approximation (INLA approach proposed by Rue, Martino, and Chopin (2009 is a computationally effective alternative to MCMC for Bayesian inference. INLA is designed for latent Gaussian models, a very wide and flexible class of models ranging from (generalized linear mixed to spatial and spatio-temporal models. Combined with the stochastic partial differential equation approach (SPDE, Lindgren, Rue, and Lindstrm 2011, one can accommodate all kinds of geographically referenced data, including areal and geostatistical ones, as well as spatial point process data. The implementation interface covers stationary spatial mod- els, non-stationary spatial models, and also spatio-temporal models, and is applicable in epidemiology, ecology, environmental risk assessment, as well as general geostatistics.
Hörning, Sebastian; Bárdossy, András
2016-04-01
Traditional geostatistical analysis is mainly based on variograms and/or covariance functions. A more advanced investigation of spatially distributed variables can be performed using rank order geostatistical methods. For example the rank correlation function in combination with the asymmetry function gives a more detailed insight in the spatial dependence structure of the data of interest. However, many physical processes, for example advection of solute in porous media, can lead to asymmetries that exhibit a certain direction, i.e. they lead to irreversibility in a spatial context. Reversibility is well known in time series analysis; however it is hardly utilized in geostatistics. Spatial reversibility or directional dependencies can neither be covered by the rank correlation function nor by the classical asymmetry function. Therefore, a statistical test based on a chi-squared test on empirical directional copulas will be introduced that enables testing for spatial reversibility. In order to quantify the strength of directional dependencies a new direction-dependent asymmetry function is introduced. Different examples, ranging from synthetical flow and transport experiments to real-world precipitation data, will be used to demonstrate the applicability of the test and the new measure. The difference to classical anisotropy will be shown and the chi-squared test will also be used to test for significance.
Reference frames in virtual spatial navigation are viewpoint dependent
Directory of Open Access Journals (Sweden)
Ágoston eTörök
2014-09-01
Full Text Available Spatial navigation in the mammalian brain relies on a cognitive map of the environment. Such cognitive maps enable us, for example, to take the optimal route from a given location to a known target. The formation of these maps is naturally influenced by our perception of the environment, meaning it is dependent on factors such as our viewpoint and choice of reference frame. Yet, it is unknown how these factors influence the construction of cognitive maps. Here, we evaluated how various combinations of viewpoints and reference frames affect subjects’ performance when they navigated in a bounded virtual environment without landmarks. We measured both their path length and time efficiency and found that (i ground perspective was associated with egocentric frame of reference, (ii aerial perspective was associated with allocentric frame of reference, (iii there was no appreciable performance difference between first and third person egocentric viewing positions and (iv while none of these effects were dependent on gender, males tended to perform better in general. Our study provides evidence that there are inherent associations between visual perspectives and cognitive reference frames. This result has implications about the mechanisms of path integration in the human brain and may also inspire designs of virtual reality applications. Lastly, we demonstrated the effective use of a tablet PC and spatial navigation tasks for studying spatial and cognitive aspects of human memory.
Spatial dependence of plasma oscillations in Josephson tunnel junctions
DEFF Research Database (Denmark)
Holst, Thorsten; Hansen, Jørn Bindslev
1991-01-01
We report on direct measurements of the plasma oscillations in Josephson tunnel junctions of various spatial dimensions. The effect of the spatial variation of the Cooper-pair phase difference (the Josephson phase) on the dynamics of the junction was investigated by application of a static magnetic...... field threading the tunneling barrier. We compare measurements where the plasma frequency was tuned either by applying a magnetic field or by raising the temperature. A crossover from short- to long-junction behavior of the functional dependence of the plasma oscillations was observed in the case...... of an applied magnetic field. Numerical simulations of the governing partial-differential sine-Gordon equation were performed and compared to the experimental results and a perturbation analysis. The theoretical results support the experiments and allow us to interpret the observed crossover as due...
Bayesian Spatial Modelling with R-INLA
Finn Lindgren; Håvard Rue
2015-01-01
The principles behind the interface to continuous domain spatial models in the R- INLA software package for R are described. The integrated nested Laplace approximation (INLA) approach proposed by Rue, Martino, and Chopin (2009) is a computationally effective alternative to MCMC for Bayesian inference. INLA is designed for latent Gaussian models, a very wide and flexible class of models ranging from (generalized) linear mixed to spatial and spatio-temporal models. Combined with the stochastic...
Binny, Rachelle N; Plank, Michael J; James, Alex
2015-05-06
The ability of cells to undergo collective movement plays a fundamental role in tissue repair, development and cancer. Interactions occurring at the level of individual cells may lead to the development of spatial structure which will affect the dynamics of migrating cells at a population level. Models that try to predict population-level behaviour often take a mean-field approach, which assumes that individuals interact with one another in proportion to their average density and ignores the presence of any small-scale spatial structure. In this work, we develop a lattice-free individual-based model (IBM) that uses random walk theory to model the stochastic interactions occurring at the scale of individual migrating cells. We incorporate a mechanism for local directional bias such that an individual's direction of movement is dependent on the degree of cell crowding in its neighbourhood. As an alternative to the mean-field approach, we also employ spatial moment theory to develop a population-level model which accounts for spatial structure and predicts how these individual-level interactions propagate to the scale of the whole population. The IBM is used to derive an equation for dynamics of the second spatial moment (the average density of pairs of cells) which incorporates the neighbour-dependent directional bias, and we solve this numerically for a spatially homogeneous case.
A Gender- and Sexual Orientation-Dependent Spatial Attentional Effect of Invisible Images
National Research Council Canada - National Science Library
Yi Jiang; Patricia Costello; Fang Fang; Miner Huang; Sheng He
2006-01-01
...) erotic pictures, can direct the distribution of spatial attention. Furthermore, invisible erotic information can either attract or repel observers' spatial attention depending on their gender and sexual orientation...
Evaluating spatial patterns in hydrological modelling
DEFF Research Database (Denmark)
Koch, Julian
of spatial information in a holistic assessment. Opposed, statistical measures typically only address a limited amount of spatial information. A web-based survey and a citizen science project are employed to quantify the collective perceptive skills of humans aiming at benchmarking spatial metrics...... of environmental science, such as meteorology, geostatistics or geography. In total, seven metrics are evaluated with respect to their capability to quantitatively compare spatial patterns. The human visual perception is often considered superior to computer based measures, because it integrates various dimensions...... with respect to their capability to mimic human evaluations. This PhD thesis aims at expanding the standard toolbox of spatial model evaluation with innovative metrics that adequately compare spatial patterns. Driven by the rise of more complex model structures and the increase of suitable remote sensing...
Modeling Dependencies in Critical Infrastructures
Nieuwenhuijs, A.H.; Luiijf, H.A.M.; Klaver, M.H.A.
2009-01-01
This paper describes a model for expressing critical infrastructure dependencies. The model addresses the limitations of existing approaches with respect to clarity of definition, support for quality and the influence of operating states of critical infrastructures and environmental factors.
Learning optimal spatially-dependent regularization parameters in total variation image denoising
Van Chung, Cao; De los Reyes, J. C.; Schönlieb, C. B.
2017-07-01
We consider a bilevel optimization approach in function space for the choice of spatially dependent regularization parameters in TV image denoising models. First- and second-order optimality conditions for the bilevel problem are studied when the spatially-dependent parameter belongs to the Sobolev space {{H}1}≤ft(Ω \\right) . A combined Schwarz domain decomposition-semismooth Newton method is proposed for the solution of the full optimality system and local superlinear convergence of the semismooth Newton method is verified. Exhaustive numerical computations are finally carried out to show the suitability of the approach.
Tonini, A.; Pede, V.
2011-01-01
In this paper, a stochastic frontier model accounting for spatial dependency is developed using generalized maximum entropy estimation. An application is made for measuring total factor productivity in European agriculture. The empirical results show that agricultural productivity growth in Europe i
A neuromorphic model of spatial lookahead planning.
Ivey, Richard; Bullock, Daniel; Grossberg, Stephen
2011-04-01
In order to create spatial plans in a complex and changing world, organisms need to rapidly adapt to novel configurations of obstacles that impede simple routes to goal acquisition. Some animals can mentally create successful multistep spatial plans in new visuo-spatial layouts that preclude direct, one-segment routes to goal acquisition. Lookahead multistep plans can, moreover, be fully developed before an animal executes any step in the plan. What neural computations suffice to yield preparatory multistep lookahead plans during spatial cognition of an obstructed two-dimensional scene? To address this question, we introduce a novel neuromorphic system for spatial lookahead planning in which a feasible sequence of actions is prepared before movement begins. The proposed system combines neurobiologically plausible mechanisms of recurrent shunting competitive networks, visuo-spatial diffusion, and inhibition-of-return. These processes iteratively prepare a multistep trajectory to the desired goal state in the presence of obstacles. The planned trajectory can be stored using a primacy gradient in a sequential working memory and enacted by a competitive queuing process. The proposed planning system is compared with prior planning models. Simulation results demonstrate system robustness to environmental variations. Notably, the model copes with many configurations of obstacles that lead other visuo-spatial planning models into selecting undesirable or infeasible routes. Our proposal is inspired by mechanisms of spatial attention and planning in primates. Accordingly, our simulation results are compared with neurophysiological and behavioral findings from relevant studies of spatial lookahead behavior.
Hierarchical modeling and analysis for spatial data
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
Directory of Open Access Journals (Sweden)
Simone Becker Lopes
2014-04-01
Full Text Available Considering the importance of spatial issues in transport planning, the main objective of this study was to analyze the results obtained from different approaches of spatial regression models. In the case of spatial autocorrelation, spatial dependence patterns should be incorporated in the models, since that dependence may affect the predictive power of these models. The results obtained with the spatial regression models were also compared with the results of a multiple linear regression model that is typically used in trips generation estimations. The findings support the hypothesis that the inclusion of spatial effects in regression models is important, since the best results were obtained with alternative models (spatial regression models or the ones with spatial variables included. This was observed in a case study carried out in the city of Porto Alegre, in the state of Rio Grande do Sul, Brazil, in the stages of specification and calibration of the models, with two distinct datasets.
On spatially explicit models of cholera epidemics
National Research Council Canada - National Science Library
Bertuzzo, E; Casagrandi, R; Gatto, M; Rodriguez-Iturbe, I; Rinaldo, A
2010-01-01
We generalize a recently proposed model for cholera epidemics that accounts for local communities of susceptibles and infectives in a spatially explicit arrangement of nodes linked by networks having...
SIMULATION MODELING SLOW SPATIALLY HETER- OGENEOUS COAGULATION
Directory of Open Access Journals (Sweden)
P. A. Zdorovtsev
2013-01-01
Full Text Available A new model of spatially inhomogeneous coagulation, i.e. formation of larger clusters by joint interaction of smaller ones, is under study. The results of simulation are compared with known analytical and numerical solutions.
Spatial averaging infiltration model for layered soil
Institute of Scientific and Technical Information of China (English)
HU HePing; YANG ZhiYong; TIAN FuQiang
2009-01-01
To quantify the influences of soil heterogeneity on infiltration, a spatial averaging infiltration model for layered soil (SAI model) is developed by coupling the spatial averaging approach proposed by Chen et al. and the Generalized Green-Ampt model proposed by Jia et al. In the SAI model, the spatial heterogeneity along the horizontal direction is described by a probability distribution function, while that along the vertical direction is represented by the layered soils. The SAI model is tested on a typical soil using Monte Carlo simulations as the base model. The results show that the SAI model can directly incorporate the influence of spatial heterogeneity on infiltration on the macro scale. It is also found that the homogeneous assumption of soil hydraulic conductivity along the horizontal direction will overestimate the infiltration rate, while that along the vertical direction will underestimate the infiltration rate significantly during rainstorm periods. The SAI model is adopted in the spatial averaging hydrological model developed by the authors, and the results prove that it can be applied in the macro-scale hydrological and land surface process modeling in a promising way.
Spatial averaging infiltration model for layered soil
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
To quantify the influences of soil heterogeneity on infiltration, a spatial averaging infiltration model for layered soil (SAI model) is developed by coupling the spatial averaging approach proposed by Chen et al. and the Generalized Green-Ampt model proposed by Jia et al. In the SAI model, the spatial hetero- geneity along the horizontal direction is described by a probability distribution function, while that along the vertical direction is represented by the layered soils. The SAI model is tested on a typical soil using Monte Carlo simulations as the base model. The results show that the SAI model can directly incorporate the influence of spatial heterogeneity on infiltration on the macro scale. It is also found that the homogeneous assumption of soil hydraulic conductivity along the horizontal direction will overes- timate the infiltration rate, while that along the vertical direction will underestimate the infiltration rate significantly during rainstorm periods. The SAI model is adopted in the spatial averaging hydrological model developed by the authors, and the results prove that it can be applied in the macro-scale hy- drological and land surface process modeling in a promising way.
Spatial occupancy models for large data sets
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.
Evaluating spatial patterns in hydrological modelling
DEFF Research Database (Denmark)
Koch, Julian
is not fully exploited by current modelling frameworks due to the lack of suitable spatial performance metrics. Furthermore, the traditional model evaluation using discharge is found unsuitable to lay confidence on the predicted catchment inherent spatial variability of hydrological processes in a fully...... the contiguous United Sates (10^6 km2). To this end, the thesis at hand applies a set of spatial performance metrics on various hydrological variables, namely land-surface-temperature (LST), evapotranspiration (ET) and soil moisture. The inspiration for the applied metrics is found in related fields...
The effect of spatial dependence on hazard validation
Iervolino, Iunio; Giorgio, Massimiliano; Cito, Pasquale
2017-03-01
In countries where best-practice probabilistic hazard studies and seismic monitoring networks are available, there is increasing interest in direct validation of hazard maps. It usually means trying to quantitatively understand whether probabilities estimated via hazard analysis are consistent with observed frequencies of exceedance of ground motion intensity thresholds. Because the exceedance events of interest are typically rare with respect to the time span covered by data from seismic networks, a common approach underlying these studies is to pool observations from different sites. The main reason for this is to collect a sample large enough to convincingly perform a statistical analysis. However, this requires accounting for the dependence among the stochastic processes counting exceedances of ground motion intensity measures thresholds at different sites. Neglecting this dependence may lead to potentially fallacious conclusions about inadequateness of probabilistic seismic hazard. This study addresses this issue revisiting a hazard validation exercise for Italy, showing that accounting for this kind of spatial dependence can change the results of formal testing.
Spatial dependencies of wind power and interrelations with spot price dynamics
Energy Technology Data Exchange (ETDEWEB)
Elberg, Christina; Hagspiel, Simeon
2013-06-15
Wind power has seen a strong growth over the last decade. Due to its high intermittency, spot prices have become more volatile and exhibit correlated behavior with wind power fed into the system. In this paper, we develop a stochastic simulation model that incorporates the spatial dependencies of wind power and its interrelations with spot prices: We employ a structural supply and demand based model for the electricity spot price that takes into account stochastic production quantities of wind power. Spatial dependencies are modeled with the help of copulas, thus linking the single turbine wind power to the aggregated wind power in a market. The model is applied to the German electricity market where wind power already today makes up a significant share of total power production. Revenue distributions and the market value of different wind power plants are analyzed. We find that the specific location of the considered wind turbine, i.e. its spatial dependency with respect to the aggregated wind power in the system, is of high relevance for its market value. Many of the analyzed locations show an upper tail dependence that adversely impacts the market value. This effect becomes more important for increasing levels of wind power penetration.
Uncertainty in spatially explicit animal dispersal models
Mooij, Wolf M.; DeAngelis, Donald L.
2003-01-01
Uncertainty in estimates of survival of dispersing animals is a vexing difficulty in conservation biology. The current notion is that this uncertainty decreases the usefulness of spatially explicit population models in particular. We examined this problem by comparing dispersal models of three levels of complexity: (1) an event-based binomial model that considers only the occurrence of mortality or arrival, (2) a temporally explicit exponential model that employs mortality and arrival rates, and (3) a spatially explicit grid-walk model that simulates the movement of animals through an artificial landscape. Each model was fitted to the same set of field data. A first objective of the paper is to illustrate how the maximum-likelihood method can be used in all three cases to estimate the means and confidence limits for the relevant model parameters, given a particular set of data on dispersal survival. Using this framework we show that the structure of the uncertainty for all three models is strikingly similar. In fact, the results of our unified approach imply that spatially explicit dispersal models, which take advantage of information on landscape details, suffer less from uncertainly than do simpler models. Moreover, we show that the proposed strategy of model development safeguards one from error propagation in these more complex models. Finally, our approach shows that all models related to animal dispersal, ranging from simple to complex, can be related in a hierarchical fashion, so that the various approaches to modeling such dispersal can be viewed from a unified perspective.
Spatial interactions in agent-based modeling
Ausloos, Marcel; Merlone, Ugo
2014-01-01
Agent Based Modeling (ABM) has become a widespread approach to model complex interactions. In this chapter after briefly summarizing some features of ABM the different approaches in modeling spatial interactions are discussed. It is stressed that agents can interact either indirectly through a shared environment and/or directly with each other. In such an approach, higher-order variables such as commodity prices, population dynamics or even institutions, are not exogenously specified but instead are seen as the results of interactions. It is highlighted in the chapter that the understanding of patterns emerging from such spatial interaction between agents is a key problem as much as their description through analytical or simulation means. The chapter reviews different approaches for modeling agents' behavior, taking into account either explicit spatial (lattice based) structures or networks. Some emphasis is placed on recent ABM as applied to the description of the dynamics of the geographical distribution o...
SPATIAL MODELLING FOR DESCRIBING SPATIAL VARIABILITY OF SOIL PHYSICAL PROPERTIES IN EASTERN CROATIA
Directory of Open Access Journals (Sweden)
Igor Bogunović
2016-06-01
Full Text Available The objectives of this study were to characterize the field-scale spatial variability and test several interpolation methods to identify the best spatial predictor of penetration resistance (PR, bulk density (BD and gravimetric water content (GWC in the silty loam soil in Eastern Croatia. The measurements were made on a 25 x 25-m grid which created 40 individual grid cells. Soil properties were measured at the center of the grid cell deep 0-10 cm and 10-20 cm. Results demonstrated that PR and GWC displayed strong spatial dependence at 0-10 cm BD, while there was moderate and weak spatial dependence of PR, BD and GWC at depth of 10-20 cm. Semi-variogram analysis suggests that future sampling intervals for investigated parameters can be increased to 35 m in order to reduce research costs. Additionally, interpolation models recorded similar root mean square values with high predictive accuracy. Results suggest that investigated properties do not have uniform interpolation method implying the need for spatial modelling in the evaluation of these soil properties in Eastern Croatia.
Models for dependent time series
Tunnicliffe Wilson, Granville; Haywood, John
2015-01-01
Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data.The first four chapters discuss the two main pillars of the subject that have been developed over the last 60 years: vector autoregressive modeling and multivariate spectral analysis. These chapters provide the foundational mater
Multivariate Receptor Models for Spatially Correlated Multipollutant Data
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.
Statistical inference and visualization in scale-space for spatially dependent images
Vaughan, Amy
2012-03-01
SiZer (SIgnificant ZERo crossing of the derivatives) is a graphical scale-space visualization tool that allows for statistical inferences. In this paper we develop a spatial SiZer for finding significant features and conducting goodness-of-fit tests for spatially dependent images. The spatial SiZer utilizes a family of kernel estimates of the image and provides not only exploratory data analysis but also statistical inference with spatial correlation taken into account. It is also capable of comparing the observed image with a specific null model being tested by adjusting the statistical inference using an assumed covariance structure. Pixel locations having statistically significant differences between the image and a given null model are highlighted by arrows. The spatial SiZer is compared with the existing independent SiZer via the analysis of simulated data with and without signal on both planar and spherical domains. We apply the spatial SiZer method to the decadal temperature change over some regions of the Earth. © 2011 The Korean Statistical Society.
Uncertainty in spatially explicit animal dispersal models
Mooij, W.M.; DeAngelis, D.L.
2003-01-01
Uncertainty in estimates of survival of dispersing animals is a vexing difficulty in conservation biology. The current notion is that this uncertainty decreases the usefulness of spatially explicit population models in particular. We examined this problem by comparing dispersal models of three level
Integrated statistical modelling of spatial landslide probability
Mergili, M.; Chu, H.-J.
2015-09-01
Statistical methods are commonly employed to estimate spatial probabilities of landslide release at the catchment or regional scale. Travel distances and impact areas are often computed by means of conceptual mass point models. The present work introduces a fully automated procedure extending and combining both concepts to compute an integrated spatial landslide probability: (i) the landslide inventory is subset into release and deposition zones. (ii) We employ a simple statistical approach to estimate the pixel-based landslide release probability. (iii) We use the cumulative probability density function of the angle of reach of the observed landslide pixels to assign an impact probability to each pixel. (iv) We introduce the zonal probability i.e. the spatial probability that at least one landslide pixel occurs within a zone of defined size. We quantify this relationship by a set of empirical curves. (v) The integrated spatial landslide probability is defined as the maximum of the release probability and the product of the impact probability and the zonal release probability relevant for each pixel. We demonstrate the approach with a 637 km2 study area in southern Taiwan, using an inventory of 1399 landslides triggered by the typhoon Morakot in 2009. We observe that (i) the average integrated spatial landslide probability over the entire study area corresponds reasonably well to the fraction of the observed landside area; (ii) the model performs moderately well in predicting the observed spatial landslide distribution; (iii) the size of the release zone (or any other zone of spatial aggregation) influences the integrated spatial landslide probability to a much higher degree than the pixel-based release probability; (iv) removing the largest landslides from the analysis leads to an enhanced model performance.
Kindt, Joel D.
A microfluidic refractometer was designed based on previous optofluidic intracavity spectroscopy (OFIS) chips utilized to distinguish healthy and cancerous cells. The optofluidic cavity is realized by adding high reflectivity dielectric mirrors to the top and bottom of a microfluidic channel. This creates a plane-plane Fabry-Perot optical cavity in which the resonant wavelengths are highly dependent on the optical path length inside the cavity. Refractometry is a useful method to determine the nature of fluids, including the concentration of a solute in a solvent as well as the temperature of the fluid. Advantages of microfluidic systems are the easy integration with lab-on-chip devices and the need for only small volumes of fluid. The unique abilities of the microfluidic refractometer in this thesis include its spatial, temperature, and wavelength dependence. Spatial dependence of the transmission spectrum is inherent through a spatial filtering process implemented with an optical fiber and microscope objective. A sequence of experimental observations guided the change from using the OFIS chip as a cell discrimination device to a complimentary refractometer. First, it was noted the electrode structure within the microfluidic channel, designed to trap and manipulate biological cells with dielectrophoretic (DEP) forces, caused the resonant wavelengths to blue-shift when the electrodes were energized. This phenomenon is consistent with the negative dn/dT property of water and water-based solutions. Next, it was necessary to develop a method to separate the optical path length into physical path length and refractive index. Air holes were placed near the microfluidic channel to exclusively measure the cavity length with the known refractive index of air. The cavity length was then interpolated across the microfluidic channel, allowing any mechanical changes to be taken into account. After the separation of physical path length and refractive index, it was of interest
Spatial Visualization Abilities of Field Dependent/Independent Preservice Teachers
Yazici, Ersen
2014-01-01
Introduction: Spatial skills have been a significant area of research in educational psychology for more years and it has two major dimensions as spatial visualization and spatial orientation. Mathematics educators acknowledge the influence of cognitive styles in the learning of mathematics. There are various recognized cognitive styles in the…
Assessing the performance of the independence method in modeling spatial extreme rainfall
Zheng, Feifei; Thibaud, Emeric; Leonard, Michael; Westra, Seth
2015-09-01
Spatial statistical methods are often employed to improve precision when estimating marginal distributions of extreme rainfall. Methods such as max-stable and copula models parameterize the spatial dependence and provide a continuous spatial representation. Alternatively, the independence method can be used to estimate marginal parameters without the need for parameterizing the spatial dependence, and this method has been under-utilized in hydrologic applications. This paper investigates the effectiveness of the independence method for marginal parameter estimation of spatially dependent extremes. Its performance is compared with three spatial dependence models (max-stable Brown-Resnick, max-stable Schlather, and Gaussian copula) by means of a simulation study. The independence method is statistically robust in estimating parameters and their associated confidence intervals for spatial extremes with various underlying dependence structures. The spatial dependence models perform comparably with the independence method when the spatial dependence structure is correctly specified; otherwise they exhibit considerably worse performance. We conclude that the independence method is more appealing for modeling the marginal distributions of spatial extremes (e.g., regional estimation of trends in rainfall extremes) due to its greater robustness and simplicity. The four statistical methods are illustrated using a spatial data set comprising 69 subdaily rainfall series from the Greater Sydney region, Australia.
Frequency dependence of signal power and spatial reach of the local field potential.
Directory of Open Access Journals (Sweden)
Szymon Łęski
Full Text Available Despite its century-old use, the interpretation of local field potentials (LFPs, the low-frequency part of electrical signals recorded in the brain, is still debated. In cortex the LFP appears to mainly stem from transmembrane neuronal currents following synaptic input, and obvious questions regarding the 'locality' of the LFP are: What is the size of the signal-generating region, i.e., the spatial reach, around a recording contact? How far does the LFP signal extend outside a synaptically activated neuronal population? And how do the answers depend on the temporal frequency of the LFP signal? Experimental inquiries have given conflicting results, and we here pursue a modeling approach based on a well-established biophysical forward-modeling scheme incorporating detailed reconstructed neuronal morphologies in precise calculations of population LFPs including thousands of neurons. The two key factors determining the frequency dependence of LFP are the spatial decay of the single-neuron LFP contribution and the conversion of synaptic input correlations into correlations between single-neuron LFP contributions. Both factors are seen to give low-pass filtering of the LFP signal power. For uncorrelated input only the first factor is relevant, and here a modest reduction (100 Hz compared to the near-DC ([Formula: see text] value of about [Formula: see text]. Much larger frequency-dependent effects are seen when populations of pyramidal neurons receive correlated and spatially asymmetric inputs: the low-frequency ([Formula: see text] LFP power can here be an order of magnitude or more larger than at 60 Hz. Moreover, the low-frequency LFP components have larger spatial reach and extend further outside the active population than high-frequency components. Further, the spatial LFP profiles for such populations typically span the full vertical extent of the dendrites of neurons in the population. Our numerical findings are backed up by an intuitive
Modelling the emergence of spatial patterns of economic activity
Yang, Jung-Hun; Frenken, Koen
2012-01-01
Understanding how spatial configurations of economic activity emerge is important when formulating spatial planning and economic policy. A simple model was proposed by Simon, who assumed that firms grow at a rate proportional to their size, and that new divisions of firms with certain probabilities relocate to other firms or to new centres of economic activity. Simon's model produces realistic results in the sense that the sizes of economic centres follow a Zipf distribution, which is also observed in reality. It lacks realism in the sense that mechanisms such as cluster formation, congestion (defined as an overly high density of the same activities) and dependence on the spatial distribution of external parties (clients, labour markets) are ignored. The present paper proposed an extension of the Simon model that includes both centripetal and centrifugal forces. Centripetal forces are included in the sense that firm divisions are more likely to settle in locations that offer a higher accessibility to other fi...
Spatial correlations in bed load transport: evidence, importance, and modelling
Heyman, J; Mettra, F; Ancey, C
2016-01-01
This article examines the spatial {dynamics of bed load particles} in water. We focus particularly on the fluctuations of particle activity, which is defined as the number of moving particles per unit bed {length}. Based on a stochastic model recently proposed by \\citet{Ancey2013}, we derive the second moment of particle activity analytically; that is the spatial correlation functions of particle activity. From these expressions, we show that large moving particle clusters can develop spatially. Also, we provide evidence that fluctuations of particle activity are scale-dependent. Two characteristic lengths emerge from the model: a saturation length $\\ell_{sat}$ describing the length needed for a perturbation in particle activity to relax to the homogeneous solution, and a correlation length $\\ell_c$ describing the typical size of moving particle clusters. A dimensionless P\\'eclet number can also be defined according to the transport model. Three different experimental data sets are used to test the theoretica...
Stochastic spatial models of plant diseases
Brown, D H
2001-01-01
I present three models of plant--pathogen interactions. The models are stochastic and spatially explicit at the scale of individual plants. For each model, I use a version of pair approximation or moment closure along with a separation of timescales argument to determine the effects of spatial clustering on threshold structure. By computing the spatial structure early in an invasion, I find explicit corrections to mean field theory. In the first chapter, I present a lattice model of a disease that is not directly lethal to its host, but affects its ability to compete with neighbors. I use a type of pair approximation to determine conditions for invasions and coexistence. In the second chapter, I study a basic SIR epidemic point process in continuous space. I implement a multiplicative moment closure scheme to compute the threshold transmission rate as a function of spatial parameters. In the final chapter, I model the evolution of pathogen resistance when two plant species share a pathogen. Evolution may lead...
Spatial Models and Networks of Living Systems
DEFF Research Database (Denmark)
Juul, Jeppe Søgaard
. Such systems are known to be stabilized by spatial structure. Finally, I analyse data from a large mobile phone network and show that people who are topologically close in the network have similar communication patterns. This main part of the thesis is based on six different articles, which I have co...... variables of the system. However, this approach disregards any spatial structure of the system, which may potentially change the behaviour drastically. An alternative approach is to construct a cellular automaton with nearest neighbour interactions, or even to model the system as a complex network...... with interactions defined by network topology. In this thesis I first describe three different biological models of ageing and cancer, in which spatial structure is important for the system dynamics. I then turn to describe characteristics of ecosystems consisting of three cyclically interacting species...
Evaluating stream health based environmental justice model performance at different spatial scales
Daneshvar, Fariborz; Nejadhashemi, A. Pouyan; Zhang, Zhen; Herman, Matthew R.; Shortridge, Ashton; Marquart-Pyatt, Sandra
2016-07-01
This study evaluated the effects of spatial resolution on environmental justice analysis concerning stream health. The Saginaw River Basin in Michigan was selected since it is an area of concern in the Great Lakes basin. Three Bayesian Conditional Autoregressive (CAR) models (ordinary regression, weighted regression and spatial) were developed for each stream health measure based on 17 socioeconomic and physiographical variables at three census levels. For all stream health measures, spatial models had better performance compared to the two non-spatial ones at the census tract and block group levels. Meanwhile no spatial dependency was found at the county level. Multilevel Bayesian CAR models were also developed to understand the spatial dependency at the three levels. Results showed that considering level interactions improved models' prediction. Residual plots also showed that models developed at the block group and census tract (in contrary to county level models) are able to capture spatial variations.
Area-to-point Kriging in spatial hedonic pricing models
Yoo, E.-H.; Kyriakidis, P. C.
2009-12-01
This paper proposes a geostatistical hedonic price model in which the effects of location on house values are explicitly modeled. The proposed geostatistical approach, namely area-to-point Kriging with External Drift (A2PKED), can take into account spatial dependence and spatial heteroskedasticity, if they exist. Furthermore, this approach has significant implications in situations where exhaustive area-averaged housing price data are available in addition to a subset of individual housing price data. In the case study, we demonstrate that A2PKED substantially improves the quality of predictions using apartment sale transaction records that occurred in Seoul, South Korea, during 2003. The improvement is illustrated via a comparative analysis, where predicted values obtained from different models, including two traditional regression-based hedonic models and a point-support geostatistical model, are compared to those obtained from the A2PKED model.
The 3-D global spatial data model foundation of the spatial data infrastructure
Burkholder, Earl F
2008-01-01
Traditional methods for handling spatial data are encumbered by the assumption of separate origins for horizontal and vertical measurements. Modern measurement systems operate in a 3-D spatial environment. The 3-D Global Spatial Data Model: Foundation of the Spatial Data Infrastructure offers a new model for handling digital spatial data, the global spatial data model or GSDM. The GSDM preserves the integrity of three-dimensional spatial data while also providing additional benefits such as simpler equations, worldwide standardization, and the ability to track spatial data accuracy with greater specificity and convenience. This groundbreaking spatial model incorporates both a functional model and a stochastic model to connect the physical world to the ECEF rectangular system. Combining horizontal and vertical data into a single, three-dimensional database, this authoritative monograph provides a logical development of theoretical concepts and practical tools that can be used to handle spatial data mo...
Modeling Spatially Unrestricted Pedestrian Traffic on Footbridges
DEFF Research Database (Denmark)
Zivanovic, Stana; Pavic, Aleksandar; Ingólfsson, Einar Thór
2010-01-01
The research into modelling walking-induced dynamic loading and its effects on footbridge structures and people using them has been intensified in the last decade after some high profile vibration serviceability failures. In particular, the crowd induced loading, characterised by spatially...... restricted movement of pedestrians, has kept attracting attention of researchers. However, it is the normal spatially unrestricted pedestrian traffic, and its vertical dynamic loading component, that are most relevant for vibration serviceability checks for most footbridges. Despite the existence of numerous...... design procedures concerned with this loading, the current confidence in its modelling is low due to lack of verification of the models on as-built structures. This is the motivation behind reviewing the existing design procedures for modelling normal pedestrian traffic in this paper and evaluating...
Modelling spatial density using continuous wavelet transforms
Indian Academy of Sciences (India)
D Sudheer Reddy; N Gopal Reddy; A K Anilkumar
2013-02-01
Due to increase in the satelite launch activities from many countries around the world the orbital debris issue has become a major concern for the space agencies to plan a collision-free orbit design. The risk of collisions is calculated using the in situ measurements and available models. Spatial density models are useful in understanding the long-term likelihood of a collision in a particular region of space and also helpful in pre-launch orbit planning. In this paper, we present a method of estimating model parameters such as number of peaks and peak locations of spatial density model using continuous wavelets. The proposed methodology was experimented with two line element data and the results are presented.
Spatial Models and Networks of Living Systems
DEFF Research Database (Denmark)
Juul, Jeppe Søgaard
variables of the system. However, this approach disregards any spatial structure of the system, which may potentially change the behaviour drastically. An alternative approach is to construct a cellular automaton with nearest neighbour interactions, or even to model the system as a complex network....... Such systems are known to be stabilized by spatial structure. Finally, I analyse data from a large mobile phone network and show that people who are topologically close in the network have similar communication patterns. This main part of the thesis is based on six different articles, which I have co...
A Non-Gaussian Spatial Generalized Linear Latent Variable Model
Irincheeva, Irina
2012-08-03
We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.
Bridging asymptotic independence and dependence in spatial exbtremes using Gaussian scale mixtures
Huser, Raphaël
2017-06-23
Gaussian scale mixtures are constructed as Gaussian processes with a random variance. They have non-Gaussian marginals and can exhibit asymptotic dependence unlike Gaussian processes, which are asymptotically independent except in the case of perfect dependence. In this paper, we study the extremal dependence properties of Gaussian scale mixtures and we unify and extend general results on their joint tail decay rates in both asymptotic dependence and independence cases. Motivated by the analysis of spatial extremes, we propose flexible yet parsimonious parametric copula models that smoothly interpolate from asymptotic dependence to independence and include the Gaussian dependence as a special case. We show how these new models can be fitted to high threshold exceedances using a censored likelihood approach, and we demonstrate that they provide valuable information about tail characteristics. In particular, by borrowing strength across locations, our parametric model-based approach can also be used to provide evidence for or against either asymptotic dependence class, hence complementing information given at an exploratory stage by the widely used nonparametric or parametric estimates of the χ and χ̄ coefficients. We demonstrate the capacity of our methodology by adequately capturing the extremal properties of wind speed data collected in the Pacific Northwest, US.
A nonlocal spatial model for Lyme disease
Yu, Xiao; Zhao, Xiao-Qiang
2016-07-01
This paper is devoted to the study of a nonlocal and time-delayed reaction-diffusion model for Lyme disease with a spatially heterogeneous structure. In the case of a bounded domain, we first prove the existence of the positive steady state and a threshold type result for the disease-free system, and then establish the global dynamics for the model system in terms of the basic reproduction number. In the case of an unbound domain, we obtain the existence of the disease spreading speed and its coincidence with the minimal wave speed. At last, we use numerical simulations to verify our analytic results and investigate the influence of model parameters and spatial heterogeneity on the disease infection risk.
Supplementary Material for: Factor Copula Models for Replicated Spatial Data
Krupskii, Pavel
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.
Linking spatial and dynamic models for traffic maneuvers
DEFF Research Database (Denmark)
Olderog, Ernst-Rüdiger; Ravn, Anders Peter; Wisniewski, Rafal
2015-01-01
For traffic maneuvers of multiple vehicles on highways we build an abstract spatial and a concrete dynamic model. In the spatial model we show the safety (collision freedom) of lane-change maneuvers. By linking the spatial and dynamic model via suitable refinements of the spatial atoms to distance...
Modeling local dependence in longitudinal IRT models
DEFF Research Database (Denmark)
Larsen, Maja Olsbjerg; Christensen, Karl Bang
2015-01-01
Measuring change in a latent variable over time is often done using the same instrument at several time points. This can lead to dependence between responses across time points for the same person yielding within person correlations that are stronger than what can be attributed to the latent...... variable. Ignoring this can lead to biased estimates of changes in the latent variable. In this paper we propose a method for modeling local dependence in the longitudinal 2PL model. It is based on the concept of item splitting, and makes it possible to correctly estimate change in the latent variable....
Developing a modelling for the spatial data infrastructure
CSIR Research Space (South Africa)
Hjelmager, J
2005-07-01
Full Text Available The Commission on Spatial Data Standards of the International Cartographic Association (ICA) is working on defining spatial models and technical characteristics of a Spatial Data Infrastructure (SDI). To date, this work has been restricted...
Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models
Hughes, John
2010-01-01
Non-gaussian spatial data are very common in many disciplines. For instance, count data are common in disease mapping, and binary data are common in ecology. When fitting spatial regressions for such data, one needs to account for dependence to ensure reliable inference for the regression coefficients. The spatial generalized linear mixed model (SGLMM) offers a very popular and flexible approach to modeling such data, but the SGLMM suffers from three major shortcomings: (1) uninterpretability of parameters due to spatial confounding, (2) variance inflation due to spatial confounding, and (3) high-dimensional spatial random effects that make fully Bayesian inference for such models computationally challenging. We propose a new parameterization of the SGLMM that alleviates spatial confounding and speeds computation by greatly reducing the dimension of the spatial random effects. We illustrate the application of our approach to simulated binary, count, and Gaussian spatial datasets, and to a large infant mortali...
Research of ERP model system of spatial data warehouse
Institute of Scientific and Technical Information of China (English)
CHEN Xue-long; WANG Yan-zhang
2004-01-01
The broad sharing of spatial information is demanded in the infrastructure construction of spatial data in our country. And the spatial data warehouse realizes the effective management and sharing of spatial information serving as an efficient tool. This article proposes ERP model system that of general-decision-oriented for constructing spatial data warehouse from the aspect of decision application. In the end of article, the construction process of spatial data warehouse based on ERP model system is discussed.
Spatial Aggregation: Data Model and Implementation
Gomez, Leticia; Kuijpers, Bart; Vaisman, Alejandro
2007-01-01
Data aggregation in Geographic Information Systems (GIS) is only marginally present in commercial systems nowadays, mostly through ad-hoc solutions. In this paper, we first present a formal model for representing spatial data. This model integrates geographic data and information contained in data warehouses external to the GIS. We define the notion of geometric aggregation, a general framework for aggregate queries in a GIS setting. We also identify the class of summable queries, which can be efficiently evaluated by precomputing the overlay of two or more of the thematic layers involved in the query. We also sketch a language, denoted GISOLAP-QL, for expressing queries that involve GIS and OLAP features. In addition, we introduce Piet, an implementation of our proposal, that makes use of overlay precomputation for answering spatial queries (aggregate or not). Our experimental evaluation showed that for a certain class of geometric queries with or without aggregation, overlay precomputation outperforms R-tre...
Human Plague Risk: Spatial-Temporal Models
Pinzon, Jorge E.
2010-01-01
This chpater reviews the use of spatial-temporal models in identifying potential risks of plague outbreaks into the human population. Using earth observations by satellites remote sensing there has been a systematic analysis and mapping of the close coupling between the vectors of the disease and climate variability. The overall result is that incidence of plague is correlated to positive El Nino/Southem Oscillation (ENSO).
The quantitative modelling of human spatial habitability
Wise, James A.
1988-01-01
A theoretical model for evaluating human spatial habitability (HuSH) in the proposed U.S. Space Station is developed. Optimizing the fitness of the space station environment for human occupancy will help reduce environmental stress due to long-term isolation and confinement in its small habitable volume. The development of tools that operationalize the behavioral bases of spatial volume for visual kinesthetic, and social logic considerations is suggested. This report further calls for systematic scientific investigations of how much real and how much perceived volume people need in order to function normally and with minimal stress in space-based settings. The theoretical model presented in this report can be applied to any size or shape interior, at any scale of consideration, for the Space Station as a whole to an individual enclosure or work station. Using as a point of departure the Isovist model developed by Dr. Michael Benedikt of the U. of Texas, the report suggests that spatial habitability can become as amenable to careful assessment as engineering and life support concerns.
Random spatial processes and geostatistical models for soil variables
Lark, R. M.
2009-04-01
Geostatistical models of soil variation have been used to considerable effect to facilitate efficient and powerful prediction of soil properties at unsampled sites or over partially sampled regions. Geostatistical models can also be used to investigate the scaling behaviour of soil process models, to design sampling strategies and to account for spatial dependence in the random effects of linear mixed models for spatial variables. However, most geostatistical models (variograms) are selected for reasons of mathematical convenience (in particular, to ensure positive definiteness of the corresponding variables). They assume some underlying spatial mathematical operator which may give a good description of observed variation of the soil, but which may not relate in any clear way to the processes that we know give rise to that observed variation in the real world. In this paper I shall argue that soil scientists should pay closer attention to the underlying operators in geostatistical models, with a view to identifying, where ever possible, operators that reflect our knowledge of processes in the soil. I shall illustrate how this can be done in the case of two problems. The first exemplar problem is the definition of operators to represent statistically processes in which the soil landscape is divided into discrete domains. This may occur at disparate scales from the landscape (outcrops, catchments, fields with different landuse) to the soil core (aggregates, rhizospheres). The operators that underly standard geostatistical models of soil variation typically describe continuous variation, and so do not offer any way to incorporate information on processes which occur in discrete domains. I shall present the Poisson Voronoi Tessellation as an alternative spatial operator, examine its corresponding variogram, and apply these to some real data. The second exemplar problem arises from different operators that are equifinal with respect to the variograms of the
Spatial modes in one-dimensional models for capillary jets
Guerrero, J.; González, H.; García, F. J.
2016-03-01
One-dimensional (1D) models are widely employed to simplify the analysis of axisymmetric capillary jets. These models postulate that, for slender deformations of the free surface, the radial profile of the axial velocity can be approximated as uniform (viscous slice, averaged, and Cosserat models) or parabolic (parabolic model). In classical works on spatial stability analysis with 1D models, considerable misinterpretation was generated about the modes yielded by each model. The already existing physical analysis of three-dimensional (3D) axisymmetric spatial modes enables us to relate these 1D spatial modes to the exact 3D counterparts. To do so, we address the surface stimulation problem, which can be treated as linear, by considering the effect of normal and tangential stresses to perturb the jet. A Green's function for a spatially local stimulation having a harmonic time dependence provides the general formalism to describe any time-periodic stimulation. The Green's function of this signaling problem is known to be a superposition of the spatial modes, but in fact these modes are of fundamental nature, i.e., not restricted to the surface stimulation problem. The smallness of the wave number associated with each mode is the criterion to validate or invalidate the 1D approaches. The proposed axial-velocity profiles (planar or parabolic) also have a remarkable influence on the outcomes of each 1D model. We also compare with the classical 3D results for (i) conditions for absolute instability, and (ii) the amplitude of the unstable mode resulting from both normal and tangential surface stress stimulation. Incidentally, as a previous task, we need to re-deduce 1D models in order to include eventual stresses of various possible origins (electrohydrodynamic, thermocapillary, etc.) applied on the free surface, which were not considered in the previous general formulations.
Dinis da Costa, Daniel
2014-01-01
This article presents student-teachers' perceptions of spatial 3D-descriptive geometry education in Mozambique. To interpret a 3D object through 2D projections and vice-versa requires spatial abilities that are deemed crucial for learning in any spatially dependent discipline such as 3D-descriptive geometry, engineering and technical-vocational…
Spatially dependent electron-positron pair creation rate
Lv, Q. Z.; Unger, J.; Li, Y. T.; Su, Q.; Grobe, R.
2016-11-01
The vacuum breakdown process in an inhomogeneous supercritical electric field of finite extension is examined. By probing the electron-positron pair creation zone with incoming electrons that have controlled velocities, we can address the fundamental question of where the electrons are being created from the vacuum. In contrast to what one might expect, the spatial regions of most likely pair creation are not necessarily those where the electric-field strength is largest. In fact, the creation regions are determined solely by energetic considerations and particles can therefore be created in spatial regions where the local field strength is far below the Schwinger threshold. These predictions are also supported independently by spatial probability densities obtained by extrapolation as well as by classical and quantum-mechanical analysis.
Indoorgml - a Standard for Indoor Spatial Modeling
Li, Ki-Joune
2016-06-01
With recent progress of mobile devices and indoor positioning technologies, it becomes possible to provide location-based services in indoor space as well as outdoor space. It is in a seamless way between indoor and outdoor spaces or in an independent way only for indoor space. However, we cannot simply apply spatial models developed for outdoor space to indoor space due to their differences. For example, coordinate reference systems are employed to indicate a specific position in outdoor space, while the location in indoor space is rather specified by cell number such as room number. Unlike outdoor space, the distance between two points in indoor space is not determined by the length of the straight line but the constraints given by indoor components such as walls, stairs, and doors. For this reason, we need to establish a new framework for indoor space from fundamental theoretical basis, indoor spatial data models, and information systems to store, manage, and analyse indoor spatial data. In order to provide this framework, an international standard, called IndoorGML has been developed and published by OGC (Open Geospatial Consortium). This standard is based on a cellular notion of space, which considers an indoor space as a set of non-overlapping cells. It consists of two types of modules; core module and extension module. While core module consists of four basic conceptual and implementation modeling components (geometric model for cell, topology between cells, semantic model of cell, and multi-layered space model), extension modules may be defined on the top of the core module to support an application area. As the first version of the standard, we provide an extension for indoor navigation.
Isard's contributions to spatial interaction modeling
O'Kelly, M. E.
. This short review, surveys Isard's role in promoting what has become known as spatial interaction modeling. Some contextual information on the milieu from which his work emerged is given, together with a selected number of works that are judged to have been influenced (directly and indirectly) by his work. It is suggested that this burgeoning field owes a lot to the foundations laid in the gravity model chapter of ``Methods''. The review is supplemented by a rather extensive bibliography of additional works that are indicative of the breadth of the impact of this field.
Estimation of Spatial Dynamic Nonparametric Durbin Models with Fixed Effects
Qian, Minghui; Hu, Ridong; Chen, Jianwei
2016-01-01
Spatial panel data models have been widely studied and applied in both scientific and social science disciplines, especially in the analysis of spatial influence. In this paper, we consider the spatial dynamic nonparametric Durbin model (SDNDM) with fixed effects, which takes the nonlinear factors into account base on the spatial dynamic panel…
The quantitative modelling of human spatial habitability
Wise, J. A.
1985-01-01
A model for the quantitative assessment of human spatial habitability is presented in the space station context. The visual aspect assesses how interior spaces appear to the inhabitants. This aspect concerns criteria such as sensed spaciousness and the affective (emotional) connotations of settings' appearances. The kinesthetic aspect evaluates the available space in terms of its suitability to accommodate human movement patterns, as well as the postural and anthrometric changes due to microgravity. Finally, social logic concerns how the volume and geometry of available space either affirms or contravenes established social and organizational expectations for spatial arrangements. Here, the criteria include privacy, status, social power, and proxemics (the uses of space as a medium of social communication).
DEFF Research Database (Denmark)
Møller, Jesper; Rasmussen, Jakob Gulddahl
2012-01-01
point’ an ‘independent cluster point’ or a ‘dependent cluster point’. The background and independent cluster points are thought to exhibit ‘complete spatial randomness’, whereas the dependent cluster points are likely to occur close to previous cluster points. We demonstrate the flexibility of the model......We introduce a flexible spatial point process model for spatial point patterns exhibiting linear structures, without incorporating a latent line process. The model is given by an underlying sequential point process model. Under this model, the points can be of one of three types: a ‘background...
Spatial Database Modeling for Indoor Navigation Systems
Gotlib, Dariusz; Gnat, Miłosz
2013-12-01
For many years, cartographers are involved in designing GIS and navigation systems. Most GIS applications use the outdoor data. Increasingly, similar applications are used inside buildings. Therefore it is important to find the proper model of indoor spatial database. The development of indoor navigation systems should utilize advanced teleinformation, geoinformatics, geodetic and cartographical knowledge. The authors present the fundamental requirements for the indoor data model for navigation purposes. Presenting some of the solutions adopted in the world they emphasize that navigation applications require specific data to present the navigation routes in the right way. There is presented original solution for indoor data model created by authors on the basis of BISDM model. Its purpose is to expand the opportunities for use in indoor navigation.
Helbich, M; Griffith, D
2016-01-01
Real estate policies in urban areas require the recognition of spatial heterogeneity in housing prices to account for local settings. In response to the growing number of spatially varying coefficient models in housing applications, this study evaluated four models in terms of their spatial patterns
A Computational Model of Spatial Development
Hiraki, Kazuo; Sashima, Akio; Phillips, Steven
Psychological experiments on children's development of spatial knowledge suggest experience at self-locomotion with visual tracking as important factors. Yet, the mechanism underlying development is unknown. We propose a robot that learns to mentally track a target object (i.e., maintaining a representation of an object's position when outside the field-of-view) as a model for spatial development. Mental tracking is considered as prediction of an object's position given the previous environmental state and motor commands, and the current environment state resulting from movement. Following Jordan & Rumelhart's (1992) forward modeling architecture the system consists of two components: an inverse model of sensory input to desired motor commands; and a forward model of motor commands to desired sensory input (goals). The robot was tested on the `three cups' paradigm (where children are required to select the cup containing the hidden object under various movement conditions). Consistent with child development, without the capacity for self-locomotion the robot's errors are self-center based. When given the ability of self-locomotion the robot responds allocentrically.
Spatial Economics Model Predicting Transport Volume
Directory of Open Access Journals (Sweden)
Lu Bo
2016-10-01
Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.
Latent spatial models and sampling design for landscape genetics
Hanks, Ephraim M.; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.
2016-01-01
We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.
Stakhovych, Stanislav; Bijmolt, Tammo H. A.; Wedel, Michel
2012-01-01
In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the
The effect of spatial light modulator (SLM) dependent dispersion on spatial beam shaping
CSIR Research Space (South Africa)
Spangenberg, D-M
2013-08-01
Full Text Available SLMs used for spatial modulation of lasers are often used in conjunction with very narrow bandwidth laser light where diffractive dispersion could be approximated as a constant. It is known that diffractive dispersion is inversely proportional...
Determination of spatially dependent diffusion parameters in bovine bone using Kalman filter.
Shokry, Abdallah; Ståhle, Per; Svensson, Ingrid
2015-11-07
Although many studies have been made for homogenous constant diffusion, bone is an inhomogeneous material. It has been suggested that bone porosity decreases from the inner boundaries to the outer boundaries of the long bones. The diffusivity of substances in the bone matrix is believed to increase as the bone porosity increases. In this study, an experimental set up is used where bovine bone samples, saturated with potassium chloride (KCl), were put into distilled water and the conductivity of the water was followed. Chloride ions in the bone samples escaped out in the water through diffusion and the increase of the conductivity was measured. A one-dimensional, spatially dependent mathematical model describing the diffusion process is used. The diffusion parameters in the model are determined using a Kalman filter technique. The parameters for spatially dependent at endosteal and periosteal surfaces are found to be (12.8 ± 4.7) × 10(-11) and (5 ± 3.5) × 10(-11)m(2)/s respectively. The mathematical model function using the obtained diffusion parameters fits very well with the experimental data with mean square error varies from 0.06 × 10(-6) to 0.183 × 10(-6) (μS/m)(2).
Modeling the impact of spatial relationships on horizontal curve safety.
Findley, Daniel J; Hummer, Joseph E; Rasdorf, William; Zegeer, Charles V; Fowler, Tyler J
2012-03-01
The curved segments of roadways are more hazardous because of the additional centripetalforces exerted on a vehicle, driver expectations, and other factors. The safety of a curve is dependent on various factors, most notably by geometric factors, but the location of a curve in relation to other curves is also thought to influence the safety of those curves because of a driver's expectation to encounter additional curves. The link between an individual curve's geometric characteristics and its safety performance has been established, but spatial considerations are typically not included in a safety analysis. The spatial considerations included in this research consisted of four components: distance to adjacent curves, direction of turn of the adjacent curves, and radius and length of the adjacent curves. The primary objective of this paper is to quantify the spatial relationship between adjacent horizontal curves and horizontal curve safety using a crash modification factor. Doing so enables a safety professional to more accurately estimate safety to allocate funding to reduce or prevent future collisions and more efficiently design new roadway sections to minimize crash risk where there will be a series of curves along a route. The most important finding from this research is the statistical significance of spatial considerations for the prediction of horizontal curve safety. The distances to adjacent curves were found to be a reliable predictor of observed collisions. This research recommends a model which utilizes spatial considerations for horizontal curve safety prediction in addition to current Highway Safety Manual prediction capabilities using individual curve geometric features.
Directory of Open Access Journals (Sweden)
I. Dandouras
2011-05-01
Full Text Available We investigate the spatial variation of magnetopause energy conversion and transfer using Cluster spacecraft observations of two magnetopause crossing events as well as using a global magnetohydrodynamic (MHD simulation GUMICS-4. These two events, (16 January 2001, and 26 January 2001 are similar in all other aspects except for the sign of the interplanetary magnetic field (IMF y-component that has earlier been found to control the spatial dependence of energy transfer. In simulations of the two events using observed solar wind parameters as input, we find that the GUMICS-4 energy transfer agrees with the Cluster observations spatially and is about 30 % lower in magnitude. According to the simulation, most of the the energy transfer takes place in the plane of the IMF (as previous modelling results have suggested, and the locations of the load and generator regions on the magnetopause are controlled by the IMF orientation. Assuming that the model results are as well in accordance with the in situ observations also on other parts of the magnetopause, we are able to pin down the total energy transfer during the two Cluster magnetopause crossings. Here, we estimate that the instantaneous total power transferring through the magnetopause during the two events is at least 1500–2000 GW, agreeing with ε scaled using the mean magnetopause area in the simulation. Hence the combination of the simulation results and the Cluster observations indicate that the ε parameter is probably underestimated by a factor of 2–3.
Yang, Wei; Sharp, Basil
2017-04-01
This paper analyses spatial dependence and determinants of the New Zealand dairy farmers' adoption of best management practices to protect water quality. A Bayesian spatial durbin probit model is used to survey data collected from farmers in the Waikato region of New Zealand. The results show that farmers located near each other exhibit similar choice behaviour, indicating the importance of farmer interactions in adoption decisions. The results also address that information acquisition is the most important determinant of farmers' adoption of best management practices. Financial problems are considered a significant barrier to adopting best management practices. Overall, the existence of distance decay effect and spatial dependence in farmers' adoption decisions highlights the importance of accounting for spatial effects in farmers' decision-making, which emerges as crucial to the formulation of sustainable agriculture policy.
Effcient Data Access for Location-Dependent Spatial Queries
Institute of Scientific and Technical Information of China (English)
Kwangjin Park
2014-01-01
When the mobile environment consists of light-weight devices, the loss of network connectivity and scarce resources, e.g., low battery power and limited memory, become primary issues of concern in order to effciently support portable wireless devices. In this paper, we propose an index-based peer-to-peer data access method that uses a new Hierarchical Location-Based Sequential (HLBS) index. We then propose a novel distributed Nearest First Broadcast (NFB) algorithm. Both HLBS and NFB are specifically designed for mobile peer-to-peer service in wireless broadcast environments. The system has a lower response time, because the client only contacts a qualified service provider by accessing the HLBS and quickly retrieves the data to answer the query by using NFB. HLBS and NFB design the index for spatial ob jects according to the positions of individual clients and transfer the index in the order arranged so that the spatial query can be processed even after the user tunes the partial index. Hence, this design can support rapid and energy-efficient service. A performance evaluation is conducted to compare the proposed algorithms with algorithms based on R-tree and Hilbert-curve air indexes. The results show that the proposed data dissemination algorithm with the HLBS index is scalable and energy efficient in both range queries and nearest neighbor queries.
DEFF Research Database (Denmark)
Møller, Jesper; Rasmussen, Jakob Gulddahl
We introduce a flexible spatial point process model for spatial point patterns exhibiting linear structures, without incorporating a latent line process. The model is given by an underlying sequential point process model, i.e. each new point is generated given the previous points. Under this model...... the points can be of one of three types: a ‘background point’, an ‘independent cluster point’, or a ‘dependent cluster point’. The background and independent cluster points are thought to exhibit ‘complete spatial randomness’, while the conditional distribution of a dependent cluster point given the previous...... points is such that the dependent cluster point is likely to occur closely to a previous cluster point. We demonstrate the flexibility of the model for producing point patterns with linear structures, and propose to use the model as the likelihood in a Bayesian setting when analyzing a spatial point...
Spatial Stochastic Point Models for Reservoir Characterization
Energy Technology Data Exchange (ETDEWEB)
Syversveen, Anne Randi
1997-12-31
The main part of this thesis discusses stochastic modelling of geology in petroleum reservoirs. A marked point model is defined for objects against a background in a two-dimensional vertical cross section of the reservoir. The model handles conditioning on observations from more than one well for each object and contains interaction between objects, and the objects have the correct length distribution when penetrated by wells. The model is developed in a Bayesian setting. The model and the simulation algorithm are demonstrated by means of an example with simulated data. The thesis also deals with object recognition in image analysis, in a Bayesian framework, and with a special type of spatial Cox processes called log-Gaussian Cox processes. In these processes, the logarithm of the intensity function is a Gaussian process. The class of log-Gaussian Cox processes provides flexible models for clustering. The distribution of such a process is completely characterized by the intensity and the pair correlation function of the Cox process. 170 refs., 37 figs., 5 tabs.
Rey, Sergio J.; Kang, Wei; Wolf, Levi
2016-10-01
Discrete Markov chain models (DMCs) have been widely applied to the study of regional income distribution dynamics and convergence. This popularity reflects the rich body of DMC theory on the one hand and the ability of this framework to provide insights on the internal and external properties of regional income distribution dynamics on the other. In this paper we examine the properties of tests for spatial effects in DMC models of regional distribution dynamics. We do so through a series of Monte Carlo simulations designed to examine the size, power and robustness of tests for spatial heterogeneity and spatial dependence in transitional dynamics. This requires that we specify a data generating process for not only the null, but also alternatives when spatial heterogeneity or spatial dependence is present in the transitional dynamics. We are not aware of any work which has examined these types of data generating processes in the spatial distribution dynamics literature. Results indicate that tests for spatial heterogeneity and spatial dependence display good power for the presence of spatial effects. However, tests for spatial heterogeneity are not robust to the presence of strong spatial dependence, while tests for spatial dependence are sensitive to the spatial configuration of heterogeneity. When the spatial configuration can be considered random, dependence tests are robust to the dynamic spatial heterogeneity, but not so to the process mean heterogeneity when the difference in process means is large relative to the variance of the time series.
Theoretical aspects of spatial-temporal modeling
Matsui, Tomoko
2015-01-01
This book provides a modern introductory tutorial on specialized theoretical aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter provides up-to-date coverage of particle association measures that underpin the theoretical properties of recently developed random set methods in space and time otherwise known as the class of probability hypothesis density framework (PHD filters). The second chapter gives an overview of recent advances in Monte Carlo methods for Bayesian filtering in high-dimensional spaces. In particular, the chapter explains how one may extend classical sequential Monte Carlo methods for filtering and static inference problems to high dimensions and big-data applications. The third chapter presents an overview of generalized families of processes that extend the class of Gaussian process models to heavy-tailed families known as alph...
Stimulus-dependent effects on tactile spatial acuity
Directory of Open Access Journals (Sweden)
Tommerdahl M
2005-10-01
"bilateral" condition, in which 25 Hz flutter was delivered to the two points on the attended hand and a second stimulus (either flutter or vibration was delivered to the unattended hand. The two-point limen was reduced (i.e., spatial acuity was improved under the complex stimulus condition when compared to the control stimulus condition. Specifically, whereas adding vibration to the unilateral two-point flutter stimulus improved spatial acuity by 20 to 25%, the two-point limen was not significantly affected by substantial changes in stimulus amplitude (between 100 – 200 μm. In contrast, simultaneous stimulation of the unattended hand (contralateral to the attended site, impaired spatial acuity by 20% with flutter stimulation and by 30% with vibration stimulation. Conclusion It was found that the addition of 200 Hz vibration to a two-point 25 Hz flutter stimulus significantly improved a subject's ability to discriminate between two points on the skin. Since previous studies showed that 200 Hz vibration preferentially evokes activity in cortical area SII and reduces or inhibits the spatial extent of activity in SI in the same hemisphere, the findings in this paper raise the possibility that although SI activity plays a major role in two-point discrimination on the skin, influences relayed to SI from SII in the same hemisphere may contribute importantly to SI's ability to differentially respond to stimuli applied to closely spaced skin points on the same side of the body midline.
Tapered composite likelihood for spatial max-stable models
Sang, Huiyan
2014-05-01
Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.
Directory of Open Access Journals (Sweden)
Jesse Whittington
Full Text Available Capture-recapture studies are frequently used to monitor the status and trends of wildlife populations. Detection histories from individual animals are used to estimate probability of detection and abundance or density. The accuracy of abundance and density estimates depends on the ability to model factors affecting detection probability. Non-spatial capture-recapture models have recently evolved into spatial capture-recapture models that directly include the effect of distances between an animal's home range centre and trap locations on detection probability. Most studies comparing non-spatial and spatial capture-recapture biases focussed on single year models and no studies have compared the accuracy of demographic parameter estimates from open population models. We applied open population non-spatial and spatial capture-recapture models to three years of grizzly bear DNA-based data from Banff National Park and simulated data sets. The two models produced similar estimates of grizzly bear apparent survival, per capita recruitment, and population growth rates but the spatial capture-recapture models had better fit. Simulations showed that spatial capture-recapture models produced more accurate parameter estimates with better credible interval coverage than non-spatial capture-recapture models. Non-spatial capture-recapture models produced negatively biased estimates of apparent survival and positively biased estimates of per capita recruitment. The spatial capture-recapture grizzly bear population growth rates and 95% highest posterior density averaged across the three years were 0.925 (0.786-1.071 for females, 0.844 (0.703-0.975 for males, and 0.882 (0.779-0.981 for females and males combined. The non-spatial capture-recapture population growth rates were 0.894 (0.758-1.024 for females, 0.825 (0.700-0.948 for males, and 0.863 (0.771-0.957 for both sexes. The combination of low densities, low reproductive rates, and predominantly negative
Whittington, Jesse; Sawaya, Michael A
2015-01-01
Capture-recapture studies are frequently used to monitor the status and trends of wildlife populations. Detection histories from individual animals are used to estimate probability of detection and abundance or density. The accuracy of abundance and density estimates depends on the ability to model factors affecting detection probability. Non-spatial capture-recapture models have recently evolved into spatial capture-recapture models that directly include the effect of distances between an animal's home range centre and trap locations on detection probability. Most studies comparing non-spatial and spatial capture-recapture biases focussed on single year models and no studies have compared the accuracy of demographic parameter estimates from open population models. We applied open population non-spatial and spatial capture-recapture models to three years of grizzly bear DNA-based data from Banff National Park and simulated data sets. The two models produced similar estimates of grizzly bear apparent survival, per capita recruitment, and population growth rates but the spatial capture-recapture models had better fit. Simulations showed that spatial capture-recapture models produced more accurate parameter estimates with better credible interval coverage than non-spatial capture-recapture models. Non-spatial capture-recapture models produced negatively biased estimates of apparent survival and positively biased estimates of per capita recruitment. The spatial capture-recapture grizzly bear population growth rates and 95% highest posterior density averaged across the three years were 0.925 (0.786-1.071) for females, 0.844 (0.703-0.975) for males, and 0.882 (0.779-0.981) for females and males combined. The non-spatial capture-recapture population growth rates were 0.894 (0.758-1.024) for females, 0.825 (0.700-0.948) for males, and 0.863 (0.771-0.957) for both sexes. The combination of low densities, low reproductive rates, and predominantly negative population growth
Directory of Open Access Journals (Sweden)
Sohair F Higazi
2013-02-01
Full Text Available Regression analysis depends on several assumptions that have to be satisfied. A major assumption that is never satisfied when variables are from contiguous observations is the independence of error terms. Spatial analysis treated the violation of that assumption by two derived models that put contiguity of observations into consideration. Data used are from Egypt's 2006 latest census, for 93 counties in middle delta seven adjacent Governorates. The dependent variable used is the percent of individuals classified as poor (those who make less than 1$ daily. Predictors are some demographic indicators. Explanatory Spatial Data Analysis (ESDA is performed to examine the existence of spatial clustering and spatial autocorrelation between neighboring counties. The ESDA revealed spatial clusters and spatial correlation between locations. Three statistical models are applied to the data, the Ordinary Least Square regression model (OLS, the Spatial Error Model (SEM and the Spatial Lag Model (SLM.The Likelihood Ratio test and some information criterions are used to compare SLM and SEM to OLS. The SEM model proved to be better than the SLM model. Recommendations are drawn regarding the two spatial models used.
Kramer, Sean; Bollt, Erik M
2013-09-01
Given multiple images that describe chaotic reaction-diffusion dynamics, parameters of a partial differential equation (PDE) model are estimated using autosynchronization, where parameters are controlled by synchronization of the model to the observed data. A two-component system of predator-prey reaction-diffusion PDEs is used with spatially dependent parameters to benchmark the methods described. Applications to modeling the ecological habitat of marine plankton blooms by nonlinear data assimilation through remote sensing are discussed.
Bollt, Erik
2012-01-01
Given multiple images that describe chaotic reaction-diffusion dynamics, parameters of a PDE model are estimated using autosynchronization, where parameters are controlled by synchronization of the model to the observed data. A two-component system of predator-prey reaction-diffusion PDEs is used with spatially dependent parameters to benchmark the methods described. Applications to modelling the ecological habitat of marine plankton blooms by nonlinear data assimilation through remote sensing is discussed.
Spatially explicit modelling of cholera epidemics
Finger, F.; Bertuzzo, E.; Mari, L.; Knox, A. C.; Gatto, M.; Rinaldo, A.
2013-12-01
Epidemiological models can provide crucial understanding about the dynamics of infectious diseases. Possible applications range from real-time forecasting and allocation of health care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. We apply a spatially explicit model to the cholera epidemic that struck Haiti in October 2010 and is still ongoing. The dynamics of susceptibles as well as symptomatic and asymptomatic infectives are modelled at the scale of local human communities. Dissemination of Vibrio cholerae through hydrological transport and human mobility along the road network is explicitly taken into account, as well as the effect of rainfall as a driver of increasing disease incidence. The model is calibrated using a dataset of reported cholera cases. We further model the long term impact of several types of interventions on the disease dynamics by varying parameters appropriately. Key epidemiological mechanisms and parameters which affect the efficiency of treatments such as antibiotics are identified. Our results lead to conclusions about the influence of different intervention strategies on the overall epidemiological dynamics.
A physically based analytical spatial air temperature and humidity model
Yang Yang; Theodore A. Endreny; David J. Nowak
2013-01-01
Spatial variation of urban surface air temperature and humidity influences human thermal comfort, the settling rate of atmospheric pollutants, and plant physiology and growth. Given the lack of observations, we developed a Physically based Analytical Spatial Air Temperature and Humidity (PASATH) model. The PASATH model calculates spatial solar radiation and heat...
Espa, Giuseppe; Arbia, Giuseppe; Giuliani, Diego
2013-01-01
A series of recent papers have introduced some explorative methods based on Ripley's K-function (Ripley in J R Stat Soc B 39(2):172-212, 1977) analyzing the micro-geographical patterns of firms. Often the spatial heterogeneity of an area is handled by referring to a case-control design, in which spatial clusters occur as over-concentrations of firms belonging to a specific industry as opposed to the distribution of firms in the whole economy. Therefore, positive, or negative, spatial dependence between firms occurs when a specific sector of industry is seen to present a more aggregated pattern (or more dispersed) than is common in the economy as a whole. This approach has led to the development of relative measures of spatial concentration which, as a consequence, are not straightforwardly comparable across different economies. In this article, we explore a parametric approach based on the inhomogeneous K-function (Baddeley et al. in Statistica Nederlandica 54(3):329-350, 2000) that makes it possible to obtain an absolute measure of the industrial agglomeration that is also able to capture spatial heterogeneity. We provide an empirical application of the approach taken with regard to the spatial distribution of high-tech industries in Milan (Italy) in 2001.
Spatial model for transmission of mosquito-borne diseases
Kon, Cynthia Mui Lian; Labadin, Jane
2015-05-01
In this paper, a generic model which takes into account spatial heterogeneity for the dynamics of mosquito-borne diseases is proposed. The dissemination of the disease is described by a system of reaction-diffusion partial differential equations. Host human and vector mosquito populations are divided into susceptible and infectious classes. Diffusion is considered to occur in all classes of both populations. Susceptible humans are infected when bitten by infectious mosquitoes. Susceptible mosquitoes bite infectious humans and become infected. The biting rate of mosquitoes is considered to be density dependent on the total human population in different locations. The system is solved numerically and results are shown.
P300 ERP Component Depends on Both Spatial Frequency and Contrast
Directory of Open Access Journals (Sweden)
Li-Ting Tsai
2011-05-01
Full Text Available Contrast perception depends on not only the early visual responses, but also top-down modulations. We measured how does P300, a well-documented event-related potential (ERP index for top-down influence, change with both spatial frequency and contrast. ERP were acquired from 10 participants, aged 18–50 years, when they were performing a visual oddball task. The target was a Gabor patch whose spatial frequency was either 4 or 8 cy/degree and contrasts 90% or 30%. The probability of target presence in a trial was 30%. All target stimuli produced a reliable P300 component. At the low spatial frequency, the amplitude of P300 was larger and the latency was shorter for the low contrast patterns than for the higher contrast ones for all electrodes. Such difference was not observed in high spatial frequency patterns. The latency was slightly longer for high spatial frequency patterns than the low spatial frequency ones. Our results showed an interaction between spatial frequency and contrast in P300. The characteristics of P300 at low spatial frequency correlated with task difficulty, but not at high spatial frequency. This suggests that the top-down influence on contrast perception may be spatial frequency depended.
Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.
Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J
2010-12-01
Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies
Spatial and frequency dependence of plasma currents in a 300 mm capacitively coupled plasma reactor
Energy Technology Data Exchange (ETDEWEB)
Miller, Paul A [Sandia National Laboratories, MS 1423, PO Box 5800, Albuquerque, NM 87185-1423 (United States); Barnat, Edward V [Sandia National Laboratories, MS 1423, PO Box 5800, Albuquerque, NM 87185-1423 (United States); Hebner, Gregory A [Sandia National Laboratories, MS 1423, PO Box 5800, Albuquerque, NM 87185-1423 (United States); Paterson, Alex M [Applied Materials, Inc., 974 Arques Avenue, Sunnyvale, CA 94086 (United States); Holland, John P [Applied Materials, Inc., 974 Arques Avenue, Sunnyvale, CA 94086 (United States)
2006-11-01
There is much interest in scaling rf-excited capacitively coupled plasma reactors to larger sizes and to higher frequencies. As the size approaches operating wavelength, concerns arise about non-uniformity across the work piece, particularly in light of the well-documented slow-surface-wave phenomenon. We present measurements and calculations of spatial and frequency dependence of rf magnetic fields inside argon plasma in an industrially relevant, 300 mm plasma-processing chamber. The results show distinct differences in the spatial distributions and harmonic content of rf fields in the plasma at the three frequencies studied (13.56, 60 and 176 MHz). Evidence of a slow-wave structure was not apparent. The results suggest that interaction between the plasma and the rf excitation circuit may strongly influence the structures of these magnetic fields and that this interaction is frequency dependent. At the higher frequencies, wave propagation becomes extremely complex; it is controlled by the strong electrical nonlinearity of the sheath and is not explained simply by previous models.
Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors
Jacobs, J.P.A.M.; Ligthart, J.E.; Vrijburg, H.
2009-01-01
We extend the three-step generalized methods of moments (GMM) approach of Kapoor, Kelejian, and Prucha (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory variables.
A Model of Colonic Crypts using SBML Spatial
Directory of Open Access Journals (Sweden)
Carlo Maj
2013-09-01
Full Text Available The Spatial Processes package enables an explicit definition of a spatial environment on top of the normal dynamic modeling SBML capabilities. The possibility of an explicit representation of spatial dynamics increases the representation power of SBML. In this work we used those new SBML features to define an extensive model of colonic crypts composed of the main cellular types (from stem cells to fully differentiated cells, alongside their spatial dynamics.
Spatial Data Web Services Pricing Model Infrastructure
Ozmus, L.; Erkek, B.; Colak, S.; Cankurt, I.; Bakıcı, S.
2013-08-01
most important law with related NSDI is the establishment of General Directorate of Geographic Information System under the Ministry of Environment and Urbanism. due to; to do or to have do works and activities with related to the establishment of National Geographic Information Systems (NGIS), usage of NGIS and improvements of NGIS. Outputs of these projects are served to not only public administration but also to Turkish society. Today for example, TAKBIS data (cadastre services) are shared more than 50 institutions by Web services, Tusaga-Aktif system has more than 3800 users who are having real-time GPS data correction, Orthophoto WMS services has been started for two years as a charge of free. Today there is great discussion about data pricing among the institutions. Some of them think that the pricing is storage of the data. Some of them think that the pricing is value of data itself. There is no certain rule about pricing. On this paper firstly, pricing of data storage and later on spatial data pricing models in different countries are investigated to improve institutional understanding in Turkey.
Stakhovych, Stanislav; Bijmolt, Tammo H. A.; Wedel, Michel
2012-01-01
In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the model parameters and demonstrates the consequences…
Stakhovych, Stanislav; Bijmolt, Tammo H. A.; Wedel, Michel
2012-01-01
In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the model parameters and demonstrates the consequences…
Platz, M.; Rapp, J.; Groessler, M.; Niehaus, E.; Babu, A.; Soman, B.
2014-11-01
A Spatial Decision Support System (SDSS) provides support for decision makers and should not be viewed as replacing human intelligence with machines. Therefore it is reasonable that decision makers are able to use a feature to analyze the provided spatial decision support in detail to crosscheck the digital support of the SDSS with their own expertise. Spatial decision support is based on risk and resource maps in a Geographic Information System (GIS) with relevant layers e.g. environmental, health and socio-economic data. Spatial fuzzy logic allows the representation of spatial properties with a value of truth in the range between 0 and 1. Decision makers can refer to the visualization of the spatial truth of single risk variables of a disease. Spatial fuzzy logic rules that support the allocation of limited resources according to risk can be evaluated with measure theory on topological spaces, which allows to visualize the applicability of this rules as well in a map. Our paper is based on the concept of a spatial fuzzy logic on topological spaces that contributes to the development of an adaptive Early Warning And Response System (EWARS) providing decision support for the current or future spatial distribution of a disease. It supports the decision maker in testing interventions based on available resources and apply risk mitigation strategies and provide guidance tailored to the geo-location of the user via mobile devices. The software component of the system would be based on open source software and the software developed during this project will also be in the open source domain, so that an open community can build on the results and tailor further work to regional or international requirements and constraints. A freely available EWARS Spatial Fuzzy Logic Demo was developed wich enables a user to visualize risk and resource maps based on individual data in several data formats.
Recent advances in technology have led to the collection of high-dimensional data not previously encountered in many scientific environments. As a result, scientists are often faced with the challenging task of including these high-dimensional data into statistical models. For example, data from sen...
Context dependent DNA evolutionary models
DEFF Research Database (Denmark)
Jensen, Jens Ledet
This paper is about stochastic models for the evolution of DNA. For a set of aligned DNA sequences, connected in a phylogenetic tree, the models should be able to explain - in probabilistic terms - the differences seen in the sequences. From the estimates of the parameters in the model one can...
Directory of Open Access Journals (Sweden)
Kostas Alexandridis
2013-06-01
Full Text Available Assessing spatial model performance often presents challenges related to the choice and suitability of traditional statistical methods in capturing the true validity and dynamics of the predicted outcomes. The stochastic nature of many of our contemporary spatial models of land use change necessitate the testing and development of new and innovative methodologies in statistical spatial assessment. In many cases, spatial model performance depends critically on the spatially-explicit prior distributions, characteristics, availability and prevalence of the variables and factors under study. This study explores the statistical spatial characteristics of statistical model assessment of modeling land use change dynamics in a seven-county study area in South-Eastern Wisconsin during the historical period of 1963–1990. The artificial neural network-based Land Transformation Model (LTM predictions are used to compare simulated with historical land use transformations in urban/suburban landscapes. We introduce a range of Bayesian information entropy statistical spatial metrics for assessing the model performance across multiple simulation testing runs. Bayesian entropic estimates of model performance are compared against information-theoretic stochastic entropy estimates and theoretically-derived accuracy assessments. We argue for the critical role of informational uncertainty across different scales of spatial resolution in informing spatial landscape model assessment. Our analysis reveals how incorporation of spatial and landscape information asymmetry estimates can improve our stochastic assessments of spatial model predictions. Finally our study shows how spatially-explicit entropic classification accuracy estimates can work closely with dynamic modeling methodologies in improving our scientific understanding of landscape change as a complex adaptive system and process.
Spatial Econometric data analysis: moving beyond traditional models
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 perspecti
Spatially explicit fate modelling of nanomaterials in natural waters
Quik, J.T.K.; Klein, de J.J.M.; Koelmans, A.A.
2015-01-01
Site specific exposure assessments for engineered nanoparticles (ENPs) require spatially explicit fate models, which however are not yet available. Here we present an ENP fate model (NanoDUFLOW) that links ENP specific process descriptions to a spatially explicit hydrological model. The link enables
Mining multilevel spatial association rules with cloud models
Institute of Scientific and Technical Information of China (English)
YANG Bin; ZHU Zhong-ying
2005-01-01
The traditional generalization-based knowledge discovery method is introduced. A new kind of multilevel spatial association of the rules mining method based on the cloud model is presented. The cloud model integrates the vague and random use of linguistic terms in a unified way. With these models, spatial and nonspatial attribute values are well generalized at multiple levels, allowing discovery of strong spatial association rules.Combining the cloud model based method with Apriori algorithms for mining association rules from a spatial database shows benefits in being effective and flexible.
Bonciocat, C A; Grosu, G; Ghiţă, S
1997-01-01
In this work a theoretical model was used in combination with testings on normal subjects to get more insight in the role of the departure from circularity or dispersion of the shapes in visual perception. The model was inspired by the observation that the intensity of the effect of a given level of contrast of a shape usually increases, for the same area, with the shape being better concentrated around a center. The model introduces as a measurable characteristic the degree of concentration or dispersion of a shape with respect to a center. The measure was based on the maximum of the convolution integral of the characteristic function of the shape with the weighting function 1/2 pi r, r being the distance between the point of convolution and the surface element to be integrated. A program for the calculation of the degree of concentration of figures and other related processing operations was developed in Turbo Pascal language on a 486 PC. The program included the possibility to generate various figures and to operate on them various transformations such as strangulation, fragmentation with separation of fragments. The model introduces a center of the figure, the point best surrounded by the whole figure, with a geometric and visual significance, as resulting from the good concordance between its calculated and perceived positioning in different relatively simple shapes. In symmetrical compact figures subjected to a central separation or narrowing two centres appear entering the two resulting nuclear parts; a good concordance between model and perception was again observed in this transition to two centres and their subsequent positions in the two nuclear parts. In accord to model prediction, testings showed a very pronounced dependence of the summation efficiency over a contrasting area on the degree of dispersion of the area. This is reflected in the drastic decrease upon figure dispersion of the intensity with which a given brightness or colour contrast is
Stakhovych, Stanislav; Bijmolt, Tammo H. A.; Wedel, Michel
2012-01-01
In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the mo
Using IBMs to Investigate Spatially-dependent Processes in Landscape Genetics Theory
Much of landscape and conservation genetics theory has been derived using non-spatialmathematical models. Here, we use a mechanistic, spatially-explicit, eco-evolutionary IBM to examine the utility of this theoretical framework in landscapes with spatial structure. Our analysis...
Using IBMs to Investigate Spatially-dependent Processes in Landscape Genetics Theory
Much of landscape and conservation genetics theory has been derived using non-spatialmathematical models. Here, we use a mechanistic, spatially-explicit, eco-evolutionary IBM to examine the utility of this theoretical framework in landscapes with spatial structure. Our analysis...
An API for Integrating Spatial Context Models with Spatial Reasoning Algorithms
DEFF Research Database (Denmark)
Kjærgaard, Mikkel Baun
2006-01-01
The integration of context-aware applications with spatial context models is often done using a common query language. However, algorithms that estimate and reason about spatial context information can benefit from a tighter integration. An object-oriented API makes such integration possible and ...... modeling. The utility of the API is evaluated in several real-world cases from an indoor location system, and spans several types of spatial reasoning algorithms.......The integration of context-aware applications with spatial context models is often done using a common query language. However, algorithms that estimate and reason about spatial context information can benefit from a tighter integration. An object-oriented API makes such integration possible...
Directory of Open Access Journals (Sweden)
Mustafa Koroglu
2016-02-01
Full Text Available This paper considers a functional-coefficient spatial Durbin model with nonparametric spatial weights. Applying the series approximation method, we estimate the unknown functional coefficients and spatial weighting functions via a nonparametric two-stage least squares (or 2SLS estimation method. To further improve estimation accuracy, we also construct a second-step estimator of the unknown functional coefficients by a local linear regression approach. Some Monte Carlo simulation results are reported to assess the finite sample performance of our proposed estimators. We then apply the proposed model to re-examine national economic growth by augmenting the conventional Solow economic growth convergence model with unknown spatial interactive structures of the national economy, as well as country-specific Solow parameters, where the spatial weighting functions and Solow parameters are allowed to be a function of geographical distance and the countries’ openness to trade, respectively.
Alaskan soil carbon stocks: spatial variability and dependence on environmental factors
Directory of Open Access Journals (Sweden)
U. Mishra
2012-09-01
Full Text Available The direction and magnitude of soil organic carbon (SOC changes in response to climate change depend on the spatial and vertical distributions of SOC. We estimated spatially resolved SOC stocks from surface to C horizon, distinguishing active-layer and permafrost-layer stocks, based on geospatial analysis of 472 soil profiles and spatially referenced environmental variables for Alaska. Total Alaska state-wide SOC stock was estimated to be 77 Pg, with 61% in the active-layer, 27% in permafrost, and 12% in non-permafrost soils. Prediction accuracy was highest for the active-layer as demonstrated by highest ratio of performance to deviation (1.5. Large spatial variability was predicted, with whole-profile, active-layer, and permafrost-layer stocks ranging from 1–296 kg C m^{−2}, 2–166 kg m^{−2}, and 0–232 kg m^{−2}, respectively. Temperature and soil wetness were found to be primary controllers of whole-profile, active-layer, and permafrost-layer SOC stocks. Secondary controllers, in order of importance, were found to be land cover type, topographic attributes, and bedrock geology. The observed importance of soil wetness rather than precipitation on SOC stocks implies that the poor representation of high-latitude soil wetness in Earth system models may lead to large uncertainty in predicted SOC stocks under future climate change scenarios. Under strict caveats described in the text and assuming temperature changes from the A1B Intergovernmental Panel on Climate Change emissions scenario, our geospatial model indicates that the equilibrium average 2100 Alaska active-layer depth could deepen by 11 cm, resulting in a thawing of 13 Pg C currently in permafrost. The equilibrium SOC loss associated with this warming would be highest under continuous permafrost (31%, followed by discontinuous (28%, isolated (24.3%, and sporadic (23.6% permafrost areas. Our high-resolution mapping of soil carbon stock reveals the
Alaskan soil carbon stocks: spatial variability and dependence on environmental factors
Directory of Open Access Journals (Sweden)
U. Mishra
2012-05-01
Full Text Available The direction and magnitude of soil organic carbon (SOC changes in response to climate change depend on the spatial and vertical distributions of SOC. We estimated spatially-resolved SOC stocks from surface to C horizon, distinguishing active-layer and permafrost-layer stocks, based on geospatial analysis of 472 soil profiles and spatially referenced environmental variables for Alaska. Total Alaska state-wide SOC stock was estimated to be 77 Pg, with 61% in the active-layer, 27% in permafrost, and 12% in non-permafrost soils. Prediction accuracy was highest for the active-layer as demonstrated by highest ratio of performance to deviation (1.5. Large spatial variability was predicted, with whole-profile, active-layer, and permafrost-layer stocks ranging from 1–296 kg C m^{−2}, 2–166 kg m^{−2}, and 0–232 kg m^{−2}, respectively. Temperature and soil wetness were found to be primary controllers of whole-profile, active-layer, and permafrost-layer SOC stocks. Secondary controllers, in order of importance, were: land cover type, topographic attributes, and bedrock geology. The observed importance of soil wetness rather than precipitation on SOC stocks implies that the poor representation of high-latitude soil wetness in Earth System Models may lead to large uncertainty in predicted SOC stocks under future climate change scenarios. Under strict caveats described in the text and assuming temperature changes from the A1B Intergovernmental Panel on Climate Change emissions scenario, our geospatial model indicates that the equilibrium average 2100 Alaska active-layer depth could deepen by 11 cm, resulting in a thawing of 13 Pg C currently in permafrost. The equilibrium SOC loss associated with this warming would be highest under continuous permafrost (31%, followed by discontinuous (28%, isolated (24.3%, and sporadic (23.6% permafrost areas. Our high resolution mapping of soil carbon stock reveals the potential
Modelling spatial vagueness based on type-2 fuzzy set
Institute of Scientific and Technical Information of China (English)
DU Guo-ning; ZHU Zhong-ying
2006-01-01
The modelling and formal characterization of spatial vagueness plays an increasingly important role in the implementation of Geographic Information System (GIS). The concepts involved in spatial objects of GIS have been investigated and acknowledged as being vague and ambiguous. Models and methods which describe and handle fuzzy or vague (rather than crisp or determinate) spatial objects, will be more necessary in GIS. This paper proposes a new method for modelling spatial vagueness based on type-2 fuzzy set, which is distinguished from the traditional type-1 fuzzy methods and more suitable for describing and implementing the vague concepts and objects in GIS.
Redox-dependent spatially resolved electrochemistry at graphene and graphite step edges.
Güell, Aleix G; Cuharuc, Anatolii S; Kim, Yang-Rae; Zhang, Guohui; Tan, Sze-yin; Ebejer, Neil; Unwin, Patrick R
2015-04-28
The electrochemical (EC) behavior of mechanically exfoliated graphene and highly oriented pyrolytic graphite (HOPG) is studied at high spatial resolution in aqueous solutions using Ru(NH3)6(3+/2+) as a redox probe whose standard potential sits close to the intrinsic Fermi level of graphene and graphite. When scanning electrochemical cell microscopy (SECCM) data are coupled with that from complementary techniques (AFM, micro-Raman) applied to the same sample area, different time-dependent EC activity between the basal planes and step edges is revealed. In contrast, other redox couples (ferrocene derivatives) whose potential is further removed from the intrinsic Fermi level of graphene and graphite show uniform and high activity (close to diffusion-control). Macroscopic voltammetric measurements in different environments reveal that the time-dependent behavior after HOPG cleavage, peculiar to Ru(NH3)6(3+/2+), is not associated particularly with any surface contaminants but is reasonably attributed to the spontaneous delamination of the HOPG with time to create partially coupled graphene layers, further supported by conductive AFM measurements. This process has a major impact on the density of states of graphene and graphite edges, particularly at the intrinsic Fermi level to which Ru(NH3)6(3+/2+) is most sensitive. Through the use of an improved voltammetric mode of SECCM, we produce movies of potential-resolved and spatially resolved HOPG activity, revealing how enhanced activity at step edges is a subtle effect for Ru(NH3)6(3+/2+). These latter studies allow us to propose a microscopic model to interpret the EC response of graphene (basal plane and edges) and aged HOPG considering the nontrivial electronic band structure.
Book review: Statistical Analysis and Modelling of Spatial Point Patterns
DEFF Research Database (Denmark)
Møller, Jesper
2009-01-01
Statistical Analysis and Modelling of Spatial Point Patterns by J. Illian, A. Penttinen, H. Stoyan and D. Stoyan. Wiley (2008), ISBN 9780470014912......Statistical Analysis and Modelling of Spatial Point Patterns by J. Illian, A. Penttinen, H. Stoyan and D. Stoyan. Wiley (2008), ISBN 9780470014912...
Proximal soil sensing to parameterize spatial environmental modeling
Spatially explicit models are important tools to understand the effects of the interaction of management and landscape factors on water and soil quality. One challenge to application of such models is the need to know spatially-distributed values for input parameters. Some such data can come from av...
Getis, Arthur
1997-01-01
In recent years, spatial analysis has become an increasingly active field, as evidenced by the establishment of educational and research programs at many universities. Its popularity is due mainly to new technologies and the development of spatial data infrastructures. This book illustrates some recent developments in spatial analysis, behavioural modelling, and computational intelligence. World renown spatial analysts explain and demonstrate their new and insightful models and methods. The applications are in areas of societal interest such as the spread of infectious diseases, migration behaviour, and retail and agricultural location strategies. In addition, there is emphasis on the uses of new technologoies for the analysis of spatial data through the application of neural network concepts.
Consequences of spatial autocorrelation for niche-based models
DEFF Research Database (Denmark)
Segurado, P.; Araújo, Miguel B.; Kunin, W. E.
2006-01-01
variables, as measured by Moran's I, was analysed and compared between models. The effects of systematic subsampling of the data set and the inclusion of a contagion term to deal with spatial autocorrelation in models were assessed with projections made with GLM, as it was with this method that estimates...... were vulnerable to the effects of spatial autocorrelation. 5. The procedures utilized to reduce the effects of spatial autocorrelation had varying degrees of success. Subsampling was partially effective in avoiding the inflation effect, whereas the inclusion of a contagion term fully eliminated......1. Spatial autocorrelation is an important source of bias in most spatial analyses. We explored the bias introduced by spatial autocorrelation on the explanatory and predictive power of species' distribution models, and make recommendations for dealing with the problem. 2. Analyses were based...
A spatial interaction model with spatially structured origin and destination effects
LeSage, James P.; Llano, Carlos
2013-07-01
We introduce a Bayesian hierarchical regression model that extends the traditional least-squares regression model used to estimate gravity or spatial interaction relations involving origin-destination flows. Spatial interaction models attempt to explain variation in flows from n origin regions to n destination regions resulting in a sample of N = n 2 observations that reflect an n by n flow matrix converted to a vector. Explanatory variables typically include origin and destination characteristics as well as distance between each region and all other regions. Our extension introduces latent spatial effects parameters structured to follow a spatial autoregressive process. Individual effects parameters are included in the model to reflect latent or unobservable influences at work that are unique to each region treated as an origin and destination. That is, we estimate 2 n individual effects parameters using the sample of N = n 2 observations. We illustrate the method using a sample of commodity flows between 18 Spanish regions during the 2002 period.
Emergent universe in spatially flat cosmological model
Zhang, Kaituo; Yu, Hongwei
2013-01-01
The scenario of an emergent universe provides a promising resolution to the big bang singularity in universes with positive or negative spatial curvature. It however remains unclear whether the scenario can be successfully implemented in a spatially flat universe which seems to be favored by present cosmological observations. In this paper, we study the stability of Einstein static state solutions in a spatially flat Shtanov-Sahni braneworld scenario. With a negative dark radiation term included and assuming a scalar field as the only matter energy component, we find that the universe can stay at an Einstein static state past eternally and then evolve to an inflation phase naturally as the scalar field climbs up its potential slowly. In addition, we also propose a concrete potential of the scalar field that realizes this scenario.
Limited dependent variable models for panel data
Charlier, E.
1997-01-01
Many economic phenomena require limited variable models for an appropriate treatment. In addition, panel data models allow the inclusion of unobserved individual-specific effects. These models are combined in this thesis. Distributional assumptions in the limited dependent variable models are
A formal model for access control with supporting spatial context
Institute of Scientific and Technical Information of China (English)
ZHANG Hong; HE YePing; SHI ZhiGuo
2007-01-01
There is an emerging recognition of the importance of utilizing contextual information in authorization decisions. Controlling access to resources in the field of wireless and mobile networking require the definition of a formal model for access control with supporting spatial context. However, traditional RBAC model does not specify these spatial requirements. In this paper, we extend the existing RBAC model and propose the SC-RBAC model that utilizes spatial and location-based information in security policy definitions. The concept of spatial role is presented,and the role is assigned a logical location domain to specify the spatial boundary.Roles are activated based on the current physical position of the user which obtained from a specific mobile terminal. We then extend SC-RBAC to deal with hierarchies, modeling permission, user and activation inheritance, and prove that the hierarchical spatial roles are capable of constructing a lattice which is a means for articulate multi-level security policy and more suitable to control the information flow security for safety-critical location-aware information systems. Next, constrained SC-RBAC allows express various spatial separations of duty constraints,location-based cardinality and temporal constraints for specify fine-grained spatial semantics that are typical in location-aware systems. Finally, we introduce 9 invariants for the constrained SC-RBAC and its basic security theorem is proven. The constrained SC-RBAC provides the foundation for applications in need of the constrained spatial context aware access control.
Modeling the spatial reach of the LFP
DEFF Research Database (Denmark)
Lindén, Henrik; Tetzlaff, Tom; Potjans, Tobias C
2011-01-01
The local field potential (LFP) reflects activity of many neurons in the vicinity of the recording electrode and is therefore useful for studying local network dynamics. Much of the nature of the LFP is, however, still unknown. There are, for instance, contradicting reports on the spatial extent...... distribution, and the correlation in synaptic activity. For uncorrelated activity, the LFP represents cells in a small region (within a radius of a few hundred micrometers). If the LFP contributions from different cells are correlated, the size of the generating region is determined by the spatial extent...
OECD/NEA benchmark for time-dependent neutron transport calculations without spatial homogenization
Energy Technology Data Exchange (ETDEWEB)
Hou, Jason, E-mail: jason.hou@ncsu.edu [Department of Nuclear Engineering, North Carolina State University, Raleigh, NC 27695 (United States); Ivanov, Kostadin N. [Department of Nuclear Engineering, North Carolina State University, Raleigh, NC 27695 (United States); Boyarinov, Victor F.; Fomichenko, Peter A. [National Research Centre “Kurchatov Institute”, Kurchatov Sq. 1, Moscow (Russian Federation)
2017-06-15
Highlights: • A time-dependent homogenization-free neutron transport benchmark was created. • The first phase, known as the kinetics phase, was described in this work. • Preliminary results for selected 2-D transient exercises were presented. - Abstract: A Nuclear Energy Agency (NEA), Organization for Economic Co-operation and Development (OECD) benchmark for the time-dependent neutron transport calculations without spatial homogenization has been established in order to facilitate the development and assessment of numerical methods for solving the space-time neutron kinetics equations. The benchmark has been named the OECD/NEA C5G7-TD benchmark, and later extended with three consecutive phases each corresponding to one modelling stage of the multi-physics transient analysis of the nuclear reactor core. This paper provides a detailed introduction of the benchmark specification of Phase I, known as the “kinetics phase”, including the geometry description, supporting neutron transport data, transient scenarios in both two-dimensional (2-D) and three-dimensional (3-D) configurations, as well as the expected output parameters from the participants. Also presented are the preliminary results for the initial state 2-D core and selected transient exercises that have been obtained using the Monte Carlo method and the Surface Harmonic Method (SHM), respectively.
Modeling fixation locations using spatial point processes.
Barthelmé, Simon; Trukenbrod, Hans; Engbert, Ralf; Wichmann, Felix
2013-10-01
Whenever eye movements are measured, a central part of the analysis has to do with where subjects fixate and why they fixated where they fixated. To a first approximation, a set of fixations can be viewed as a set of points in space; this implies that fixations are spatial data and that the analysis of fixation locations can be beneficially thought of as a spatial statistics problem. We argue that thinking of fixation locations as arising from point processes is a very fruitful framework for eye-movement data, helping turn qualitative questions into quantitative ones. We provide a tutorial introduction to some of the main ideas of the field of spatial statistics, focusing especially on spatial Poisson processes. We show how point processes help relate image properties to fixation locations. In particular we show how point processes naturally express the idea that image features' predictability for fixations may vary from one image to another. We review other methods of analysis used in the literature, show how they relate to point process theory, and argue that thinking in terms of point processes substantially extends the range of analyses that can be performed and clarify their interpretation.
Modelling spatial patterns of economic activity in the Netherlands
Yang, Jung-Hun; Frenken, Koen; Van Oort, Frank; Visser, Evert-Jan
2012-01-01
Understanding how spatial configurations of economic activity emerge is important when formulating spatial planning and economic policy. Not only micro-simulation and agent-based model such as UrbanSim, ILUMAS and SIMFIRMS, but also Simon's model of hierarchical concentration have widely applied, for this purpose. These models, however, have limitations with respect to simulating structural changes in spatial economic systems and the impact of proximity. The present paper proposes a model of firm development that is based on behavioural rules such as growth, closure, spin-off and relocation. An important aspect of the model is that locational preferences of firms are based on agglomeration advantages, accessibility of markets and congestion, allowing for a proper description of concentration and deconcentration tendencies. By comparing the outcomes of the proposed model with real world data, we will calibrate the parameters and assess how well the model predicts existing spatial configurations and decide. The...
Spatial emission modelling for residential wood combustion in Denmark
DEFF Research Database (Denmark)
Plejdrup, Marlene Schmidt; Nielsen, Ole-Kenneth; Brandt, Jørgen
2016-01-01
Residential wood combustion (RWC) is a major contributor to atmospheric pollution especially for particulate matter. Air pollution has significant impact on human health, and it is therefore important to know the human exposure. For this purpose, it is necessary with a detailed high resolution...... spatial distribution of emissions. In previous studies as well as in the model previously used in Denmark, the spatial resolution is limited, e.g. municipality or county level. Further, in many cases models are mainly relying on population density data as the spatial proxy for distributing the emissions....... This paper describes the new Danish model for high resolution spatial distribution of emissions from RWC to air. The new spatial emission model is based on information regarding building type, and primary and supplementary heating installations from the Danish Building and Dwelling Register (BBR), which...
Spatial Fleming-Viot models with selection and mutation
Dawson, Donald A
2014-01-01
This book constructs a rigorous framework for analysing selected phenomena in evolutionary theory of populations arising due to the combined effects of migration, selection and mutation in a spatial stochastic population model, namely the evolution towards fitter and fitter types through punctuated equilibria. The discussion is based on a number of new methods, in particular multiple scale analysis, nonlinear Markov processes and their entrance laws, atomic measure-valued evolutions and new forms of duality (for state-dependent mutation and multitype selection) which are used to prove ergodic theorems in this context and are applicable for many other questions and renormalization analysis for a variety of phenomena (stasis, punctuated equilibrium, failure of naive branching approximations, biodiversity) which occur due to the combination of rare mutation, mutation, resampling, migration and selection and make it necessary to mathematically bridge the gap (in the limit) between time and space scales.
Free-streaming radiation in cosmological models with spatial curvature
Wilson, M. L.
1982-01-01
The effects of spatial curvature on radiation anisotropy are examined for the standard Friedmann-Robertson-Walker model universes. The effect of curvature is found to be very important when considering fluctuations with wavelengths comparable to the horizon. It is concluded that the behavior of radiation fluctuations in models with spatial curvature is quite different from that in spatially flat models, and that models with negative curvature are most strikingly different. It is therefore necessary to take the curvature into account in careful studies of the anisotropy of the microwave background.
Institute of Scientific and Technical Information of China (English)
Yan Xia REN
2008-01-01
The global supports of super-Poisson processes and super-random walks with a branching mechanism ψ(z)=z2 and constant branching rate are known to be noncompact. It turns out that, for any spatially dependent branching rate, this property remains true. However, the asymptotic extinction property for these two kinds of superprocesses depends on the decay rate of the branching-rate function at infinity.
Modality dependence and intermodal transfer in the Corsi Spatial Sequence Task: Screen vs. Floor.
Röser, Andrea; Hardiess, Gregor; Mallot, Hanspeter A
2016-07-01
Four versions of the Corsi Spatial Sequence Task (CSST) were tested in a complete within-subject design, investigating whether participants' performance depends on the modality of task presentation and reproduction that put different demands on spatial processing. Presentation of the sequence (encoding phase) and the reproduction (recall phase) were each carried out either on a computer screen or on the floor of a room, involving actual walking in the recall phase. Combinations of the two different encoding and recall procedures result in the modality conditions Screen-Screen, Screen-Floor, Floor-Screen, and Floor-Floor. Results show the expected decrease in performance with increasing sequence length, which is likely due to processing limitations of working memory. We also found differences in performance between the modality conditions indicating different involvements of spatial working memory processes. Participants performed best in the Screen-Screen modality condition. Floor-Screen and Floor-Floor modality conditions require additional working memory resources for reference frame transformation and spatial updating, respectively; the resulting impairment of the performance was about the same in these two conditions. Finally, the Screen-Floor modality condition requires both types of additional spatial demands and led to the poorest performance. Therefore, we suggest that besides the well-known spatial requirements of CSST, additional working memory resources are demanded in walking CSST supporting processes such as spatial updating, mental rotation, reference frame transformation, and the control of walking itself.
Spatial-temporal assessment of climate model drifts
Zanchettin, Davide; Woldeyes Arisido, Maeregu; Gaetan, Carlo; Rubino, Angelo
2016-04-01
Decadal climate forecasts with full-field initialized coupled climate models are affected by a growing error signal that develops due to the adjustment of the simulations from the assimilated state consistent with observations to the state consistent with the biased model's climatology. Sea-surface temperature (SST) drifts and biases are a major concern due to the central role of SST properties for the dynamical coupling between the atmosphere and the ocean, and for the associated variability. Therefore, strong SST drifts complicate the initialization and assessment of decadal climate prediction experiments, and can be detrimental for their overall quality. We propose a dynamic linear model based on a state-space approach and developed within a Bayesian hierarchical framework for probabilistic assessment of spatial and temporal characteristics of SST drifts in ensemble climate simulations. The state-space approach uses unobservable state variables to directly model the processes generating the observed variability. The statistical model is based on a sequential definition of the process having a conditional dependency only on the previous time step, which therefore corresponds to the Kalman filter formulas. In our formulation, the statistical model distinguishes between seasonal and longer-term drift components, and between large-scale and local drifts. We apply the Bayesian method to make inferences on the variance components of the Gaussian errors in both the observation and system equations of the state-space model. To this purpose, we draw samples from their posterior distributions using a Monte Carlo Markov Chain simulation technique with a Gibbs sampler. In this contribution we illustrate a first application of the model using the MiKlip prototype system for decadal climate predictions. We focus on the tropical Atlantic Ocean - a region where climate models are typically affected by a severe warm SST bias - to demonstrate how our approach allows for a more
A Top-Down Spatially Resolved Electrical Load Model
Directory of Open Access Journals (Sweden)
Martin Robinius
2017-03-01
Full Text Available The increasing deployment of variable renewable energy sources (VRES is changing the source regime in the electrical energy sector. However, VRES feed-in from wind turbines and photovoltaic systems is dependent on the weather and only partially predictable. As a result, existing energy sector models must be re-evaluated and adjusted as necessary. In long-term forecast models, the expansion of VRES must be taken into account so that future local overloads can be identified and measures taken. This paper focuses on one input factor for electrical energy models: the electrical load. We compare two different types to describe this, namely vertical grid load and total load. For the total load, an approach for a spatially-resolved electrical load model is developed and applied at the municipal level in Germany. This model provides detailed information about the load at a quarterly-hour resolution across 11,268 German municipalities. In municipalities with concentrations of energy-intensive industry, high loads are expected, which our simulation reproduces with a good degree of accuracy. Our results also show that municipalities with energy-intensive industry have a higher simulated electric load than neighboring municipalities that do not host energy-intensive industries. The underlying data was extracted from publically accessible sources and therefore the methodology introduced is also applicable to other countries.
DEFF Research Database (Denmark)
Møller, Jesper; Rasmussen, Jakob Gulddahl
points is such that the dependent cluster point is likely to occur closely to a previous cluster point. We demonstrate the flexibility of the model for producing point patterns with linear structures, and propose to use the model as the likelihood in a Bayesian setting when analyzing a spatial point...
Directory of Open Access Journals (Sweden)
Wu Hanguang
2007-01-01
Full Text Available The performance of multiple-input multiple-output (MIMO systems is greatly influenced by the spatial-temporal correlation properties of the underlying MIMO channels. This paper investigates the spatial-temporal correlation characteristics of the spatial channel model (SCM in the Third Generation Partnership Project (3GPP and the Kronecker-based stochastic model (KBSM at three levels, namely, the cluster level, link level, and system level. The KBSM has both the spatial separability and spatial-temporal separability at all the three levels. The spatial-temporal separability is observed for the SCM only at the system level, but not at the cluster and link levels. The SCM shows the spatial separability at the link and system levels, but not at the cluster level since its spatial correlation is related to the joint distribution of the angle of arrival (AoA and angle of departure (AoD. The KBSM with the Gaussian-shaped power azimuth spectrum (PAS is found to fit best the 3GPP SCM in terms of the spatial correlations. Despite its simplicity and analytical tractability, the KBSM is restricted to model only the average spatial-temporal behavior of MIMO channels. The SCM provides more insights of the variations of different MIMO channel realizations, but the implementation complexity is relatively high.
Directory of Open Access Journals (Sweden)
Cheng-Xiang Wang
2007-02-01
Full Text Available The performance of multiple-input multiple-output (MIMO systems is greatly influenced by the spatial-temporal correlation properties of the underlying MIMO channels. This paper investigates the spatial-temporal correlation characteristics of the spatial channel model (SCM in the Third Generation Partnership Project (3GPP and the Kronecker-based stochastic model (KBSM at three levels, namely, the cluster level, link level, and system level. The KBSM has both the spatial separability and spatial-temporal separability at all the three levels. The spatial-temporal separability is observed for the SCM only at the system level, but not at the cluster and link levels. The SCM shows the spatial separability at the link and system levels, but not at the cluster level since its spatial correlation is related to the joint distribution of the angle of arrival (AoA and angle of departure (AoD. The KBSM with the Gaussian-shaped power azimuth spectrum (PAS is found to fit best the 3GPP SCM in terms of the spatial correlations. Despite its simplicity and analytical tractability, the KBSM is restricted to model only the average spatial-temporal behavior of MIMO channels. The SCM provides more insights of the variations of different MIMO channel realizations, but the implementation complexity is relatively high.
Kilifarska, N. A.
There are some models that describe the spatial distribution of greatest frequency yielding reflection from the F2 ionospheric layer (foF2). However, the distribution of the models' errors over the globe and how they depend on seasons, solar activity, etc., are unknown till this time. So the aim of the present paper is to compare the accuracy in describing the latitudinal and longitudinal variation of the mid-latitude maximum electron density, of CCIR, URSI, and a new created theoretical model. A comparison between the above mentioned models and all available from Boulder's data bank VI data (among 35 deg and 70 deg) have been made. Data for three whole years with different solar activity - 1976 (F_10.7 = 73.6), 1981 (F_10.7 = 20.6), 1983 (F_10.7 = 119.6) have been compared. The final results show that: 1. the areas with greatest and smallest errors depend on UT, season and solar activity; 2. the error distribution of CCIR and URSI models are very similar and are not coincident with these ones of theoretical model. The last result indicates that the theoretical model, described briefly bellow, may be a real alternative to the empirical CCIR and URSI models. The different spatial distribution of the models' errors gives a chance for the users to choose the most appropriate model, depending on their needs. Taking into account that the theoretical models have equal accuracy in region with many or without any ionosonde station, this result shows that our model can be used to improve the global mapping of the mid-latitude ionosphere. Moreover, if Re values of the input aeronomical parameters (neutral composition, temperatures and winds), are used - it may be expected that this theoretical model can be applied for Re or almost Re-time mapping of the main ionospheric parameters (foF2 and hmF2).
Estimation of exposure to toxic releases using spatial interaction modeling
Directory of Open Access Journals (Sweden)
Conley Jamison F
2011-03-01
Full Text Available Abstract Background The United States Environmental Protection Agency's Toxic Release Inventory (TRI data are frequently used to estimate a community's exposure to pollution. However, this estimation process often uses underdeveloped geographic theory. Spatial interaction modeling provides a more realistic approach to this estimation process. This paper uses four sets of data: lung cancer age-adjusted mortality rates from the years 1990 through 2006 inclusive from the National Cancer Institute's Surveillance Epidemiology and End Results (SEER database, TRI releases of carcinogens from 1987 to 1996, covariates associated with lung cancer, and the EPA's Risk-Screening Environmental Indicators (RSEI model. Results The impact of the volume of carcinogenic TRI releases on each county's lung cancer mortality rates was calculated using six spatial interaction functions (containment, buffer, power decay, exponential decay, quadratic decay, and RSEI estimates and evaluated with four multivariate regression methods (linear, generalized linear, spatial lag, and spatial error. Akaike Information Criterion values and P values of spatial interaction terms were computed. The impacts calculated from the interaction models were also mapped. Buffer and quadratic interaction functions had the lowest AIC values (22298 and 22525 respectively, although the gains from including the spatial interaction terms were diminished with spatial error and spatial lag regression. Conclusions The use of different methods for estimating the spatial risk posed by pollution from TRI sites can give different results about the impact of those sites on health outcomes. The most reliable estimates did not always come from the most complex methods.
Spatial uncertainty model for visual features using a Kinect™ sensor.
Park, Jae-Han; Shin, Yong-Deuk; Bae, Ji-Hun; Baeg, Moon-Hong
2012-01-01
This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.
Spatial Uncertainty Model for Visual Features Using a Kinect™ Sensor
Directory of Open Access Journals (Sweden)
Jae-Han Park
2012-06-01
Full Text Available This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.
Full feature data model for spatial information network integration
Institute of Scientific and Technical Information of China (English)
DENG Ji-qiu; BAO Guang-shu
2006-01-01
In allusion to the difficulty of integrating data with different models in integrating spatial information,the characteristics of raster structure, vector structure and mixed model were analyzed, and a hierarchical vectorraster integrative full feature model was put forward by integrating the advantage of vector and raster model and using the object-oriented method. The data structures of the four basic features, i.e. point, line, surface and solid,were described. An application was analyzed and described, and the characteristics of this model were described. In this model, all objects in the real world are divided into and described as features with hierarchy, and all the data are organized in vector. This model can describe data based on feature, field, network and other models, and avoid the disadvantage of inability to integrate data based on different models and perform spatial analysis on them in spatial information integration.
FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model
Russell A. Parsons
2006-01-01
Efforts to quantitatively evaluate the effectiveness of fuels treatments are hampered by inconsistencies between the spatial scale at which fuel treatments are implemented and the spatial scale, and detail, with which we model fire and fuel interactions. Central to this scale inconsistency is the resolution at which variability within the fuel bed is considered. Crown...
Modelling cell polarization driven by synthetic spatially graded Rac activation.
Directory of Open Access Journals (Sweden)
William R Holmes
Full Text Available The small GTPase Rac is known to be an important regulator of cell polarization, cytoskeletal reorganization, and motility of mammalian cells. In recent microfluidic experiments, HeLa cells endowed with appropriate constructs were subjected to gradients of the small molecule rapamycin leading to synthetic membrane recruitment of a Rac activator and direct graded activation of membrane-associated Rac. Rac activation could thus be triggered independent of upstream signaling mechanisms otherwise responsible for transducing activating gradient signals. The response of the cells to such stimulation depended on exceeding a threshold of activated Rac. Here we develop a minimal reaction-diffusion model for the GTPase network alone and for GTPase-phosphoinositide crosstalk that is consistent with experimental observations for the polarization of the cells. The modeling suggests that mutual inhibition is a more likely mode of cell polarization than positive feedback of Rac onto its own activation. We use a new analytical tool, Local Perturbation Analysis, to approximate the partial differential equations by ordinary differential equations for local and global variables. This method helps to analyze the parameter space and behaviour of the proposed models. The models and experiments suggest that (1 spatially uniform stimulation serves to sensitize a cell to applied gradients. (2 Feedback between phosphoinositides and Rho GTPases sensitizes a cell. (3 Cell lengthening/flattening accompanying polarization can increase the sensitivity of a cell and stabilize an otherwise unstable polarization.
Hijmering, R. A.; Verhoeve, P.; Kozorezov, A. G.; Martin, D. D. E.; Wigmore, J. K.; Jerjen, I.; Venn, R.; Groot, P. J.
2008-04-01
We present direct measurements of spatial and spectral resolution of cryogenic distributed readout imaging detectors (DROIDs). The spatial and spectral resolutions have been experimentally determined by scanning a 10μm spot of monochromatic visible light across the detector. The influences of the photon energy, bias voltage, and absorber length and width on the spatial and spectral resolutions have been examined. The confinement of quasiparticles in the readout sensors (superconducting tunnel junctions) as well as the detector's signal amplitude can be optimized by tuning the bias voltage, thereby improving both the spatial and spectral resolutions. Changing the length of the absorber affects the spatial and spectral resolutions in opposite manner, making it an important parameter to optimize the DROID for the application at hand. The results have been used to test expressions for photon energy, position, and spatial and spectral resolutions which have been derived by using an existing one-dimensional model. The model is found to accurately describe the experimental data, but some limitations have been identified. In particular, the model's assumption that the two sensors have identical response characteristics and noise, the approximation of the detailed quasiparticle dynamics in the sensors by border conditions, and the use of a one-dimensional diffusion process is not always adequate.
Badger, Daniel; Barnden, Leighton
2014-09-01
The aim of this study is to determine the dependence of single photon emission computed tomography (SPECT) spatial resolution on the content of images for iterative reconstruction with distance dependent resolution (DDR) correction. An experiment was performed using a perturbation technique to measure change in resolution of line sources in simple and complex images with iterative reconstruction with increasing iteration. Projections of the line sources were reconstructed alone and again after the addition of projections of a uniform flood or a complex phantom. An alternative experiment used images of a realistic brain phantom and evaluated an effective spatial resolution by matching the images to the digital version of the phantom convolved with 3D Gaussian kernels. The experiments were performed using ordered subset expectation maximisation iterative reconstruction with and without the use of DDR correction. The results show a significant difference in reconstructed resolution between images of line sources depending on the content of the added image. The full width at half maximum of images of a line source reconstructed using DDR correction increased by 20-30 % when the added image was complex. Without DDR this difference was much smaller and disappeared with increasing iteration. Reported SPECT resolution should be taken as indicative only with regard to clinical imaging if the measurement is made using a point or line source alone and an iterative reconstruction algorithm is used.
Liu, Jianfeng; Zhang, Xiang; Wu, Bi; Pan, Guoyan; Xu, Jing; Wu, Shaofei
2017-07-19
Land use pattern is an effective reflection of anthropic activities, which are primarily responsible for water quality deterioration. A detailed understanding of relationship between water quality and land use is critical for effective land use management to improve water quality. Linear mixed effects and multiple regression models were applied to water quality data collected from 2003 to 2010 from 36 stations in the Huai River basin together with topography and climate data, to characterize the land use impacts on water quality and their spatial scale and seasonal dependence. The results indicated that the influence of land use categories on specific water quality parameter was multiple and varied with spatial scales and seasons. Land use exhibited strongest association with dissolved oxygen (DO) and ammonia nitrogen (NH3-N) concentrations at entire watershed scale and with total phosphorus (TP) and fluoride concentrations at finer scales. However, the spatial scale, at which land use exerted strongest influence on instream chemical oxygen demand (COD) and biochemical oxygen demand (BOD) levels, varied with seasons. In addition, land use composition was responsible for the seasonal pattern observed in contaminant concentrations. COD, NH3-N, and fluoride generally peaked during dry seasons in highly urbanized regions and during rainy seasons in less urbanized regions. High proportion of agricultural and rural areas was associated with high nutrient contamination risk during spring. The results highlight the spatial scale and seasonal dependence of land use impacts on water quality and can provide scientific basis for scale-specific land management and seasonal contamination control.
A Spatial Model for the Instantaneous Estimation of Wind Power at a Large Number of Unobserved Sites
DEFF Research Database (Denmark)
Lenzi, Amanda; Guillot, Gilles; Pinson, Pierre
2015-01-01
We propose a hierarchical Bayesian spatial model to obtain predictive densities of wind power at a set of un-monitored locations. The model consists of a mixture of Gamma density for the non-zero values and degenerated distributions at zero. The spatial dependence is described through a common Ga...
Spatial Statistical Procedures to Validate Input Data in Energy Models
Energy Technology Data Exchange (ETDEWEB)
Johannesson, G.; Stewart, J.; Barr, C.; Brady Sabeff, L.; George, R.; Heimiller, D.; Milbrandt, A.
2006-01-01
Energy modeling and analysis often relies on data collected for other purposes such as census counts, atmospheric and air quality observations, economic trends, and other primarily non-energy related uses. Systematic collection of empirical data solely for regional, national, and global energy modeling has not been established as in the abovementioned fields. Empirical and modeled data relevant to energy modeling is reported and available at various spatial and temporal scales that might or might not be those needed and used by the energy modeling community. The incorrect representation of spatial and temporal components of these data sets can result in energy models producing misleading conclusions, especially in cases of newly evolving technologies with spatial and temporal operating characteristics different from the dominant fossil and nuclear technologies that powered the energy economy over the last two hundred years. Increased private and government research and development and public interest in alternative technologies that have a benign effect on the climate and the environment have spurred interest in wind, solar, hydrogen, and other alternative energy sources and energy carriers. Many of these technologies require much finer spatial and temporal detail to determine optimal engineering designs, resource availability, and market potential. This paper presents exploratory and modeling techniques in spatial statistics that can improve the usefulness of empirical and modeled data sets that do not initially meet the spatial and/or temporal requirements of energy models. In particular, we focus on (1) aggregation and disaggregation of spatial data, (2) predicting missing data, and (3) merging spatial data sets. In addition, we introduce relevant statistical software models commonly used in the field for various sizes and types of data sets.
Spatial Statistical Procedures to Validate Input Data in Energy Models
Energy Technology Data Exchange (ETDEWEB)
Lawrence Livermore National Laboratory
2006-01-27
Energy modeling and analysis often relies on data collected for other purposes such as census counts, atmospheric and air quality observations, economic trends, and other primarily non-energy-related uses. Systematic collection of empirical data solely for regional, national, and global energy modeling has not been established as in the above-mentioned fields. Empirical and modeled data relevant to energy modeling is reported and available at various spatial and temporal scales that might or might not be those needed and used by the energy modeling community. The incorrect representation of spatial and temporal components of these data sets can result in energy models producing misleading conclusions, especially in cases of newly evolving technologies with spatial and temporal operating characteristics different from the dominant fossil and nuclear technologies that powered the energy economy over the last two hundred years. Increased private and government research and development and public interest in alternative technologies that have a benign effect on the climate and the environment have spurred interest in wind, solar, hydrogen, and other alternative energy sources and energy carriers. Many of these technologies require much finer spatial and temporal detail to determine optimal engineering designs, resource availability, and market potential. This paper presents exploratory and modeling techniques in spatial statistics that can improve the usefulness of empirical and modeled data sets that do not initially meet the spatial and/or temporal requirements of energy models. In particular, we focus on (1) aggregation and disaggregation of spatial data, (2) predicting missing data, and (3) merging spatial data sets. In addition, we introduce relevant statistical software models commonly used in the field for various sizes and types of data sets.
Error Threshold for Spatially Resolved Evolution in the Quasispecies Model
Energy Technology Data Exchange (ETDEWEB)
Altmeyer, S.; McCaskill, J. S.
2001-06-18
The error threshold for quasispecies in 1, 2, 3, and {infinity} dimensions is investigated by stochastic simulation and analytically. The results show a monotonic decrease in the maximal sustainable error probability with decreasing diffusion coefficient, independently of the spatial dimension. It is thereby established that physical interactions between sequences are necessary in order for spatial effects to enhance the stabilization of biological information. The analytically tractable behavior in an {infinity} -dimensional (simplex) space provides a good guide to the spatial dependence of the error threshold in lower dimensional Euclidean space.
Study on spatial temporal model in property management information system
Institute of Scientific and Technical Information of China (English)
李良宝; 李晓东
2004-01-01
Time is an important dimension for information in the geographical information system. Data, such as the historical state of target property space and related events causing the state to be changed, should be saved as important files. This should be applied to property management. This paper designs and constructs a spatial temporal model, which is suitable to the property data changing management and spatial temporal query by analyzing the basic types and characteristics of property management spatial changing time and date. This model uses current and historical situational layers to organize and set up the relationship between current situation data and historical dates according to spatial temporal topological relations in property entities. By using Map Basic, housing property management and spatial query is realized.
Valid auto-models for spatially autocorrelated occupancy and abundance data
Bardos, David C.; Guillera-Arroita, Gurutzeta; Wintle, Brendan A.
2015-01-01
Auto-logistic and related auto-models, implemented approximately as autocovariate regression, provide simple and direct modelling of spatial dependence. The autologistic model has been widely applied in ecology since Augustin, Mugglestone and Buckland (J. Appl. Ecol., 1996, 33, 339) analysed red deer census data using a hybrid estimation approach, combining maximum pseudo-likelihood estimation with Gibbs sampling of missing data. However Dormann (Ecol. Model., 2007, 207, 234) questioned the v...
Neutrino flavor instabilities in a time-dependent supernova model
Directory of Open Access Journals (Sweden)
Sajad Abbar
2015-12-01
Full Text Available A dense neutrino medium such as that inside a core-collapse supernova can experience collective flavor conversion or oscillations because of the neutral-current weak interaction among the neutrinos. This phenomenon has been studied in a restricted, stationary supernova model which possesses the (spatial spherical symmetry about the center of the supernova and the (directional axial symmetry around the radial direction. Recently it has been shown that these spatial and directional symmetries can be broken spontaneously by collective neutrino oscillations. In this letter we analyze the neutrino flavor instabilities in a time-dependent supernova model. Our results show that collective neutrino oscillations start at approximately the same radius in both the stationary and time-dependent supernova models unless there exist very rapid variations in local physical conditions on timescales of a few microseconds or shorter. Our results also suggest that collective neutrino oscillations can vary rapidly with time in the regimes where they do occur which need to be studied in time-dependent supernova models.
Scale-based spatial data model for GIS
Institute of Scientific and Technical Information of China (English)
WEI Zu-kuan
2004-01-01
Being the primary media of geographical information and the elementary objects manipulated, almost all of maps adopt the layer-based model to represent geographic information in the existent GIS. However, it is difficult to extend the map represented in layer-based model. Furthermore, in Web-Based GIS, It is slow to transmit the spatial data for map viewing. In this paper, for solving the questions above, we have proposed a new method for representing the spatial data. That is scale-based model. In this model we represent maps in three levels: scale-view, block, and spatial object, and organize the maps in a set of map layers, named Scale-View, which associates some given scales.Lastly, a prototype Web-Based GIS using the proposed spatial data representation is described briefly.
Spatial modelling of wind speed around windbreaks
Vigiak, O.; Sterk, G.; Warren, A.; Hagen, L.J.
2003-01-01
This paper presents a model to integrate windbreak shelter effects into a Geographic Information System (GIS). The GIS procedure incorporates the 1999 version windbreak sub-model of the Wind Erosion Prediction System (WEPS). Windbreak shelter is modeled in terms of friction velocity reduction, which
Time-dependent models of dense PDRs with complex molecules
Morata, O.; Herbst, E.
2008-01-01
We present a study of the chemistry of a dense photon-dominated region (PDR) using a time-dependent chemical model. Our major interest is to study the spatial distribution of complex molecules such as hydrocarbons and cyanopolyynes in the cool dense material bordering regions where star formation has taken place. Our standard model uses a homogeneous cloud of density 2x10e4 cm-3 and temperature T=40 K, which is irradiated by a far-ultraviolet radiation field of intermediate intensity, given b...
Modeling signalized intersection safety with corridor-level spatial correlations.
Guo, Feng; Wang, Xuesong; Abdel-Aty, Mohamed A
2010-01-01
Intersections in close spatial proximity along a corridor should be considered as correlated due to interacted traffic flows as well as similar road design and environmental characteristics. It is critical to incorporate this spatial correlation for assessing the true safety impacts of risk factors. In this paper, several Bayesian models were developed to model the crash data from 170 signalized intersections in the state of Florida. The safety impacts of risk factors such as geometric design features, traffic control, and traffic flow characteristics were evaluated. The Poisson and Negative Binomial Bayesian models with non-informative priors were fitted but the focus is to incorporate spatial correlations among intersections. Two alternative models were proposed to capture this correlation: (1) a mixed effect model in which the corridor-level correlation is incorporated through a corridor-specific random effect and (2) a conditional autoregressive model in which the magnitude of correlations is determined by spatial distances among intersections. The models were compared using the Deviance Information Criterion. The results indicate that the Poisson spatial model provides the best model fitting. Analysis of the posterior distributions of model parameters indicated that the size of intersection, the traffic conditions by turning movement, and the coordination of signal phase have significant impacts on intersection safety.
Regression-based air temperature spatial prediction models: an example from Poland
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Mariusz Szymanowski
2013-10-01
Full Text Available A Geographically Weighted Regression ? Kriging (GWRK algorithm, based on the local Geographically Weighted Regression (GWR, is applied for spatial prediction of air temperature in Poland. Hengl's decision tree for selecting a suitable prediction model is extended for varying spatial relationships between the air temperature and environmental predictors with an assumption of existing environmental dependence of analyzed temperature variables. The procedure includes the potential choice of a local GWR instead of the global Multiple Linear Regression (MLR method for modeling the deterministic part of spatial variation, which is usual in the standard regression (residual kriging model (MLRK. The analysis encompassed: testing for environmental correlation, selecting an appropriate regression model, testing for spatial autocorrelation of the residual component, and validating the prediction accuracy. The proposed approach was performed for 69 air temperature cases, with time aggregation ranging from daily to annual average air temperatures. The results show that, irrespective of the level of data aggregation, the spatial distribution of temperature is better fitted by local models, and hence is the reason for choosing a GWR instead of the MLR for all variables analyzed. Additionally, in most cases (78% there is spatial autocorrelation in the residuals of the deterministic part, which suggests that the GWR model should be extended by ordinary kriging of residuals to the GWRK form. The decision tree used in this paper can be considered as universal as it encompasses either spatially varying relationships of modeled and explanatory variables or random process that can be modeled by a stochastic extension of the regression model (residual kriging. Moreover, for all cases analyzed, the selection of a method based on the local regression model (GWRK or GWR does not depend on the data aggregation level, showing the potential versatility of the technique.
Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.
2015-01-01
Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.
Roy, Vivekananda; Evangelou, Evangelos; Zhu, Zhengyuan
2016-03-01
Spatial generalized linear mixed models (SGLMMs) are popular models for spatial data with a non-Gaussian response. Binomial SGLMMs with logit or probit link functions are often used to model spatially dependent binomial random variables. It is known that for independent binomial data, the robit regression model provides a more robust (against extreme observations) alternative to the more popular logistic and probit models. In this article, we introduce a Bayesian spatial robit model for spatially dependent binomial data. Since constructing a meaningful prior on the link function parameter as well as the spatial correlation parameters in SGLMMs is difficult, we propose an empirical Bayes (EB) approach for the estimation of these parameters as well as for the prediction of the random effects. The EB methodology is implemented by efficient importance sampling methods based on Markov chain Monte Carlo (MCMC) algorithms. Our simulation study shows that the robit model is robust against model misspecification, and our EB method results in estimates with less bias than full Bayesian (FB) analysis. The methodology is applied to a Celastrus Orbiculatus data, and a Rhizoctonia root data. For the former, which is known to contain outlying observations, the robit model is shown to do better for predicting the spatial distribution of an invasive species. For the latter, our approach is doing as well as the classical models for predicting the disease severity for a root disease, as the probit link is shown to be appropriate. Though this article is written for Binomial SGLMMs for brevity, the EB methodology is more general and can be applied to other types of SGLMMs. In the accompanying R package geoBayes, implementations for other SGLMMs such as Poisson and Gamma SGLMMs are provided.
S4: A Spatial-Spectral model for Speckle Suppression
Fergus, Rob; Oppenheimer, Rebecca; Brenner, Douglas; Pueyo, Laurent
2014-01-01
High dynamic-range imagers aim to block out or null light from a very bright primary star to make it possible to detect and measure far fainter companions; in real systems a small fraction of the primary light is scattered, diffracted, and unocculted. We introduce S4, a flexible data-driven model for the unocculted (and highly speckled) light in the P1640 spectroscopic coronograph. The model uses Principal Components Analysis (PCA) to capture the spatial structure and wavelength dependence of the speckles but not the signal produced by any companion. Consequently, the residual typically includes the companion signal. The companion can thus be found by filtering this error signal with a fixed companion model. The approach is sensitive to companions that are of order a percent of the brightness of the speckles, or up to $10^{-7}$ times the brightness of the primary star. This outperforms existing methods by a factor of 2-3 and is close to the shot-noise physical limit.
S4: A spatial-spectral model for speckle suppression
Energy Technology Data Exchange (ETDEWEB)
Fergus, Rob [Department of Computer Science, New York University, New York, NY 10003 (United States); Hogg, David W. [Center for Cosmology and Particle Physics, Department of Physics, New York University, New York, NY 10003 (United States); Oppenheimer, Rebecca; Brenner, Douglas [Department of Astrophysics, American Museum of Natural History, New York, NY 10024-5192 (United States); Pueyo, Laurent, E-mail: fergus@cs.nyu.edu [Space Telescope Science Institute, Baltimore, MD 21218 (United States)
2014-10-20
High dynamic range imagers aim to block or eliminate light from a very bright primary star in order to make it possible to detect and measure far fainter companions; in real systems, a small fraction of the primary light is scattered, diffracted, and unocculted. We introduce S4, a flexible data-driven model for the unocculted (and highly speckled) light in the P1640 spectroscopic coronagraph. The model uses principal components analysis (PCA) to capture the spatial structure and wavelength dependence of the speckles, but not the signal produced by any companion. Consequently, the residual typically includes the companion signal. The companion can thus be found by filtering this error signal with a fixed companion model. The approach is sensitive to companions that are of the order of a percent of the brightness of the speckles, or up to 10{sup –7} times the brightness of the primary star. This outperforms existing methods by a factor of two to three and is close to the shot-noise physical limit.
How does spatial study design influence density estimates from spatial capture-recapture models?
Directory of Open Access Journals (Sweden)
Rahel Sollmann
Full Text Available When estimating population density from data collected on non-invasive detector arrays, recently developed spatial capture-recapture (SCR models present an advance over non-spatial models by accounting for individual movement. While these models should be more robust to changes in trapping designs, they have not been well tested. Here we investigate how the spatial arrangement and size of the trapping array influence parameter estimates for SCR models. We analysed black bear data collected with 123 hair snares with an SCR model accounting for differences in detection and movement between sexes and across the trapping occasions. To see how the size of the trap array and trap dispersion influence parameter estimates, we repeated analysis for data from subsets of traps: 50% chosen at random, 50% in the centre of the array and 20% in the South of the array. Additionally, we simulated and analysed data under a suite of trap designs and home range sizes. In the black bear study, we found that results were similar across trap arrays, except when only 20% of the array was used. Black bear density was approximately 10 individuals per 100 km(2. Our simulation study showed that SCR models performed well as long as the extent of the trap array was similar to or larger than the extent of individual movement during the study period, and movement was at least half the distance between traps. SCR models performed well across a range of spatial trap setups and animal movements. Contrary to non-spatial capture-recapture models, they do not require the trapping grid to cover an area several times the average home range of the studied species. This renders SCR models more appropriate for the study of wide-ranging mammals and more flexible to design studies targeting multiple species.
Validating a spatially distributed hydrological model with soil morphology data
Directory of Open Access Journals (Sweden)
T. Doppler
2013-10-01
Full Text Available Spatially distributed hydrological models are popular tools in hydrology and they are claimed to be useful to support management decisions. Despite the high spatial resolution of the computed variables, calibration and validation is often carried out only on discharge time-series at specific locations due to the lack of spatially distributed reference data. Because of this restriction, the predictive power of these models, with regard to predicted spatial patterns, can usually not be judged. An example of spatial predictions in hydrology is the prediction of saturated areas in agricultural catchments. These areas can be important source areas for the transport of agrochemicals to the stream. We set up a spatially distributed model to predict saturated areas in a 1.2 km2 catchment in Switzerland with moderate topography. Around 40% of the catchment area are artificially drained. We measured weather data, discharge and groundwater levels in 11 piezometers for 1.5 yr. For broadening the spatially distributed data sets that can be used for model calibration and validation, we translated soil morphological data available from soil maps into an estimate of the duration of soil saturation in the soil horizons. We used redox-morphology signs for these estimates. This resulted in a data set with high spatial coverage on which the model predictions were validated. In general, these saturation estimates corresponded well to the measured groundwater levels. We worked with a model that would be applicable for management decisions because of its fast calculation speed and rather low data requirements. We simultaneously calibrated the model to the groundwater levels in the piezometers and discharge. The model was able to reproduce the general hydrological behavior of the catchment in terms of discharge and absolute groundwater levels. However, the accuracy of the groundwater level predictions was not high enough to be used for the prediction of saturated areas
Spatial heterogeneity, frequency-dependent selection and polymorphism in host-parasite interactions
Directory of Open Access Journals (Sweden)
Tellier Aurélien
2011-11-01
Full Text Available Abstract Background Genomic and pathology analysis has revealed enormous diversity in genes involved in disease, including those encoding host resistance and parasite effectors (also known in plant pathology as avirulence genes. It has been proposed that such variation may persist when an organism exists in a spatially structured metapopulation, following the geographic mosaic of coevolution. Here, we study gene-for-gene relationships governing the outcome of plant-parasite interactions in a spatially structured system and, in particular, investigate the population genetic processes which maintain balanced polymorphism in both species. Results Following previous theory on the effect of heterogeneous environments on maintenance of polymorphism, we analysed a model with two demes in which the demes have different environments and are coupled by gene flow. Environmental variation is manifested by different coefficients of natural selection, the costs to the host of resistance and to the parasite of virulence, the cost to the host of being diseased and the cost to an avirulent parasite of unsuccessfully attacking a resistant host. We show that migration generates negative direct frequency-dependent selection, a condition for maintenance of stable polymorphism in each deme. Balanced polymorphism occurs preferentially if there is heterogeneity for costs of resistance and virulence alleles among populations and to a lesser extent if there is variation in the cost to the host of being diseased. We show that the four fitness costs control the natural frequency of oscillation of host resistance and parasite avirulence alleles. If demes have different costs, their frequencies of oscillation differ and when coupled by gene flow, there is amplitude death of the oscillations in each deme. Numerical simulations show that for a multiple deme island model, costs of resistance and virulence need not to be present in each deme for stable polymorphism to occur
Spatial emission modelling for residential wood combustion in Denmark
Plejdrup, Marlene S.; Nielsen, Ole-Kenneth; Brandt, Jørgen
2016-11-01
Residential wood combustion (RWC) is a major contributor to atmospheric pollution especially for particulate matter. Air pollution has significant impact on human health, and it is therefore important to know the human exposure. For this purpose, it is necessary with a detailed high resolution spatial distribution of emissions. In previous studies as well as in the model previously used in Denmark, the spatial resolution is limited, e.g. municipality or county level. Further, in many cases models are mainly relying on population density data as the spatial proxy for distributing the emissions. This paper describes the new Danish model for high resolution spatial distribution of emissions from RWC to air. The new spatial emission model is based on information regarding building type, and primary and supplementary heating installations from the Danish Building and Dwelling Register (BBR), which holds detailed data for all buildings in Denmark. The new model provides a much more accurate distribution of emissions than the previous model used in Denmark, as the resolution has been increased from municipality level to a 1 km × 1 km resolution, and the distribution key has been significantly improved so that it no longer puts an excessive weight on population density. The new model has been verified for the city of Copenhagen, where emissions estimated using both the previous and the new model have been compared to the emissions estimated in a case study. This comparison shows that the new model with the developed weighting factors (76 ton PM2.5) is in good agreement with the case study (95 ton PM2.5), and that the new model has improved the spatial emission distribution significantly compared to the previous model (284 ton PM2.5). Additionally, a sensitivity analysis was done to illustrate the impact of the weighting factors on the result, showing that the new model independently of the weighting factors chosen produce a more accurate result than the old model.
Spatial Error Metrics for Oceanographic Model Verification
2012-02-01
quantitatively and qualitatively for this oceano - graphic data and successfully separates the model error into displacement and intensity components. This... oceano - graphic models as well, though one would likely need to make special modifications to handle the often-used nonuniform spacing between depth layers
Learning Anatomy: Do New Computer Models Improve Spatial Understanding?
Garg, Amit; Norman, Geoff; Spero, Lawrence; Taylor, Ian
1999-01-01
Assesses desktop-computer models that rotate in virtual three-dimensional space. Compares spatial learning with a computer carpal-bone model horizontally rotating at 10-degree views with the same model rotating at 90-degree views. (Author/CCM)
Isothermal Langevin dynamics in systems with power-law spatially dependent friction.
Regev, Shaked; Grønbech-Jensen, Niels; Farago, Oded
2016-07-01
We study the dynamics of Brownian particles in a heterogeneous one-dimensional medium with a spatially dependent diffusion coefficient of the form D(x)∼|x|^{c}, at constant temperature. The particle's probability distribution function (PDF) is calculated both analytically, by solving Fick's diffusion equation, and from numerical simulations of the underdamped Langevin equation. At long times, the PDFs calculated by both approaches yield identical results, corresponding to subdiffusion for c1, the diffusion equation predicts that the particles accelerate. Here we show that this phenomenon, previously considered in several works as an illustration for the possible dramatic effects of spatially dependent thermal noise, is unphysical. We argue that in an isothermal medium, the motion cannot exceed the ballistic limit (〈x^{2}〉∼t^{2}). The ballistic limit is reached when the friction coefficient drops sufficiently fast at large distances from the origin and is correctly captured by Langevin's equation.
Spatial phase sensitivity of complex cells in primary visual cortex depends on stimulus contrast.
Meffin, H; Hietanen, M A; Cloherty, S L; Ibbotson, M R
2015-12-01
Neurons in primary visual cortex are classified as simple, which are phase sensitive, or complex, which are significantly less phase sensitive. Previously, we have used drifting gratings to show that the phase sensitivity of complex cells increases at low contrast and after contrast adaptation while that of simple cells remains the same at all contrasts (Cloherty SL, Ibbotson MR. J Neurophysiol 113: 434-444, 2015; Crowder NA, van Kleef J, Dreher B, Ibbotson MR. J Neurophysiol 98: 1155-1166, 2007; van Kleef JP, Cloherty SL, Ibbotson MR. J Physiol 588: 3457-3470, 2010). However, drifting gratings confound the influence of spatial and temporal summation, so here we have stimulated complex cells with gratings that are spatially stationary but continuously reverse the polarity of the contrast over time (contrast-reversing gratings). By varying the spatial phase and contrast of the gratings we aimed to establish whether the contrast-dependent phase sensitivity of complex cells results from changes in spatial or temporal processing or both. We found that most of the increase in phase sensitivity at low contrasts could be attributed to changes in the spatial phase sensitivities of complex cells. However, at low contrasts the complex cells did not develop the spatiotemporal response characteristics of simple cells, in which paired response peaks occur 180° out of phase in time and space. Complex cells that increased their spatial phase sensitivity at low contrasts were significantly overrepresented in the supragranular layers of cortex. We conclude that complex cells in supragranular layers of cat cortex have dynamic spatial summation properties and that the mechanisms underlying complex cell receptive fields differ between cortical layers.
Comparison of spatial extreme value models for snow depth extremes in Austria
Schellander, Harald; Hell, Tobias
2017-04-01
In Alpine regions like Austria a spatial representation of extreme snow depth is of crucial importance for numerous purposes such as the designing of construction projects. Extreme value theory builds the well-established foundation of modeling extremes. Two different approaches for the spatial modeling of snow depth extremes have been extensively investigated lately: Smooth Spatial Modeling (Blanchet and Lehning, 2010) and different classes of max-stable processes (Blanchet and Davison, 2011; Nicolet et al., 2015), both outperforming classical interpolation techniques. While max-stable models are generally considered as improvement over smooth modeling, the methods have not been compared in the context of extreme snow depth. In the present study a great variety of different GEV models is fitted to seasonal snow depth maxima measured at more than 200 Austrian weather stations. Return levels of smooth spatial models and several max-stable representations (Schlather, Brown-Resnick, Geometric Gaussian, Extremal-t) and covariance models (Powered Exponential, Brown, Whittle-Matern), also allowing for anisotropic extremal dependence are compared by a modified Anderson-Darling score and a normalized RMSE. Preliminary results show, that for snow depth extremes in Austria smooth spatial modeling and a version with extremal coefficients as covariates deliver slightly better scores than (an)-isotropic max-stable models.
Directory of Open Access Journals (Sweden)
Jolley Damien
2011-10-01
Full Text Available Abstract Background Analytic methods commonly used in epidemiology do not account for spatial correlation between observations. In regression analyses, omission of that autocorrelation can bias parameter estimates and yield incorrect standard error estimates. Methods We used age standardised incidence ratios (SIRs of esophageal cancer (EC from the Babol cancer registry from 2001 to 2005, and extracted socioeconomic indices from the Statistical Centre of Iran. The following models for SIR were used: (1 Poisson regression with agglomeration-specific nonspatial random effects; (2 Poisson regression with agglomeration-specific spatial random effects. Distance-based and neighbourhood-based autocorrelation structures were used for defining the spatial random effects and a pseudolikelihood approach was applied to estimate model parameters. The Bayesian information criterion (BIC, Akaike's information criterion (AIC and adjusted pseudo R2, were used for model comparison. Results A Gaussian semivariogram with an effective range of 225 km best fit spatial autocorrelation in agglomeration-level EC incidence. The Moran's I index was greater than its expected value indicating systematic geographical clustering of EC. The distance-based and neighbourhood-based Poisson regression estimates were generally similar. When residual spatial dependence was modelled, point and interval estimates of covariate effects were different to those obtained from the nonspatial Poisson model. Conclusions The spatial pattern evident in the EC SIR and the observation that point estimates and standard errors differed depending on the modelling approach indicate the importance of accounting for residual spatial correlation in analyses of EC incidence in the Caspian region of Iran. Our results also illustrate that spatial smoothing must be applied with care.
Can spatial statistical river temperature models be transferred between catchments?
Jackson, Faye L.; Fryer, Robert J.; Hannah, David M.; Malcolm, Iain A.
2017-09-01
There has been increasing use of spatial statistical models to understand and predict river temperature (Tw) from landscape covariates. However, it is not financially or logistically feasible to monitor all rivers and the transferability of such models has not been explored. This paper uses Tw data from four river catchments collected in August 2015 to assess how well spatial regression models predict the maximum 7-day rolling mean of daily maximum Tw (Twmax) within and between catchments. Models were fitted for each catchment separately using (1) landscape covariates only (LS models) and (2) landscape covariates and an air temperature (Ta) metric (LS_Ta models). All the LS models included upstream catchment area and three included a river network smoother (RNS) that accounted for unexplained spatial structure. The LS models transferred reasonably to other catchments, at least when predicting relative levels of Twmax. However, the predictions were biased when mean Twmax differed between catchments. The RNS was needed to characterise and predict finer-scale spatially correlated variation. Because the RNS was unique to each catchment and thus non-transferable, predictions were better within catchments than between catchments. A single model fitted to all catchments found no interactions between the landscape covariates and catchment, suggesting that the landscape relationships were transferable. The LS_Ta models transferred less well, with particularly poor performance when the relationship with the Ta metric was physically implausible or required extrapolation outside the range of the data. A single model fitted to all catchments found catchment-specific relationships between Twmax and the Ta metric, indicating that the Ta metric was not transferable. These findings improve our understanding of the transferability of spatial statistical river temperature models and provide a foundation for developing new approaches for predicting Tw at unmonitored locations across
Directory of Open Access Journals (Sweden)
Karsten Schönrogge
Full Text Available Revealing the interactions between alien species and native communities is central to understanding the ecological consequences of range expansion. Much has been learned through study of the communities developing around invading herbivorous insects. Much less, however, is known about the significance of such aliens for native vertebrate predators for which invaders may represent a novel food source. We quantified spatial patterns in native bird predation of invading gall-inducing Andricus wasps associated with introduced Turkey oak (Quercus cerris at eight sites across the UK. These gallwasps are available at high density before the emergence of caterpillars that are the principle spring food of native insectivorous birds. Native birds showed positive spatial density dependence in gall attack rates at two sites in southern England, foraging most extensively on trees with highest gall densities. In a subsequent study at one of these sites, positive spatial density dependence persisted through four of five sequential week-long periods of data collection. Both patterns imply that invading galls are a significant resource for at least some native bird populations. Density dependence was strongest in southern UK bird populations that have had longest exposure to the invading gallwasps. We hypothesise that this pattern results from the time taken for native bird populations to learn how to exploit this novel resource.
Maestre, F.T.; Castillo-Monroy, A. P.; Bowker, M.A.; Ochoa-Hueso, R.
2012-01-01
1. Recent studies have suggested that the simultaneous maintenance of multiple ecosystem functions (multifunctionality) is positively supported by species richness. However, little is known regarding the relative importance of other community attributes (e.g. spatial pattern, species evenness) as drivers of multifunctionality. 2. We conducted two microcosm experiments using model biological soil crust communities dominated by lichens to: (i) evaluate the joint effects and relative importance of changes in species composition, spatial pattern (clumped and random distribution of lichens), evenness (maximal and low evenness) and richness (from two to eight species) on soil functions related to nutrient cycling (β-glucosidase, urease and acid phosphatase enzymes, in situ N availability, total N, organic C, and N fixation), and (ii) assess how these community attributes affect multifunctionality. 3. Species richness, composition and spatial pattern affected multiple ecosystem functions (e.g. organic C, total N, N availability, β-glucosidase activity), albeit the magnitude and direction of their effects varied with the particular function, experiment and soil depth considered. Changes in species composition had effects on organic C, total N and the activity of β-glucosidase. Significant species richness × evenness and spatial pattern × evenness interactions were found when analysing functions such as organic C, total N and the activity of phosphatase. 4. The probability of sustaining multiple ecosystem functions increased with species richness, but this effect was largely modulated by attributes such as species evenness, composition and spatial pattern. Overall, we found that model communities with high species richness, random spatial pattern and low evenness increased multifunctionality. 5. Synthesis. Our results illustrate how different community attributes have a diverse impact on ecosystem functions related to nutrient cycling, and provide new
Hanks, Ephraim M.; Schliep, Erin M.; Hooten, Mevin B.; Hoeting, Jennifer A.
2015-01-01
In spatial generalized linear mixed models (SGLMMs), covariates that are spatially smooth are often collinear with spatially smooth random effects. This phenomenon is known as spatial confounding and has been studied primarily in the case where the spatial support of the process being studied is discrete (e.g., areal spatial data). In this case, the most common approach suggested is restricted spatial regression (RSR) in which the spatial random effects are constrained to be orthogonal to the fixed effects. We consider spatial confounding and RSR in the geostatistical (continuous spatial support) setting. We show that RSR provides computational benefits relative to the confounded SGLMM, but that Bayesian credible intervals under RSR can be inappropriately narrow under model misspecification. We propose a posterior predictive approach to alleviating this potential problem and discuss the appropriateness of RSR in a variety of situations. We illustrate RSR and SGLMM approaches through simulation studies and an analysis of malaria frequencies in The Gambia, Africa.
Empirical spatial econometric modelling of small scale neighbourhood
Gerkman, Linda
2012-07-01
The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.
Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.
2013-01-01
In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.
Unleashing spatially distributed ecohydrology modeling using Big Data tools
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
Spatial memory tasks in rodents: what do they model?
Morellini, Fabio
2013-10-01
The analysis of spatial learning and memory in rodents is commonly used to investigate the mechanisms underlying certain forms of human cognition and to model their dysfunction in neuropsychiatric and neurodegenerative diseases. Proper interpretation of rodent behavior in terms of spatial memory and as a model of human cognitive functions is only possible if various navigation strategies and factors controlling the performance of the animal in a spatial task are taken into consideration. The aim of this review is to describe the experimental approaches that are being used for the study of spatial memory in rats and mice and the way that they can be interpreted in terms of general memory functions. After an introduction to the classification of memory into various categories and respective underlying neuroanatomical substrates, I explain the concept of spatial memory and its measurement in rats and mice by analysis of their navigation strategies. Subsequently, I describe the most common paradigms for spatial memory assessment with specific focus on methodological issues relevant for the correct interpretation of the results in terms of cognitive function. Finally, I present recent advances in the use of spatial memory tasks to investigate episodic-like memory in mice.
Upscaling of Mixing Processes using a Spatial Markov Model
Bolster, Diogo; Sund, Nicole; Porta, Giovanni
2016-11-01
The Spatial Markov model is a model that has been used to successfully upscale transport behavior across a broad range of spatially heterogeneous flows, with most examples to date coming from applications relating to porous media. In its most common current forms the model predicts spatially averaged concentrations. However, many processes, including for example chemical reactions, require an adequate understanding of mixing below the averaging scale, which means that knowledge of subscale fluctuations, or closures that adequately describe them, are needed. Here we present a framework, consistent with the Spatial Markov modeling framework, that enables us to do this. We apply and present it as applied to a simple example, a spatially periodic flow at low Reynolds number. We demonstrate that our upscaled model can successfully predict mixing by comparing results from direct numerical simulations to predictions with our upscaled model. To this end we focus on predicting two common metrics of mixing: the dilution index and the scalar dissipation. For both metrics our upscaled predictions very closely match observed values from the DNS. This material is based upon work supported by NSF Grants EAR-1351625 and EAR-1417264.
Spatially correlated disturbances in a locally dispersing population model.
Hiebeler, David
2005-01-01
The basic contact process in continuous time is studied, where instead of single occupied sites becoming empty independently, larger-scale disturbance events simultaneously remove the population from contiguous blocks of sites. Stochastic spatial simulations and pair approximations were used to investigate the model. Increasing the spatial scale of disturbance events increases spatial clustering of the population and variability in growth rates within localized regions, reduces the effective overall population density, and increases the critical reproductive rate necessary for the population to persist. Pair approximations yield a closed-form analytic expression for equilibrium population density and the critical value necessary for persistence.
Spatial Modeling Tools for Cell Biology
2006-10-01
34 iv Figure 5.1: Computational results for a diffusion problem on planar square thin film............ 36 Figure 5.2... Wisc . Open Microscopy Env. Pre-CoBi Model Lib. CFDRC CoBi Tools CFDRC CoBi Tools Simulation Environment JigCell Tools Figure 4.1: Cell biology
An Evolutionary Model of Spatial Competition
DEFF Research Database (Denmark)
Knudsen, Thorbjørn; Winter, Sidney G.
This paper sets forth an evolutionary model in which diverse businesses, with diverse offerings, compete in a stylized physical space. When a business firm attempts to expand its activity, so as to profit further from the capabilities it has developed, it necessarily does so in a "new location...
Properties of spatial Cox process models
DEFF Research Database (Denmark)
Møller, Jesper
Probabilistic properties of Cox processes of relevance for statistical modelling and inference are studied. Particularly, we study the most important classes of Cox processes, including log Gaussian Cox processes, shot noise Cox processes, and permanent Cox processes. We consider moment propertie...
Dresvyannikov, M. A.; Chernyaev, A. P.; Karuzskii, A. L.; Mityagin, Yu. A.; Perestoronin, A. V.; Volchkov, N. A.
2016-12-01
An operator of the permittivity can completely describe alone a microwave response of conductors with the spatial dispersion. An eigenvalue problem for the nonself-adjoint permittivity operator Ễa was considered generally to search the wave solutions for conductors and superconductors. An appearance of additional solutions (additional waves) due to the spatial dispersion can strongly influence the properties of nanoelectronic devices or novel superconducting materials in the form of anomalous losses for example, and should be accounted in simulation and modeling of micro- and nanoelectronic devices. It was concluded that the modulus |Ž| of the surface impedance is proportional to the degree of frequency ω2/3 for all normal conductor solutions except that for the superconductor. There was some criticism related to the idea that the electrodynamics of superconductors should be in principle reduced to those for conductors as the temperature approaches and beyond the critical temperature. We demonstrate that appropriately taken into account effects of the spatial dispersion can give the general frequency dependence of the surface impedance for the obtained solutions including that for the superconductor. It is shown that an incorporation of the spatial dispersion leads to an appearance of the Meissner effect in perfect conductors in the same manner as in superconductors.
Nathoo, F S
2010-06-01
In this article, we present a new statistical methodology for longitudinal studies in forestry, where trees are subject to recurrent infection, and the hazard of infection depends on tree growth over time. Understanding the nature of this dependence has important implications for reforestation and breeding programs. Challenges arise for statistical analysis in this setting with sampling schemes leading to panel data, exhibiting dynamic spatial variability, and incomplete covariate histories for hazard regression. In addition, data are collected at a large number of locations, which poses computational difficulties for spatiotemporal modeling. A joint model for infection and growth is developed wherein a mixed nonhomogeneous Poisson process, governing recurring infection, is linked with a spatially dynamic nonlinear model representing the underlying height growth trajectories. These trajectories are based on the von Bertalanffy growth model and a spatially varying parameterization is employed. Spatial variability in growth parameters is modeled through a multivariate spatial process derived through kernel convolution. Inference is conducted in a Bayesian framework with implementation based on hybrid Monte Carlo. Our methodology is applied for analysis in an 11-year study of recurrent weevil infestation of white spruce in British Columbia.
Spatial capture-recapture models allowing Markovian transience or dispersal
Royle, J. Andrew; Fuller, Angela K.; Sutherland, Chris
2016-01-01
Spatial capture–recapture (SCR) models are a relatively recent development in quantitative ecology, and they are becoming widely used to model density in studies of animal populations using camera traps, DNA sampling and other methods which produce spatially explicit individual encounter information. One of the core assumptions of SCR models is that individuals possess home ranges that are spatially stationary during the sampling period. For many species, this assumption is unlikely to be met and, even for species that are typically territorial, individuals may disperse or exhibit transience at some life stages. In this paper we first conduct a simulation study to evaluate the robustness of estimators of density under ordinary SCR models when dispersal or transience is present in the population. Then, using both simulated and real data, we demonstrate that such models can easily be described in the BUGS language providing a practical framework for their analysis, which allows us to evaluate movement dynamics of species using capture–recapture data. We find that while estimators of density are extremely robust, even to pathological levels of movement (e.g., complete transience), the estimator of the spatial scale parameter of the encounter probability model is confounded with the dispersal/transience scale parameter. Thus, use of ordinary SCR models to make inferences about density is feasible, but interpretation of SCR model parameters in relation to movement should be avoided. Instead, when movement dynamics are of interest, such dynamics should be parameterized explicitly in the model.
Neural correlates of reward-based spatial learning in persons with cocaine dependence.
Tau, Gregory Z; Marsh, Rachel; Wang, Zhishun; Torres-Sanchez, Tania; Graniello, Barbara; Hao, Xuejun; Xu, Dongrong; Packard, Mark G; Duan, Yunsuo; Kangarlu, Alayar; Martinez, Diana; Peterson, Bradley S
2014-02-01
Dysfunctional learning systems are thought to be central to the pathogenesis of and impair recovery from addictions. The functioning of the brain circuits for episodic memory or learning that support goal-directed behavior has not been studied previously in persons with cocaine dependence (CD). Thirteen abstinent CD and 13 healthy participants underwent MRI scanning while performing a task that requires the use of spatial cues to navigate a virtual-reality environment and find monetary rewards, allowing the functional assessment of the brain systems for spatial learning, a form of episodic memory. Whereas both groups performed similarly on the reward-based spatial learning task, we identified disturbances in brain regions involved in learning and reward in CD participants. In particular, CD was associated with impaired functioning of medial temporal lobe (MTL), a brain region that is crucial for spatial learning (and episodic memory) with concomitant recruitment of striatum (which normally participates in stimulus-response, or habit, learning), and prefrontal cortex. CD was also associated with enhanced sensitivity of the ventral striatum to unexpected rewards but not to expected rewards earned during spatial learning. We provide evidence that spatial learning in CD is characterized by disturbances in functioning of an MTL-based system for episodic memory and a striatum-based system for stimulus-response learning and reward. We have found additional abnormalities in distributed cortical regions. Consistent with findings from animal studies, we provide the first evidence in humans describing the disruptive effects of cocaine on the coordinated functioning of multiple neural systems for learning and memory.
Zhou, Yixuan; Yiwen, E.; Xu, Xinlong; Li, Weilong; Wang, Huan; Zhu, Lipeng; Bai, Jintao; Ren, Zhaoyu; Wang, Li
2016-12-01
Spatial dispersion effect of aligned carbon nanotubes (CNTs) in the terahertz (THz) region has significance for both theoretical and applied consideration due to the unique intrinsically anisotropic physical properties of CNTs. Herein, we report the angular dependent reflection of p-polarized THz wave from vertically aligned multi-walled CNT arrays in both experiment and theory. The spectra indicate that the reflection depends on the film thickness of vertically aligned CNTs, the incident angle, and the frequency. The calculation model is based on the spatial dispersion effect of aligned CNTs and performed with effective impedance method and the Maxwell-Garnett approximation. The results fit well with the experiment when the thickness of CNT film is thin, which reveals a coherent superposition mechanism of the CNT surface reflection and CNTs/Si interface reflection. For thick CNT films, the CNTs/Si interface response determines the reflection at small incident angles, while the CNTs surface effect dominates at large incident angles. This work investigates the spatial dispersion effect of vertically aligned CNT arrays in the THz region, and paves a way for potential anisotropic THz applications based on CNTs with oblique incidence requirements.
Spatial Bayesian hierarchical modelling of extreme sea states
Clancy, Colm; O'Sullivan, John; Sweeney, Conor; Dias, Frédéric; Parnell, Andrew C.
2016-11-01
A Bayesian hierarchical framework is used to model extreme sea states, incorporating a latent spatial process to more effectively capture the spatial variation of the extremes. The model is applied to a 34-year hindcast of significant wave height off the west coast of Ireland. The generalised Pareto distribution is fitted to declustered peaks over a threshold given by the 99.8th percentile of the data. Return levels of significant wave height are computed and compared against those from a model based on the commonly-used maximum likelihood inference method. The Bayesian spatial model produces smoother maps of return levels. Furthermore, this approach greatly reduces the uncertainty in the estimates, thus providing information on extremes which is more useful for practical applications.
GIS application on spatial landslide analysis using statistical based models
Pradhan, Biswajeet; Lee, Saro; Buchroithner, Manfred F.
2009-09-01
This paper presents the assessment results of spatially based probabilistic three models using Geoinformation Techniques (GIT) for landslide susceptibility analysis at Penang Island in Malaysia. Landslide locations within the study areas were identified by interpreting aerial photographs, satellite images and supported with field surveys. Maps of the topography, soil type, lineaments and land cover were constructed from the spatial data sets. There are ten landslide related factors were extracted from the spatial database and the frequency ratio, fuzzy logic, and bivariate logistic regression coefficients of each factor was computed. Finally, landslide susceptibility maps were drawn for study area using frequency ratios, fuzzy logic and bivariate logistic regression models. For verification, the results of the analyses were compared with actual landslide locations in study area. The verification results show that bivariate logistic regression model provides slightly higher prediction accuracy than the frequency ratio and fuzzy logic models.
Temperature dependent extension of a hysteresis model
Sixdenier, Fabien; MESSAL, Oualid; Hilal, Alaa; Martin, Christian; Raulet, Marie-Ange
2015-01-01
International audience; Some soft magnetic materials (like ferrites but not only) are strongly dependent of the temperature. In order to predict their behaviour in electrical devices, engineers need hysteresis models able to take into account the temperature. This paper is an attempt to take into account the temperature in an existing model of hysteresis through its parameters. Variations of some parameters are issued from Weiss’s works and others have to be fitted numerically. Simulation res...
Uncertainty in a spatial evacuation model
Mohd Ibrahim, Azhar; Venkat, Ibrahim; Wilde, Philippe De
2017-08-01
Pedestrian movements in crowd motion can be perceived in terms of agents who basically exhibit patient or impatient behavior. We model crowd motion subject to exit congestion under uncertainty conditions in a continuous space and compare the proposed model via simulations with the classical social force model. During a typical emergency evacuation scenario, agents might not be able to perceive with certainty the strategies of opponents (other agents) owing to the dynamic changes entailed by the neighborhood of opponents. In such uncertain scenarios, agents will try to update their strategy based on their own rules or their intrinsic behavior. We study risk seeking, risk averse and risk neutral behaviors of such agents via certain game theory notions. We found that risk averse agents tend to achieve faster evacuation time whenever the time delay in conflicts appears to be longer. The results of our simulations also comply with previous work and conform to the fact that evacuation time of agents becomes shorter once mutual cooperation among agents is achieved. Although the impatient strategy appears to be the rational strategy that might lead to faster evacuation times, our study scientifically shows that the more the agents are impatient, the slower is the egress time.
Was Thebes Necessary? Contingency in Spatial Modelling
Evans, Tim S
2016-01-01
When data is poor we resort to theory modelling. This is a two-step process. We have first to identify the appropriate type of model for the system under consideration and then to tailor it to the specifics of the case. To understand settlement formation, which is the concern of this paper, this not only involves choosing input parameter values such as site separations but also input functions which characterises the ease of travel between sites. Although the generic behaviour of the model is understood, the details are not. Different choices will necessarily lead to different outputs (for identical inputs). We can only proceed if choices that are "close" give outcomes are similar. Where there are local differences it suggests that there was no compelling reason for one outcome rather than the other. If these differences are important for the historic record we may interpret this as sensitivity to contingency. We re-examine the rise of Greek city states as first formulated by Rihll and Wilson in 1979, initial...
Comparing spatial and temporal transferability of hydrological model parameters
Patil, Sopan D.; Stieglitz, Marc
2015-06-01
Operational use of hydrological models requires the transfer of calibrated parameters either in time (for streamflow forecasting) or space (for prediction at ungauged catchments) or both. Although the effects of spatial and temporal parameter transfer on catchment streamflow predictions have been well studied individually, a direct comparison of these approaches is much less documented. Here, we compare three different schemes of parameter transfer, viz., temporal, spatial, and spatiotemporal, using a spatially lumped hydrological model called EXP-HYDRO at 294 catchments across the continental United States. Results show that the temporal parameter transfer scheme performs best, with lowest decline in prediction performance (median decline of 4.2%) as measured using the Kling-Gupta efficiency metric. More interestingly, negligible difference in prediction performance is observed between the spatial and spatiotemporal parameter transfer schemes (median decline of 12.4% and 13.9% respectively). We further demonstrate that the superiority of temporal parameter transfer scheme is preserved even when: (1) spatial distance between donor and receiver catchments is reduced, or (2) temporal lag between calibration and validation periods is increased. Nonetheless, increase in the temporal lag between calibration and validation periods reduces the overall performance gap between the three parameter transfer schemes. Results suggest that spatiotemporal transfer of hydrological model parameters has the potential to be a viable option for climate change related hydrological studies, as envisioned in the "trading space for time" framework. However, further research is still needed to explore the relationship between spatial and temporal aspects of catchment hydrological variability.
Spatial mixture multiscale modeling for aggregated health data.
Aregay, Mehreteab; Lawson, Andrew B; Faes, Christel; Kirby, Russell S; Carroll, Rachel; Watjou, Kevin
2016-09-01
One of the main goals in spatial epidemiology is to study the geographical pattern of disease risks. For such purpose, the convolution model composed of correlated and uncorrelated components is often used. However, one of the two components could be predominant in some regions. To investigate the predominance of the correlated or uncorrelated component for multiple scale data, we propose four different spatial mixture multiscale models by mixing spatially varying probability weights of correlated (CH) and uncorrelated heterogeneities (UH). The first model assumes that there is no linkage between the different scales and, hence, we consider independent mixture convolution models at each scale. The second model introduces linkage between finer and coarser scales via a shared uncorrelated component of the mixture convolution model. The third model is similar to the second model but the linkage between the scales is introduced through the correlated component. Finally, the fourth model accommodates for a scale effect by sharing both CH and UH simultaneously. We applied these models to real and simulated data, and found that the fourth model is the best model followed by the second model.
Environmental dependence in the ellipsoidal collapse model
Desjacques, Vincent
2007-01-01
N-body simulations have demonstrated a correlation between the properties of haloes and their environment. In this paper, we assess whether the ellipsoidal collapse model can produce a similar dependence. First, we explore the statistical correlation that originates from Gaussian initial conditions. We derive analytic expressions for a number of joint statistics of the shear tensor and estimate the sensitivity of the local characteristics of the shear to the global geometry of the large scale environment. Next, we concentrate on the dynamical aspect of the environmental dependence using a simplified model that takes into account the interaction between a collapsing halo and its environment. We find that the tidal force exerted by the surrounding mass distribution causes haloes embedded in overdense regions to virialize earlier. An effective density threshold whose shape depends on the large scale density provides a good description of this environmental effect. We show that, using this approach, a correlation...
Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification
Liu, Da; Li, Jianxun
2016-01-01
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. PMID:27999259
Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification.
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.
Modelling the spatial distribution of ammonia emissions in the UK
Energy Technology Data Exchange (ETDEWEB)
Hellsten, S. [Centre for Ecology and Hydrology Edinburgh, Bush Estate, Penicuik, Midlothian EH26 0QB (United Kingdom); Institute of Geography, School of Geosciences, University of Edinburgh, Drummond Street, Edinburgh EH8 9XP (United Kingdom); IVL Swedish Environmental Research Institute Ltd, P.O. Box 5302, SE-400 14 Gothenburg (Sweden)], E-mail: sofie.hellsten@ivl.se; Dragosits, U. [Centre for Ecology and Hydrology Edinburgh, Bush Estate, Penicuik, Midlothian EH26 0QB (United Kingdom); Place, C.J. [Institute of Geography, School of Geosciences, University of Edinburgh, Drummond Street, Edinburgh EH8 9XP (United Kingdom); Vieno, M. [Centre for Ecology and Hydrology Edinburgh, Bush Estate, Penicuik, Midlothian EH26 0QB (United Kingdom); Institute of Atmospheric and Environmental Science, School of GeoSciences, University of Edinburgh, Crew Building, The King' s buildings, West Mains Road, Edinburgh EH9 3JN (United Kingdom); Dore, A.J. [Centre for Ecology and Hydrology Edinburgh, Bush Estate, Penicuik, Midlothian EH26 0QB (United Kingdom); Misselbrook, T.H. [Institute of Grassland and Environmental Research, North Wyke, Okehampton, Exeter EX 2SB (United Kingdom); Tang, Y.S.; Sutton, M.A. [Centre for Ecology and Hydrology Edinburgh, Bush Estate, Penicuik, Midlothian EH26 0QB (United Kingdom)
2008-08-15
Ammonia emissions (NH{sub 3}) are characterised by a high spatial variability at a local scale. When modelling the spatial distribution of NH{sub 3} emissions, it is important to provide robust emission estimates, since the model output is used to assess potential environmental impacts, e.g. exceedance of critical loads. The aim of this study was to provide a new, updated spatial NH{sub 3} emission inventory for the UK for the year 2000, based on an improved modelling approach and the use of updated input datasets. The AENEID model distributes NH{sub 3} emissions from a range of agricultural activities, such as grazing and housing of livestock, storage and spreading of manures, and fertilizer application, at a 1-km grid resolution over the most suitable landcover types. The results of the emission calculation for the year 2000 are analysed and the methodology is compared with a previous spatial emission inventory for 1996. - It is important to provide robust estimates of the spatial distribution of ammonia emissions, since the model output is used to assess potential environmental impacts, e.g. through the exceedance of critical loads.
Nadeem, Khurram; Moore, Jeffrey E; Zhang, Ying; Chipman, Hugh
2016-07-01
Stochastic versions of Gompertz, Ricker, and various other dynamics models play a fundamental role in quantifying strength of density dependence and studying long-term dynamics of wildlife populations. These models are frequently estimated using time series of abundance estimates that are inevitably subject to observation error and missing data. This issue can be addressed with a state-space modeling framework that jointly estimates the observed data model and the underlying stochastic population dynamics (SPD) model. In cases where abundance data are from multiple locations with a smaller spatial resolution (e.g., from mark-recapture and distance sampling studies), models are conventionally fitted to spatially pooled estimates of yearly abundances. Here, we demonstrate that a spatial version of SPD models can be directly estimated from short time series of spatially referenced distance sampling data in a unified hierarchical state-space modeling framework that also allows for spatial variance (covariance) in population growth. We also show that a full range of likelihood based inference, including estimability diagnostics and model selection, is feasible in this class of models using a data cloning algorithm. We further show through simulation experiments that the hierarchical state-space framework introduced herein efficiently captures the underlying dynamical parameters and spatial abundance distribution. We apply our methodology by analyzing a time series of line-transect distance sampling data for fin whales (Balaenoptera physalus) off the U.S. west coast. Although there were only seven surveys conducted during the study time frame, 1991-2014, our analysis detected presence of strong density regulation and provided reliable estimates of fin whale densities. In summary, we show that the integrative framework developed herein allows ecologists to better infer key population characteristics such as presence of density regulation and spatial variability in a
Directory of Open Access Journals (Sweden)
Cohen S.X.
2014-03-01
Full Text Available In this article, we describe a novel unsupervised spectral image segmentation algorithm. This algorithm extends the classical Gaussian Mixture Model-based unsupervised classification technique by incorporating a spatial flavor into the model: the spectra are modelized by a mixture of K classes, each with a Gaussian distribution, whose mixing proportions depend on the position. Using a piecewise constant structure for those mixing proportions, we are able to construct a penalized maximum likelihood procedure that estimates the optimal partition as well as all the other parameters, including the number of classes. We provide a theoretical guarantee for this estimation, even when the generating model is not within the tested set, and describe an efficient implementation. Finally, we conduct some numerical experiments of unsupervised segmentation from a real dataset.
Spatial flood extent modelling. A performance based comparison
Werner, M.G.F.
2004-01-01
The rapid development of Geographical Information Systems (GIS) has together with the inherent spatial nature of hydrological modelling led to an equally rapid development in the integration between GIS and hydrological models. The advantages of integration are particularly apparent in flood extent
Spatial dependence of diurnal temperature range trends on precipitation from 1950 to 2004
Energy Technology Data Exchange (ETDEWEB)
Zhou, Liming [Georgia Institute of Technology, School of Earth and Atmospheric Sciences, Atlanta, GA (United States); Dai, Aiguo [National Center for Atmospheric Research, Boulder, CO (United States); Dai, Yongjiu [Beijing Normal University, School of Geography, Beijing (China); Vose, Russell S. [National Climatic Data Center, Climate Analysis Branch, Asheville, NC (United States); Zou, Cheng-Zhi [NOAA/NESDIS, Office of Research and Applications, Camp Springs, MD (United States); Tian, Yuhong [IMSG at NOAA/NESDIS, Camp Springs, MD (United States); Chen, Haishan [Nanjing University of Information Science and Technology, Jiangsu Key Laboratory of Meteorological Disaster, Nanjing (China)
2009-02-15
This paper analyzes the spatial dependence of annual diurnal temperature range (DTR) trends from 1950-2004 on the annual climatology of three variables: precipitation, cloud cover, and leaf area index (LAI), by classifying the global land into various climatic regions based on the climatological annual precipitation. The regional average trends for annual minimum temperature (T{sub min}) and DTR exhibit significant spatial correlations with the climatological values of these three variables, while such correlation for annual maximum temperature (T{sub max}) is very weak. In general, the magnitude of the downward trend of DTR and the warming trend of T{sub min} decreases with increasing precipitation amount, cloud cover, and LAI, i.e., with stronger DTR decreasing trends over drier regions. Such spatial dependence of T{sub min} and DTR trends on the climatological precipitation possibly reflects large-scale effects of increased global greenhouse gases and aerosols (and associated changes in cloudiness, soil moisture, and water vapor) during the later half of the twentieth century. (orig.)
Integrating models that depend on variable data
Banks, A. T.; Hill, M. C.
2016-12-01
Models of human-Earth systems are often developed with the goal of predicting the behavior of one or more dependent variables from multiple independent variables, processes, and parameters. Often dependent variable values range over many orders of magnitude, which complicates evaluation of the fit of the dependent variable values to observations. Many metrics and optimization methods have been proposed to address dependent variable variability, with little consensus being achieved. In this work, we evaluate two such methods: log transformation (based on the dependent variable being log-normally distributed with a constant variance) and error-based weighting (based on a multi-normal distribution with variances that tend to increase as the dependent variable value increases). Error-based weighting has the advantage of encouraging model users to carefully consider data errors, such as measurement and epistemic errors, while log-transformations can be a black box for typical users. Placing the log-transformation into the statistical perspective of error-based weighting has not formerly been considered, to the best of our knowledge. To make the evaluation as clear and reproducible as possible, we use multiple linear regression (MLR). Simulations are conducted with MatLab. The example represents stream transport of nitrogen with up to eight independent variables. The single dependent variable in our example has values that range over 4 orders of magnitude. Results are applicable to any problem for which individual or multiple data types produce a large range of dependent variable values. For this problem, the log transformation produced good model fit, while some formulations of error-based weighting worked poorly. Results support previous suggestions fthat error-based weighting derived from a constant coefficient of variation overemphasizes low values and degrades model fit to high values. Applying larger weights to the high values is inconsistent with the log
Density-dependent prey mortality is determined by the spatial scale of predator foraging.
McCarthy, Erin K; White, J Wilson
2016-02-01
Foraging theory predicts which prey patches predators should target. However, in most habitats, what constitutes a 'patch' and how prey density is calculated are subjective concepts and depend on the spatial scale at which the predator (or scientist) is observing. Moreover, the predator's 'foraging scale' affects prey population dynamics: predators should produce directly density-dependent (DDD) prey mortality at the foraging scale, but inversely density-dependent (IDD) mortality (safety-in-numbers) at smaller scales. We performed the first experimental test of these predictions using behavioral assays with guppies (Poecilia reticulata) feeding on bloodworm 'prey' patches. The guppy's foraging scale had already been estimated in a prior study. Our experimental results confirmed theoretical predictions: predation was IDD when prey were aggregated at a scale smaller than the foraging scale, but not when prey were aggregated at larger scales. These results could be used to predict outcomes of predator-prey interactions in continuous, non-discrete habitats in the field.
Distributed multi-criteria model evaluation and spatial association analysis
Scherer, Laura; Pfister, Stephan
2015-04-01
Model performance, if evaluated, is often communicated by a single indicator and at an aggregated level; however, it does not embrace the trade-offs between different indicators and the inherent spatial heterogeneity of model efficiency. In this study, we simulated the water balance of the Mississippi watershed using the Soil and Water Assessment Tool (SWAT). The model was calibrated against monthly river discharge at 131 measurement stations. Its time series were bisected to allow for subsequent validation at the same gauges. Furthermore, the model was validated against evapotranspiration which was available as a continuous raster based on remote sensing. The model performance was evaluated for each of the 451 sub-watersheds using four different criteria: 1) Nash-Sutcliffe efficiency (NSE), 2) percent bias (PBIAS), 3) root mean square error (RMSE) normalized to standard deviation (RSR), as well as 4) a combined indicator of the squared correlation coefficient and the linear regression slope (bR2). Conditions that might lead to a poor model performance include aridity, a very flat and steep relief, snowfall and dams, as indicated by previous research. In an attempt to explain spatial differences in model efficiency, the goodness of the model was spatially compared to these four phenomena by means of a bivariate spatial association measure which combines Pearson's correlation coefficient and Moran's index for spatial autocorrelation. In order to assess the model performance of the Mississippi watershed as a whole, three different averages of the sub-watershed results were computed by 1) applying equal weights, 2) weighting by the mean observed river discharge, 3) weighting by the upstream catchment area and the square root of the time series length. Ratings of model performance differed significantly in space and according to efficiency criterion. The model performed much better in the humid Eastern region than in the arid Western region which was confirmed by the
Sang, Huiyan
2011-12-01
This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models. Our method allows for a nonseparable and nonstationary cross-covariance structure. We also present a covariance approximation approach to facilitate the computation in the modeling and analysis of very large multivariate spatial data sets. The covariance approximation consists of two parts: a reduced-rank part to capture the large-scale spatial dependence, and a sparse covariance matrix to correct the small-scale dependence error induced by the reduced rank approximation. We pay special attention to the case that the second part of the approximation has a block-diagonal structure. Simulation results of model fitting and prediction show substantial improvement of the proposed approximation over the predictive process approximation and the independent blocks analysis. We then apply our computational approach to the joint statistical modeling of multiple climate model errors. © 2012 Institute of Mathematical Statistics.
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.
A spatial model of mosquito host-seeking behavior.
Cummins, Bree; Cortez, Ricardo; Foppa, Ivo M; Walbeck, Justin; Hyman, James M
2012-01-01
Mosquito host-seeking behavior and heterogeneity in host distribution are important factors in predicting the transmission dynamics of mosquito-borne infections such as dengue fever, malaria, chikungunya, and West Nile virus. We develop and analyze a new mathematical model to describe the effect of spatial heterogeneity on the contact rate between mosquito vectors and hosts. The model includes odor plumes generated by spatially distributed hosts, wind velocity, and mosquito behavior based on both the prevailing wind and the odor plume. On a spatial scale of meters and a time scale of minutes, we compare the effectiveness of different plume-finding and plume-tracking strategies that mosquitoes could use to locate a host. The results show that two different models of chemotaxis are capable of producing comparable results given appropriate parameter choices and that host finding is optimized by a strategy of flying across the wind until the odor plume is intercepted. We also assess the impact of changing the level of host aggregation on mosquito host-finding success near the end of the host-seeking flight. When clusters of hosts are more tightly associated on smaller patches, the odor plume is narrower and the biting rate per host is decreased. For two host groups of unequal number but equal spatial density, the biting rate per host is lower in the group with more individuals, indicative of an attack abatement effect of host aggregation. We discuss how this approach could assist parameter choices in compartmental models that do not explicitly model the spatial arrangement of individuals and how the model could address larger spatial scales and other probability models for mosquito behavior, such as Lévy distributions.
A spatial model of mosquito host-seeking behavior.
Directory of Open Access Journals (Sweden)
Bree Cummins
Full Text Available Mosquito host-seeking behavior and heterogeneity in host distribution are important factors in predicting the transmission dynamics of mosquito-borne infections such as dengue fever, malaria, chikungunya, and West Nile virus. We develop and analyze a new mathematical model to describe the effect of spatial heterogeneity on the contact rate between mosquito vectors and hosts. The model includes odor plumes generated by spatially distributed hosts, wind velocity, and mosquito behavior based on both the prevailing wind and the odor plume. On a spatial scale of meters and a time scale of minutes, we compare the effectiveness of different plume-finding and plume-tracking strategies that mosquitoes could use to locate a host. The results show that two different models of chemotaxis are capable of producing comparable results given appropriate parameter choices and that host finding is optimized by a strategy of flying across the wind until the odor plume is intercepted. We also assess the impact of changing the level of host aggregation on mosquito host-finding success near the end of the host-seeking flight. When clusters of hosts are more tightly associated on smaller patches, the odor plume is narrower and the biting rate per host is decreased. For two host groups of unequal number but equal spatial density, the biting rate per host is lower in the group with more individuals, indicative of an attack abatement effect of host aggregation. We discuss how this approach could assist parameter choices in compartmental models that do not explicitly model the spatial arrangement of individuals and how the model could address larger spatial scales and other probability models for mosquito behavior, such as Lévy distributions.
Koschinsky, Julia; Lozano-Gracia, Nancy; Piras, Gianfranco
2012-07-01
This article compares results from non-spatial and new spatial methods to examine the reliability of welfare estimates (direct and multiplier effects) for locational housing attributes in Seattle, WA. In particular, we assess if OLS with spatial fixed effects is able to account for the spatial structure in a way that represents a viable alternative to spatial econometric methods. We find that while OLS with spatial fixed effects accounts for more of the spatial structure than simple OLS, it does not account for all of the spatial structure. It thus does not present a viable alternative to the spatial methods. Similar to existing comparisons between results from non-spatial and established spatial methods, we also find that OLS generates higher coefficient and direct effect estimates for both structural and locational housing characteristics than spatial methods do. OLS with spatial fixed effects is closer to the spatial estimates than OLS without fixed effects but remains higher. Finally, a comparison of the direct effects with locally weighted regression results highlights spatial threshold effects that are missed in the global models. Differences between spatial estimators are almost negligible in this study.
Spatially random models, estimation theory, and robot arm dynamics
Rodriguez, G.
1987-01-01
Spatially random models provide an alternative to the more traditional deterministic models used to describe robot arm dynamics. These alternative models can be used to establish a relationship between the methodologies of estimation theory and robot dynamics. A new class of algorithms for many of the fundamental robotics problems of inverse and forward dynamics, inverse kinematics, etc. can be developed that use computations typical in estimation theory. The algorithms make extensive use of the difference equations of Kalman filtering and Bryson-Frazier smoothing to conduct spatial recursions. The spatially random models are very easy to describe and are based on the assumption that all of the inertial (D'Alembert) forces in the system are represented by a spatially distributed white-noise model. The models can also be used to generate numerically the composite multibody system inertia matrix. This is done without resorting to the more common methods of deterministic modeling involving Lagrangian dynamics, Newton-Euler equations, etc. These methods make substantial use of human knowledge in derivation and minipulation of equations of motion for complex mechanical systems.
Modeling Urban Spatial Growth in Mountainous Regions of Western China
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Guoping Huang
2017-08-01
Full Text Available The scale and speed of urbanization in the mountainous regions of western China have received little attention from researchers. These cities are facing rapid population growth and severe environmental degradation. This study analyzed historical urban growth trends in this mountainous region to better understand the interaction between the spatial growth pattern and the mountainous topography. Three major factors—slope, accessibility, and land use type—were studied in light of their relationships with urban spatial growth. With the analysis of historical data as the basis, a conceptual urban spatial growth model was devised. In this model, slope, accessibility, and land use type together create resistance to urban growth, while accessibility controls the sequence of urban development. The model was tested and evaluated using historical data. It serves as a potential tool for planners to envision and assess future urban growth scenarios and their potential environmental impacts to make informed decisions.
Spatial distribution of emissions to air – the SPREAD model
DEFF Research Database (Denmark)
Plejdrup, Marlene Schmidt; Gyldenkærne, Steen
The National Environmental Research Institute (NERI), Aarhus University, completes the annual national emission inventories for greenhouse gases and air pollutants according to Denmark’s obligations under international conventions, e.g. the climate convention, UNFCCC and the convention on long......-range transboundary air pollution, CLRTAP. NERI has developed a model to distribute emissions from the national emission inventories on a 1x1 km grid covering the Danish land and sea territory. The new spatial high resolution distribution model for emissions to air (SPREAD) has been developed according...... to the requirements for reporting of gridded emissions to CLRTAP. Spatial emission data is e.g. used as input for air quality modelling, which again serves as input for assessment and evaluation of health effects. For these purposes distributions with higher spatial resolution have been requested. Previously...
A spatially-averaged mathematical model of kidney branching morphogenesis
Zubkov, V.S.
2015-08-01
© 2015 Published by Elsevier Ltd. Kidney development is initiated by the outgrowth of an epithelial ureteric bud into a population of mesenchymal cells. Reciprocal morphogenetic responses between these two populations generate a highly branched epithelial ureteric tree with the mesenchyme differentiating into nephrons, the functional units of the kidney. While we understand some of the mechanisms involved, current knowledge fails to explain the variability of organ sizes and nephron endowment in mice and humans. Here we present a spatially-averaged mathematical model of kidney morphogenesis in which the growth of the two key populations is described by a system of time-dependant ordinary differential equations. We assume that branching is symmetric and is invoked when the number of epithelial cells per tip reaches a threshold value. This process continues until the number of mesenchymal cells falls below a critical value that triggers cessation of branching. The mathematical model and its predictions are validated against experimentally quantified C57Bl6 mouse embryonic kidneys. Numerical simulations are performed to determine how the final number of branches changes as key system parameters are varied (such as the growth rate of tip cells, mesenchyme cells, or component cell population exit rate). Our results predict that the developing kidney responds differently to loss of cap and tip cells. They also indicate that the final number of kidney branches is less sensitive to changes in the growth rate of the ureteric tip cells than to changes in the growth rate of the mesenchymal cells. By inference, increasing the growth rate of mesenchymal cells should maximise branch number. Our model also provides a framework for predicting the branching outcome when ureteric tip or mesenchyme cells change behaviour in response to different genetic or environmental developmental stresses.
Local dependence in random graph models: characterization, properties and statistical inference.
Schweinberger, Michael; Handcock, Mark S
2015-06-01
Dependent phenomena, such as relational, spatial and temporal phenomena, tend to be characterized by local dependence in the sense that units which are close in a well-defined sense are dependent. In contrast with spatial and temporal phenomena, though, relational phenomena tend to lack a natural neighbourhood structure in the sense that it is unknown which units are close and thus dependent. Owing to the challenge of characterizing local dependence and constructing random graph models with local dependence, many conventional exponential family random graph models induce strong dependence and are not amenable to statistical inference. We take first steps to characterize local dependence in random graph models, inspired by the notion of finite neighbourhoods in spatial statistics and M-dependence in time series, and we show that local dependence endows random graph models with desirable properties which make them amenable to statistical inference. We show that random graph models with local dependence satisfy a natural domain consistency condition which every model should satisfy, but conventional exponential family random graph models do not satisfy. In addition, we establish a central limit theorem for random graph models with local dependence, which suggests that random graph models with local dependence are amenable to statistical inference. We discuss how random graph models with local dependence can be constructed by exploiting either observed or unobserved neighbourhood structure. In the absence of observed neighbourhood structure, we take a Bayesian view and express the uncertainty about the neighbourhood structure by specifying a prior on a set of suitable neighbourhood structures. We present simulation results and applications to two real world networks with 'ground truth'.
Image categorization based on spatial visual vocabulary model
Wang, Yuxin; He, Changqin; Guo, He; Feng, Zhen; Jia, Qi
2010-08-01
In this paper, we propose an approach to recognize scene categories by means of a novel method named spatial visual vocabulary. Firstly, we hierarchically divide images into sub regions and construct the spatial visual vocabulary by grouping the low-level features collected from every corresponding spatial sub region into a specified number of clusters using k-means algorithm. To recognize the category of a scene, the visual vocabulary distributions of all spatial sub regions are concatenated to form a global feature vector. The classification is obtained using LIBSVM, a support vector machine classifier. Our goal is to find a universal framework which is applicable to various types of features, so two kinds of features are used in the experiments: "V1-like" filters and PACT features. In almost all experimental cases, the proposed model achieves superior results. Source codes are available by email.
Spatial resolution dependence on spectral frequency in human speech cortex electrocorticography
Muller, Leah; Hamilton, Liberty S.; Edwards, Erik; Bouchard, Kristofer E.; Chang, Edward F.
2016-10-01
Objective. Electrocorticography (ECoG) has become an important tool in human neuroscience and has tremendous potential for emerging applications in neural interface technology. Electrode array design parameters are outstanding issues for both research and clinical applications, and these parameters depend critically on the nature of the neural signals to be recorded. Here, we investigate the functional spatial resolution of neural signals recorded at the human cortical surface. We empirically derive spatial spread functions to quantify the shared neural activity for each frequency band of the electrocorticogram. Approach. Five subjects with high-density (4 mm center-to-center spacing) ECoG grid implants participated in speech perception and production tasks while neural activity was recorded from the speech cortex, including superior temporal gyrus, precentral gyrus, and postcentral gyrus. The cortical surface field potential was decomposed into traditional EEG frequency bands. Signal similarity between electrode pairs for each frequency band was quantified using a Pearson correlation coefficient. Main results. The correlation of neural activity between electrode pairs was inversely related to the distance between the electrodes; this relationship was used to quantify spatial falloff functions for cortical subdomains. As expected, lower frequencies remained correlated over larger distances than higher frequencies. However, both the envelope and phase of gamma and high gamma frequencies (30-150 Hz) are largely uncorrelated (<90%) at 4 mm, the smallest spacing of the high-density arrays. Thus, ECoG arrays smaller than 4 mm have significant promise for increasing signal resolution at high frequencies, whereas less additional gain is achieved for lower frequencies. Significance. Our findings quantitatively demonstrate the dependence of ECoG spatial resolution on the neural frequency of interest. We demonstrate that this relationship is consistent across patients and
Spatial econometrics using microdata
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
Hydrological model uncertainty due to spatial evapotranspiration estimation methods
Yu, Xuan; Lamačová, Anna; Duffy, Christopher; Krám, Pavel; Hruška, Jakub
2016-05-01
Evapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index [LAI], interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50°03‧N, 12°40‧E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models.
Porzycki, Jakub; WÄ s, Jarosław; Hedayatifar, Leila; Hassanibesheli, Forough; Kułakowski, Krzysztof
2017-08-01
The aim of the paper is an analysis of self-organization patterns observed in the unidirectional flow of pedestrians. On the basis of experimental data from Zhang et al. [J. Zhang et al., J. Stat. Mech. (2011) P06004, 10.1088/1742-5468/2011/06/P06004], we analyze the mutual positions and velocity correlations between pedestrians when walking along a corridor. The angular and spatial dependencies of the mutual positions reveal a spatial structure that remains stable during the crowd motion. This structure differs depending on the value of n , for the consecutive n th -nearest-neighbor position set. The preferred position for the first-nearest neighbor is on the side of the pedestrian, while for further neighbors, this preference shifts to the axis of movement. The velocity correlations vary with the angle formed by the pair of neighboring pedestrians and the direction of motion and with the time delay between pedestrians' movements. The delay dependence of the correlations shows characteristic oscillations, produced by the velocity oscillations when striding; however, a filtering of the main frequency of individual striding out reduces the oscillations only partially. We conclude that pedestrians select their path directions so as to evade the necessity of continuously adjusting their speed to their neighbors'. They try to keep a given distance, but follow the person in front of them, as well as accepting and observing pedestrians on their sides. Additionally, we show an empirical example that illustrates the shape of a pedestrian's personal space during movement.
ECoS, a framework for modelling hierarchical spatial systems.
Harris, John R W; Gorley, Ray N
2003-10-01
A general framework for modelling hierarchical spatial systems has been developed and implemented as the ECoS3 software package. The structure of this framework is described, and illustrated with representative examples. It allows the set-up and integration of sets of advection-diffusion equations representing multiple constituents interacting in a spatial context. Multiple spaces can be defined, with zero, one or two-dimensions and can be nested, and linked through constituent transfers. Model structure is generally object-oriented and hierarchical, reflecting the natural relations within its real-world analogue. Velocities, dispersions and inter-constituent transfers, together with additional functions, are defined as properties of constituents to which they apply. The resulting modular structure of ECoS models facilitates cut and paste model development, and template model components have been developed for the assembly of a range of estuarine water quality models. Published examples of applications to the geochemical dynamics of estuaries are listed.
Analysing earthquake slip models with the spatial prediction comparison test
Zhang, L.
2014-11-10
Earthquake rupture models inferred from inversions of geophysical and/or geodetic data exhibit remarkable variability due to uncertainties in modelling assumptions, the use of different inversion algorithms, or variations in data selection and data processing. A robust statistical comparison of different rupture models obtained for a single earthquake is needed to quantify the intra-event variability, both for benchmark exercises and for real earthquakes. The same approach may be useful to characterize (dis-)similarities in events that are typically grouped into a common class of events (e.g. moderate-size crustal strike-slip earthquakes or tsunamigenic large subduction earthquakes). For this purpose, we examine the performance of the spatial prediction comparison test (SPCT), a statistical test developed to compare spatial (random) fields by means of a chosen loss function that describes an error relation between a 2-D field (‘model’) and a reference model. We implement and calibrate the SPCT approach for a suite of synthetic 2-D slip distributions, generated as spatial random fields with various characteristics, and then apply the method to results of a benchmark inversion exercise with known solution. We find the SPCT to be sensitive to different spatial correlations lengths, and different heterogeneity levels of the slip distributions. The SPCT approach proves to be a simple and effective tool for ranking the slip models with respect to a reference model.
Modeling of Spatially Correlated Energetic Disorder in Organic Semiconductors.
Kordt, Pascal; Andrienko, Denis
2016-01-12
Mesoscale modeling of organic semiconductors relies on solving an appropriately parametrized master equation. Essential ingredients of the parametrization are site energies (driving forces), which enter the charge transfer rate between pairs of neighboring molecules. Site energies are often Gaussian-distributed and are spatially correlated. Here, we propose an algorithm that generates these energies with a given Gaussian distribution and spatial correlation function. The method is tested on an amorphous organic semiconductor, DPBIC, illustrating that the accurate description of correlations is essential for the quantitative modeling of charge transport in amorphous mesophases.
Nethery, Rachel C; Warren, Joshua L; Herring, Amy H; Moore, Kari A B; Evenson, Kelly R; Diez-Roux, Ana V
2015-11-01
The purpose of this study was to reduce the dimensionality of a set of neighborhood-level variables collected on participants in the Multi-Ethnic Study of Atherosclerosis (MESA) while appropriately accounting for the spatial structure of the data. A common spatial factor analysis model in the Bayesian setting was utilized in order to properly characterize dependencies in the data. Results suggest that use of the spatial factor model can result in more precise estimation of factor scores, improved insight into the spatial patterns in the data, and the ability to more accurately assess associations between the neighborhood environment and health outcomes.
Nethery, Rachel C.; Warren, Joshua L.; Herring, Amy H.; Moore, Kari A.B.; Evenson, Kelly R.; Diez-Roux, Ana V.
2015-01-01
The purpose of this study was to reduce the dimensionality of a set of neighborhood-level variables collected on participants in the Multi-Ethnic Study of Atherosclerosis (MESA) while appropriately accounting for the spatial structure of the data. A common spatial factor analysis model in the Bayesian setting was utilized in order to properly characterize dependencies in the data. Results suggest that use of the spatial factor model can result in more precise estimation of factor scores, improved insight into the spatial patterns in the data, and the ability to more accurately assess associations between the neighborhood environment and health outcomes. PMID:26372887
Directory of Open Access Journals (Sweden)
M. Liu
2012-02-01
Full Text Available In this paper, simulations with the Soil Water Atmosphere Plant (SWAP model are performed to quantify the spatial variability of both potential and actual evapotranspiration (ET, and soil moisture content (SMC caused by topography-induced spatial wind and radiation differences. To obtain the spatially distributed ET/SMC patterns, the field scale SWAP model is applied in a distributed way for both pointwise and catchment wide simulations. An adapted radiation model from r.sun and the physically-based meso-scale wind model METRAS PC are applied to obtain the spatial radiation and wind patterns respectively, which show significant spatial variation and correlation with aspect and elevation respectively. Such topographic dependences and spatial variations further propagate to ET/SMC. A strong spatial, seasonal-dependent, scale-relevant intra-catchment variability in daily/annual ET and less variability in SMC can be observed from the numerical experiments. The study concludes that topography has a significant effect on ET/SMC in the humid region where ET is a energy limited rather than water availability limited process. It affects the spatial runoff generation through spatial radiation and wind, therefore should be applied to inform hydrological model development. In addition, the methodology used in the study can serve as a general method for physically-based ET estimation for data sparse regions.
Management model application at nested spatial levels in Mediterranean Basins
Lo Porto, Antonio; De Girolamo, Anna Maria; Froebrich, Jochen
2014-05-01
In the EU Water Framework Directive (WFD) implementation processes, hydrological and water quality models can be powerful tools that allow to design and test alternative management strategies, as well as judging their general feasibility and acceptance. Although in recent decades several models have been developed, their use in Mediterranean basins, where rivers have a temporary character, is quite complex and there is limited information in literature which can facilitate model applications and result evaluations in this region. The high spatial variability which characterizes rainfall events, soil hydrological properties and land uses of Mediterranean basin makes more difficult to simulate hydrological and water quality in this region than in other Countries. This variability also has several implications in modeling simulations results especially when simulations at different spatial scale are needed for watershed management purpose. It is well known that environmental processes operating at different spatial scale determine diverse impacts on water quality status (hydrological, chemical, ecological). Hence, the development of management strategies have to include both large scale (watershed) and local spatial scales approaches (e.g. stream reach). This paper presents the results of a study which analyzes how the spatial scale affects the results of hydrologic process and water quality of model simulations in a Mediterranean watershed. Several aspects involved in modeling hydrological and water quality processes at different spatial scale for river basin management are investigated including model data requirements, data availability, model results and uncertainty. A hydrologic and water quality model (SWAT) was used to simulate hydrologic processes and water quality at different spatial scales in the Candelaro river basin (Puglia, S-E Italy) and to design management strategies to reach as possible WFD goals. When studying a basin to assess its current status
Vörtler, K.; Mamivand, M.; Barnard, L.; Szlufarska, I.; Garner, F. A.; Morgan, D.
2016-10-01
In this work we develop an ab initio informed rate theory model to track the spatial and temporal evolution of implanted ions (Cr+) in Fe and Fesbnd 14%Cr during high dose irradiation. We focus on the influence of the specimen surface, the depth dependence of ion-induced damage, the damage rate, and the consequences of ion implantation, all of which influence the depth dependence of alloy composition evolving with continued irradiation. We investigate chemical segregation effects in the material by considering the diffusion of the irradiation-induced defects. Moreover, we explore how temperature, grain size, grain boundary sink strength, and defect production bias modify the resulting distribution of alloy composition. Our results show that the implanted ion profile can be quite different than the predicted SRIM implantation profile due to radiation enhanced transport and segregation.
Spatial Reasoning Training Through Light Curves Of Model Asteroids
Ziffer, Julie; Nakroshis, Paul A.; Rudnick, Benjamin T.; Brautigam, Maxwell J.; Nelson, Tyler W.
2015-11-01
Recent research has demonstrated that spatial reasoning skills, long known to be crucial to math and science success, are teachable. Even short stints of training can improve spatial reasoning skills among students who lack them (Sorby et al., 2006). Teaching spatial reasoning is particularly valuable to women and minorities who, through societal pressure, often doubt their spatial reasoning skill (Hill et al., 2010). We have designed a hands on asteroid rotation lab that provides practice in spatial reasoning tasks while building the student’s understanding of photometry. For our tool, we mount a model asteroid, with any shape of our choosing, on a slowly rotating motor shaft, whose speed is controlled by the experimenter. To mimic an asteroid light curve, we place the model asteroid in a dark box, shine a movable light source upon our asteroid, and record the light reflected onto a moveable camera. Students may then observe changes in the light curve that result from varying a) the speed of rotation, b) the model asteroid’s orientation with respect to the motor axis, c) the model asteroid’s shape or albedo, and d) the phase angle. After practicing with our tool, students are asked to pair new objects to their corresponding light curves. To correctly pair objects to their light curves, students must imagine how light scattering off of a three dimensional rotating object is imaged on a ccd sensor plane, and then reduced to a series of points on a light curve plot. Through the use of our model asteroid, the student develops confidence in spatial reasoning skills.
On the time-dependent extra spatial dimensions in six dimensional space-time
Lien, Phan Hong; Hai, Do Thi Hong
2016-06-01
In this paper, we analyze the time-dependent extra spatial dimensions in six dimensional (6D) space-time. The 4-brane is assumed to be a de Sitter space. Based on the form of the brane-world energy-momentum tensor proposed by Shiromizu et al. and the five dimensions by Peter K. F. Kuhfittic, we extended the theory to the 2-codimension embedded in higher dimensions. The inflation scenario in 6D is investigated in two cases of cosmological constant: Ʌ > 0 and Ʌ < 0. The energy of two extra dimensions is calculated too.
Spatial organization and time dependence of Jupiter's tropospheric temperatures, 1980-1993
Orton, Glenn S.; Friedson, A. James; Yanamandra-Fisher, Padmavati A.; Caldwell, John; Hammel, Heidi B.; Baines, Kevin H.; Bergstralh, Jay T.; Martin, Terry Z.; West, Robert A.; Veeder, Glenn J., Jr.
1994-01-01
The spatial organization and time dependence of Jupiter's temperature near 250-millibar pressure were measured through a jovian year by imaging thermal emission at 18 micrometers. The temperature field is influenced by seasonal radiative forcing, and its banded organization is closely correlated with the visible cloud field. Evidence was found for a quasi-periodic oscillation of temperatures in the Equatorial Zone, a correlation between tropospheric and stratospheric waves in the North Equatorial Belt, and slowly moving thermal features in the North and South Equatorial Belts. There appears to be no common relation between temporal changes of temperature and changes in the visual albedo of the various axisymmetric bands.
Fu, Jin; Wu, Sheng; Li, Hong; Petzold, Linda R.
2014-10-01
The inhomogeneous stochastic simulation algorithm (ISSA) is a fundamental method for spatial stochastic simulation. However, when diffusion events occur more frequently than reaction events, simulating the diffusion events by ISSA is quite costly. To reduce this cost, we propose to use the time dependent propensity function in each step. In this way we can avoid simulating individual diffusion events, and use the time interval between two adjacent reaction events as the simulation stepsize. We demonstrate that the new algorithm can achieve orders of magnitude efficiency gains over widely-used exact algorithms, scales well with increasing grid resolution, and maintains a high level of accuracy.
Energy Technology Data Exchange (ETDEWEB)
Fu, Jin, E-mail: iamfujin@hotmail.com [Department of Computer Science, University of California, Santa Barbara (United States); Wu, Sheng, E-mail: sheng@cs.ucsb.edu [Department of Computer Science, University of California, Santa Barbara (United States); Li, Hong, E-mail: hong.li@teradata.com [Teradata Inc., El Segundo, California (United States); Petzold, Linda R., E-mail: petzold@cs.ucsb.edu [Department of Computer Science, University of California, Santa Barbara (United States)
2014-10-01
The inhomogeneous stochastic simulation algorithm (ISSA) is a fundamental method for spatial stochastic simulation. However, when diffusion events occur more frequently than reaction events, simulating the diffusion events by ISSA is quite costly. To reduce this cost, we propose to use the time dependent propensity function in each step. In this way we can avoid simulating individual diffusion events, and use the time interval between two adjacent reaction events as the simulation stepsize. We demonstrate that the new algorithm can achieve orders of magnitude efficiency gains over widely-used exact algorithms, scales well with increasing grid resolution, and maintains a high level of accuracy.
A Spatial Lattice Model Applied for Meteorological Visualization and Analysis
Directory of Open Access Journals (Sweden)
Mingyue Lu
2017-03-01
Full Text Available Meteorological information has obvious spatial-temporal characteristics. Although it is meaningful to employ a geographic information system (GIS to visualize and analyze the meteorological information for better identification and forecasting of meteorological weather so as to reduce the meteorological disaster loss, modeling meteorological information based on a GIS is still difficult because meteorological elements generally have no stable shape or clear boundary. To date, there are still few GIS models that can satisfy the requirements of both meteorological visualization and analysis. In this article, a spatial lattice model based on sampling particles is proposed to support both the representation and analysis of meteorological information. In this model, a spatial sampling particle is regarded as the basic element that contains the meteorological information, and the location where the particle is placed with the time mark. The location information is generally represented using a point. As these points can be extended to a surface in two dimensions and a voxel in three dimensions, if these surfaces and voxels can occupy a certain space, then this space can be represented using these spatial sampling particles with their point locations and meteorological information. In this case, the full meteorological space can then be represented by arranging numerous particles with their point locations in a certain structure and resolution, i.e., the spatial lattice model, and extended at a higher resolution when necessary. For practical use, the meteorological space is logically classified into three types of spaces, namely the projection surface space, curved surface space, and stereoscopic space, and application-oriented spatial lattice models with different organization forms of spatial sampling particles are designed to support the representation, inquiry, and analysis of meteorological information within the three types of surfaces. Cases
A spatial emergy model for Alachua County, Florida
Lambert, James David
A spatial model of the distribution of energy flows and storages in Alachua County, Florida, was created and used to analyze spatial patterns of energy transformation hierarchy in relation to spatial patterns of human settlement. Emergy, the available energy of one kind previously required directly or indirectly to make a product or service, was used as a measure of the quality of the different forms of energy flows and storages. Emergy provides a common unit of measure for comparing the productive contributions of natural processes with those of economic and social processes---it is an alternative to using money for measuring value. A geographic information system was used to create a spatial model and make maps that show the distribution and magnitude of different types of energy and emergy flows and storages occurring in one-hectare land units. Energy transformities were used to convert individual energy flows and storages into emergy units. Maps of transformities were created that reveal a clear spatial pattern of energy transformation hierarchy. The maps display patterns of widely-dispersed areas with lower transformity energy flows and storages, and smaller, centrally-located areas with higher transformities. Energy signature graphs and spatial unit transformities were used to characterize and compare the types and amounts of energy being consumed and stored according to land use classification, planning unit, and neighborhood categories. Emergy ratio maps and spatial unit ratios were created by dividing the values for specific emergy flows or storages by the values for other emergy flows or storages. Spatial context analysis was used to analyze the spatial distribution patterns of mean and maximum values for emergy flows and storages. The modeling method developed for this study is general and applicable to all types of landscapes and could be applied at any scale. An advantage of this general approach is that the results of other studies using this method
Analysis of the impact of immigration on labour market using spatial models
Polonyankina, Tatiana
2017-07-01
This paper investigates the impact of immigration on employment and unemployment of a host country. The question to answer is: How does employment/unemployment in the host country change after an increase in number of immigrants? The analysis is taking into account only legal immigrants in recession period. The model is combining classical regression of cross-sectional data with spatial econometrics models where cross-section dependencies are captured by a spatial matrix. The intention is by using spatial models analyse the sensitivity of employment/unemployment rate on change in a share of immigration in a region. The used panel data are based on the Labour force survey and on available macro data in Eurostat for 3 European countries (Germany, Austria and Czech Republic) grouped into cells by NUTS regions in a recession period.
Spatial object model[l]ing in fuzzy topological spaces : with applications to land cover change
Tang, Xinming
2004-01-01
The central topic of this thesis focuses on the accommodation of fuzzy spatial objects in a GIS. Several issues are discussed theoretically and practically, including the definition of fuzzy spatial objects, the topological relations between them, the modeling of fuzzy spatial objects, the generatio
Mixtures of Polya trees for flexible spatial frailty survival modelling.
Zhao, Luping; Hanson, Timothy E; Carlin, Bradley P
2009-06-01
Mixtures of Polya trees offer a very flexible nonparametric approach for modelling time-to-event data. Many such settings also feature spatial association that requires further sophistication, either at the point level or at the lattice level. In this paper, we combine these two aspects within three competing survival models, obtaining a data analytic approach that remains computationally feasible in a fully hierarchical Bayesian framework using Markov chain Monte Carlo methods. We illustrate our proposed methods with an analysis of spatially oriented breast cancer survival data from the Surveillance, Epidemiology and End Results program of the National Cancer Institute. Our results indicate appreciable advantages for our approach over competing methods that impose unrealistic parametric assumptions, ignore spatial association or both.
Noncausal spatial prediction filtering based on an ARMA model
Institute of Scientific and Technical Information of China (English)
Liu Zhipeng; Chen Xiaohong; Li Jingye
2009-01-01
Conventional f-x prediction filtering methods are based on an autoregressive model. The error section is first computed as a source noise but is removed as additive noise to obtain the signal, which results in an assumption inconsistency before and after filtering. In this paper, an autoregressive, moving-average model is employed to avoid the model inconsistency. Based on the ARMA model, a noncasual prediction filter is computed and a self-deconvolved projection filter is used for estimating additive noise in order to suppress random noise. The 1-D ARMA model is also extended to the 2-D spatial domain, which is the basis for noncasual spatial prediction filtering for random noise attenuation on 3-D seismic data. Synthetic and field data processing indicate this method can suppress random noise more effectively and preserve the signal simultaneously and does much better than other conventional prediction filtering methods.
A heuristic model of alcohol dependence.
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Zhen Qi
Full Text Available BACKGROUND: Substance dependence poses a critical health problem. Sadly, its neurobiological mechanisms are still unclear, and this lack of real understanding is reflected in insufficient treatment options. It has been hypothesized that alcohol effects are due to an imbalance between neuroexcitatory and neuroinhibitory amino acids. However, glutamate and GABA interact with other neurotransmitters, which form a complicated network whose functioning evades intuition and should be investigated systemically with methods of biomedical systems analysis. METHODS AND RESULTS: We present a heuristic model of neurotransmitters that combines a neurochemical interaction matrix at the biochemical level with a mobile describing the balances between pairs of neurotransmitters at the physiological and behavioral level. We investigate the effects of alcohol on the integrated neurotransmitter systems at both levels. The model simulation results are consistent with clinical and experimental observations. The model demonstrates that the drug diazepam for symptoms of alcohol withdrawal effectively reduces the imbalances between neurotransmitters. Moreover, the acetylcholine signal is suggested as a novel target for treatment of symptoms associated with alcohol withdrawal. CONCLUSIONS: Efficient means of integrating clinical symptoms across multiple levels are still scarce and difficult to establish. We present a heuristic model of systemic neurotransmitter functionality that permits the assessment of genetic, biochemical, and pharmacological perturbations. The model can serve as a tool to represent clinical and biological observations and explore various scenarios associated with alcohol dependence and its treatments. It also is very well suited for educational purposes.
Colour dependence of zodiacal light models
Giese, R. H.; Hanner, M. S.; Leinert, C.
1973-01-01
Colour models of the zodiacal light in the ecliptic have been calculated for both dielectric and metallic particles in the sub-micron and micron size range. Two colour ratios were computed, a blue ratio and a red ratio. The models with a size distribution proportional to s to the -2.5 power ds (where s is the particle radius) generally show a colour close to the solar colour and almost independent of elongation. Especially in the blue colour ratio there is generally no significant dependence on the lower cutoff size (0.1-1 micron). The main feature of absorbing particles is a reddening at small elongations. The models for size distributions proportional to s to the -4 power ds show larger departures from solar colour and more variation with model parameters. Colour measurements, including red and near infra-red, therefore are useful to distinguish between flat and steep size spectra and to verify the presence of slightly absorbing particles.
Spatial scale-dependent policy planning for land management in southern Europe.
Papadimitriou, F; Mairota, P
1996-01-01
This study outlines and original tool for rural policy planning in southern Europe. This new tool is a process-based, scale-dependent, rural policy-making approach, which is designed to address increasing land degradation problems in southern Europe. Seven important processes are identified (land abandonment, devegetation, intensification in agriculture, global climate change, accelerated soil erosion, increasing water demands, urbanisation) and plotted on a space-time diagram, which clearly shows the spatial and temporal scales for which these processes are significant for landscape change in southern Europe. Conclusions are derived concerning, in particular, sustainable (optimal) rural policy-making for southern Europe's problematic land management. An optimal spatial-temporal scale for land management in southern Europe may range spatially from the "farm" (0.5 km(2)) to "sub-provincial" level (450 km(2)) and temporally from 7 to 30 years. The study delineates methods and results derivable from such a new policy-planning approach and suggests the usefulness of combining this approach with ecological land classification at the landscape level.
Spatial cognition and crime: the study of mental models of spatial relations in crime analysis.
Luini, Lorenzo P; Scorzelli, Marco; Mastroberardino, Serena; Marucci, Francesco S
2012-08-01
Several studies employed different algorithms in order to investigate criminal's spatial behaviour and to identify mental models and cognitive strategies related to it. So far, a number of geographic profiling (GP) software have been implemented to analyse mobility and its relation to the way criminals are using spatial environment when committing a crime. Since crimes are usually perpetrated in the offender's high-awareness areas, those cognitive maps can be employed to create a map of the criminal's operating area to help investigators to circumscribe search areas. The aim of the present study was to verify accuracy of simple statistical analysis in predicting spatial mobility of a group of 30 non-criminal subjects. Results showed that statistics such as Mean Centre and Standard Distance were accurate in elaborating a GP for each subject according to the mobility area provided. Future analysis will be implemented using mobility information of criminal subjects and location-based software to verify whether there is a cognitive spatial strategy employed by them when planning and committing a crime.
Qi, Feng; Tavakol, Vahid; Ocket, Ilja; Xu, Peng; Schreurs, Dominique; Wang, Jinkuan; Nauwelaers, Bart
2010-01-01
Active millimeter wave imaging systems have become a promising candidate for indoor security applications and industrial inspection. However, there is a lack of simulation tools at the system level. We introduce and evaluate two modeling approaches that are applied to active millimeter wave imaging systems. The first approach originates in Fourier optics and concerns the calculation in the spatial frequency domain. The second approach is based on wave propagation and corresponds to calculation in the spatial domain. We compare the two approaches in the case of both rough and smooth objects and point out that the spatial frequency domain calculation may suffer from a large error in amplitude of 50% in the case of rough objects. The comparison demonstrates that the concepts of point-spread function and f-number should be applied with careful consideration in coherent millimeter wave imaging systems. In the case of indoor applications, the near-field effect should be considered, and this is included in the spatial domain calculation.
Off-resonant vibrational excitation: Orientational dependence and spatial control of photofragments
DEFF Research Database (Denmark)
Machholm, Mette; Henriksen, Niels Engholm
2000-01-01
-dependent response to the IR fields is due to the anharmonicity of the potential. A subsequent ultraviolet laser pulse in resonance at the outer turning point of the vibrational motion can then dissociate the oscillating molecules, all with the same orientation, leading to spatial control of the photofragment......Off-resonant and resonant vibrational excitation with short intense infrared (IR) laser pulses creates localized oscillating wave packets, but differs by the efficiency of the excitation and surprisingly by the orientational dependence. Orientational selectivity of the vibrational excitation...... of randomly oriented heteronuclear diatomic molecules can be obtained under simultaneous irradiation by a resonant and an off-resonant intense IR laser pulse: Molecules with one initial orientation will be vibrationally excited, while those with the opposite orientation will be at rest. The orientation...
Spatial estimation of debris flows-triggering rainfall and its dependence on rainfall severity
Destro, Elisa; Marra, Francesco; Nikolopoulos, Efthymios; Zoccatelli, Davide; Creutin, Jean-Dominique; Borga, Marco
2016-04-01
Forecasting the occurrence of landslides and debris flows (collectively termed 'debris flows' hereinafter) is fundamental for issuing hazard warnings, and focuses largely on rainfall as a triggering agent. Debris flow forecasting relies very often on the identification of combinations of depth and duration of rainfall - rainfall thresholds - that trigger widespread debris flows. Rainfall estimation errors related to the sparse nature of raingauge data are enhanced in case of convective rainfall events characterized by limited spatial extent. Such errors have been shown to cause underestimation of the rainfall thresholds and, thus, less efficient forecasts of debris flows occurrence. This work examines the spatial organization of debris flows-triggering rainfall around the debris flow initiation points using high-resolution, carefully corrected radar data for a set of short duration (debris-flow triggering rainfall events that occurred in the study area between 2005 and 2014. The selected events are among the most severe in the region during this period and triggered a total of 99 debris flows that caused significant damage to people and infrastructures. We show that the spatial rainfall organisation depends on the severity (measured via the estimated return time-RT) of the debris flow-triggering rainfall. For more frequent events (RTdebris flow location coincides with a local minimum, whereas for less frequent events (RT>20 yrs) the triggering rainfall presents a local peak corresponding to the debris flow initiation point. Dependence of these features on rainfall duration is quite limited. The characteristics of the spatial rainfall organisation are exploited to understand the performances and results of three different rainfall interpolation techniques: nearest neighbour (NN), inverse distance weighting (IDW) and ordinary kriging (OK). We show that the features of the spatial organization of the debris flow triggering rainfall explain the biases in the
Lattice three-species models of the spatial spread of rabies among foxes
Benyoussef, A; Chakib, H; Ez-Zahraouy, H
1999-01-01
Lattice models describing the spatial spread of rabies among foxes are studied. In these models, the fox population is divided into three-species: susceptible, infected or incubating, and infectious or rabid. They are based on the fact that susceptible and incubating foxes are territorial while rabid foxes have lost their sense of direction and move erratically. Two different models are investigated: a one-dimensional coupled-map lattice model, and a two-dimensional automata network model. Both models take into account the short-range character of the infection process and the diffusive motion of rabid foxes. Numerical simulations show how the spatial distribution of rabies, and the speed of propagation of the epizootic front depend upon the carrying capacity of the environment and diffusion of rabid foxes out of their territory.
Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models
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Chuanglin Fang
2015-11-01
Full Text Available Urban air pollution is one of the most visible environmental problems to have accompanied China’s rapid urbanization. Based on emission inventory data from 2014, gathered from 289 cities, we used Global and Local Moran’s I to measure the spatial autorrelation of Air Quality Index (AQI values at the city level, and employed Ordinary Least Squares (OLS, Spatial Lag Model (SAR, and Geographically Weighted Regression (GWR to quantitatively estimate the comprehensive impact and spatial variations of China’s urbanization process on air quality. The results show that a significant spatial dependence and heterogeneity existed in AQI values. Regression models revealed urbanization has played an important negative role in determining air quality in Chinese cities. The population, urbanization rate, automobile density, and the proportion of secondary industry were all found to have had a significant influence over air quality. Per capita Gross Domestic Product (GDP and the scale of urban land use, however, failed the significance test at 10% level. The GWR model performed better than global models and the results of GWR modeling show that the relationship between urbanization and air quality was not constant in space. Further, the local parameter estimates suggest significant spatial variation in the impacts of various urbanization factors on air quality.
Spatial optimum collocation model of urban land and its algorithm
Kong, Xiangqiang; Li, Xinyun
2007-06-01
Optimizing the allocation of urban land is that layout and fix position the various types of land-use in space, maximize the overall benefits of urban space (including economic, social, environment) using a certain method and technique. There is two problems need to deal with in optimizing the allocation of urban land in the technique: one is the quantitative structure, the other is the space structure. In allusion to these problems, according to the principle of spatial coordination, a kind of new optimum collocation model about urban land was put forward in this text. In the model, we give a target function and a set of "soft" constraint conditions, and the area proportions of various types of land-use are restricted to the corresponding allowed scope. Spatial genetic algorithm is used to manipulate and calculate the space of urban land, the optimum spatial collocation scheme can be gradually approached, in which the three basic operations of reproduction, crossover and mutation are all operated on the space. Taking the built-up areas of Jinan as an example, we did the spatial optimum collocation experiment of urban land, the spatial aggregation of various types is better, and an approving result was got.
DEFF Research Database (Denmark)
Antón Castro, Francesc/François; Musiige, Deogratius; Mioc, Darka
2016-01-01
This paper presents a case study for comparing different multidimensional mathematical modeling methodologies used in multidimensional spatial big data modeling and proposing a new technique. An analysis of multidimensional modeling approaches (neural networks, polynomial interpolation and homotopy...
A theory and a computational model of spatial reasoning with preferred mental models.
Ragni, Marco; Knauff, Markus
2013-07-01
Inferences about spatial arrangements and relations like "The Porsche is parked to the left of the Dodge and the Ferrari is parked to the right of the Dodge, thus, the Porsche is parked to the left of the Ferrari," are ubiquitous. However, spatial descriptions are often interpretable in many different ways and compatible with several alternative mental models. This article suggests that individuals tackle such indeterminate multiple-model problems by constructing a single, simple, and typical mental model but neglect other possible models. The model that first comes to reasoners' minds is the preferred mental model. It helps save cognitive resources but also leads to reasoning errors and illusory inferences. The article presents a preferred model theory and an instantiation of this theory in the form of a computational model, preferred inferences in reasoning with spatial mental models (PRISM). PRISM can be used to simulate and explain how preferred models are constructed, inspected, and varied in a spatial array that functions as if it were a spatial working memory. A spatial focus inserts tokens into the array, inspects the array to find new spatial relations, and relocates tokens in the array to generate alternative models of the problem description, if necessary. The article also introduces a general measure of difficulty based on the number of necessary focus operations (rather than the number of models). A comparison with results from psychological experiments shows that the theory can explain preferences, errors, and the difficulty of spatial reasoning problems.
Function modeling improves the efficiency of spatial modeling using big data from remote sensing
John Hogland; Nathaniel Anderson
2017-01-01
Spatial modeling is an integral component of most geographic information systems (GISs). However, conventional GIS modeling techniques can require substantial processing time and storage space and have limited statistical and machine learning functionality. To address these limitations, many have parallelized spatial models using multiple coding libraries and have...
Rockfall hazard analysis using LiDAR and spatial modeling
Lan, Hengxing; Martin, C. Derek; Zhou, Chenghu; Lim, Chang Ho
2010-05-01
Rockfalls have been significant geohazards along the Canadian Class 1 Railways (CN Rail and CP Rail) since their construction in the late 1800s. These rockfalls cause damage to infrastructure, interruption of business, and environmental impacts, and their occurrence varies both spatially and temporally. The proactive management of these rockfall hazards requires enabling technologies. This paper discusses a hazard assessment strategy for rockfalls along a section of a Canadian railway using LiDAR and spatial modeling. LiDAR provides accurate topographical information of the source area of rockfalls and along their paths. Spatial modeling was conducted using Rockfall Analyst, a three dimensional extension to GIS, to determine the characteristics of the rockfalls in terms of travel distance, velocity and energy. Historical rockfall records were used to calibrate the physical characteristics of the rockfall processes. The results based on a high-resolution digital elevation model from a LiDAR dataset were compared with those based on a coarse digital elevation model. A comprehensive methodology for rockfall hazard assessment is proposed which takes into account the characteristics of source areas, the physical processes of rockfalls and the spatial attribution of their frequency and energy.
Lateral specialization in unilateral spatial neglect: a cognitive robotics model.
Conti, Daniela; Di Nuovo, Santo; Cangelosi, Angelo; Di Nuovo, Alessandro
2016-08-01
In this paper, we present the experimental results of an embodied cognitive robotic approach for modelling the human cognitive deficit known as unilateral spatial neglect (USN). To this end, we introduce an artificial neural network architecture designed and trained to control the spatial attentional focus of the iCub robotic platform. Like the human brain, the architecture is divided into two hemispheres and it incorporates bio-inspired plasticity mechanisms, which allow the development of the phenomenon of the specialization of the right hemisphere for spatial attention. In this study, we validate the model by replicating a previous experiment with human patients affected by the USN and numerical results show that the robot mimics the behaviours previously exhibited by humans. We also simulated recovery after the damage to compare the performance of each of the two hemispheres as additional validation of the model. Finally, we highlight some possible advantages of modelling cognitive dysfunctions of the human brain by means of robotic platforms, which can supplement traditional approaches for studying spatial impairments in humans.
Spatial modeling on the nutrient retention of an estuary wetland
Li, X.; Xiao, D.; Jongman, R.H.G.; Harms, W.B.; Bregt, A.K.
2003-01-01
There is a great potential to use the estuary wetland as a final filter for nutrient enriched river water, and reduce the possibility of coastal water eutrophication. Based upon field data, spatial models were designed on a stepwise basis to simulate the nutrient reduction function of the wetland in
Design of spatial experiments: Model fitting and prediction
Energy Technology Data Exchange (ETDEWEB)
Fedorov, V.V.
1996-03-01
The main objective of the paper is to describe and develop model oriented methods and algorithms for the design of spatial experiments. Unlike many other publications in this area, the approach proposed here is essentially based on the ideas of convex design theory.
On Angular Sampling Methods for 3-D Spatial Channel Models
DEFF Research Database (Denmark)
Fan, Wei; Jämsä, Tommi; Nielsen, Jesper Ødum
2015-01-01
This paper discusses generating three dimensional (3D) spatial channel models with emphasis on the angular sampling methods. Three angular sampling methods, i.e. modified uniform power sampling, modified uniform angular sampling, and random pairing methods are proposed and investigated in detail....
Differences in spatial understanding between physical and virtual models
Directory of Open Access Journals (Sweden)
Lei Sun
2014-03-01
Full Text Available In the digital age, physical models are still used as major tools in architectural and urban design processes. The reason why designers still use physical models remains unclear. In addition, physical and 3D virtual models have yet to be differentiated. The answers to these questions are too complex to account for in all aspects. Thus, this study only focuses on the differences in spatial understanding between physical and virtual models. In particular, it emphasizes on the perception of scale. For our experiment, respondents were shown a physical model and a virtual model consecutively. A questionnaire was then used to ask the respondents to evaluate these models objectively and to establish which model was more accurate in conveying object size. Compared with the virtual model, the physical model tended to enable quicker and more accurate comparisons of building heights.
A Spatially Continuous Model of Carbohydrate Digestion and Transport Processes in the Colon.
Directory of Open Access Journals (Sweden)
Arun S Moorthy
Full Text Available A spatially continuous mathematical model of transport processes, anaerobic digestion and microbial complexity as would be expected in the human colon is presented. The model is a system of first-order partial differential equations with context determined number of dependent variables, and stiff, non-linear source terms. Numerical simulation of the model is used to elucidate information about the colon-microbiota complex. It is found that the composition of materials on outflow of the model does not well-describe the composition of material in other model locations, and inferences using outflow data varies according to model reactor representation. Additionally, increased microbial complexity allows the total microbial community to withstand major system perturbations in diet and community structure. However, distribution of strains and functional groups within the microbial community can be modified depending on perturbation length and microbial kinetic parameters. Preliminary model extensions and potential investigative opportunities using the computational model are discussed.
Chronic BDNF deficiency leads to an age-dependent impairment in spatial learning.
Petzold, Anne; Psotta, Laura; Brigadski, Tanja; Endres, Thomas; Lessmann, Volkmar
2015-04-01
Brain-derived neurotrophic factor (BDNF) is a crucial mediator of neural plasticity and, consequently, of memory formation. In hippocampus-dependent learning tasks BDNF also seems to play an essential role. However, there are conflicting results concerning the spatial learning ability of aging BDNF(+/-) mice in the Morris water maze paradigm. To evaluate the effect of chronic BDNF deficiency in the hippocampus on spatial learning throughout life, we conducted a comprehensive study to test differently aged BDNF(+/-) mice and their wild type littermates in the Morris water maze and to subsequently quantify their hippocampal BDNF protein levels as well as expression levels of TrkB receptors. We observed an age-dependent learning deficit in BDNF(+/-) animals, starting at seven months of age, despite stable hippocampal BDNF protein expression and continual decline of TrkB receptor expression throughout aging. Furthermore, we detected a positive correlation between hippocampal BDNF protein levels and learning performance during the probe trial in animals that showed a good learning performance during the long-term memory test.
Cho, J.-H.; Lee, D.-Y.; Noh, S.-J.; Kim, H.; Choi, C. R.; Lee, J.; Hwang, J.
2017-05-01
In this paper, using the multisatellite (the Van Allen Probes and two GOES satellites) observations in the inner magnetosphere, we examine two electromagnetic ion cyclotron (EMIC) wave events that are triggered by Pdyn enhancements under prolonged northward interplanetary magnetic field quiet time preconditions. For both events, the impact of enhanced Pdyn causes EMIC waves at multiple points. However, we find a strong spatial dependence that EMIC waves due to enhanced Pdyn impact can occur at multiple points (likely globally but not necessarily everywhere) but with different wave properties. For Event 1, three satellites situated at a nearly same dawnside zone but at slightly different L shells see occurrence of EMIC waves but in different frequencies relative to local ion gyrofrequencies and with different polarizations. These waves are found inside or at the outer edge of the plasmasphere. Another satellite near noon observes no dramatic EMIC wave despite the strongest magnetic compression there. For Event 2, the four satellites are situated at widely separated magnetic local time zones when they see occurrence of EMIC waves. They are again found at different frequencies relative to local ion gyrofrequencies with different polarizations and all outside the plasmasphere. We propose two possible explanations that (i) if triggered by enhanced Pdyn impact, details of ion cyclotron instability growth can be sensitive to local plasma conditions related to background proton distributions, and (ii) there can be preexisting waves with a specific spatial distribution, which determines occurrence and specific properties of EMIC waves depending on satellite's relative position after an enhanced Pdyn arrives.
Modeling of spatial dependence in wind power forecast uncertainty
DEFF Research Database (Denmark)
Papaefthymiou, George; Pinson, Pierre
2008-01-01
It is recognized today that short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a paramount information on the uncertainty of expected wind generation. When considering different areas covering a region, they are produced independently, and thus neg...
Dong, Guanpeng; Harris, Richard; Jones, Kelvyn; Yu, Jianhui
2015-01-01
This paper develops a methodology for extending multilevel modelling to incorporate spatial interaction effects. The motivation is that classic multilevel models are not specifically spatial. Lower level units may be nested into higher level ones based on a geographical hierarchy (or a membership structure—for example, census zones into regions) but the actual locations of the units and the distances between them are not directly considered: what matters is the groupings but not how close together any two units are within those groupings. As a consequence, spatial interaction effects are neither modelled nor measured, confounding group effects (understood as some sort of contextual effect that acts ‘top down’ upon members of a group) with proximity effects (some sort of joint dependency that emerges between neighbours). To deal with this, we incorporate spatial simultaneous autoregressive processes into both the outcome variable and the higher level residuals. To assess the performance of the proposed method and the classic multilevel model, a series of Monte Carlo simulations are conducted. The results show that the proposed method performs well in retrieving the true model parameters whereas the classic multilevel model provides biased and inefficient parameter estimation in the presence of spatial interactions. An important implication of the study is to be cautious of an apparent neighbourhood effect in terms of both its magnitude and statistical significance if spatial interaction effects at a lower level are suspected. Applying the new approach to a two-level land price data set for Beijing, China, we find significant spatial interactions at both the land parcel and district levels. PMID:26086913
Modern methodology and applications in spatial-temporal modeling
Matsui, Tomoko
2015-01-01
This book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant influence in machine learning disciplines. The second chapter takes up non-parametric statistical methods for spatial field reconstruction and exceedance probability estimation based on Gaussian process-based models in the context of wireless sensor network data. The third chapter presents signal-processing methods applied to acoustic mood analysis based on music signal analysis. The fourth chapter covers models that are applicable to time series modeling in the domain of speech and language processing. This includes aspects of factor analysis, independent component an...
Practical likelihood analysis for spatial generalized linear mixed models
DEFF Research Database (Denmark)
Bonat, W. H.; Ribeiro, Paulo Justiniano
2016-01-01
We investigate an algorithm for maximum likelihood estimation of spatial generalized linear mixed models based on the Laplace approximation. We compare our algorithm with a set of alternative approaches for two datasets from the literature. The Rhizoctonia root rot and the Rongelap are, respectiv......We investigate an algorithm for maximum likelihood estimation of spatial generalized linear mixed models based on the Laplace approximation. We compare our algorithm with a set of alternative approaches for two datasets from the literature. The Rhizoctonia root rot and the Rongelap are...... of Laplace approximation include the computation of the maximized log-likelihood value, which can be used for model selection and tests, and the possibility to obtain realistic confidence intervals for model parameters based on profile likelihoods. The Laplace approximation also avoids the tuning...
Spatial Development Modeling Methodology Application Possibilities in Vilnius
Directory of Open Access Journals (Sweden)
Lina Panavaitė
2017-05-01
Full Text Available In order to control the continued development of high-rise buildings and their irreversible visual impact on the overall silhouette of the city, the great cities of the world introduced new methodological principles to city’s spatial development models. These methodologies and spatial planning guidelines are focused not only on the controlled development of high-rise buildings, but on the spatial modelling of the whole city by defining main development criteria and estimating possible consequences. Vilnius city is no exception, however the re-establishment of independence of Lithuania caused uncontrolled urbanization process, so most of the city development regulations emerged as a consequence of unmanaged processes of investors’ expectations legalization. The importance of consistent urban fabric as well as conservation and representation of city’s most important objects gained attention only when an actual threat of overshadowing them with new architecture along with unmanaged urbanization in the city center or urban sprawl at suburbia, caused by land-use projects, had emerged. Current Vilnius’ spatial planning documents clearly define urban structure and key development principles, however the definitions are relatively abstract, causing uniform building coverage requirements for territories with distinct qualities and simplifying planar designs which do not meet quality standards. The overall quality of urban architecture is not regulated. The article deals with current spatial modeling methods, their individual parts, principles, the criteria for quality assessment and their applicability in Vilnius. The text contains an outline of possible building coverage regulations and impact assessment criteria for new development. The article contains a compendium of requirements for high-quality spatial planning and building design.
Comparing spatial and temporal transferability of hydrological model parameters
Patil, Sopan; Stieglitz, Marc
2015-04-01
Operational use of hydrological models requires the transfer of calibrated parameters either in time (for streamflow forecasting) or space (for prediction at ungauged catchments) or both. Although the effects of spatial and temporal parameter transfer on catchment streamflow predictions have been well studied individually, a direct comparison of these approaches is much less documented. In our view, such comparison is especially pertinent in the context of increasing appeal and popularity of the "trading space for time" approaches that are proposed for assessing the hydrological implications of anthropogenic climate change. Here, we compare three different schemes of parameter transfer, viz., temporal, spatial, and spatiotemporal, using a spatially lumped hydrological model called EXP-HYDRO at 294 catchments across the continental United States. Results show that the temporal parameter transfer scheme performs best, with lowest decline in prediction performance (median decline of 4.2%) as measured using the Kling-Gupta efficiency metric. More interestingly, negligible difference in prediction performance is observed between the spatial and spatiotemporal parameter transfer schemes (median decline of 12.4% and 13.9% respectively). We further demonstrate that the superiority of temporal parameter transfer scheme is preserved even when: (1) spatial distance between donor and receiver catchments is reduced, or (2) temporal lag between calibration and validation periods is increased. Nonetheless, increase in the temporal lag between calibration and validation periods reduces the overall performance gap between the three parameter transfer schemes. Results suggest that spatiotemporal transfer of hydrological model parameters has the potential to be a viable option for climate change related hydrological studies, as envisioned in the "trading space for time" framework. However, further research is still needed to explore the relationship between spatial and temporal
Patterns of variability in early-life traits of fishes depend on spatial scale of analysis.
Di Franco, Antonio; Guidetti, Paolo
2011-06-23
Estimates of early-life traits of fishes (e.g. pelagic larval duration (PLD) and spawning date) are essential for investigating and assessing patterns of population connectivity. Such estimates are available for a large number of both tropical and temperate fish species, but few studies have assessed their variability in space, especially across multiple scales. The present study, where a Mediterranean fish (i.e. the white seabream Diplodus sargus sargus) was used as a model, shows that spawning date and PLD are spatially more variable at a scale of kilometres than at a scale of tens to hundreds of kilometres. This study indicates the importance of considering spatial variability of early-life traits of fishes in order to properly delineate connectivity patterns at larval stages (e.g. by means of Lagrangian simulations), thus providing strategically useful information on connectivity and relevant management goals (e.g. the creation of networks of marine reserves).
Integrating remote sensing and spatially explicit epidemiological modeling
Finger, Flavio; Knox, Allyn; Bertuzzo, Enrico; Mari, Lorenzo; Bompangue, Didier; Gatto, Marino; Rinaldo, Andrea
2015-04-01
Spatially explicit epidemiological models are a crucial tool for the prediction of epidemiological patterns in time and space as well as for the allocation of health care resources. In addition they can provide valuable information about epidemiological processes and allow for the identification of environmental drivers of the disease spread. Most epidemiological models rely on environmental data as inputs. They can either be measured in the field by the means of conventional instruments or using remote sensing techniques to measure suitable proxies of the variables of interest. The later benefit from several advantages over conventional methods, including data availability, which can be an issue especially in developing, and spatial as well as temporal resolution of the data, which is particularly crucial for spatially explicit models. Here we present the case study of a spatially explicit, semi-mechanistic model applied to recurring cholera outbreaks in the Lake Kivu area (Democratic Republic of the Congo). The model describes the cholera incidence in eight health zones on the shore of the lake. Remotely sensed datasets of chlorophyll a concentration in the lake, precipitation and indices of global climate anomalies are used as environmental drivers. Human mobility and its effect on the disease spread is also taken into account. Several model configurations are tested on a data set of reported cases. The best models, accounting for different environmental drivers, and selected using the Akaike information criterion, are formally compared via cross validation. The best performing model accounts for seasonality, El Niño Southern Oscillation, precipitation and human mobility.
Stochastic geometry, spatial statistics and random fields models and algorithms
2015-01-01
Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistics and random fields, this volume places a special emphasis on fundamental classes of models and algorithms as well as on their applications, for example in materials science, biology and genetics. This book has a strong focus on simulations and includes extensive codes in Matlab and R, which are widely used in the mathematical community. It can be regarded as a continuation of the recent volume 2068 of Lecture Notes in Mathematics, where other issues of stochastic geometry, spatial statistics and random fields were considered, with a focus on asymptotic methods.
Spatial distribution of emissions to air – the SPREAD model
DEFF Research Database (Denmark)
Plejdrup, Marlene Schmidt; Gyldenkærne, Steen
The National Environmental Research Institute (NERI), Aarhus University, completes the annual national emission inventories for greenhouse gases and air pollutants according to Denmark’s obligations under international conventions, e.g. the climate convention, UNFCCC and the convention on long...... to the requirements for reporting of gridded emissions to CLRTAP. Spatial emission data is e.g. used as input for air quality modelling, which again serves as input for assessment and evaluation of health effects. For these purposes distributions with higher spatial resolution have been requested. Previously...
Constitutive model with time-dependent deformations
DEFF Research Database (Denmark)
Krogsbøll, Anette
1998-01-01
are common in time as well as size. This problem is adressed by means of a new constitutive model for soils. It is able to describe the behavior of soils at different deformation rates. The model defines time-dependent and stress-related deformations separately. They are related to each other and they occur......In many geological and Engineering problems it is necessary to transform information from one scale to another. Data collected at laboratory scale are often used to evaluate field problems on a much larger scale. This is certainly true for geological problems where extreme scale differences...... simultanelously. The model is based on concepts from elasticity and viscoplasticity theories. In addition to Hooke's law for the elastic behavior, the framework for the viscoplastic behavior consists, in the general case (two-dimensional or three-dimensional), of a yield surface, an associated flow rule...
Uniqueness of Petrov type D spatially inhomogeneous irrotational silent models
Apostolopoulos, P S; Apostolopoulos, Pantelis S; Carot, Jaume
2006-01-01
The consistency of the constraint with the evolution equations for spatially inhomogeneous and irrotational silent (SIIS) models of Petrov type I, demands that the former are preserved along the timelike congruence represented by the velocity of the dust fluid, leading to an infinite set of non-trivial constraints. This fact has been used to conjecture that the resulting models correspond to the spatially homogeneous (SH) models of Bianchi type I, at least for the case where the cosmological constant vanish. By exploiting the full set of the constraint equations as expressed in the 1+3 covariant formalism and using elements from the theory of the spacelike congruences, we provide a direct and simple proof of this conjecture for vacuum and dust fluid models, which shows that the Szekeres family of solutions represents the most general class of SIIS models. The suggested procedure also shows that, the uniqueness of the spatially inhomogeneous and irrotational models of Petrov type D is not affected by the prese...
Spatial Temporal Modelling of Particulate Matter for Health Effects Studies
Hamm, N. A. S.
2016-10-01
Epidemiological studies of the health effects of air pollution require estimation of individual exposure. It is not possible to obtain measurements at all relevant locations so it is necessary to predict at these space-time locations, either on the basis of dispersion from emission sources or by interpolating observations. This study used data obtained from a low-cost sensor network of 32 air quality monitoring stations in the Dutch city of Eindhoven, which make up the ILM (innovative air (quality) measurement system). These stations currently provide PM10 and PM2.5 (particulate matter less than 10 and 2.5 m in diameter), aggregated to hourly means. The data provide an unprecedented level of spatial and temporal detail for a city of this size. Despite these benefits the time series of measurements is characterized by missing values and noisy values. In this paper a space-time analysis is presented that is based on a dynamic model for the temporal component and a Gaussian process geostatistical for the spatial component. Spatial-temporal variability was dominated by the temporal component, although the spatial variability was also substantial. The model delivered accurate predictions for both isolated missing values and 24-hour periods of missing values (RMSE = 1.4 μg m-3 and 1.8 μg m-3 respectively). Outliers could be detected by comparison to the 95% prediction interval. The model shows promise for predicting missing values, outlier detection and for mapping to support health impact studies.
Kim, Yura; Jun, Mikyoung; Min, Seung-Ki; Suh, Myoung-Seok; Kang, Hyun-Suk
2016-05-01
CORDEX-East Asia, a branch of the coordinated regional climate downscaling experiment (CORDEX) initiative, provides high-resolution climate simulations for the domain covering East Asia. This study analyzes temperature data from regional climate models (RCMs) participating in the CORDEX - East Asia region, accounting for the spatial dependence structure of the data. In particular, we assess similarities and dissimilarities of the outputs from two RCMs, HadGEM3-RA and RegCM4, over the region and over time. A Bayesian functional analysis of variance (ANOVA) approach is used to simultaneously model the temperature patterns from the two RCMs for the current and future climate. We exploit nonstationary spatial models to handle the spatial dependence structure of the temperature variable, which depends heavily on latitude and altitude. For a seasonal comparison, we examine changes in the winter temperature in addition to the summer temperature data. We find that the temperature increase projected by RegCM4 tends to be smaller than the projection of HadGEM3-RA for summers, and that the future warming projected by HadGEM3-RA tends to be weaker for winters. Also, the results show that there will be a warming of 1-3°C over the region in 45 years. More specifically, the warming pattern clearly depends on the latitude, with greater temperature increases in higher latitude areas, which implies that warming may be more severe in the northern part of the domain.
Space in multi-agent systems modelling spatial processes
Directory of Open Access Journals (Sweden)
Petr Rapant
2007-06-01
Full Text Available Need for modelling of spatial processes arise in the spehere of geoinformation systems in the last time. Some processes (espetially natural ones can be modeled by means of using external tools, e. g. for modelling of contaminant transport in the environment. But in the case of socio-economic processes suitable tools interconnected with GIS are still in quest of reserch and development. One of the candidate technologies are so called multi-agent systems. Their theory is developed quite well, but they lack suitable means for dealing with space. This article deals with this problem and proposes solution for the field of a road transport modelling.
A spatial operator algebra for manipulator modeling and control
Rodriguez, G.; Kreutz, K.; Milman, M.
1988-01-01
A powerful new spatial operator algebra for modeling, control, and trajectory design of manipulators is discussed along with its implementation in the Ada programming language. Applications of this algebra to robotics include an operator representation of the manipulator Jacobian matrix; the robot dynamical equations formulated in terms of the spatial algebra, showing the complete equivalence between the recursive Newton-Euler formulations to robot dynamics; the operator factorization and inversion of the manipulator mass matrix which immediately results in O(N) recursive forward dynamics algorithms; the joint accelerations of a manipulator due to a tip contact force; the recursive computation of the equivalent mass matrix as seen at the tip of a manipulator; and recursive forward dynamics of a closed chain system. Finally, additional applications and current research involving the use of the spatial operator algebra are discussed in general terms.
Classification of missing values in spatial data using spin models
Žukovič, Milan; 10.1103/PhysRevE.80.011116
2013-01-01
A problem of current interest is the estimation of spatially distributed processes at locations where measurements are missing. Linear interpolation methods rely on the Gaussian assumption, which is often unrealistic in practice, or normalizing transformations, which are successful only for mild deviations from the Gaussian behavior. We propose to address the problem of missing values estimation on two-dimensional grids by means of spatial classification methods based on spin (Ising, Potts, clock) models. The "spin" variables provide an interval discretization of the process values, and the spatial correlations are captured in terms of interactions between the spins. The spins at the unmeasured locations are classified by means of the "energy matching" principle: the correlation energy of the entire grid (including prediction sites) is estimated from the sample-based correlations. We investigate the performance of the spin classifiers in terms of computational speed, misclassification rate, class histogram an...
Spatial-angular modeling of ground-based biaxial lidar
Agishev, Ravil R.
1997-10-01
Results of spatial-angular LIDAR modeling based on an efficiency criterion introduced are represented. Their analysis shows that a low spatial-angular efficiency of traditional VIS and NIR systems is a main cause of a low S/BR ratio at the photodetector input. It determines the considerable measurements errors and the following low accuracy of atmospheric optical parameters retrieval. As we have shown, the most effective protection against intensive sky background radiation for ground-based biaxial LIDAR's consist in forming of their angular field according to spatial-angular efficiency criterion G. Some effective approaches to high G-parameter value achievement to achieve the receiving system optimization are discussed.
Modelling spatial patterns of urban growth in Africa.
Linard, Catherine; Tatem, Andrew J; Gilbert, Marius
2013-10-01
The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5-10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers.
Modelling spatial patterns of urban growth in Africa
Linard, Catherine; Tatem, Andrew J.; Gilbert, Marius
2013-01-01
The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5–10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers. PMID:25152552
Indoor 3D Route Modeling Based On Estate Spatial Data
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.
Filgueira, Ramon; Grant, Jon; Strand, Øivind
2014-06-01
Shellfish carrying capacity is determined by the interaction of a cultured species with its ecosystem, which is strongly influenced by hydrodynamics. Water circulation controls the exchange of matter between farms and the adjacent areas, which in turn establishes the nutrient supply that supports phytoplankton populations. The complexity of water circulation makes necessary the use of hydrodynamic models with detailed spatial resolution in carrying capacity estimations. This detailed spatial resolution also allows for the study of processes that depend on specific spatial arrangements, e.g., the most suitable location to place farms, which is crucial for marine spatial planning, and consequently for decision support systems. In the present study, a fully spatial physical-biogeochemical model has been combined with scenario building and optimization techniques as a proof of concept of the use of ecosystem modeling as an objective tool to inform marine spatial planning. The object of this exercise was to generate objective knowledge based on an ecosystem approach to establish new mussel aquaculture areas in a Norwegian fjord. Scenario building was used to determine the best location of a pump that can be used to bring nutrient-rich deep waters to the euphotic layer, increasing primary production, and consequently, carrying capacity for mussel cultivation. In addition, an optimization tool, parameter estimation (PEST), was applied to the optimal location and mussel standing stock biomass that maximize production, according to a preestablished carrying capacity criterion. Optimization tools allow us to make rational and transparent decisions to solve a well-defined question, decisions that are essential for policy makers. The outcomes of combining ecosystem models with scenario building and optimization facilitate planning based on an ecosystem approach, highlighting the capabilities of ecosystem modeling as a tool for marine spatial planning.
Spatial-Dependent Diffusion of Cosmic Rays and the Ratio of pbar/p, B/C
Guo, Yi-Qing; Jin, Chao
2015-01-01
Recent precise measurements of cosmic ray spectral revealed an anomalous hardening at ~200 GV for nuclei from PAMELA, CREAM, ATIC, AMS02 experiments and at tens of GeV for primary electron derived from AMS02 experiment. Particularly, the latest observation of pbar/p ratio by AMS02 demonstrated a flat distribution, which further validated the spectrum anomalies of secondary particles. All those new phenomena indicated that the conventional propagation model of cosmic rays meet challenge. In this work, the spatial-dependent propagation coefficient D(r,z,\\rho) is employed by tracing the source distribution under the physical picture of two-halo model in DRAGON package. Under such scenario, the model calculation will result in a two-component spectral for primary nuclei and electron. Simultaneously, due to the smaller rigidity dependence of D(r,z,\\rho) in galactic disk, the ratio of secondary-to-primary will be inevitablly flatter than the calculation in the conventional propagation model. As a result, we can rep...
Chaotic and stable perturbed maps: 2-cycles and spatial models
Braverman, E.; Haroutunian, J.
2010-06-01
As the growth rate parameter increases in the Ricker, logistic and some other maps, the models exhibit an irreversible period doubling route to chaos. If a constant positive perturbation is introduced, then the Ricker model (but not the classical logistic map) experiences period doubling reversals; the break of chaos finally gives birth to a stable two-cycle. We outline the maps which demonstrate a similar behavior and also study relevant discrete spatial models where the value in each cell at the next step is defined only by the values at the cell and its nearest neighbors. The stable 2-cycle in a scalar map does not necessarily imply 2-cyclic-type behavior in each cell for the spatial generalization of the map.
Neuromorphic model of magnocellular and parvocellular visual paths: spatial resolution
Energy Technology Data Exchange (ETDEWEB)
Aguirre, Rolando C [Departamento de Luminotecnia, Luz y Vision, FACET, Universidad Nacional de Tucuman, Tucuman (Argentina); Felice, Carmelo J [Departamento de BioingenierIa, FACET, Universidad Nacional de Tucuman Argentina, Tucuman (Argentina); Colombo, Elisa M [Departamento de Luminotecnia, Luz y Vision, FACET, Universidad Nacional de Tucuman, Tucuman (Argentina)
2007-11-15
Physiological studies of the human retina show the existence of at least two visual information processing channels, the magnocellular and the parvocellular ones. Both have different spatial, temporal and chromatic features. This paper focuses on the different spatial resolution of these two channels. We propose a neuromorphic model, so that they match the retina's physiology. Considering the Deutsch and Deutsch model (1992), we propose two configurations (one for each visual channel) of the connection between the retina's different cell layers. The responses of the proposed model have similar behaviour to those of the visual cells: each channel has an optimum response corresponding to a given stimulus size which decreases for larger or smaller stimuli. This size is bigger for the magno path than for the parvo path and, in the end, both channels produce a magnifying of the borders of a stimulus.
Spatial probabilistic pulsatility model for enhancing photoplethysmographic imaging systems
Amelard, Robert; Clausi, David A.; Wong, Alexander
2016-11-01
Photoplethysmographic imaging (PPGI) is a widefield noncontact biophotonic technology able to remotely monitor cardiovascular function over anatomical areas. Although spatial context can provide insight into physiologically relevant sampling locations, existing PPGI systems rely on coarse spatial averaging with no anatomical priors for assessing arterial pulsatility. Here, we developed a continuous probabilistic pulsatility model for importance-weighted blood pulse waveform extraction. Using a data-driven approach, the model was constructed using a 23 participant sample with a large demographic variability (11/12 female/male, age 11 to 60 years, BMI 16.4 to 35.1 kg·m-2). Using time-synchronized ground-truth blood pulse waveforms, spatial correlation priors were computed and projected into a coaligned importance-weighted Cartesian space. A modified Parzen-Rosenblatt kernel density estimation method was used to compute the continuous resolution-agnostic probabilistic pulsatility model. The model identified locations that consistently exhibited pulsatility across the sample. Blood pulse waveform signals extracted with the model exhibited significantly stronger temporal correlation (W=35,pbpm].
INHOMOGENEITY IN SPATIAL COX POINT PROCESSES – LOCATION DEPENDENT THINNING IS NOT THE ONLY OPTION
Directory of Open Access Journals (Sweden)
Michaela Prokešová
2010-11-01
Full Text Available In the literature on point processes the by far most popular option for introducing inhomogeneity into a point process model is the location dependent thinning (resulting in a second-order intensity-reweighted stationary point process. This produces a very tractable model and there are several fast estimation procedures available. Nevertheless, this model dilutes the interaction (or the geometrical structure of the original homogeneous model in a special way. When concerning the Markov point processes several alternative inhomogeneous models were suggested and investigated in the literature. But it is not so for the Cox point processes, the canonical models for clustered point patterns. In the contribution we discuss several other options how to define inhomogeneous Cox point process models that result in point patterns with different types of geometric structure. We further investigate the possible parameter estimation procedures for such models.
Animal models of alcohol and drug dependence
Directory of Open Access Journals (Sweden)
Cleopatra S. Planeta
2013-01-01
Full Text Available Drug addiction has serious health and social consequences. In the last 50 years, a wide range of techniques have been developed to model specific aspects of drug-taking behaviors and have greatly contributed to the understanding of the neurobiological basis of drug abuse and addiction. In the last two decades, new models have been proposed in an attempt to capture the more genuine aspects of addiction-like behaviors in laboratory animals. The goal of the present review is to provide an overview of the preclinical procedures used to study drug abuse and dependence and describe recent progress that has been made in studying more specific aspects of addictive behavior in animals.
Greilich, A.; Markus, M.; Goles, E.
2005-05-01
We investigate the control of spatiotemporal chaos by external forcing at equidistant points (pinning sites) in one-dimensional systems. A monotonic decrease of the minimum distance between pinning sites versus the spatial measure entropy (in the absence of forcing) can be obtained for an appropriate choice of the forcing procedure. Such a relation between a feature for control and the disorder of the uncontrolled system is shown for four systems: binary cellular automata, coupled logistic equations, a stick-slip model and coupled differential equations.
Spatial Impairment and Memory in Genetic Disorders: Insights from Mouse Models
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Sang Ah Lee
2017-02-01
Full Text Available Research across the cognitive and brain sciences has begun to elucidate some of the processes that guide navigation and spatial memory. Boundary geometry and featural landmarks are two distinct classes of environmental cues that have dissociable neural correlates in spatial representation and follow different patterns of learning. Consequently, spatial navigation depends both on the type of cue available and on the type of learning provided. We investigated this interaction between spatial representation and memory by administering two different tasks (working memory, reference memory using two different environmental cues (rectangular geometry, striped landmark in mouse models of human genetic disorders: Prader-Willi syndrome (PWScrm+/p− mice, n = 12 and Beta-catenin mutation (Thr653Lys-substituted mice, n = 12. This exploratory study provides suggestive evidence that these models exhibit different abilities and impairments in navigating by boundary geometry and featural landmarks, depending on the type of memory task administered. We discuss these data in light of the specific deficits in cognitive and brain function in these human syndromes and their animal model counterparts.
Spatial Impairment and Memory in Genetic Disorders: Insights from Mouse Models
Lee, Sang Ah; Tucci, Valter; Vallortigara, Giorgio
2017-01-01
Research across the cognitive and brain sciences has begun to elucidate some of the processes that guide navigation and spatial memory. Boundary geometry and featural landmarks are two distinct classes of environmental cues that have dissociable neural correlates in spatial representation and follow different patterns of learning. Consequently, spatial navigation depends both on the type of cue available and on the type of learning provided. We investigated this interaction between spatial representation and memory by administering two different tasks (working memory, reference memory) using two different environmental cues (rectangular geometry, striped landmark) in mouse models of human genetic disorders: Prader-Willi syndrome (PWScrm+/p− mice, n = 12) and Beta-catenin mutation (Thr653Lys-substituted mice, n = 12). This exploratory study provides suggestive evidence that these models exhibit different abilities and impairments in navigating by boundary geometry and featural landmarks, depending on the type of memory task administered. We discuss these data in light of the specific deficits in cognitive and brain function in these human syndromes and their animal model counterparts. PMID:28208764
Effect of Spatial Dispersion on Evolutionary Stability: A Two-Phenotype and Two-Patch Model.
Li, Qing; Zhang, Jiahua; Zhang, Boyu; Cressman, Ross; Tao, Yi
2015-01-01
In this paper, we investigate a simple two-phenotype and two-patch model that incorporates both spatial dispersion and density effects in the evolutionary game dynamics. The migration rates from one patch to another are considered to be patch-dependent but independent of individual's phenotype. Our main goal is to reveal the dynamical properties of the evolutionary game in a heterogeneous patchy environment. By analyzing the equilibria and their stabilities, we find that the dynamical behavior of the evolutionary game dynamics could be very complicated. Numerical analysis shows that the simple model can have twelve equilibria where four of them are stable. This implies that spatial dispersion can significantly complicate the evolutionary game, and the evolutionary outcome in a patchy environment should depend sensitively on the initial state of the patches.
Ostoja, Steven M.; Schupp, Eugene W.; Klinger, Rob
2013-01-01
Granivore foraging decisions affect consumer success and determine the quantity and spatial pattern of seed survival. These decisions are influenced by environmental variation at spatial scales ranging from landscapes to local foraging patches. In a field experiment, the effects of seed patch variation across three spatial scales on seed removal by western harvester ants Pogonomyrmex occidentalis were evaluated. At the largest scale we assessed harvesting in different plant communities, at the intermediate scale we assessed harvesting at different distances from ant mounds, and at the smallest scale we assessed the effects of interactions among seed species in local seed neighborhoods on seed harvesting (i.e. resource–consumer interface). Selected seed species were presented alone (monospecific treatment) and in mixture with Bromus tectorum (cheatgrass; mixture treatment) at four distances from P. occidentalis mounds in adjacent intact sagebrush and non-native cheatgrass-dominated communities in the Great Basin, Utah, USA. Seed species differed in harvest, with B. tectorum being least preferred. Large and intermediate scale variation influenced harvest. More seeds were harvested in sagebrush than in cheatgrass-dominated communities (largest scale), and the quantity of seed harvested varied with distance from mounds (intermediate-scale), although the form of the distance effect differed between plant communities. At the smallest scale, seed neighborhood affected harvest, but the patterns differed among seed species considered. Ants harvested fewer seeds from mixed-seed neighborhoods than from monospecific neighborhoods, suggesting context dependence and potential associational resistance. Further, the effects of plant community and distance from mound on seed harvest in mixtures differed from their effects in monospecific treatments. Beyond the local seed neighborhood, selection of seed resources is better understood by simultaneously evaluating removal at
Nakamura, Tomoe Y.; Nakao, Shu; Nakajo, Yukako; Takahashi, Jun C.; Wakabayashi, Shigeo; Yanamoto, Hiroji
2017-01-01
Intracellular Ca2+ signaling regulates diverse functions of the nervous system. Many of these neuronal functions, including learning and memory, are regulated by neuronal calcium sensor-1 (NCS-1). However, the pathways by which NCS-1 regulates these functions remain poorly understood. Consistent with the findings of previous reports, we revealed that NCS-1 deficient (Ncs1-/-) mice exhibit impaired spatial learning and memory function in the Morris water maze test, although there was little change in their exercise activity, as determined via treadmill-analysis. Expression of brain-derived neurotrophic factor (BDNF; a key regulator of memory function) and dopamine was significantly reduced in the Ncs1-/- mouse brain, without changes in the levels of glial cell-line derived neurotrophic factor or nerve growth factor. Although there were no gross structural abnormalities in the hippocampi of Ncs1-/- mice, electron microscopy analysis revealed that the density of large dense core vesicles in CA1 presynaptic neurons, which release BDNF and dopamine, was decreased. Phosphorylation of Ca2+/calmodulin-dependent protein kinase II-α (CaMKII-α, which is known to trigger long-term potentiation and increase BDNF levels, was significantly reduced in the Ncs1-/- mouse brain. Furthermore, high voltage electric potential stimulation, which increases the levels of BDNF and promotes spatial learning, significantly increased the levels of NCS-1 concomitant with phosphorylated CaMKII-α in the hippocampus; suggesting a close relationship between NCS-1 and CaMKII-α. Our findings indicate that NCS-1 may regulate spatial learning and memory function at least in part through activation of CaMKII-α signaling, which may directly or indirectly increase BDNF production. PMID:28122057
Modified Spatial Channel Model for MIMO Wireless Systems
Directory of Open Access Journals (Sweden)
Pekka Kyösti
2007-12-01
Full Text Available Ã¯Â»Â¿The third generation partnership Project's (3GPP spatial channel model (SCM is a stochastic channel model for MIMO systems. Due to fixed subpath power levels and angular directions, the SCM model does not show the degree of variation which is encountered in real channels. In this paper, we propose a modified SCM model which has random subpath powers and directions and still produces Laplace shape angular power spectrum. Simulation results on outage MIMO capacity with basic and modified SCM models show that the modified SCM model gives constantly smaller capacity values. Accordingly, it seems that the basic SCM gives too small correlation between MIMO antennas. Moreover, the variance in capacity values is larger using the proposed SCM model. Simulation results were supported by the outage capacity results from a measurement campaign conducted in the city centre of Oulu, Finland.
Spatial Modeling of Iron Transformations Within Artificial Soil Aggregates
Kausch, M.; Meile, C.; Pallud, C.
2008-12-01
Structured soils exhibit significant variations in transport characteristics at the aggregate scale. Preferential flow occurs through macropores while predominantly diffusive exchange takes place in intra-aggregate micropores. Such environments characterized by mass transfer limitations are conducive to the formation of small-scale chemical gradients and promote strong spatial variation in processes controlling the fate of redox-sensitive elements such as Fe. In this study, we present a reactive transport model used to spatially resolve iron bioreductive processes occurring within a spherical aggregate at the interface between advective and diffusive domains. The model is derived from current conceptual models of iron(hydr)oxide (HFO) transformations and constrained by literature and experimental data. Data were obtained from flow-through experiments on artificial soil aggregates inoculated with Shewanella putrefaciens strain CN32, and include the temporal evolution of the bulk solution composition, as well as spatial information on the final solid phase distribution within aggregates. With all iron initially in the form of ferrihydrite, spatially heterogeneous formation of goethite/lepidocrocite, magnetite and siderite was observed during the course of the experiments. These transformations were reproduced by the model, which ascribes a central role to divalent iron as a driver of HFO transformations and master variable in the rate laws of the considered reaction network. The predicted dissolved iron breakthrough curves also match the experimental ones closely. Thus, the computed chemical concentration fields help identify factors governing the observed trends in the solid phase distribution patterns inside the aggregate. Building on a mechanistic description of transformation reactions, fluid flow and solute transport, the model was able to describe the observations and hence illustrates the importance of small-scale gradients and dynamics of bioreductive
Sparse modeling of spatial environmental variables associated with asthma.
Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W
2015-02-01
Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors.
Spatial distribution of emissions to air - the SPREAD model
Energy Technology Data Exchange (ETDEWEB)
Plejdrup, M.S.; Gyldenkaerne, S.
2011-04-15
The National Environmental Research Institute (NERI), Aarhus University, completes the annual national emission inventories for greenhouse gases and air pollutants according to Denmark's obligations under international conventions, e.g. the climate convention, UNFCCC and the convention on long-range transboundary air pollution, CLRTAP. NERI has developed a model to distribute emissions from the national emission inventories on a 1x1 km grid covering the Danish land and sea territory. The new spatial high resolution distribution model for emissions to air (SPREAD) has been developed according to the requirements for reporting of gridded emissions to CLRTAP. Spatial emission data is e.g. used as input for air quality modelling, which again serves as input for assessment and evaluation of health effects. For these purposes distributions with higher spatial resolution have been requested. Previously, a distribution on the 17x17 km EMEP grid has been set up and used in research projects combined with detailed distributions for a few sectors or sub-sectors e.g. a distribution for emissions from road traffic on 1x1 km resolution. SPREAD is developed to generate improved spatial emission data for e.g. air quality modelling in exposure studies. SPREAD includes emission distributions for each sector in the Danish inventory system; stationary combustion, mobile sources, fugitive emissions from fuels, industrial processes, solvents and other product use, agriculture and waste. This model enables generation of distributions for single sectors and for a number of sub-sectors and single sources as well. This report documents the methodologies in this first version of SPREAD and presents selected results. Further, a number of potential improvements for later versions of SPREAD are addressed and discussed. (Author)
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.
Spatial Modelling of Sediment Transport over the Upper Citarum Catchment
Directory of Open Access Journals (Sweden)
Poerbandono
2006-05-01
Full Text Available This paper discusses set up of a spatial model applied in Geographic Information System (GIS environment for predicting annual erosion rate and sediment yield of a watershed. The study area is situated in the Upper Citarum Catchment of West Java. Annual sediment yield is considered as product of erosion rate and sediment delivery ratio to be modelled under similar modeling tool. Sediment delivery ratio is estimated on the basis of sediment resident time. The modeling concept is based on the calculation of water flow velocity through sub-catchment surface, which is controlled by topography, rainfall, soil characteristics and various types of land use. Relating velocity to known distance across digital elevation model, sediment resident time can be estimated. Data from relevance authorities are used. Bearing in mind limited knowledge of some governing factors due to lack of observation, the result has shown the potential of GIS for spatially modeling regional sediment transport. Validation of model result is carried out by evaluating measured and computed total sediment yield at the main outlet. Computed total sediment yields for 1994 and 2001 are found to be 1.96×106 and 2.10×106tons/year. They deviate roughly 54 and 8% with respect to those measured in the field. Model response due to land use change observed in 2001 and 1994 is also recognised. Under presumably constant rainfall depth, an increase of overall average annual erosion rate of 11% resulted in an increase of overall average sediment yield of 7%.
Efficient rate control scheme using modified inter-layer dependency for spatial scalability
Indian Academy of Sciences (India)
PARUL JADHAV; SHIRISH KSHIRSAGAR
2016-12-01
Scalable video coding extension has been added to H.264AVC to support compression and encoding of multiple resolution video sequences, having different frame rates and fidelities in a single bit stream.The motion vectors and the residual data of the enhancement layers are derived from up-sampling the co-located macroblock (MB) of the base layer. The peak signal to noise ratio (PSNR) across the enhancement layers isdegraded as up-sampling introduces distortion of high-frequency components. In this paper, a spatial-resolutionratio-based MB mode decision scheme is proposed for spatially enhanced layers. The scheme uses the motion estimated at the base layer, to encode the respective MBs in the enhancement layers. The spatial–temporalsearch schemes at the enhancement layers are used to derive motion vectors and residues that are encoded using a quantization parameter obtained using independent rate control (IRC) scheme. The IRC from the prior art is modified to achieve better rate control per layer by recursive updates for mean absolute difference values of eachbasic unit. Proposed modified inter-layer dependency shows improvement in the PSNR for enhancement layers while the updated IRC enforces better IRC for all the layers.
Poulin, Brett A; Ryan, Joseph N; Nagy, Kathryn L; Stubbins, Aron; Dittmar, Thorsten; Orem, William H; Krabbenhoft, David P; Aiken, George R
2017-03-01
Sulfate inputs to the Florida Everglades stimulate sulfidic conditions in freshwater wetland sediments that affect ecological and biogeochemical processes. An unexplored implication of sulfate enrichment is alteration of the content and speciation of sulfur in dissolved organic matter (DOM), which influences the reactivity of DOM with trace metals. Here, we describe the vertical and lateral spatial dependence of sulfur chemistry in the hydrophobic organic acid (HPOA) fraction of DOM from unimpacted and sulfate-impacted Everglades wetlands using X-ray absorption spectroscopy and ultrahigh-resolution mass spectrometry. Spatial variation in DOM sulfur content and speciation reflects the degree of sulfate enrichment and resulting sulfide concentrations in sediment pore waters. Sulfur is incorporated into DOM predominantly as highly reduced species in sulfidic pore waters. Sulfur-enriched DOM in sediment pore waters exchanges with overlying surface waters and the sulfur likely undergoes oxidative transformations in the water column. Across all wetland sites and depths, the total sulfur content of DOM correlated with the relative abundance of highly reduced sulfur functionality. The results identify sulfate input as a primary determinant on DOM sulfur chemistry to be considered in the context of wetland restoration and sulfur and trace metal cycling.
Poulin, Brett A.; Ryan, Joseph N.; Nagy, Kathryn L.; Stubbins, Aron; Dittmar, Thorsten; Orem, William H.; Krabbenhoft, David P.; Aiken, George R.
2017-01-01
Sulfate inputs to the Florida Everglades stimulate sulfidic conditions in freshwater wetland sediments that affect ecological and biogeochemical processes. An unexplored implication of sulfate enrichment is alteration of the content and speciation of sulfur in dissolved organic matter (DOM), which influences the reactivity of DOM with trace metals. Here, we describe the vertical and lateral spatial dependence of sulfur chemistry in the hydrophobic organic acid fraction of DOM from unimpacted and sulfate-impacted Everglades wetlands using X-ray absorption spectroscopy and ultrahigh-resolution mass spectrometry. Spatial variation in DOM sulfur content and speciation reflects the degree of sulfate enrichment and resulting sulfide concentrations in sediment pore waters. Sulfur is incorporated into DOM predominantly as highly reduced species in sulfidic pore waters. Sulfur-enriched DOM in sediment pore waters exchanges with overlying surface waters and the sulfur likely undergoes oxidative transformations in the water column. Across all wetland sites and depths, the total sulfur content of DOM correlated with the relative abundance of highly reduced sulfur functionality. The results identify sulfate input as a primary determinant on DOM sulfur chemistry to be considered in the context of wetland restoration and sulfur and trace metal cycling.
Think continuous: Markovian Gaussian models in spatial statistics
Simpson, Daniel; Rue, Håvard
2011-01-01
Gaussian Markov random fields (GMRFs) are frequently used as computationally efficient models in spatial statistics. Unfortunately, it has traditionally been difficult to link GMRFs with the more traditional Gaussian random field models as the Markov property is difficult to deploy in continuous space. Following the pioneering work of Lindgren et al. (2011), we expound on the link between Markovian Gaussian random fields and GMRFs. In particular, we discuss the theoretical and practical aspects of fast computation with continuously specified Markovian Gaussian random fields, as well as the clear advantages they offer in terms of clear, parsimonious and interpretable models of anisotropy and non-stationarity.
Spatial emission modelling for residential wood combustion in Denmark
DEFF Research Database (Denmark)
Plejdrup, Marlene Schmidt; Nielsen, Ole-Kenneth; Brandt, Jørgen
2016-01-01
Residential wood combustion (RWC) is a major contributor to atmospheric pollution especially for particulate matter. Air pollution has significant impact on human health, and it is therefore important to know the human exposure. For this purpose, it is necessary with a detailed high resolution...... model with the developed weighting factors (76 ton PM2.5) is in good agreement with the case study (95 ton PM2.5), and that the new model has improved the spatial emission distribution significantly compared to the previous model (284 ton PM2.5). Additionally, a sensitivity analysis was done...
Orton, Glenn S.; Friedson, A. James; Baines, Kevin H.; Martin, Terry Z.; West, Robert A.; Caldwell, John; Hammel, Heidi B.; Bergstralh, Jay T.; Malcolm, Michael E.
1991-01-01
The spatial organization and time dependence of Jupiter's stratospheric temperatures have been measured by observing thermal emission from the 7.8-micrometer CH4 band. These temperatures, observed through the greater part of a Jovian year, exhibit the influence of seasonal radiative forcing. Distinct bands of high temperature are located at the poles and midlatitudes, while the equator alternates between warm and cold with a period of approximately 4 years. Substantial longitudinal variability is often observed within the warm midlatitude bands, and occasionally elsewhere on the planet. This variability includes small, localized structures, as well as large-scale waves with wavelengths longer than about 30,000 kilometers. The amplitudes of the waves vary on a time scale of about 1 month; structures on a smaller scale may have lifetimes of only days. Waves observed in 1985, 1987, and 1988 propagated with group velocities less than + or - 30 meters/sec.
Polarons with Spatially Dependent Mass in a Finite Parabolic Quantum Well
Institute of Scientific and Technical Information of China (English)
赵凤岐; 梁希侠
2002-01-01
We study the energy levels of an electron (or hole) polaron in a parabolic quantum well structure, includingthe spatial dependence of the effective mass. We also consider the two-mode behaviour of longitudinal opticalphonon modes of the ternary mixed crystals in the structure, in the calculation of the effect of the electron-phonon interaction. We calculate the ground state, the first excited state and the transition energy ofan electron(or hole) in the GaAs/Alx Ga1-xAs parabolic quantum well structure. The numerical results show that theelectron-phonon interaction obviously affects the energy levels of the electron (or hole), which are in agreementwith experimental results.
Generalization of visuomotor adaptation depends on the spatial characteristic of visual workspace.
Wang, Lei; Müsseler, Jochen
2012-11-01
The present study aims to address a novel aspect of visuomotor adaptation and its generalization. It is based on the assumption that the spatial structure of the distal action space is crucial for generalization. In the experiments, the distal action spaces could manifest either a symmetric or parallel structure. The imposed visuomotor rotations in the adaptation and the following generalization were either the same or opposing each other. In the generalization phase, motor bias resulting from prior adaptation was observed, and it turned out to substantially depend on the property of the workspace. In Experiment 1 with a parallel workspace, preceding adaptation to the same rotation was more advantageous than adaptation to an opposing rotation. This observation was reversed in Experiment 2 with the symmetrical workspace: prior adaptation to an opposing rotation was more advantageous for the generalization than prior adaptation to the same rotation. Mechanisms possibly underlying the observed influence of the workspace configuration were discussed.
Flavour Dependent Gauged Radiative Neutrino Mass Model
Baek, Seungwon; Yagyu, Kei
2015-01-01
We propose a one-loop induced radiative neutrino mass model with anomaly free flavour dependent gauge symmetry: $\\mu$ minus $\\tau$ symmetry $U(1)_{\\mu-\\tau}$. A neutrino mass matrix satisfying current experimental data can be obtained by introducing a weak isospin singlet scalar boson that breaks $U(1)_{\\mu-\\tau}$ symmetry, an inert doublet scalar field, and three right-handed neutrinos in addition to the fields in the standard model. We find that a characteristic structure appears in the neutrino mass matrix: two-zero texture form which predicts three non-zero neutrino masses and three non-zero CP-phases which can be determined five well measured experimental inputs of two squared mass differences and three mixing angles. Furthermore, it is clarified that only the inverted mass hierarchy is allowed in our model. In a favored parameter set from the neutrino sector, the discrepancy in the muon anomalous magnetic moment between the experimental data and the the standard model prediction can be explained by the ...
Parameter and uncertainty estimation for mechanistic, spatially explicit epidemiological models
Finger, Flavio; Schaefli, Bettina; Bertuzzo, Enrico; Mari, Lorenzo; Rinaldo, Andrea
2014-05-01
Epidemiological models can be a crucially important tool for decision-making during disease outbreaks. The range of possible applications spans from real-time forecasting and allocation of health-care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. Our spatially explicit, mechanistic models for cholera epidemics have been successfully applied to several epidemics including, the one that struck Haiti in late 2010 and is still ongoing. Calibration and parameter estimation of such models represents a major challenge because of properties unusual in traditional geoscientific domains such as hydrology. Firstly, the epidemiological data available might be subject to high uncertainties due to error-prone diagnosis as well as manual (and possibly incomplete) data collection. Secondly, long-term time-series of epidemiological data are often unavailable. Finally, the spatially explicit character of the models requires the comparison of several time-series of model outputs with their real-world counterparts, which calls for an appropriate weighting scheme. It follows that the usual assumption of a homoscedastic Gaussian error distribution, used in combination with classical calibration techniques based on Markov chain Monte Carlo algorithms, is likely to be violated, whereas the construction of an appropriate formal likelihood function seems close to impossible. Alternative calibration methods, which allow for accurate estimation of total model uncertainty, particularly regarding the envisaged use of the models for decision-making, are thus needed. Here we present the most recent developments regarding methods for parameter and uncertainty estimation to be used with our mechanistic, spatially explicit models for cholera epidemics, based on informal measures of goodness of fit.
Spatial uncertainty assessment in modelling reference evapotranspiration at regional scale
Directory of Open Access Journals (Sweden)
G. Buttafuoco
2010-07-01
Full Text Available Evapotranspiration is one of the major components of the water balance and has been identified as a key factor in hydrological modelling. For this reason, several methods have been developed to calculate the reference evapotranspiration (ET_{0}. In modelling reference evapotranspiration it is inevitable that both model and data input will present some uncertainty. Whatever model is used, the errors in the input will propagate to the output of the calculated ET_{0}. Neglecting information about estimation uncertainty, however, may lead to improper decision-making and water resources management. One geostatistical approach to spatial analysis is stochastic simulation, which draws alternative and equally probable, realizations of a regionalized variable. Differences between the realizations provide a measure of spatial uncertainty and allow to carry out an error propagation analysis. Among the evapotranspiration models, the Hargreaves-Samani model was used.
The aim of this paper was to assess spatial uncertainty of a monthly reference evapotranspiration model resulting from the uncertainties in the input attributes (mainly temperature at regional scale. A case study was presented for Calabria region (southern Italy. Temperature data were jointly simulated by conditional turning bands simulation with elevation as external drift and 500 realizations were generated.
The ET_{0} was then estimated for each set of the 500 realizations of the input variables, and the ensemble of the model outputs was used to infer the reference evapotranspiration probability distribution function. This approach allowed to delineate the areas characterized by greater uncertainty, to improve supplementary sampling strategies and ET_{0} value predictions.
A spatially constrained generative model and an EM algorithm for image segmentation.
Diplaros, Aristeidis; Vlassis, Nikos; Gevers, Theo
2007-05-01
In this paper, we present a novel spatially constrained generative model and an expectation-maximization (EM) algorithm for model-based image segmentation. The generative model assumes that the unobserved class labels of neighboring pixels in the image are generated by prior distributions with similar parameters, where similarity is defined by entropic quantities relating to the neighboring priors. In order to estimate model parameters from observations, we derive a spatially constrained EM algorithm that iteratively maximizes a lower bound on the data log-likelihood, where the penalty term is data-dependent. Our algorithm is very easy to implement and is similar to the standard EM algorithm for Gaussian mixtures with the main difference that the labels posteriors are "smoothed" over pixels between each E- and M-step by a standard image filter. Experiments on synthetic and real images show that our algorithm achieves competitive segmentation results compared to other Markov-based methods, and is in general faster.
An alternative to the standard spatial econometric approaches in hedonic house price models
DEFF Research Database (Denmark)
Veie, Kathrine Lausted; Panduro, Toke Emil
Hedonic models are subject to spatially correlated errors which are a symptom of omitted spatial variables, mis-speciﬁcation or mismeasurement. Methods have been developed to address this problem through the use of spatial econometrics or spatial ﬁxed eﬀects. However, often spatial correlation...
Representing spatial information in a computational model for network management
Blaisdell, James H.; Brownfield, Thomas F.
1994-01-01
While currently available relational database management systems (RDBMS) allow inclusion of spatial information in a data model, they lack tools for presenting this information in an easily comprehensible form. Computer-aided design (CAD) software packages provide adequate functions to produce drawings, but still require manual placement of symbols and features. This project has demonstrated a bridge between the data model of an RDBMS and the graphic display of a CAD system. It is shown that the CAD system can be used to control the selection of data with spatial components from the database and then quickly plot that data on a map display. It is shown that the CAD system can be used to extract data from a drawing and then control the insertion of that data into the database. These demonstrations were successful in a test environment that incorporated many features of known working environments, suggesting that the techniques developed could be adapted for practical use.
An exactly solvable, spatial model of mutation accumulation in cancer
Paterson, Chay; Nowak, Martin A.; Waclaw, Bartlomiej
2016-12-01
One of the hallmarks of cancer is the accumulation of driver mutations which increase the net reproductive rate of cancer cells and allow them to spread. This process has been studied in mathematical models of well mixed populations, and in computer simulations of three-dimensional spatial models. But the computational complexity of these more realistic, spatial models makes it difficult to simulate realistically large and clinically detectable solid tumours. Here we describe an exactly solvable mathematical model of a tumour featuring replication, mutation and local migration of cancer cells. The model predicts a quasi-exponential growth of large tumours, even if different fragments of the tumour grow sub-exponentially due to nutrient and space limitations. The model reproduces clinically observed tumour growth times using biologically plausible rates for cell birth, death, and migration rates. We also show that the expected number of accumulated driver mutations increases exponentially in time if the average fitness gain per driver is constant, and that it reaches a plateau if the gains decrease over time. We discuss the realism of the underlying assumptions and possible extensions of the model.
A marginal revenue equilibrium model for spatial water allocation
Institute of Scientific and Technical Information of China (English)
王劲峰; 刘昌明; 王智勇; 于静洁
2002-01-01
The outside water is transported into the water-shorted area. It is allocated among many sub-areas that composed the water-shorted area, in order to maximize the total benefit from the input water for the areas. This paper presents a model for spatial water allocation based on the marginal revenue of water utilization, taking the six southern districts of Hebei Province as an example.
Spatial memory impairments in a prediabetic rat model
Soares,E.; Prediger, R. D.; Nunes, S.; A.A. Castro; Viana, S .D.; Lemos, C.; C. M. Souza; Agostinho, P; Cunha, R. A.; E. Carvalho; Ribeiro, C. A. Fontes; Reis, F.; PEREIRA, F. C.
2013-01-01
Diabetes is associated with an increased risk for brain disorders, namely cognitive impairments associated with hippocampal dysfunction underlying diabetic encephalopathy. However, the impact of a prediabetic state on cognitive function is unknown. Therefore, we now investigated whether spatial learning and memory deficits and the underlying hippocampal dysfunction were already present in a prediabetic animal model. Adult Wistar rats drinking high-sucrose (HSu) diet (35% sucrose solution duri...
Rule-based spatial modeling with diffusing, geometrically constrained molecules
Directory of Open Access Journals (Sweden)
Lohel Maiko
2010-06-01
Full Text Available Abstract Background We suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our approach molecules possess a location in the reactor as well as an orientation and geometry, while the reactions are carried out according to a list of implicitly specified reaction rules. Because the reaction rules can contain patterns for molecules, a combinatorially complex or even infinitely sized reaction network can be defined. For our implementation (based on LAMMPS, we have chosen an already existing formalism (BioNetGen for the implicit specification of the reaction network. This compatibility allows to import existing models easily, i.e., only additional geometry data files have to be provided. Results Our simulations show that the obtained dynamics can be fundamentally different from those simulations that use classical reaction-diffusion approaches like Partial Differential Equations or Gillespie-type spatial stochastic simulation. We show, for example, that the combination of combinatorial complexity and geometric effects leads to the emergence of complex self-assemblies and transportation phenomena happening faster than diffusion (using a model of molecular walkers on microtubules. When the mentioned classical simulation approaches are applied, these aspects of modeled systems cannot be observed without very special treatment. Further more, we show that the geometric information can even change the organizational structure of the reaction system. That is, a set of chemical species that can in principle form a stationary state in a Differential Equation formalism, is potentially unstable when geometry is considered, and vice versa. Conclusions We conclude that our approach provides a new general framework filling a gap in between approaches with no or rigid spatial representation like Partial Differential Equations and specialized coarse-grained spatial
Rule-based spatial modeling with diffusing, geometrically constrained molecules
Lohel Maiko; Lenser Thorsten; Ibrahim Bashar; Gruenert Gerd; Hinze Thomas; Dittrich Peter
2010-01-01
Abstract Background We suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our approach molecules possess a location in the reactor as well as an orientation and geometry, while the reactions are carried out according to a list of implicitly specified reaction rules. Because the reaction rules can contain patterns for molecules, a combinatorially complex or even infinitely sized reaction net...
Osei, Frank B.; Duker, Alfred A.; Augustijn, Ellen-Wien; Stein, Alfred
2010-10-01
Cholera has been a public health burden in Ghana since the early 1970s. Between 1999 and 2005, a total of 25,636 cases and 620 deaths were officially reported to the WHO. In one of the worst affected urban cities, fecal contamination of surface water is extremely high, and the disease is reported to be prevalent among inhabitants living in close proximity to surface water bodies. Surface runoff from dump sites is a major source of fecal and bacterial contamination of rivers and streams in the study area. This study aims to determine (a) the impacts of surface water contamination on cholera infection and (b) detect and map arbitrary shaped clusters of cholera. A Geographic Information System (GIS) based spatial analysis is used to delineate potential reservoirs of the cholera vibrios; possibly contaminated by surface runoff from open space refuse dumps. Statistical modeling using OLS model reveals a significant negative association between (a) cholera prevalence and proximity to all the potential cholera reservoirs ( R2 = 0.18, p cholera prevalence and proximity to upstream potential cholera reservoirs ( R2 = 0.25, p cholera prevalence on the spatial neighbors of the communities. A flexible scan statistic identifies a most likely cluster with a higher relative risk (RR = 2.04, p cholera infection.
A Matérn model of the spatial covariance structure of point rain rates
Sun, Ying
2014-07-15
It is challenging to model a precipitation field due to its intermittent and highly scale-dependent nature. Many models of point rain rates or areal rainfall observations have been proposed and studied for different time scales. Among them, the spectral model based on a stochastic dynamical equation for the instantaneous point rain rate field is attractive, since it naturally leads to a consistent space–time model. In this paper, we note that the spatial covariance structure of the spectral model is equivalent to the well-known Matérn covariance model. Using high-quality rain gauge data, we estimate the parameters of the Matérn model for different time scales and demonstrate that the Matérn model is superior to an exponential model, particularly at short time scales.
Rodhouse, T.J.; Irvine, K.M.; Vierling, K.T.; Vierling, L.A.
2011-01-01
Monitoring programs that evaluate restoration and inform adaptive management are important for addressing environmental degradation. These efforts may be well served by spatially explicit hierarchical approaches to modeling because of unavoidable spatial structure inherited from past land use patterns and other factors. We developed Bayesian hierarchical models to estimate trends from annual density counts observed in a spatially structured wetland forb (Camassia quamash [camas]) population following the cessation of grazing and mowing on the study area, and in a separate reference population of camas. The restoration site was bisected by roads and drainage ditches, resulting in distinct subpopulations ("zones") with different land use histories. We modeled this spatial structure by fitting zone-specific intercepts and slopes. We allowed spatial covariance parameters in the model to vary by zone, as in stratified kriging, accommodating anisotropy and improving computation and biological interpretation. Trend estimates provided evidence of a positive effect of passive restoration, and the strength of evidence was influenced by the amount of spatial structure in the model. Allowing trends to vary among zones and accounting for topographic heterogeneity increased precision of trend estimates. Accounting for spatial autocorrelation shifted parameter coefficients in ways that varied among zones depending on strength of statistical shrinkage, autocorrelation and topographic heterogeneity-a phenomenon not widely described. Spatially explicit estimates of trend from hierarchical models will generally be more useful to land managers than pooled regional estimates and provide more realistic assessments of uncertainty. The ability to grapple with historical contingency is an appealing benefit of this approach.
Spatial Models of Prebiotic Evolution: Soup Before Pizza?
Scheuring, István; Czárán, Tamás; Szabó, Péter; Károlyi, György; Toroczkai, Zoltán
2003-10-01
The problem of information integration and resistance to the invasion of parasitic mutants in prebiotic replicator systems is a notorious issue of research on the origin of life. Almost all theoretical studies published so far have demonstrated that some kind of spatial structure is indispensable for the persistence and/or the parasite resistance of any feasible replicator system. Based on a detailed critical survey of spatial models on prebiotic information integration, we suggest a possible scenario for replicator system evolution leading to the emergence of the first protocells capable of independent life. We show that even the spatial versions of the hypercycle model are vulnerable to selfish parasites in heterogeneous habitats. Contrary, the metabolic system remains persistent and coexistent with its parasites both on heterogeneous surfaces and in chaotically mixing flowing media. Persistent metabolic parasites can be converted to metabolic cooperators, or they can gradually obtain replicase activity. Our simulations show that, once replicase activity emerged, a gradual and simultaneous evolutionary improvement of replicase functionality (speed and fidelity) and template efficiency is possible only on a surface that constrains the mobility of macromolecule replicators. Based on the results of the models reviewed, we suggest that open chaotic flows (`soup') and surface dynamics (`pizza') both played key roles in the sequence of evolutionary events ultimately concluding in the appearance of the first living cell on Earth.
Bayesian Network Models for Local Dependence among Observable Outcome Variables
Almond, Russell G.; Mulder, Joris; Hemat, Lisa A.; Yan, Duanli
2009-01-01
Bayesian network models offer a large degree of flexibility for modeling dependence among observables (item outcome variables) from the same task, which may be dependent. This article explores four design patterns for modeling locally dependent observations: (a) no context--ignores dependence among observables; (b) compensatory context--introduces…
The dependence of optimal fractionation schemes on the spatial dose distribution
Unkelbach, Jan; Craft, David; Salari, Ehsan; Ramakrishnan, Jagdish; Bortfeld, Thomas
2013-01-01
We consider the fractionation problem in radiation therapy. Tumor sites in which the dose-limiting organ at risk (OAR) receives a substantially lower dose than the tumor, bear potential for hypofractionation even if the α/β-ratio of the tumor is larger than the α/β-ratio of the OAR. In this work, we analyze the interdependence of the optimal fractionation scheme and the spatial dose distribution in the OAR. In particular, we derive a criterion under which a hypofractionation regimen is indicated for both a parallel and a serial OAR. The approach is based on the concept of the biologically effective dose (BED). For a hypothetical homogeneously irradiated OAR, it has been shown that hypofractionation is suggested by the BED model if the α/β-ratio of the OAR is larger than α/β-ratio of the tumor times the sparing factor, i.e. the ratio of the dose received by the tumor and the OAR. In this work, we generalize this result to inhomogeneous dose distributions in the OAR. For a parallel OAR, we determine the optimal fractionation scheme by minimizing the integral BED in the OAR for a fixed BED in the tumor. For a serial structure, we minimize the maximum BED in the OAR. This leads to analytical expressions for an effective sparing factor for the OAR, which provides a criterion for hypofractionation. The implications of the model are discussed for lung tumor treatments. It is shown that the model supports hypofractionation for small tumors treated with rotation therapy, i.e. highly conformal techniques where a large volume of lung tissue is exposed to low but nonzero dose. For larger tumors, the model suggests hyperfractionation. We further discuss several non-intuitive interdependencies between optimal fractionation and the spatial dose distribution. For instance, lowering the dose in the lung via proton therapy does not necessarily provide a biological rationale for hypofractionation.
A hierarchical model for spatial capture-recapture data
Royle, J. Andrew; Young, K.V.
2008-01-01
Estimating density is a fundamental objective of many animal population studies. Application of methods for estimating population size from ostensibly closed populations is widespread, but ineffective for estimating absolute density because most populations are subject to short-term movements or so-called temporary emigration. This phenomenon invalidates the resulting estimates because the effective sample area is unknown. A number of methods involving the adjustment of estimates based on heuristic considerations are in widespread use. In this paper, a hierarchical model of spatially indexed capture recapture data is proposed for sampling based on area searches of spatial sample units subject to uniform sampling intensity. The hierarchical model contains explicit models for the distribution of individuals and their movements, in addition to an observation model that is conditional on the location of individuals during sampling. Bayesian analysis of the hierarchical model is achieved by the use of data augmentation, which allows for a straightforward implementation in the freely available software WinBUGS. We present results of a simulation study that was carried out to evaluate the operating characteristics of the Bayesian estimator under variable densities and movement patterns of individuals. An application of the model is presented for survey data on the flat-tailed horned lizard (Phrynosoma mcallii) in Arizona, USA.
A modal approach to modeling spatially distributed vibration energy dissipation.
Energy Technology Data Exchange (ETDEWEB)
Segalman, Daniel Joseph
2010-08-01
The nonlinear behavior of mechanical joints is a confounding element in modeling the dynamic response of structures. Though there has been some progress in recent years in modeling individual joints, modeling the full structure with myriad frictional interfaces has remained an obstinate challenge. A strategy is suggested for structural dynamics modeling that can account for the combined effect of interface friction distributed spatially about the structure. This approach accommodates the following observations: (1) At small to modest amplitudes, the nonlinearity of jointed structures is manifest primarily in the energy dissipation - visible as vibration damping; (2) Correspondingly, measured vibration modes do not change significantly with amplitude; and (3) Significant coupling among the modes does not appear to result at modest amplitudes. The mathematical approach presented here postulates the preservation of linear modes and invests all the nonlinearity in the evolution of the modal coordinates. The constitutive form selected is one that works well in modeling spatially discrete joints. When compared against a mathematical truth model, the distributed dissipation approximation performs well.
Statistical models for spatial patterns of inertial particles in turbulence
Gustavsson, K
2014-01-01
The dynamics of particles suspended in turbulent flows is of fundamental importance for a wide range of questions in astrophysics, cloud physics, oceanography, and in technology. Laboratory experiments and direct numerical simulations have demonstrated that heavy particles respond in intricate ways to turbulent fluctuations of the carrying fluid: independent particles may cluster together and form spatial patterns even though the fluid is incompressible, and the relative speeds of nearby particles may fluctuate strongly. Both phenomena depend sensitively on the parameters of the system, affect collision rates and outcomes, and thus the long-term fate of the system. This is a hard problem to describe theoretically: the turbulence determines the particle paths, but at the same time the turbulent fluctuations encountered by a particle depend sensitively upon its path through the medium. In recent years it has become clear that important aspects of the particle dynamics in turbulence can be understood in terms of...
Evaluation of Spatial Agreement of Distinct Landslide Prediction Models
Sterlacchini, Simone; Bordogna, Gloria; Frigerio, Ivan
2013-04-01
The aim of the study was to assess the degree of spatial agreement of different predicted patterns in a majority of coherent landslide prediction maps with almost similar success and prediction rate curves. If two or more models have a similar performance, the choice of the best one is not a trivial operation and cannot be based on success and prediction rate curves only. In fact, it may happen that two or more prediction maps with similar accuracy and predictive power do not have the same degree of agreement in terms of spatial predicted patterns. The selected study area is the high Valtellina valley, in North of Italy, covering a surface of about 450 km2 where mapping of historical landslides is available. In order to assess landslide susceptibility, we applied the Weights of Evidence (WofE) modeling technique implemented by USGS by means of ARC-SDM tool. WofE efficiently investigate the spatial relationships among past events and multiple predisposing factors, providing useful information to identify the most probable location of future landslide occurrences. We have carried out 13 distinct experiments by changing the number of morphometric and geo-environmental explanatory variables in each experiment with the same training set and thus generating distinct models of landslide prediction, computing probability degrees of occurrence of landslides in each pixel. Expert knowledge and previous results from indirect statistically-based methods suggested slope, land use, and geology the best "driving controlling factors". The Success Rate Curve (SRC) was used to estimate how much the results of each model fit the occurrence of landslides used for the training of the models. The Prediction Rate Curve (PRC) was used to estimate how much the model predict the occurrence of landslides in the validation set. We found that the performances were very similar for different models. Also the dendrogram of the Cohen's kappa statistic and Principal Component Analysis (PCA) were
Dispersal leads to spatial autocorrelation in species distributions: A simulation model
Bahn, V.; Krohn, W.B.; O'Connor, R.J.
2008-01-01
Compared to population growth regulated by local conditions, dispersal has been underappreciated as a central process shaping the spatial distribution of populations. This paper asks: (a) which conditions increase the importance of dispersers relative to local recruits in determining population sizes? and (b) how does dispersal influence the spatial distribution patterns of abundances among connected populations? We approached these questions with a simulation model of populations on a coupled lattice with cells of continuously varying habitat quality expressed as carrying capacities. Each cell contained a population with the basic dynamics of density-regulated growth, and was connected to other populations by immigration and emigration. The degree to which dispersal influenced the distribution of population sizes depended most strongly on the absolute amount of dispersal, and then on the potential population growth rate. Dispersal decaying in intensity with distance left close neighbours more alike in population size than distant populations, leading to an increase in spatial autocorrelation. The spatial distribution of species with low potential growth rates is more dependent on dispersal than that of species with high growth rates; therefore, distribution modelling for species with low growth rates requires particular attention to autocorrelation, and conservation management of these species requires attention to factors curtailing dispersal, such as fragmentation and dispersal barriers. ?? 2007 Elsevier B.V. All rights reserved.
The flexible focus: whether spatial attention is unitary or divided depends on observer goals.
Jefferies, Lisa N; Enns, James T; Di Lollo, Vincent
2014-04-01
The distribution of visual attention has been the topic of much investigation, and various theories have posited that attention is allocated either as a single unitary focus or as multiple independent foci. In the present experiment, we demonstrate that attention can be flexibly deployed as either a unitary or a divided focus in the same experimental task, depending on the observer's goals. To assess the distribution of attention, we used a dual-stream Attentional Blink (AB) paradigm and 2 target pairs. One component of the AB, Lag-1 sparing, occurs only if the second target pair appears within the focus of attention. By varying whether the first-target-pair could be expected in a predictable location (always in-stream) or not (unpredictably in-stream or between-streams), observers were encouraged to deploy a divided or a unitary focus, respectively. When the second-target-pair appeared between the streams, Lag-1 sparing occurred for the Unpredictable group (consistent with a unitary focus) but not for the Predictable group (consistent with a divided focus). Thus, diametrically different outcomes occurred for physically identical displays, depending on the expectations of the observer about where spatial attention would be required.
Spatial self-organization in hybrid models of multicellular adhesion
Bonforti, Adriano; Duran-Nebreda, Salva; Montañez, Raúl; Solé, Ricard
2016-10-01
Spatial self-organization emerges in distributed systems exhibiting local interactions when nonlinearities and the appropriate propagation of signals are at work. These kinds of phenomena can be modeled with different frameworks, typically cellular automata or reaction-diffusion systems. A different class of dynamical processes involves the correlated movement of agents over space, which can be mediated through chemotactic movement or minimization of cell-cell interaction energy. A classic example of the latter is given by the formation of spatially segregated assemblies when cells display differential adhesion. Here, we consider a new class of dynamical models, involving cell adhesion among two stochastically exchangeable cell states as a minimal model capable of exhibiting well-defined, ordered spatial patterns. Our results suggest that a whole space of pattern-forming rules is hosted by the combination of physical differential adhesion and the value of probabilities modulating cell phenotypic switching, showing that Turing-like patterns can be obtained without resorting to reaction-diffusion processes. If the model is expanded allowing cells to proliferate and die in an environment where diffusible nutrient and toxic waste are at play, different phases are observed, characterized by regularly spaced patterns. The analysis of the parameter space reveals that certain phases reach higher population levels than other modes of organization. A detailed exploration of the mean-field theory is also presented. Finally, we let populations of cells with different adhesion matrices compete for reproduction, showing that, in our model, structural organization can improve the fitness of a given cell population. The implications of these results for ecological and evolutionary models of pattern formation and the emergence of multicellularity are outlined.
Spatial interpolation schemes of daily precipitation for hydrologic modeling
Hwang, Y.; Clark, M.; Rajagopalan, B.; Leavesley, G.
2012-01-01
Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.
Combining Spatial and Telemetric Features for Learning Animal Movement Models
Kapicioglu, Berk; Wikelski, Martin; Broderick, Tamara
2012-01-01
We introduce a new graphical model for tracking radio-tagged animals and learning their movement patterns. The model provides a principled way to combine radio telemetry data with an arbitrary set of userdefined, spatial features. We describe an efficient stochastic gradient algorithm for fitting model parameters to data and demonstrate its effectiveness via asymptotic analysis and synthetic experiments. We also apply our model to real datasets, and show that it outperforms the most popular radio telemetry software package used in ecology. We conclude that integration of different data sources under a single statistical framework, coupled with appropriate parameter and state estimation procedures, produces both accurate location estimates and an interpretable statistical model of animal movement.
Induced gelation in a two-site spatial coagulation model
Siegmund-Schultze, Rainer; Wagner, Wolfgang
2006-01-01
A two-site spatial coagulation model is considered. Particles of masses $m$ and $n$ at the same site form a new particle of mass $m+n$ at rate $mn$. Independently, particles jump to the other site at a constant rate. The limit (for increasing particle numbers) of this model is expected to be nondeterministic after the gelation time, namely, one or two giant particles randomly jump between the two sites. Moreover, a new effect of induced gelation is observed--the gelation happening at the site...
Spatial Pattern of an Epidemic Model with Cross-diffusion
Institute of Scientific and Technical Information of China (English)
LI Li; JIN Zhen; SUN Gui-Quan
2008-01-01
Pattern formation of a spatial epidemic model with both serf- and cross-diffusion is investigated. From the Turing theory, it is well known that Thring pattern formation cannot occur for the equal self-diffusion coefficients.However, combined with cross-diffusion, the system will show emergence of isolated groups, i.e., stripe-like or spotted or coexistence of both, which we show by both mathematical ana/ysis and numerical simulations. Our study shows that the interaction of self- and cross-diffusion can be considered as an important mechanism for the appearance of complex spatiotemporal dynamics in epidemic models.
Modeling spatial accessibility to parks: a national study
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Lu Hua
2011-05-01
Full Text Available Abstract Background Parks provide ideal open spaces for leisure-time physical activity and important venues to promote physical activity. The spatial configuration of parks, the number of parks and their spatial distribution across neighborhood areas or local regions, represents the basic park access potential for their residential populations. A new measure of spatial access to parks, population-weighted distance (PWD to parks, combines the advantages of current park access approaches and incorporates the information processing theory and probability access surface model to more accurately quantify residential population's potential spatial access to parks. Results The PWD was constructed at the basic level of US census geography - blocks - using US park and population data. This new measure of population park accessibility was aggregated to census tract, county, state and national levels. On average, US residential populations are expected to travel 6.7 miles to access their local neighborhood parks. There are significant differences in the PWD to local parks among states. The District of Columbia and Connecticut have the best access to local neighborhood parks with PWD of 0.6 miles and 1.8 miles, respectively. Alaska, Montana, and Wyoming have the largest PWDs of 62.0, 37.4, and 32.8 miles, respectively. Rural states in the western and Midwestern US have lower neighborhood park access, while urban states have relatively higher park access. Conclusions The PWD to parks provides a consistent platform for evaluating spatial equity of park access and linking with population health outcomes. It could be an informative evaluation tool for health professionals and policy makers. This new method could be applied to quantify geographic accessibility of other types of services or destinations, such as food, alcohol, and tobacco outlets.
Hamzalouh, L.; Ismail, M. T.; Rahman, R. A.
2017-09-01
In this paper, spatial panel models were used and the method for selecting the best model amongst the spatial fixed effects model and the spatial random effects model to estimate the fitting model by using the robust Hausman test for analysis of the exports pattern of the Common Market for Eastern and Southern African (COMESA) countries. And examine the effects of the interactions of the economic statistic of explanatory variables on the exports of the COMESA. Results indicated that the spatial Durbin model with fixed effects specification should be tested and considered in most cases of this study. After that, the direct and indirect effects among COMESA regions were assessed, and the role of indirect spatial effects in estimating exports was empirically demonstrated. Regarding originality and research value, and to the best of the authors’ knowledge, this is the first attempt to examine exports between COMESA and its member countries through spatial panel models using XSMLE, which is a new command for spatial analysis using STATA.
Modelling H5N1 in Bangladesh across spatial scales: Model complexity and zoonotic transmission risk.
Hill, Edward M; House, Thomas; Dhingra, Madhur S; Kalpravidh, Wantanee; Morzaria, Subhash; Osmani, Muzaffar G; Yamage, Mat; Xiao, Xiangming; Gilbert, Marius; Tildesley, Michael J
2017-09-01
Highly pathogenic avian influenza H5N1 remains a persistent public health threat, capable of causing infection in humans with a high mortality rate while simultaneously negatively impacting the livestock industry. A central question is to determine regions that are likely sources of newly emerging influenza strains with pandemic causing potential. A suitable candidate is Bangladesh, being one of the most densely populated countries in the world and having an intensifying farming system. It is therefore vital to establish the key factors, specific to Bangladesh, that enable both continued transmission within poultry and spillover across the human-animal interface. We apply a modelling framework to H5N1 epidemics in the Dhaka region of Bangladesh, occurring from 2007 onwards, that resulted in large outbreaks in the poultry sector and a limited number of confirmed human cases. This model consisted of separate poultry transmission and zoonotic transmission components. Utilising poultry farm spatial and population information a set of competing nested models of varying complexity were fitted to the observed case data, with parameter inference carried out using Bayesian methodology and goodness-of-fit verified by stochastic simulations. For the poultry transmission component, successfully identifying a model of minimal complexity, which enabled the accurate prediction of the size and spatial distribution of cases in H5N1 outbreaks, was found to be dependent on the administration level being analysed. A consistent outcome of non-optimal reporting of infected premises materialised in each poultry epidemic of interest, though across the outbreaks analysed there were substantial differences in the estimated transmission parameters. The zoonotic transmission component found the main contributor to spillover transmission of H5N1 in Bangladesh was found to differ from one poultry epidemic to another. We conclude by discussing possible explanations for these discrepancies in
Modelling H5N1 in Bangladesh across spatial scales: Model complexity and zoonotic transmission risk
Directory of Open Access Journals (Sweden)
Edward M. Hill
2017-09-01
Full Text Available Highly pathogenic avian influenza H5N1 remains a persistent public health threat, capable of causing infection in humans with a high mortality rate while simultaneously negatively impacting the livestock industry. A central question is to determine regions that are likely sources of newly emerging influenza strains with pandemic causing potential. A suitable candidate is Bangladesh, being one of the most densely populated countries in the world and having an intensifying farming system. It is therefore vital to establish the key factors, specific to Bangladesh, that enable both continued transmission within poultry and spillover across the human–animal interface. We apply a modelling framework to H5N1 epidemics in the Dhaka region of Bangladesh, occurring from 2007 onwards, that resulted in large outbreaks in the poultry sector and a limited number of confirmed human cases. This model consisted of separate poultry transmission and zoonotic transmission components. Utilising poultry farm spatial and population information a set of competing nested models of varying complexity were fitted to the observed case data, with parameter inference carried out using Bayesian methodology and goodness-of-fit verified by stochastic simulations. For the poultry transmission component, successfully identifying a model of minimal complexity, which enabled the accurate prediction of the size and spatial distribution of cases in H5N1 outbreaks, was found to be dependent on the administration level being analysed. A consistent outcome of non-optimal reporting of infected premises materialised in each poultry epidemic of interest, though across the outbreaks analysed there were substantial differences in the estimated transmission parameters. The zoonotic transmission component found the main contributor to spillover transmission of H5N1 in Bangladesh was found to differ from one poultry epidemic to another. We conclude by discussing possible explanations for
Spatial modeling for groundwater arsenic levels in North Carolina.
Kim, Dohyeong; Miranda, Marie Lynn; Tootoo, Joshua; Bradley, Phil; Gelfand, Alan E
2011-06-01
To examine environmental and geologic determinants of arsenic in groundwater, detailed geologic data were integrated with well water arsenic concentration data and well construction data for 471 private wells in Orange County, NC, via a geographic information system. For the statistical analysis, the geologic units were simplified into four generalized categories based on rock type and interpreted mode of deposition/emplacement. The geologic transitions from rocks of a primary pyroclastic origin to rocks of volcaniclastic sedimentary origin were designated as polylines. The data were fitted to a left-censored regression model to identify key determinants of arsenic levels in groundwater. A Bayesian spatial random effects model was then developed to capture any spatial patterns in groundwater arsenic residuals into model estimation. Statistical model results indicate (1) wells close to a transition zone or fault are more likely to contain detectible arsenic; (2) welded tuffs and hydrothermal quartz bodies are associated with relatively higher groundwater arsenic concentrations and even higher for those proximal to a pluton; and (3) wells of greater depth are more likely to contain elevated arsenic. This modeling effort informs policy intervention by creating three-dimensional maps of predicted arsenic levels in groundwater for any location and depth in the area.
Spatial Model of Deforestation in Sumatra Islands Using Typological Approach
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Nurdin Sulistiyono
2015-12-01
Full Text Available High rate of deforestation occurred in Sumatra Islands had been allegedly triggered by various factors. This study examined how the deforestation pattern was related to the typology of the area, as well as how the deforestation is being affected by many factors such as physical, biological, and socio-economic of the local community. The objective of this study was to formulate a spatial model of deforestation based on triggering factors within each typology in Sumatra Islands. The typology classes were developed on the basis of socio-economic factors using the standardized-euclidean distance measure and the memberships of each cluster was determined using the furthest neighbor method. The logistic regression method was used for modeling and estimating the spatial distribution of deforestation.Two deforestation typologies were distinguished in this study, namely typology 1 (regencies/cities with low deforestation rate and typology 2 (regencies/cities with high deforestation rate. The study found that growth rate of farm households could be used to assign each regencies or cities in Sumatra Islands into their corresponding typology. The resulted spatial model of deforestation from logistic regression analysis were logit (deforestation = 1.355 + (0.012*total of farm households – (0.08*elevation – (0.019*distance from road for typology 1 and logit (deforestation = 1.714 + (0.007*total of farm households – (0.021*slope – (0.051*elevation – (0.038* distance from road + (0.039* distance from river for typology 2, respectively. The accuracy test of deforestation model in 2000–2006 showed overall accuracy of 68.52% (typology 1 and 74.49% (typology 2, while model of deforestation in 2006–2012 showed overall accuracy of 65.37% (typology 1 and 72.24% (typology 2, respectively.
Philimon, Sheena P.; Huong, Audrey K. C.; Ngu, Xavier T. I.
2017-08-01
This paper aims to investigate the variation in one’s percent mean transcutaneous oxygen saturation (StO2) with differences in spatial resolution of data. This work required the knowledge of extinction coefficient of hemoglobin derivatives in the wavelength range of 520 - 600 nm to solve for the StO2 value via an iterative fitting procedure. A pilot study was conducted on three healthy subjects with spectroscopic data collected from their right index finger at different arbitrarily selected distances. The StO2 value estimated by Extended Modified Lambert Beer (EMLB) model revealed a higher mean StO2 of 91.1 ± 1.3% at a proximity distance of 30 mm compared to 60.83 ± 2.8% at 200 mm. The results showed a high correlation between data spatial resolution and StO2 value, and revealed a decrease in StO2 value as the sampling distance increased. The preliminary findings from this study contribute to the knowledge of the appropriate distance range for consistent and high repeatability measurement of skin oxygenation.
Forecasting the behaviour of complex landslides with a spatially distributed hydrological model
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J.-P. Malet
2005-01-01
Full Text Available The relationships between rainfall, hydrology and landslide movement are often difficult to establish. In this context, ground-water flow analyses and dynamic modelling can help to clarify these complex relations, simulate the landslide hydrological behaviour in real or hypothetical situations, and help to forecast future scenarios based on environmental change. The primary objective of this study is to investigate the possibility of including more temporal and spatial information in landslide hydrology forecasting, by using a physically based spatially distributed model. Results of the hydrological and geomorphological investigation of the Super-Sauze earthflow, one of the persistently active landslide occurring in clay-rich material of the French Alps, are presented. Field surveys, continuous monitoring and interpretation of the data have shown that, in such material, the groundwater level fluctuates on a seasonal time scale, with a strong influence of the unsaturated zone. Therefore a coupled unsaturated/saturated model, incorporating Darcian saturated flow, fissure flow and meltwater flow is needed to adequately represent the landslide hydrology. The conceptual model is implemented in a 2.5-D spatially distributed hydrological model. The model is calibrated and validated on a multi-parameters database acquired on the site since 1997. The complex time-dependent and three-dimensional groundwater regime is well described, in both the short- and long-term. The hydrological model is used to forecast the future hydrological behaviour of the earthflow in response to potential environmental changes.
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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
Tatem, Andrew J; Adamo, Susana; Bharti, Nita; Burgert, Clara R; Castro, Marcia; Dorelien, Audrey; Fink, Gunter; Linard, Catherine; John, Mendelsohn; Montana, Livia; Montgomery, Mark R; Nelson, Andrew; Noor, Abdisalan M; Pindolia, Deepa; Yetman, Greg; Balk, Deborah
2012-05-16
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 population datasets and
Transient,spatially-varied recharge for groundwater modeling
Assefa, Kibreab; Woodbury, Allan
2013-04-01
This study is aimed at producing spatially and temporally varying groundwater recharge for transient groundwater modeling in a pilot watershed in the North Okanagan, Canada. The recharge modeling is undertaken by using a Richard's equation based finite element code (HYDRUS-1D) [Simunek et al., 2002], ArcGISTM [ESRI, 2011], ROSETTA [Schaap et al., 2001], in situ observations of soil temperature and soil moisture and a long term gridded climate data [Nielsen et al., 2010]. The public version of HYDUS-1D [Simunek et al., 2002] and another beta version with a detailed freezing and thawing module [Hansson et al., 2004] are first used to simulate soil temperature, snow pack and soil moisture over a one year experimental period. Statistical analysis of the results show both versions of HYDRUS-1D reproduce observed variables to the same degree. Correlation coefficients for soil temperature simulation were estimated at 0.9 and 0.8, at depths of 10 cm and 50 cm respectively; and for soil moisture, 0.8 and 0.6 at 10 cm and 50 cm respectively. This and other standard measures of model performance (root mean square error and average error) showed a promising performance of the HYDRUS-1D code in our pilot watershed. After evaluating model performance using field data and ROSETTA derived soil hydraulic parameters, the HYDRUS-1D code is coupled with ArcGISTM to produce spatially and temporally varying recharge maps throughout the Deep Creek watershed. Temporal and spatial analysis of 25 years daily recharge results at various representative points across the study watershed reveal significant temporal and spatial variations; average recharge estimated at 77.8 ± 50.8mm /year. This significant variation over the years, caused by antecedent soil moisture condition and climatic condition, illustrates the common flaw of assigning a constant percentage of precipitation throughout the simulation period. Groundwater recharge modeling has previously been attempted in the Okanagan Basin
Spatial and spatio-temporal bayesian models with R - INLA
Blangiardo, Marta
2015-01-01
Dedication iiiPreface ix1 Introduction 11.1 Why spatial and spatio-temporal statistics? 11.2 Why do we use Bayesian methods for modelling spatial and spatio-temporal structures? 21.3 Why INLA? 31.4 Datasets 32 Introduction to 212.1 The language 212.2 objects 222.3 Data and session management 342.4 Packages 352.5 Programming in 362.6 Basic statistical analysis with 393 Introduction to Bayesian Methods 533.1 Bayesian Philosophy 533.2 Basic Probability Elements 573.3 Bayes Theorem 623.4 Prior and Posterior Distributions 643.5 Working with the Posterior Distribution 663.6 Choosing the Prior Distr
Spatial, temporal, and density-dependent components of habitat quality for a desert owl.
Directory of Open Access Journals (Sweden)
Aaron D Flesch
Full Text Available Spatial variation in resources is a fundamental driver of habitat quality but the realized value of resources at any point in space may depend on the effects of conspecifics and stochastic factors, such as weather, which vary through time. We evaluated the relative and combined effects of habitat resources, weather, and conspecifics on habitat quality for ferruginous pygmy-owls (Glaucidium brasilianum in the Sonoran Desert of northwest Mexico by monitoring reproductive output and conspecific abundance over 10 years in and around 107 territory patches. Variation in reproductive output was much greater across space than time, and although habitat resources explained a much greater proportion of that variation (0.70 than weather (0.17 or conspecifics (0.13, evidence for interactions among each of these components of the environment was strong. Relative to habitat that was persistently low in quality, high-quality habitat buffered the negative effects of conspecifics and amplified the benefits of favorable weather, but did not buffer the disadvantages of harsh weather. Moreover, the positive effects of favorable weather at low conspecific densities were offset by intraspecific competition at high densities. Although realized habitat quality declined with increasing conspecific density suggesting interference mechanisms associated with an Ideal Free Distribution, broad spatial heterogeneity in habitat quality persisted. Factors linked to food resources had positive effects on reproductive output but only where nest cavities were sufficiently abundant to mitigate the negative effects of heterospecific enemies. Annual precipitation and brooding-season temperature had strong multiplicative effects on reproductive output, which declined at increasing rates as drought and temperature increased, reflecting conditions predicted to become more frequent with climate change. Because the collective environment influences habitat quality in complex ways
Spatially explicit models for inference about density in unmarked or partially marked populations
Chandler, Richard B.; Royle, J. Andrew
2013-01-01
Recently developed spatial capture–recapture (SCR) models represent a major advance over traditional capture–recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area. Furthermore, unlike nonspatial CR methods, SCR models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers and sample locations. Although the utility of SCR methods is gaining recognition, the requirement that all individuals can be uniquely identified excludes their use in many contexts. In this paper, we develop models for situations in which individual recognition is not possible, thereby allowing SCR concepts to be applied in studies of unmarked or partially marked populations. The data required for our model are spatially referenced counts made on one or more sample occasions at a collection of closely spaced sample units such that individuals can be encountered at multiple locations. Our approach includes a spatial point process for the animal activity centers and uses the spatial correlation in counts as information about the number and location of the activity centers. Camera-traps, hair snares, track plates, sound recordings, and even point counts can yield spatially correlated count data, and thus our model is widely applicable. A simulation study demonstrated that while the posterior mean exhibits frequentist bias on the order of 5–10% in small samples, the posterior mode is an accurate point estimator as long as adequate spatial correlation is present. Marking a subset of the population substantially increases posterior precision and is recommended whenever possible. We applied our model to avian point count data collected on an unmarked population of the northern parula (Parula americana) and obtained a density estimate (posterior mode) of 0.38 (95% CI: 0.19–1.64) birds/ha. Our paper challenges sampling and analytical conventions in ecology by demonstrating
Ababaei, Behnam; Sohrabi, Teymour; Mirzaei, Farhad
2014-10-01
Most stochastic weather generators have their focus on precipitation because it is the most important variable affecting environmental processes. One of the methods to reproduce the precipitation occurrence time series is to use a Markov process. But, in addition to the simulation of short-term autocorrelations in one station, it is sometimes important to preserve the spatial linear correlations (SLC) between neighboring stations as well. In this research, an extension of one-site Markov models was proposed to preserve the SLC between neighboring stations. Qazvin station was utilized as the reference station and Takestan (TK), Magsal, Nirougah, and Taleghan stations were used as the target stations. The performances of different models were assessed in relation to the simulation of dry and wet spells and short-term dependencies in precipitation time series. The results revealed that in TK station, a Markov model with a first-order spatial model could be selected as the best model, while in the other stations, a model with the order of two or three could be selected. The selected (i.e., best) models were assessed in relation to preserving the SLC between neighboring stations. The results depicted that these models were very capable in preserving the SLC between the reference station and any of the target stations. But, their performances were weaker when the SLC between the other stations were compared. In order to resolve this issue, spatially correlated random numbers were utilized instead of independent random numbers while generating synthetic time series using the Markov models. Although this method slightly reduced the model performances in relation to dry and wet spells and short-term dependencies, the improvements related to the simulation of the SLC between the other stations were substantial.
Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach
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Hongqiang Liu
2017-01-01
Full Text Available A multivariate Bayesian spatial modeling approach was used to jointly model the counts of two types of crime, i.e., burglary and non-motor vehicle theft, and explore the geographic pattern of crime risks and relevant risk factors. In contrast to the univariate model, which assumes independence across outcomes, the multivariate approach takes into account potential correlations between crimes. Six independent variables are included in the model as potential risk factors. In order to fully present this method, both the multivariate model and its univariate counterpart are examined. We fitted the two models to the data and assessed them using the deviance information criterion. A comparison of the results from the two models indicates that the multivariate model was superior to the univariate model. Our results show that population density and bar density are clearly associated with both burglary and non-motor vehicle theft risks and indicate a close relationship between these two types of crime. The posterior means and 2.5% percentile of type-specific crime risks estimated by the multivariate model were mapped to uncover the geographic patterns. The implications, limitations and future work of the study are discussed in the concluding section.
Bhat, Chandra R.; Sener, Ipek N.
2009-09-01
This study focuses on accommodating spatial dependency in data indexed by geographic location. In particular, the emphasis is on accommodating spatial error correlation across observational units in binary discrete choice models. We propose a copula-based approach to spatial dependence modeling based on a spatial logit structure rather than a spatial probit structure. In this approach, the dependence between the logistic error terms of different observational units is directly accommodated using a multivariate logistic distribution based on the Farlie-Gumbel-Morgenstein (FGM) copula. The approach represents a simple and powerful technique that results in a closed-form analytic expression for the joint probability of choice across observational units, and is straightforward to apply using a standard and direct maximum likelihood inference procedure. There is no simulation machinery involved, leading to substantial computation gains relative to current methods to address spatial correlation. The approach is applied to teenagers’ physical activity participation levels, a subject of considerable interest in the public health, transportation, sociology, and adolescence development fields. The results indicate that failing to accommodate heteroscedasticity and spatial correlation can lead to inconsistent and inefficient parameter estimates, as well as incorrect conclusions regarding the elasticity effects of exogenous variables.
Jin, Ick Hoon; Yuan, Ying; Bandyopadhyay, Dipankar
2016-01-01
Research in dental caries generates data with two levels of hierarchy: that of a tooth overall and that of the different surfaces of the tooth. The outcomes often exhibit spatial referencing among neighboring teeth and surfaces, i.e., the disease status of a tooth or surface might be influenced by the status of a set of proximal teeth/surfaces. Assessments of dental caries (tooth decay) at the tooth level yield binary outcomes indicating the presence/absence of teeth, and trinary outcomes at the surface level indicating healthy, decayed, or filled surfaces. The presence of these mixed discrete responses complicates the data analysis under a unified framework. To mitigate complications, we develop a Bayesian two-level hierarchical model under suitable (spatial) Markov random field assumptions that accommodates the natural hierarchy within the mixed responses. At the first level, we utilize an autologistic model to accommodate the spatial dependence for the tooth-level binary outcomes. For the second level and conditioned on a tooth being non-missing, we utilize a Potts model to accommodate the spatial referencing for the surface-level trinary outcomes. The regression models at both levels were controlled for plausible covariates (risk factors) of caries, and remain connected through shared parameters. To tackle the computational challenges in our Bayesian estimation scheme caused due to the doubly-intractable normalizing constant, we employ a double Metropolis-Hastings sampler. We compare and contrast our model performances to the standard non-spatial (naive) model using a small simulation study, and illustrate via an application to a clinical dataset on dental caries. PMID:27807470
Modeling temporal and spatial variability of crop yield
Bonetti, S.; Manoli, G.; Scudiero, E.; Morari, F.; Putti, M.; Teatini, P.
2014-12-01
In a world of increasing food insecurity the development of modeling tools capable of supporting on-farm decision making processes is highly needed to formulate sustainable irrigation practices in order to preserve water resources while maintaining adequate crop yield. The design of these practices starts from the accurate modeling of soil-plant-atmosphere interaction. We present an innovative 3D Soil-Plant model that couples 3D hydrological soil dynamics with a mechanistic description of plant transpiration and photosynthesis, including a crop growth module. Because of its intrinsically three dimensional nature, the model is able to capture spatial and temporal patterns of crop yield over large scales and under various climate and environmental factors. The model is applied to a 25 ha corn field in the Venice coastland, Italy, that has been continuously monitored over the years 2010 and 2012 in terms of both hydrological dynamics and yield mapping. The model results satisfactorily reproduce the large variability observed in maize yield (from 2 to 15 ton/ha). This variability is shown to be connected to the spatial heterogeneities of the farmland, which is characterized by several sandy paleo-channels crossing organic-rich silty soils. Salt contamination of soils and groundwater in a large portion of the area strongly affects the crop yield, especially outside the paleo-channels, where measured salt concentrations are lower than the surroundings. The developed model includes a simplified description of the effects of salt concentration in soil water on transpiration. The results seem to capture accurately the effects of salt concentration and the variability of the climatic conditions occurred during the three years of measurements. This innovative modeling framework paves the way to future large scale simulations of farmland dynamics.
Modelling spatial patterns and temporal trends of wildfires in Galicia (NW Spain
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Jesús Barreal
2015-08-01
Full Text Available Aim of study: The goal of this paper is to analyse the importance of the main contributing factors to the occurrence of wildfires. Area of study: We employ data from the region of Galicia during 2001-2010; although the similarities shared between this area and other rural areas may allow extrapolation of the present results. Material and Methods: The spatial dependence is analysed by using the Moran’s I and LISA statistics. We also conduct an econometric analysis modelling both, the number of fires and the relative size of afflicted woodland area as dependent variables, which depend on the climatic, land cover variables, and socio-economic characteristics of the affected areas. Fixed effects and random effect models are estimated in order to control for the heterogeneity between the Forest Districts in Galicia. Main results: Moran’s I and LISA statistics show that there is spatial dependence in the occurrence of Galician wildfires. Econometrics models show that climatology, socioeconomic variables, and temporal trends are also important to study both, the number of wildfires and the burned-forest ratio. Research highlights: We conclude that in addition to direct forest actions, other agricultural or social public plans, can help to reduce wildfires in rural areas or wildland-urban areas. Based on these conclusions, a number of guidelines are provided that may foster the development of better forest management policies in order to reduce the occurrence of wildfires.
Assessing fit in Bayesian models for spatial processes
Jun, M.
2014-09-16
© 2014 John Wiley & Sons, Ltd. Gaussian random fields are frequently used to model spatial and spatial-temporal data, particularly in geostatistical settings. As much of the attention of the statistics community has been focused on defining and estimating the mean and covariance functions of these processes, little effort has been devoted to developing goodness-of-fit tests to allow users to assess the models\\' adequacy. We describe a general goodness-of-fit test and related graphical diagnostics for assessing the fit of Bayesian Gaussian process models using pivotal discrepancy measures. Our method is applicable for both regularly and irregularly spaced observation locations on planar and spherical domains. The essential idea behind our method is to evaluate pivotal quantities defined for a realization of a Gaussian random field at parameter values drawn from the posterior distribution. Because the nominal distribution of the resulting pivotal discrepancy measures is known, it is possible to quantitatively assess model fit directly from the output of Markov chain Monte Carlo algorithms used to sample from the posterior distribution on the parameter space. We illustrate our method in a simulation study and in two applications.
Krause, Stefan; Blaen, Phillip; Hannah, David; Romejn, Paul; Gomez, Jesus; Kurz, Marie; Fleckenstein, Jan; Schmidt, Christian; Zarnetske, Jay; Cullin, Joe; Ward, Adam; Marti, Eugenia; Drummond, Jennifer; Schmadel, Noah; Knapp, Julia; Klaar, Megan; Mendoza, Clara
2016-04-01
The transport and transformation of carbon and nitrogen across aquifer - river interfaces are significantly altered along the streambed passage. Recent investigations have substantially improved the understanding of controls on streambed biogeochemical cycling, outlining a critical impact of exchange fluxes, temporal and spatial coincidence of reaction partners and streambed residence time distributions. Still, there is little understanding of the drivers of the widely observed strong spatial and temporal variability of interlinked carbon and nitrogen turnover at aquifer-river interfaces, including hotspots (locations) and hot moments (time periods) of increased reactivity. Previous research, predominantly with a surface water perspective, has mainly focused on the impact of bedform controlled hyporheic exchange fluxes and the chemical transformation of surface solutes transported along a hyporheic flow path. While such studies may explain nutrient turnover in the hyporheic zones of low-order streams in rather pristine headwater catchments, they fail to explain observations of spatially and temporally more variable nutrient turnover in streambeds with higher structural heterogeneity and relevant concentrations of autochthonous carbon and nitrogen. Here we combine laboratory, field and numerical modeling experiments from plot to stream reach/subcatchment scales to quantify the impacts of variability in physical and biogeochemical streambed properties on hyporheic nutrient (C, N, O) cycling. At the plot scale, hotspots of biogeochemical cycling have been found to be associated with peat and clay layers within streambed sediments, representing areas of significantly increased residence times and oxygen consumption what results in enhanced microbial metabolic activity and nitrogen removal capacity. We present distributed sensor network based up-scaling methods that allow identification of such features at larger reach scale. Numerical modeling based generalization
Spatial extrapolation of light use efficiency model parameters to predict gross primary production
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Karsten Schulz
2011-12-01
Full Text Available To capture the spatial and temporal variability of the gross primary production as a key component of the global carbon cycle, the light use efficiency modeling approach in combination with remote sensing data has shown to be well suited. Typically, the model parameters, such as the maximum light use efficiency, are either set to a universal constant or to land class dependent values stored in look-up tables. In this study, we employ the machine learning technique support vector regression to explicitly relate the model parameters of a light use efficiency model calibrated at several FLUXNET sites to site-specific characteristics obtained by meteorological measurements, ecological estimations and remote sensing data. A feature selection algorithm extracts the relevant site characteristics in a cross-validation, and leads to an individual set of characteristic attributes for each parameter. With this set of attributes, the model parameters can be estimated at sites where a parameter calibration is not possible due to the absence of eddy covariance flux measurement data. This will finally allow a spatially continuous model application. The performance of the spatial extrapolation scheme is evaluated with a cross-validation approach, which shows the methodology to be well suited to recapture the variability of gross primary production across the study sites.
Investigation of Spatial Variation of Sea States Offshore of Humboldt Bay CA Using a Hindcast Model.
Energy Technology Data Exchange (ETDEWEB)
Dallman, Ann Renee; Neary, Vincent Sinclair
2014-10-01
Spatial variability of sea states is an important consideration when performing wave resource assessments and wave resource characterization studies for wave energy converter (WEC) test sites and commercial WEC deployments. This report examines the spatial variation of sea states offshore of Humboldt Bay, CA, using the wave model SWAN . The effect of depth and shoaling on bulk wave parameters is well resolved using the model SWAN with a 200 m grid. At this site, the degree of spatial variation of these bulk wave parameters, with shoaling generally perpendicular to the depth contours, is found to depend on the season. The variation in wave height , for example, was higher in the summer due to the wind and wave sheltering from the protruding land on the coastline north of the model domain. Ho wever, the spatial variation within an area of a potential Tier 1 WEC test site at 45 m depth and 1 square nautical mile is almost negligible; at most about 0.1 m in both winter and summer. The six wave characterization parameters recommended by the IEC 6 2600 - 101 TS were compared at several points along a line perpendicular to shore from the WEC test site . As expected, these parameters varied based on depth , but showed very similar seasonal trends.
Khaki, M.; Schumacher, M.; Forootan, E.; Kuhn, M.; Awange, J. L.; van Dijk, A. I. J. M.
2017-10-01
Assimilation of terrestrial water storage (TWS) information from the Gravity Recovery And Climate Experiment (GRACE) satellite mission can provide significant improvements in hydrological modelling. However, the rather coarse spatial resolution of GRACE TWS and its spatially correlated errors pose considerable challenges for achieving realistic assimilation results. Consequently, successful data assimilation depends on rigorous modelling of the full error covariance matrix of the GRACE TWS estimates, as well as realistic error behavior for hydrological model simulations. In this study, we assess the application of local analysis (LA) to maximize the contribution of GRACE TWS in hydrological data assimilation. For this, we assimilate GRACE TWS into the World-Wide Water Resources Assessment system (W3RA) over the Australian continent while applying LA and accounting for existing spatial correlations using the full error covariance matrix. GRACE TWS data is applied with different spatial resolutions including 1° to 5° grids, as well as basin averages. The ensemble-based sequential filtering technique of the Square Root Analysis (SQRA) is applied to assimilate TWS data into W3RA. For each spatial scale, the performance of the data assimilation is assessed through comparison with independent in-situ ground water and soil moisture observations. Overall, the results demonstrate that LA is able to stabilize the inversion process (within the implementation of the SQRA filter) leading to less errors for all spatial scales considered with an average RMSE improvement of 54% (e.g., 52.23 mm down to 26.80 mm) for all the cases with respect to groundwater in-situ measurements. Validating the assimilated results with groundwater observations indicates that LA leads to 13% better (in terms of RMSE) assimilation results compared to the cases with Gaussian errors assumptions. This highlights the great potential of LA and the use of the full error covariance matrix of GRACE TWS
Energy and spatial dependence of MCNP simulations for ZED-2 critical experiments
Energy Technology Data Exchange (ETDEWEB)
Kozier, K.S. [Atomic Energy of Canada Limited, Chalk River, Ontario (Canada)], E-mail: kozierk@aecl.ca
2008-07-01
MCNP simulations of ZED-2 critical experiments provide a good test of the reliability of the nuclear data involved in the simulation of reactor physics phenomena of importance to CANDU reactors, particularly the coolant void reactivity. Recent work has therefore focused on the impact of the new ENDF/B-VII.0 nuclear data library. One feature of this library is the provision of thermal scattering law data for UO{sub 2}. Initial MCNP results using preliminary ACE-format data files for UO{sub 2} thermal scattering suggested that a consistent reduction was obtained in the coolant void reactivity simulation bias, especially for ZED-2 critical experiments involving slightly enriched uranium (0.95 wt% {sup 235}U) and H{sub 2}O/air coolant. However, subsequent work using UO{sub 2} thermal scattering data files that correctly include the coherent elastic scattering component indicated that the net reactivity impact is quite small. The present work extends this investigation to examine in detail the energy dependence of the impact of the UO{sub 2} thermal scattering data and, more generally, the energy and spatial dependence of the coolant void reactivity simulation bias for some of these experiments. In addition, results are presented using MCNPX with an improved treatment for thermal scattering. It is found that the net reactivity impact results from the cancellation of larger positive and negative effects at different energies and in different fuel regions, and which generally highlight the reactor physics changes that occur when the coolant is removed. (author)
Provenzano, Giovanni; Pangrazzi, Luca; Poli, Andrea; Berardi, Nicoletta; Bozzi, Yuri
2015-05-12
Induction of phosphorylated extracellular-regulated kinase (pERK) is a reliable molecular readout of learning-dependent neuronal activation. Here, we describe a pERK immunohistochemistry protocol to study the profile of hippocampal neuron activation following exposure to a spatial learning task in a mouse model characterized by cognitive deficits of neurodevelopmental origin. Specifically, we used pERK immunostaining to study neuronal activation following Morris water maze (MWM, a classical hippocampal-dependent learning task) in Engrailed-2 knockout (En2(-/-)) mice, a model of autism spectrum disorders (ASD). As compared to wild-type (WT) controls, En2(-/-) mice showed significant spatial learning deficits in the MWM. After MWM, significant differences in the number of pERK-positive neurons were detected in specific hippocampal subfields of En2(-/-) mice, as compared to WT animals. Thus, our protocol can robustly detect differences in pERK-positive neurons associated to hippocampal-dependent learning impairment in a mouse model of ASD. More generally, our protocol can be applied to investigate the profile of hippocampal neuron activation in both genetic or pharmacological mouse models characterized by cognitive deficits.
Dependency of energy and spatial distributions of photons on edge of object in brain SPECT
Deloar, H M; Kudomi, N; Kim, K M; Aoi, T; Iida, H
2003-01-01
Accurate mu maps are important for quantitative image reconstruction in SPECT. The Compton scatter energy window (CSW) technique has been proposed to define the outline of objects. In this technique, a lower energy window image is acquired in addition to the main photo-peak energy window. The image of the lower energy window is used to estimate the edge of the scanned object to produce a constant attenuation map. The aim of this study was to investigate the dependency of CSW on the spatial and energy distribution of radioisotope to predict the edges of objects. Two particular cases of brain study were considered, namely uniform distribution and non-uniform distribution using Monte Carlo simulation and experiments with uniform cylindrical phantom and hotspot phantom. The phantoms were filled with water and a radioactive solution of sup 9 sup 9 sup m Tc. For each phantom, 20%, 30%, 40% and 50% thresholds of the mean profile were applied to estimate E sub w sub t , the energy window for minimum difference betwee...
Spatial changes of lakes on the Greenland Ice Sheet and their dependence on subglacial topography
Kjær, K. H.; Korsgaard, N. J.
2009-04-01
Recent research has shown that meltwater discharge from supraglacial lakes on the Greenland Ice Sheet increase the lubrication at the bottom and enhance the ice velocity. Supraglacial lakes are considered a positive feedback to the Greenland Ice Sheet as Arctic warming continues. In this context, we want to know how the Inter-annual and inter-regional variation to area and distribution of supraglacial lakes on the entire Greenland Ice Sheet has evolved during the last decade. Also how the distribution of lakes is linked to the subglacial topography. We used 180 satellite images covering the melt seasons for the period 2000-2008. The BRDF adjusted MCD43A4 MODIS surface reflectance product was re-projected and re-sampled with a grid resolution of 500 m, with each individual image covering Greenland up to 80 degrees latitude. Lake area estimates and distributions were extracted using an automated supervised classification method and assigned to their respective large-scale drainage areas. Elevation has been added to the classification data for analysis of mean altitude over seasons. Longer term lake distribution was established through older satellite and aerial photographs. Our results show the temporal migration of lakes from north-south over the ice sheet. Many lakes seem to hold a stable spatial position over several decades reflecting dependence of the bedrock topography.
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Azevedo E.L.
2001-01-01
Full Text Available We investigated the dependency of the early facilitatory effect of a prime stimulus (S1 on the physical characteristics of the target stimulus (S2. A go-no go reaction time paradigm was used. The S1 was a gray ring and the S2s were a white vertical line, a white horizontal line, a white cross and a white small ring, all inside a white ring with the same dimensions as the S1. S1 onset-S2 onset asynchrony was 100 ms. The stimuli appeared randomly in any one of the quadrants of a monitor screen. The S2 could occur at the same position as the S1 or at a different one. We observed a strong facilitatory effect when the vertical line or the horizontal line was the go stimulus and no effect when the cross was the go stimulus. These results show that the features of the target stimulus can be decisive for the appearance of the facilitatory effect of a peripheral spatially noninformative prime stimulus.
Azevedo, E L; Squella, S A; Ribeiro-do-Valle, L E
2001-06-01
We investigated the dependency of the early facilitatory effect of a prime stimulus (S1) on the physical characteristics of the target stimulus (S2). A go-no go reaction time paradigm was used. The S1 was a gray ring and the S2s were a white vertical line, a white horizontal line, a white cross and a white small ring, all inside a white ring with the same dimensions as the S1. S1 onset-S2 onset asynchrony was 100 ms. The stimuli appeared randomly in any one of the quadrants of a monitor screen. The S2 could occur at the same position as the S1 or at a different one. We observed a strong facilitatory effect when the vertical line or the horizontal line was the go stimulus and no effect when the cross was the go stimulus. These results show that the features of the target stimulus can be decisive for the appearance of the facilitatory effect of a peripheral spatially noninformative prime stimulus.
Spatial regulation of the cAMP-dependent protein kinase during chemotactic cell migration.
Howe, Alan K; Baldor, Linda C; Hogan, Brian P
2005-10-04
Historically, the cAMP-dependent protein kinase (PKA) has a paradoxical role in cell motility, having been shown to both facilitate and inhibit actin cytoskeletal dynamics and cell migration. In an effort to understand this dichotomy, we show here that PKA is regulated in subcellular space during cell migration. Immunofluorescence microscopy and biochemical enrichment of pseudopodia showed that type II regulatory subunits of PKA and PKA activity are enriched in protrusive cellular structures formed during chemotaxis. This enrichment correlates with increased phosphorylation of key cytoskeletal substrates for PKA, including the vasodilator-stimulated phosphoprotein (VASP) and the protein tyrosine phosphatase containing a PEST motif. Importantly, inhibition of PKA activity or its ability to interact with A kinase anchoring proteins inhibited the activity of the Rac GTPase within pseudopodia. This effect correlated with both decreased guanine nucleotide exchange factor activity and increased GTPase activating protein activity. Finally, inhibition of PKA anchoring, like inhibition of total PKA activity, inhibited pseudopod formation and chemotactic cell migration. These data demonstrate that spatial regulation of PKA via anchoring is an important facet of normal chemotactic cell movement.
Combining microsimulation and spatial interaction models for retail location analysis
Nakaya, Tomoki; Fotheringham, A. Stewart; Hanaoka, Kazumasa; Clarke, Graham; Ballas, Dimitris; Yano, Keiji
2007-12-01
Although the disaggregation of consumers is crucial in understanding the fragmented markets that are dominant in many developed countries, it is not always straightforward to carry out such disaggregation within conventional retail modelling frameworks due to the limitations of data. In particular, consumer grouping based on sampled data is not assured to link with the other statistics that are vital in estimating sampling biases and missing variables in the sampling survey. To overcome this difficulty, we propose a useful combination of spatial interaction modelling and microsimulation approaches for the reliable estimation of retail interactions based on a sample survey of consumer behaviour being linked with other areal statistics. We demonstrate this approach by building an operational retail interaction model to estimate expenditure flows from households to retail stores in a local city in Japan, Kusatsu City.
Spatial Modeling of Geometallurgical Properties: Techniques and a Case Study
Energy Technology Data Exchange (ETDEWEB)
Deutsch, Jared L., E-mail: jdeutsch@ualberta.ca [University of Alberta, School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering (Canada); Palmer, Kevin [Teck Resources Limited (Canada); Deutsch, Clayton V.; Szymanski, Jozef [University of Alberta, School of Mining and Petroleum Engineering, Department of Civil and Environmental Engineering (Canada); Etsell, Thomas H. [University of Alberta, Department of Chemical and Materials Engineering (Canada)
2016-06-15
High-resolution spatial numerical models of metallurgical properties constrained by geological controls and more extensively by measured grade and geomechanical properties constitute an important part of geometallurgy. Geostatistical and other numerical techniques are adapted and developed to construct these high-resolution models accounting for all available data. Important issues that must be addressed include unequal sampling of the metallurgical properties versus grade assays, measurements at different scale, and complex nonlinear averaging of many metallurgical parameters. This paper establishes techniques to address each of these issues with the required implementation details and also demonstrates geometallurgical mineral deposit characterization for a copper–molybdenum deposit in South America. High-resolution models of grades and comminution indices are constructed, checked, and are rigorously validated. The workflow demonstrated in this case study is applicable to many other deposit types.
Stochastic population oscillations in spatial predator-prey models
Energy Technology Data Exchange (ETDEWEB)
Taeuber, Uwe C, E-mail: tauber@vt.edu [Department of Physics, Virginia Tech, Blacksburg, VA 24061-0435 (United States)
2011-09-15
It is well-established that including spatial structure and stochastic noise in models for predator-prey interactions invalidates the classical deterministic Lotka-Volterra picture of neutral population cycles. In contrast, stochastic models yield long-lived, but ultimately decaying erratic population oscillations, which can be understood through a resonant amplification mechanism for density fluctuations. In Monte Carlo simulations of spatial stochastic predator-prey systems, one observes striking complex spatio-temporal structures. These spreading activity fronts induce persistent correlations between predators and prey. In the presence of local particle density restrictions (finite prey carrying capacity), there exists an extinction threshold for the predator population. The accompanying continuous non-equilibrium phase transition is governed by the directed-percolation universality class. We employ field-theoretic methods based on the Doi-Peliti representation of the master equation for stochastic particle interaction models to (i) map the ensuing action in the vicinity of the absorbing state phase transition to Reggeon field theory, and (ii) to quantitatively address fluctuation-induced renormalizations of the population oscillation frequency, damping, and diffusion coefficients in the species coexistence phase.
Spatial differentiation in characterisation modelling – what difference does it make?
DEFF Research Database (Denmark)
Hauschild, Michael Zwicky; Potting, José
2004-01-01
In the life cycle of a product, emissions take place at many different locations. The location of the sources and its surrounding condition influence the fate of the emission and the exposure it leads to but this source of variation is currently neglected in life cycle impact assessment, although...... is spatially resolved and comprises a larger part of the cause-effect chain including exposure assessment and in some cases exceeding of threshold values, which positions it closer to endpoint modelling. Examples are given where conclusions are reversed when the site-dependent characterisation modelling...
Mobility dependent recombination models for organic solar cells
Wagenpfahl, Alexander
2017-09-01
Modern solar cell technologies are driven by the effort to enhance power conversion efficiencies. A main mechanism limiting power conversion efficiencies is charge carrier recombination which is a direct function of the encounter probability of both recombination partners. In inorganic solar cells with rather high charge carrier mobilities, charge carrier recombination is often dominated by energetic states which subsequently trap both recombination partners for recombination. Free charge carriers move fast enough for Coulomb attraction to be irrelevant for the encounter probability. Thus, charge carrier recombination is independent of charge carrier mobilities. In organic semiconductors charge carrier mobilities are much lower. Therefore, electrons and holes have more time react to mutual Coulomb-forces. This results in the strong charge carrier mobility dependencies of the observed charge carrier recombination rates. In 1903 Paul Langevin published a fundamental model to describe the recombination of ions in gas-phase or aqueous solutions, known today as Langevin recombination. During the last decades this model was used to interpret and model recombination in organic semiconductors. However, certain experiments especially with bulk-heterojunction solar cells reveal much lower recombination rates than predicted by Langevin. In search of an explanation, many material and device properties such as morphology and energetic properties have been examined in order to extend the validity of the Langevin model. A key argument for most of these extended models is, that electron and hole must find each other at a mutual spatial location. This encounter may be limited for instance by trapping of charges in trap states, by selective electrodes separating electrons and holes, or simply by the morphology of the involved semiconductors, making it impossible for electrons and holes to recombine at high rates. In this review, we discuss the development of mobility limited
Energy Technology Data Exchange (ETDEWEB)
Zinoveva, S.
2008-04-15
A crucial step towards controlled synthesis of nanoparticles is the detailed understanding of the various chemical processes that take place during the synthesis. X-ray Absorption Spectroscopy (XAS) is especially suitable for elucidating the type and structure of the intermediate metal species. It is applicable to materials that have no long range order and provides information on both electronic and geometric structures. Here a comparative study is reported of the formation of cobalt nanoparticles via thermolysis of two organometallic precursors dicobalt octacarbonyl (DCO) and alkyne-bridged dicobalt hexacarbonyl (ADH) in the presence of aluminum organics. Using time-dependent XAS a reaction pathway different from both the atom based La Mer model and the Watzky and Finsky autocatalytic surface growth model is observed. Where prior to the nucleation several intermediates are formed and the initial nucleus is composed of Co atoms coordinated with ligands Co{sub n}(CO){sub m} with n=2-3, m=3-5. The formation of Co nanoparticles was also investigated using a reaction different from thermolysis of cobalt carbonyls, namely reduction of Co (II) acetate by sodium borohydrate. Here the combination of microreactor system and spatially resolved XAS allowed ''in situ'' monitoring of the wet chemical synthesis. Several steps of the reaction were spatially resolved in the microreactor. The vertical size of the X-ray beam (50 {mu}m) focused with Kirkpatrick-Baez mirror system, determines the time resolution (better than 2 ms). The results provide direct insight into rapid process of nanoparticles formation and demonstrate the potential of this new technique for the fundamental studies of such type of processes where miniaturization and timeresolution are important. Like in the carbonyls thermolysis no evidence for the reduction of the starting complex to isolated Co{sup 0} atoms followed by nucleation of Co{sup 0} atoms was observed. (orig.)
Spatial modeling of cutaneous leishmaniasis in the Andean region of Colombia
Pérez-Flórez, Mauricio; Ocampo, Clara Beatriz; Valderrama-Ardila, Carlos; Alexander, Neal
2016-01-01
The objective of this research was to identify environmental risk factors for cutaneous leishmaniasis (CL) in Colombia and map high-risk municipalities. The study area was the Colombian Andean region, comprising 715 rural and urban municipalities. We used 10 years of CL surveillance: 2000-2009. We used spatial-temporal analysis - conditional autoregressive Poisson random effects modelling - in a Bayesian framework to model the dependence of municipality-level incidence on land use, climate, elevation and population density. Bivariable spatial analysis identified rainforests, forests and secondary vegetation, temperature, and annual precipitation as positively associated with CL incidence. By contrast, livestock agroecosystems and temperature seasonality were negatively associated. Multivariable analysis identified land use - rainforests and agro-livestock - and climate - temperature, rainfall and temperature seasonality - as best predictors of CL. We conclude that climate and land use can be used to identify areas at high risk of CL and that this approach is potentially applicable elsewhere in Latin America. PMID:27355214
A Spatial Clustering Approach for Stochastic Fracture Network Modelling
Seifollahi, S.; Dowd, P. A.; Xu, C.; Fadakar, A. Y.
2014-07-01
Fracture network modelling plays an important role in many application areas in which the behaviour of a rock mass is of interest. These areas include mining, civil, petroleum, water and environmental engineering and geothermal systems modelling. The aim is to model the fractured rock to assess fluid flow or the stability of rock blocks. One important step in fracture network modelling is to estimate the number of fractures and the properties of individual fractures such as their size and orientation. Due to the lack of data and the complexity of the problem, there are significant uncertainties associated with fracture network modelling in practice. Our primary interest is the modelling of fracture networks in geothermal systems and, in this paper, we propose a general stochastic approach to fracture network modelling for this application. We focus on using the seismic point cloud detected during the fracture stimulation of a hot dry rock reservoir to create an enhanced geothermal system; these seismic points are the conditioning data in the modelling process. The seismic points can be used to estimate the geographical extent of the reservoir, the amount of fracturing and the detailed geometries of fractures within the reservoir. The objective is to determine a fracture model from the conditioning data by minimizing the sum of the distances of the points from the fitted fracture model. Fractures are represented as line segments connecting two points in two-dimensional applications or as ellipses in three-dimensional (3D) cases. The novelty of our model is twofold: (1) it comprises a comprehensive fracture modification scheme based on simulated annealing and (2) it introduces new spatial approaches, a goodness-of-fit measure for the fitted fracture model, a measure for fracture similarity and a clustering technique for proposing a locally optimal solution for fracture parameters. We use a simulated dataset to demonstrate the application of the proposed approach
Spatial transferability of landscape-based hydrological models
Gao, Hongkai; Hrachowitz, Markus; Fenicia, Fabrizio; Gharari, Shervan; Sriwongsitanon, Nutchanart; Savenije, Hubert
2015-04-01
Landscapes, mainly distinguished by land surface topography and vegetation cover, are crucial in defining runoff generation mechanisms, interception capacity and transpiration processes. Landscapes information provides modelers with a way to take into account catchment heterogeneity, while simultaneously keeping model complexity low. A landscape-based hydrological modelling framework (FLEX-Topo), with parallel model structures, was developed and tested in various catchments with diverse climate, topography and land cover conditions. Landscape classification is the basic and most crucial procedure to create a tailor-made model for a certain catchment, as it explicitly relates hydrologic similarity to landscape similarity, which is the base of this type of models. Therefore, the study catchment is classified into different landscapes units that fulfil similar hydrological function, based on classification criteria such as the height above the nearest drainage, slope, aspect and land cover. At present, to suggested model includes four distinguishable landscapes: hillslopes, terraces/plateaus, riparian areas, and glacierized areas. Different parallel model structures are then associated with the different landscape units to describe their different dominant runoff generation mechanisms. These hydrological units are parallel and only connected by groundwater reservoir. The transferability of this landscape-based model can then be compared with the transferability of a lumped model. In this study, FLEX-Topo was developed and tested in three study sites: two cold-arid catchments in China (the upper Heihe River and the Urumqi Glacier No1 catchment), and one tropical catchment in Thailand (the upper Ping River). Stringent model tests indicate that FLEX-Topo, allowing for more process heterogeneity than lumped model formulations, exhibits higher capabilities to be spatially transferred. Furthermore, the simulated water balances, including internal fluxes, hydrograph
A dependence modelling study of extreme rainfall in Madeira Island
Gouveia-Reis, Délia; Guerreiro Lopes, Luiz; Mendonça, Sandra
2016-08-01
The dependence between variables plays a central role in multivariate extremes. In this paper, spatial dependence of Madeira Island's rainfall data is addressed within an extreme value copula approach through an analysis of maximum annual data. The impact of altitude, slope orientation, distance between rain gauge stations and distance from the stations to the sea are investigated for two different periods of time. The results obtained highlight the influence of the island's complex topography on the spatial distribution of extreme rainfall in Madeira Island.
Spatial air pollution modelling for a West-African town
Directory of Open Access Journals (Sweden)
Sirak Zenebe Gebreab
2015-11-01
Full Text Available Land use regression (LUR modelling is a common approach used in European and Northern American epidemiological studies to assess urban and traffic related air pollution exposures. Studies applying LUR in Africa are lacking. A need exists to understand if this approach holds for an African setting, where urban features, pollutant exposures and data availability differ considerably from other continents. We developed a parsimonious regression model based on 48-hour nitrogen dioxide (NO2 concentrations measured at 40 sites in Kaédi, a medium sized West-African town, and variables generated in a geographic information system (GIS. Road variables and settlement land use characteristics were found to be important predictors of 48-hour NO2 concentration in the model. About 68% of concentration variability in the town was explained by the model. The model was internally validated by leave-one-out cross-validation and it was found to perform moderately well. Furthermore, its parameters were robust to sampling variation. We applied the model at 100 m pixels to create a map describing the broad spatial pattern of NO2 across Kaédi. In this research, we demonstrated the potential for LUR as a valid, cost-effective approach for air pollution modelling and mapping in an African town. If the methodology were to be adopted by environmental and public health authorities in these regions, it could provide a quick assessment of the local air pollution burden and potentially support air pollution policies and guidelines.
Time-dependent models of dense PDRs with complex molecules
Morata, O
2008-01-01
We present a study of the chemistry of a dense photon-dominated region (PDR) using a time-dependent chemical model. Our major interest is to study the spatial distribution of complex molecules such as hydrocarbons and cyanopolyynes in the cool dense material bordering regions where star formation has taken place. Our standard model uses a homogeneous cloud of density 2x10e4 cm-3 and temperature T=40 K, which is irradiated by a far-ultraviolet radiation field of intermediate intensity, given by X=100. We find that over a range of times unsaturated hydrocarbons (e.g., C2H, C4H, C3H2) have relatively high fractional abundances in the more external layers of the PDR, whereas their abundances in the innermost layers are several orders of magnitudes lower. On the other hand, molecules that are typical of late-time chemistry are usually more abundant in the inner parts of the PDR. We also present results for models with different density, temperature, intensity of the radiation field and initial fractional abundance...
Spatial dependence of 2MASS luminosity and mass functions in the old open cluster NGC 188
Bonatto, C; Santos, J F C
2005-01-01
Luminosity and mass functions in the old open cluster NGC 188 are analysed by means of J and H 2MASS photometry. Within the uncertainties, the observed projected radial density profile of NGC 188 departs from the two-parameter King model in two inner regions, which reflects the non-virialized dynamical state and possibly, some degree of non-sphericity in the spatial shape of this old open cluster. Fits with two and three-parameter King models to the radial distribution of stars resulted in a core radius of 1.3 pc and a tidal radius of 21 pc. The present 2MASS analysis resulted in significant slope variations with distance in the mass function $\\phi(m)\\propto m^{-(1+\\chi)}$, being flat in the central parts ($\\chi=0.6\\pm0.7$) and steep in the cluster outskirts ($\\chi=7.2\\pm0.6$). The overall mass function has a slope $\\chi=1.9\\pm0.7$, slightly steeper than a standard Salpeter mass function. Solar metallicity Padova isochrone fits to the near-infrared colour-magnitude diagram of NGC 188 resulted in an age of $7....
An alternative to the standard spatial econometric approaches in hedonic house price models
DEFF Research Database (Denmark)
von Graevenitz, Kathrine; Panduro, Toke Emil
2015-01-01
Omitted, misspecified, or mismeasured spatially varying characteristics are a cause for concern in hedonic house price models. Spatial econometrics or spatial fixed effects have become popular ways of addressing these concerns. We discuss the limitations of standard spatial approaches to hedonic...
Integrated hydrologic modeling: Effects of spatial scale, discretization and initialization
Seck, A.; Welty, C.; Maxwell, R. M.
2011-12-01
Groundwater discharge contributes significantly to the annual flows of Chesapeake Bay tributaries and is presumed to contribute to the observed lag time between the implementation of management actions and the environmental response in the Chesapeake Bay. To investigate groundwater fluxes and flow paths and interaction with surface flow, we have developed a fully distributed integrated hydrologic model of the Chesapeake Bay Watershed using ParFlow. Here we present a comparison of model spatial resolution and initialization methods. We have studied the effect of horizontal discretization on overland flow processes at a range of scales. Three nested model domains have been considered: the Monocacy watershed (5600 sq. km), the Potomac watershed (92000 sq. km) and the Chesapeake Bay watershed (400,000 sq. km). Models with homogeneous subsurface and topographically-derived slopes were evaluated at 500-m, 1000-m, 2000-m, and 4000-m grid resolutions. Land surface slopes were derived from resampled DEMs and corrected using stream networks. Simulation results show that the overland flow processes are reasonably well represented with a resolution up to 2000 m. We observe that the effects of horizontal resolution dissipate with larger scale models. Using a homogeneous model that includes subsurface and surface terrain characteristics, we have evaluated various initialization methods for the integrated Monocacy watershed model. This model used several options for water table depths and two rainfall forcing methods including (1) a synthetic rainfall-recession cycle corresponding to the region's average annual rainfall rate, and (2) an initial shut-off of rainfall forcing followed by a rainfall-recession cycling. Results show the dominance of groundwater generated runoff during a first phase of the simulation followed by a convergence towards more balanced runoff generation mechanisms. We observe that the influence of groundwater runoff increases in dissected relief areas
Wachulak, Przemyslaw W; Bartnik, Andrzej; Fiedorowicz, Henryk; Kostecki, Jerzy
2011-05-09
In this paper we report a desk-top microscopy reaching 50 nm spatial resolution in very compact setup using a gas-puff laser plasma EUV source. The thickness of an object and the bandwidth of illuminating radiation were studied in order to estimate their quantitative influence on the EUV microscope spatial resolution. EUV images of various thickness objects obtained by illumination with variable bandwidth EUV radiation were compared in terms of knife-edge spatial resolution to study the bandwidth/object thickness parasitic influence on spatial resolution of the EUV microscope. © 2011 Optical Society of America
NOTE: Spatial dependence of the phase in localized bioelectrical impedance analysis
Shiffman, C. A.; Aaron, R.; Altman, A.
2001-04-01
The variety of phase functions, θ(z) = arctan X(z)/R(z), observed earlier on the thighs of healthy and seriously ill subjects via localized bioelectrical impedance analysis, can be represented by a model which combines realistic thigh shapes with homogeneous, axially symmetric conductivity tensors. While quantitative results depend sensitively on the way current is injected, it appears to be generally true that dθ/dz φz (and vice versa), where φr and φz are the phases of the radial and longitudinal conductivity components.
Spatial Modeling in The Coastal Area of East Java Province
Fadlilah Kurniawati, Ummi
2017-07-01
The existence of gaps that occur between regions, shows that it is a reasonable process considering that each region has different initial endowment factors. The first step that can be done to controll disparity is know what is the benchmark of the gap. The revenue growth indicator is one of benchmark for measuring regional disparities. The regional output is represented by the gross domestic regional income per capita. Concerning the phenomenon of regional disparity, East Java Province is concentrated in the north-south part, especially in coastal areas is an early indication of the gap. This is what prompted the analysis of predictor factors affecting the disparity in East Java Coastal Areas through a spatial modeling approach. Spatial modeling is done on the consideration that there are different local characteristics or potentials in each regency / city. Factors Economic growth, social factors, and physical development factors are the main factors in this study will be described in derived variables to obtain a clear picture of the influence of each factor to the disparity that occurred in the Coastal Region of East Java Province.
Spatial Model of Sky Brightness Magnitude in Langkawi Island, Malaysia
Redzuan Tahar, Mohammad; Kamarudin, Farahana; Umar, Roslan; Khairul Amri Kamarudin, Mohd; Hazmin Sabri, Nor; Ahmad, Karzaman; Rahim, Sobri Abdul; Sharul Aikal Baharim, Mohd
2017-03-01
Sky brightness is an essential topic in the field of astronomy, especially for optical astronomical observations that need very clear and dark sky conditions. This study presents the spatial model of sky brightness magnitude in Langkawi Island, Malaysia. Two types of Sky Quality Meter (SQM) manufactured by Unihedron are used to measure the sky brightness on a moonless night (or when the Moon is below the horizon), when the sky is cloudless and the locations are at least 100 m from the nearest light source. The selected locations are marked by their GPS coordinates. The sky brightness data obtained in this study were interpolated and analyzed using a Geographic Information System (GIS), thus producing a spatial model of sky brightness that clearly shows the dark and bright sky areas in Langkawi Island. Surprisingly, our results show the existence of a few dark sites nearby areas of high human activity. The sky brightness of 21.45 mag arcsec{}-2 in the Johnson-Cousins V-band, as the average of sky brightness equivalent to 2.8 × {10}-4{cd} {{{m}}}-2 over the entire island, is an indication that the island is, overall, still relatively dark. However, the amount of development taking place might reduce the number in the near future as the island is famous as a holiday destination.
Spatial Model of Sky Brightness Magnitude in Langkawi Island, Malaysia
Redzuan Tahar, Mohammad; Kamarudin, Farahana; Umar, Roslan; Khairul Amri Kamarudin, Mohd; Sabri, Nor Hazmin; Ahmad, Karzaman; Rahim, Sobri Abdul; Sharul Aikal Baharim, Mohd
2017-03-01
Sky brightness is an essential topic in the field of astronomy, especially for optical astronomical observations that need very clear and dark sky conditions. This study presents the spatial model of sky brightness magnitude in Langkawi Island, Malaysia. Two types of Sky Quality Meter (SQM) manufactured by Unihedron are used to measure the sky brightness on a moonless night (or when the Moon is below the horizon), when the sky is cloudless and the locations are at least 100 m from the nearest light source. The selected locations are marked by their GPS coordinates. The sky brightness data obtained in this study were interpolated and analyzed using a Geographic Information System (GIS), thus producing a spatial model of sky brightness that clearly shows the dark and bright sky areas in Langkawi Island. Surprisingly, our results show the existence of a few dark sites nearby areas of high human activity. The sky brightness of 21.45 mag arcsec{}-2 in the Johnson-Cousins V-band, as the average of sky brightness equivalent to 2.8 × {10}-4{cd} {{{m}}}-2 over the entire island, is an indication that the island is, overall, still relatively dark. However, the amount of development taking place might reduce the number in the near future as the island is famous as a holiday destination.
Moving from spatially segregated to transparent motion: A modelling approach.
Durant, Szonya; Donoso-Barrera, Alejandra; Tan, Sovira; Johnston, Alan
2006-03-22
Motion transparency, in which patterns of moving elements group together to give the impression of lacy overlapping surfaces, provides an important challenge to models of motion perception. It has been suggested that we perceive transparent motion when the shape of the velocity histogram of the stimulus is bimodal. To investigate this further, random-dot kinematogram motion sequences were created to simulate segregated (perceptually spatially separated) and transparent (perceptually overlapping) motion. The motion sequences were analysed using the multi-channel gradient model (McGM) to obtain the speed and direction at every pixel of each frame of the motion sequences. The velocity histograms obtained were found to be quantitatively similar and all were bimodal. However, the spatial and temporal properties of the velocity field differed between segregated and transparent stimuli. Transparent stimuli produced patches of rightward and leftward motion that varied in location over time. This demonstrates that we can successfully differentiate between these two types of motion on the basis of the time varying local velocity field. However, the percept of motion transparency cannot be based simply on the presence of a bimodal velocity histogram.
Spatially-explicit models of global tree density
Glick, Henry B.; Bettigole, Charlie; Maynard, Daniel S.; Covey, Kristofer R.; Smith, Jeffrey R.; Crowther, Thomas W.
2016-08-01
Remote sensing and geographic analysis of woody vegetation provide means of evaluating the distribution of natural resources, patterns of biodiversity and ecosystem structure, and socio-economic drivers of resource utilization. While these methods bring geographic datasets with global coverage into our day-to-day analytic spheres, many of the studies that rely on these strategies do not capitalize on the extensive collection of existing field data. We present the methods and maps associated with the first spatially-explicit models of global tree density, which relied on over 420,000 forest inventory field plots from around the world. This research is the result of a collaborative effort engaging over 20 scientists and institutions, and capitalizes on an array of analytical strategies. Our spatial data products offer precise estimates of the number of trees at global and biome scales, but should not be used for local-level estimation. At larger scales, these datasets can contribute valuable insight into resource management, ecological modelling efforts, and the quantification of ecosystem services.
Modeling local item dependence with the hierarchical generalized linear model.
Jiao, Hong; Wang, Shudong; Kamata, Akihito
2005-01-01
Local item dependence (LID) can emerge when the test items are nested within common stimuli or item groups. This study proposes a three-level hierarchical generalized linear model (HGLM) to model LID when LID is due to such contextual effects. The proposed three-level HGLM was examined by analyzing simulated data sets and was compared with the Rasch-equivalent two-level HGLM that ignores such a nested structure of test items. The results demonstrated that the proposed model could capture LID and estimate its magnitude. Also, the two-level HGLM resulted in larger mean absolute differences between the true and the estimated item difficulties than those from the proposed three-level HGLM. Furthermore, it was demonstrated that the proposed three-level HGLM estimated the ability distribution variance unaffected by the LID magnitude, while the two-level HGLM with no LID consideration increasingly underestimated the ability variance as the LID magnitude increased.
Royle, J. Andrew; Converse, Sarah J.
2014-01-01
Capture–recapture studies are often conducted on populations that are stratified by space, time or other factors. In this paper, we develop a Bayesian spatial capture–recapture (SCR) modelling framework for stratified populations – when sampling occurs within multiple distinct spatial and temporal strata.We describe a hierarchical model that integrates distinct models for both the spatial encounter history data from capture–recapture sampling, and also for modelling variation in density among strata. We use an implementation of data augmentation to parameterize the model in terms of a latent categorical stratum or group membership variable, which provides a convenient implementation in popular BUGS software packages.We provide an example application to an experimental study involving small-mammal sampling on multiple trapping grids over multiple years, where the main interest is in modelling a treatment effect on population density among the trapping grids.Many capture–recapture studies involve some aspect of spatial or temporal replication that requires some attention to modelling variation among groups or strata. We propose a hierarchical model that allows explicit modelling of group or strata effects. Because the model is formulated for individual encounter histories and is easily implemented in the BUGS language and other free software, it also provides a general framework for modelling individual effects, such as are present in SCR models.
Modelling Spatial Patterns of Vegetation in Desert Sand Dunes
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
A stochastic numerical approach was developed to model the actual standing biomass in the sand dunes of the northwestern Negev (Israel) and probable boundary conditions that may be responsible for the vegetation patterns investigated in detail. Our results for several variables characteristic for the prevailing climate, geomorphology, hydrology and biologicy at four measurement stations along a transect from northwest to southeast allowed for the development of a stochastic model for biomass distribution over the entire sand dune field (mesoscale) and at Nizzana experimental station (microscale). With this equation it was possible to compute andinterpolate a biomass index value for each grid point on the mesoscale and micro scale. The spatial distribution of biomass is negatively linked to distance from the sea, to rainfall and relief energy.
Fundamental Frequency and Model Order Estimation Using Spatial Filtering
DEFF Research Database (Denmark)
Karimian-Azari, Sam; Jensen, Jesper Rindom; Christensen, Mads Græsbøll
2014-01-01
extend this procedure to account for inharmonicity using unconstrained model order estimation. The simulations show that beamforming improves the performance of the joint estimates of fundamental frequency and the number of harmonics in low signal to interference (SIR) levels, and an experiment......In signal processing applications of harmonic-structured signals, estimates of the fundamental frequency and number of harmonics are often necessary. In real scenarios, a desired signal is contaminated by different levels of noise and interferers, which complicate the estimation of the signal...... parameters. In this paper, we present an estimation procedure for harmonic-structured signals in situations with strong interference using spatial filtering, or beamforming. We jointly estimate the fundamental frequency and the constrained model order through the output of the beamformers. Besides that, we...
The role of basolateral amygdala adrenergic receptors in hippocampus dependent spatial memory in rat
Directory of Open Access Journals (Sweden)
Vafaei A.L.
2008-03-01
Full Text Available Background and the purpose of the study: There are extensive evidences indicating that the noradrenergic system of the basolateral nucleus of the amygdala (BLA is involved in memory processes. The present study investigated the role of the BLA adrenergic receptors (ARs in hippocampus dependent spatial memory in place avoidance task in male rat. Material and Methods: Long Evans rats (n=150 were trained to avoid footshock in a 60° segment while foraging for scattered food on a circular (80-cm diameter arena. The rats were injected bilaterally in the BLA specific ARS (Adrenergic receptors agonist norepinephrine (NE, 0.5 and 1 µg/µl and specific β-ARs antagonist propranolol (PRO, 0.5 and 1 µg/µl before acquisition, after training or before retrieval of the place avoidance task. Control rats received vehicle at the same volume. The learning in a single 30-min session was assessed 24h later by a 30-min extinction trial in which the time to first entrance and the number of entrances to the shocked area measured the avoidance memory. Results: Acquisition and consolidation were enhanced and impaired significantly by NE and PRO when the drugs were injected 10 min before or immediately after training, respectively. In contrast, neither NE nor PRO influenced animal performances when injected before retention testing. Conclusion: Findings of this study indicates that adrenergic system of the BLA plays an important role in regulation of memory storage and show further evidences for the opinion that the BLA plays an important role in integrating hormonal and neurotransmitter influences on memory storage.
Simulation of time-dependent Heisenberg models in one dimension
DEFF Research Database (Denmark)
Volosniev, A. G.; Hammer, H. -W.; Zinner, N. T.
2016-01-01
In this Letter, we provide a theoretical analysis of strongly interacting quantum systems confined by a time-dependent external potential in one spatial dimension. We show that such systems can be used to simulate spin chains described by Heisenberg Hamiltonians in which the exchange coupling...
Caillaud, Damien; Crofoot, Margaret C.; Scarpino, Samuel V.; Jansen, Patrick A.; Garzon-Lopez, Carol X.; Winkelhagen, Annemarie J. S.; Bohlman, Stephanie A.; Walsh, Peter D.
2010-01-01
Background: The movement patterns of wild animals depend crucially on the spatial and temporal availability of resources in their habitat. To date, most attempts to model this relationship were forced to rely on simplified assumptions about the spatiotemporal distribution of food resources. Here we
Eicken, H.; Perovich, D. K.; Grenfell, T. C.; Richter-Menge, J. A.; Frey, K.
2001-12-01
Next to ice extent and thickness, the area-averaged albedo of the summer sea-ice cover is a key parameter in determining the large-scale heat exchange over the Arctic Ocean. Various remote sensing applications have yielded a substantial data base for the former two parameters, not least due to the efforts of the National Snow and Ice Data Center (NSIDC) over the past 25 years. In contrast, the spatial and temporal variability of Arctic summer sea-ice albedo is much less well described. Despite its importance (incl. for ice-albedo feedback processes), few if any large-scale sea-ice and global circulation models actually predict summer ice based on the underlying physical processes. Most models employ simple parameterization schemes instead. Remote sensing of surface ice albedo also faces substantial challenges, some of which still need to be addressed in more detail. Here, we report on albedo measurements completed over first- and multi-year sea ice in the summers of 1998, 2000 and 2001 in the North American at the SHEBA drifting ice camp and in fast ice near Barrow, Alaska. As has been established in a number of studies, spatial and temporal variability in summer sea-ice albedo is mostly determined by the areal extent of meltwater ponding at the ice surface. Given the importance of this process, a comprehensive ice hydrological program (meltwater distribution, surface topography, meltwater flow and discharge, ice permeability) has been carried out in conjunction with the optical measurements. Measurements demonstrate that Arctic summer sea-ice albedo is critically dependent on the hydrology of surface melt ponds, as controlled by meltwater production rate, ice permeability and topography. Both, remarkable short-term variability (a reduction of albedo by 43% within two days) as well as the seasonal evolution of the pond fraction and hence area-averaged albedo are forced by changes in pond water level on the order of a few centimeters. While some of these forcing
Spatial Modelling of Solar energy Potential in Kenya
Directory of Open Access Journals (Sweden)
Francis Omondi Oloo
2015-06-01
Full Text Available Solar energy is one of the readily available renewable energy resources in the developing countries within the tropical region. Kenya is one of the countries which receive an average of approximately 6.5 sunshine hours in a single day throughout the year. However, there is slow adoption of solar energy resources in the country due to limited information on the spatial variability solar energy potential. This study aims at assessing the potential of photovoltaic solar energy in Kenya. The factors that influence incident solar radiation which were considered in this task included atmospheric transmissivity and topography. The influence of atmospheric transmissivity was factored in by modelling monthly transmissivity factors from a combination of cloud cover, diffuse ratios and the effect of altitude. The contribution of topography was included by applying hemispherical viewshed analysis to determine the amount of incident global radiation on the surface based on the orientation of the terrain. GIS concepts were used to integrate the spatial datasets from different themes. The results showed that, about 70% of the land area in Kenya has the potential of receiving approximately 5kWh/m2/day throughout the year. In outline, this work successfully assessed the spatio-temporal variability in the characteristics of solar energy potential in Kenya and can be used as a basis for policy support in the country.
Spatial Modeling of Risk in Natural Resource Management
Directory of Open Access Journals (Sweden)
Peter Jones
2002-01-01
Full Text Available Making decisions in natural resource management involves an understanding of the risk and uncertainty of the outcomes, such as crop failure or cattle starvation, and of the normal spread of the expected production. Hedging against poor outcomes often means lack of investment and slow adoption of new methods. At the household level, production instability can have serious effects on income and food security. At the national level, it can have social and economic impacts that may affect all sectors of society. Crop models such as CERES-Maize are excellent tools for assessing weather-related production variability. WATBAL is a water balance model that can provide robust estimates of the potential growing days for a pasture. These models require large quantities of daily weather data that are rarely available. MarkSim is an application for generating synthetic daily weather files by estimating the third-order Markov model parameters from interpolated climate surfaces. The models can then be run for each distinct point on the map. This paper examines the growth of maize and pasture in dryland agriculture in southern Africa. Weather simulators produce independent estimates for each point on the map; however, we know that a spatial coherence of weather exists. We investigated a method of incorporating spatial coherence into MarkSim and show that it increases the variance of production. This means that all of the farmers in a coherent area share poor yields, with important consequences for food security, markets, transport, and shared grazing lands. The long-term aspects of risk are associated with global climate change. We used the results of a Global Circulation Model to extrapolate to the year 2055. We found that low maize yields would become more likely in the marginal areas, whereas they may actually increase in some areas. The same trend was found with pasture growth. We outline areas where further work is required before these tools and methods
Xu, Lei; Johnson, Timothy D.; Nichols, Thomas E.; Nee, Derek E.
2010-01-01
Summary The aim of this work is to develop a spatial model for multi-subject fMRI data. There has been extensive work on univariate modeling of each voxel for single and multi-subject data, some work on spatial modeling of single-subject data, and some recent work on spatial modeling of multi-subject data. However, there has been no work on spatial models that explicitly account for inter-subject variability in activation locations. In this work, we use the idea of activation centers and model the inter-subject variability in activation locations directly. Our model is specified in a Bayesian hierarchical frame work which allows us to draw inferences at all levels: the population level, the individual level and the voxel level. We use Gaussian mixtures for the probability that an individual has a particular activation. This helps answer an important question which is not addressed by any of the previous methods: What proportion of subjects had a significant activity in a given region. Our approach incorporates the unknown number of mixture components into the model as a parameter whose posterior distribution is estimated by reversible jump Markov Chain Monte Carlo. We demonstrate our method with a fMRI study of resolving proactive interference and show dramatically better precision of localization with our method relative to the standard mass-univariate method. Although we are motivated by fMRI data, this model could easily be modified to handle other types of imaging data. PMID:19210732
de la Mata, Tamara; Llano, Carlos
2013-07-01
Recent literature on border effect has fostered research on informal barriers to trade and the role played by network dependencies. In relation to social networks, it has been shown that intensity of trade in goods is positively correlated with migration flows between pairs of countries/regions. In this article, we investigate whether such a relation also holds for interregional trade of services. We also consider whether interregional trade flows in services linked with tourism exhibit spatial and/or social network dependence. Conventional empirical gravity models assume the magnitude of bilateral flows between regions is independent of flows to/from regions located nearby in space, or flows to/from regions related through social/cultural/ethic network connections. With this aim, we provide estimates from a set of gravity models showing evidence of statistically significant spatial and network (demographic) dependence in the bilateral flows of the trade of services considered. The analysis has been applied to the Spanish intra- and interregional monetary flows of services from the accommodation, restaurants and travel agencies for the period 2000-2009, using alternative datasets for the migration stocks and definitions of network effects.
Stationary states and spatial patterning in an SIS epidemiology model with implicit mobility
Ilnytskyi, Jaroslav; Kozitsky, Yuri; Ilnytskyi, Hryhoriy; Haiduchok, Olena
2016-11-01
By means of the asynchronous cellular automata algorithm we study stationary states and spatial patterning in an SIS model, in which the individuals are attached to the vertices of a graph and their mobility is mimicked by varying the neighbourhood size q. Here we consider the following cases: q is fixed at certain value; and q is taken at random at each step and for each individual. The obtained numerical data are then mapped onto the solution of its version, corresponding to the limit q → ∞. This allows for deducing an explicit form of the dependence of the fraction of infected individuals on the curing rate γ. A detailed analysis of the appearance of spatial patterns of infected individuals in the stationary state is performed.
Spatial Segregation, Redistribution and Welfare: A Theoretical Model
Directory of Open Access Journals (Sweden)
Tommaso Gabrieli
2016-03-01
Full Text Available This paper develops a theoretical model focusing on the effect that different neighborhood compositions can have on the formation of individual beliefs about economic opportunities. Specifically we highlight two effects that spatial segregation may have: (1 it can efficiently separate the individual effort choices of highly and low productive individuals, (2 it may imply that the median voter imposes a level of redistribution that is inefficient from the aggregate point of view. The trade-off implies that segregated and non-segregated cities may present very similar levels of aggregate welfare. We employ this framework to discuss how the structure of cities can play a role in the determination of US-type and Europe-type politico-economic equilibria and the implications for planning policies.
Parameter estimation in a spatial unit root autoregressive model
Baran, Sándor
2011-01-01
Spatial autoregressive model $X_{k,\\ell}=\\alpha X_{k-1,\\ell}+\\beta X_{k,\\ell-1}+\\gamma X_{k-1,\\ell-1}+\\epsilon_{k,\\ell}$ is investigated in the unit root case, that is when the parameters are on the boundary of the domain of stability that forms a tetrahedron with vertices $(1,1,-1), \\ (1,-1,1),\\ (-1,1,1)$ and $(-1,-1,-1)$. It is shown that the limiting distribution of the least squares estimator of the parameters is normal and the rate of convergence is $n$ when the parameters are in the faces or on the edges of the tetrahedron, while on the vertices the rate is $n^{3/2}$.
Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals.
Engemann, Denis A; Gramfort, Alexandre
2015-03-01
Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electromagnetic fields induced by post-synaptic neural currents. The estimation of the spatial covariance of the signals recorded on M/EEG sensors is a building block of modern data analysis pipelines. Such covariance estimates are used in brain-computer interfaces (BCI) systems, in nearly all source localization methods for spatial whitening as well as for data covariance estimation in beamformers. The rationale for such models is that the signals can be modeled by a zero mean Gaussian distribution. While maximizing the Gaussian likelihood seems natural, it leads to a covariance estimate known as empirical covariance (EC). It turns out that the EC is a poor estimate of the true covariance when the number of samples is small. To address this issue the estimation needs to be regularized. The most common approach downweights off-diagonal coefficients, while more advanced regularization methods are based on shrinkage techniques or generative models with low rank assumptions: probabilistic PCA (PPCA) and factor analysis (FA). Using cross-validation all of these models can be tuned and compared based on Gaussian likelihood computed on unseen data. We investigated these models on simulations, one electroencephalography (EEG) dataset as well as magnetoencephalography (MEG) datasets from the most common MEG systems. First, our results demonstrate that different models can be the best, depending on the number of samples, heterogeneity of sensor types and noise properties. Second, we show that the models tuned by cross-validation are superior to models with hand-selected regularization. Hence, we propose an automated solution to the often overlooked problem of covariance estimation of M/EEG signals. The relevance of the procedure is demonstrated here for spatial whitening and source localization of MEG signals.
A simple bioclogging model that accounts for spatial spreading of bacteria
Directory of Open Access Journals (Sweden)
Laurent Demaret
2009-04-01
Full Text Available An extension of biobarrier formation and bioclogging models is presented that accounts for spatial expansion of the bacterial population in the soil. The bacteria move into neighboring sites if locally almost all of the available pore space is occupied and the environmental conditions are such that further growth of the bacterial population is sustained. This is described by a density-dependent, double degenerate diffusion-equation that is coupled with the Darcy equations and a transport-reaction equation for growth limiting substrates. We conduct computational simulations of the governing differential equation system.
Alexeeff, Stacey E; Carroll, Raymond J; Coull, Brent
2016-04-01
Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts.
The backbone of a City Information Model (CIM): Implementing a spatial data model for urban design
Gil, J.A.; Almeida, J.; Duarte, J.P.
2011-01-01
We have been witnessing an increased interest in a more holistic approach to urban design practice and education. In this paper we present a spatial data model for urban design that proposes the combination of urban environment feature classes with design process feature classes. This data model is
Modeling the spatial dynamics of regional land use: the clue-s model
Verburg, P.H.; Soepboer, W.; Veldkamp, A.; Limpiada, R.; Espaldon, V.; Mastura, S.S.A.
2002-01-01
Land-use change models are important tools for integrated environmental management. Through scenario analysis they can help to identify near-future critical locations in the face of environmental change. A dynamic, spatially explicit, land-use change model is presented for the regional scale: CLUE-S
The backbone of a City Information Model (CIM): Implementing a spatial data model for urban design
Gil, J.A.; Almeida, J.; Duarte, J.P.
2011-01-01
We have been witnessing an increased interest in a more holistic approach to urban design practice and education. In this paper we present a spatial data model for urban design that proposes the combination of urban environment feature classes with design process feature classes. This data model is
Geo-Scape, a Granularity Depended Spatialization Tool for Visualizing Multidimensional Data Sets
Institute of Scientific and Technical Information of China (English)
Kontaxaki Sofia; Kokla Margarita; Kavouras Marinos
2010-01-01
Recently, the expertise accumulated in the field of geovisualization has found application in the visualization of abstract multidimensional data, on the basis of methods called spatialization methods. Spatialization methods aim at visualizing multidimensional data into low-dimensional representational spaces by making use of spatial metaphors and applying dimension reduction techniques. Spatial metaphors are able to provide a metaphoric framework for the visualization of information at different levels of granularity. The present paper makes an investigation on how the issue of granularity is handled in the context of representative examples of spatialization methods. Furthermore, this paper introduces the prototyping tool Geo-Scape, which provides an interactive spatialization environment for representing and exploring multidimensional data at different levels of granularity, by making use of a kernel density estimation technique and on the landscape "smoothness" metaphor. A demonstration scenario is presented next to show how Geo-Scape helps to discover knowledge into a large set of data, by grouping them into meaningful clusters on the basis ora similarity measure and organizing them at different levels of granularity.
Institute of Scientific and Technical Information of China (English)
Katsuaki Koike
2011-01-01
Sample data in the Earth and environmental sciences are limited in quantity and sampling location and therefore, sophisticated spatial modeling techniques are indispensable for accurate imaging of complicated structures and properties of geomaterials. This paper presents several effective methods that are grouped into two categories depending on the nature of regionalized data used. Type I data originate from plural populations and type II data satisfy the prerequisite of stationarity and have distinct spatial correlations. For the type I data, three methods are shown to be effective and demonstrated to produce plausible results: (1) a spline-based method, (2) a combination of a spline-based method with a stochastic simulation, and (3) a neural network method. Geostatistics proves to be a powerful tool for type II data. Three new approaches of geostatistics are presented with case studies: an application to directional data such as fracture, multi-scale modeling that incorporates a scaling law,and space-time joint analysis for multivariate data. Methods for improving the contribution of such spatial modeling to Earth and environmental sciences are also discussed and future important problems to be solved are summarized.
Aloisio, Jason M; Palmer, Matthew I; Giampieri, Mario A; Tuininga, Amy R; Lewis, James D
2017-01-01
Plant survivorship depends on biotic and abiotic factors that vary at local and regional scales. This survivorship, in turn, has cascading effects on community composition and the physical structure of vegetation. Survivorship of native plant species is variable among populations planted in environmentally stressful habitats like urban roofs, but the degree to which factors at different spatial scales affect survivorship in urban systems is not well understood. We evaluated the effects of biotic and abiotic factors on survivorship, composition, and physical structure of two native perennial species assemblages, one characterized by a mixture of C4 grasses and forbs (Hempstead Plains, HP) and one characterized by a mixture of C3 grasses and forbs (Rocky Summit, RS), that were initially sown at equal ratios of growth forms (5:1:4; grass, N-fixing forb and non-N-fixing forb) in replicate 2-m(2) plots p