Showing papers on "Spatial analysis published in 2009"

Method, system, and program for an improved enterprise spatial system

Abstract: Disclosed is a method, system, and program for providing access to spatial data. A request for data is received. Enterprise and third party data are integrated. The integrated data is processed. Spatially referenced results are generated using the processed data. The spatially referenced results are returned in response to the request.

Ecological information from spatial patterns of plants: insights from point process theory

Abstract: Summary 1. This article reviews the application of some summary statistics from current theory of spatial point processes for extracting information from spatial patterns of plants. Theoretical measures and issues connected with their estimation are described. Results are illustrated in the context of specific ecological questions about spatial patterns of trees in two forests. 2. The pair correlation function, related to Ripley’s K function, provides a formal measure of the density of neighbouring plants and makes precise the general notion of a ‘plant’s-eye’ view of a community. The pair correlation function can also be used to describe spatial relationships of neighbouring plants with different qualitative properties, such as species identity and size class. 3. The mark correlation function can be used to describe the spatial relationships of quantitative measures (e.g. biomass). We discuss two types of correlation function for quantitative marks. Applying these functions to the distribution of biomass in a temperate forest, it is shown that the spatial pattern of biomass is uncoupled from the spatial pattern of plant locations. 4. The inhomogeneous pair correlation function enables first-order heterogeneity in the environment to be removed from second-order spatial statistics. We illustrate this for a tree species in a forest of high topographic heterogeneity and show that spatial aggregation remains after allowing for spatial variation in density. An alternative method, the master function, takes a weighted average of homogeneous pair correlation functions computed in subareas; when applied to the same data and compared with the former method, the spatial aggregations are smaller in size. 5. Synthesis. These spatial statistics, especially those derived from pair densities, will help ecologists to extract important ecological information from intricate spatially correlated plants in populations and communities.

The Sage handbook of spatial analysis

Abstract: Introduction - Stewart Fotheringham and Peter A Rogerson The Special Nature of Spatial Data - Robert Haining The Role of GIS - David Martin Geovisualisation and Geovisual Analytics - Urska Demsar Availability of Spatial Data Mining Techniques - Shashi Shekhar et al Spatial Autocorrelation - Marie-Jose Fortin and Mark R T Dale The Modifiable Areal Unit Problem (MAUP) - David Wong Spatial Weights - Robin Dubin Geostatistics and Spatial Interpolation - Peter M Atkinson and Christopher D Lloyd Spatial Sampling - Eric Delmelle Statistical Inference for Geographical Processes - Chris Brunsdon Fuzzy Sets in Spatial Analysis - Vincent B Robinson Geographically Weighted Regression - Stewart Fotheringham Spatial Regression - Luc Anselin Spatial Microsimulation - D Ballas and G P Clarke Detection of Clustering in Spatial Data - Lance Waller Bayesian Spatial Analysis - Andrew B Lawson and Sudipto Banerjee Monitoring Changes in Spatial Patterns - Peter A Rogerson Case-Control Clustering for Mobile Populations - Geoffrey M Jacquez and Jaymie R Meliker Neural Networks for Spatial Data Analysis - Manfred M Fischer Geocomputation - Harvey J Miller Applied Retail Location Models Using Spatial Interaction Tools - Morton E O'Kelly Spatial Analysis on a Network - Atsuyuki Okabe and Toshiaki Satoh Challenges in Spatial Analysis - Michael F Goodchild The Future for Spatial Analysis - Reginald G Golledge

Bayesian inference in camera trapping studies for a class of spatial capture–recapture models

Abstract: We develop a class of models for inference about abundance or density using spatial capture-recapture data from studies based on camera trapping and related methods. The model is a hierarchical model composed of two components: a point process model describing the distribution of individuals in space (or their home range centers) and a model describing the observation of individuals in traps. We suppose that trap- and individual-specific capture probabilities are a function of distance between individual home range centers and trap locations. We show that the models can be regarded as generalized linear mixed models, where the individual home range centers are random effects. We adopt a Bayesian framework for inference under these models using a formulation based on data augmentation. We apply the models to camera trapping data on tigers from the Nagarahole Reserve, India, collected over 48 nights in 2006. For this study, 120 camera locations were used, but cameras were only operational at 30 locations during any given sample occasion. Movement of traps is common in many camera-trapping studies and represents an important feature of the observation model that we address explicitly in our application.

LinkedGeoData: Adding a Spatial Dimension to the Web of Data

Abstract: In order to employ the Web as a medium for data and information integration, comprehensive datasets and vocabularies are required as they enable the disambiguation and alignment of other data and information. Many real-life information integration and aggregation tasks are impossible without comprehensive background knowledge related to spatial features of the ways, structures and landscapes surrounding us. In this paper we contribute to the generation of a spatial dimension for the Data Web by elaborating on how the collaboratively collected OpenStreetMap data can be transformed and represented adhering to the RDF data model. We describe how this data can be interlinked with other spatial data sets, how it can be made accessible for machines according to the linked data paradigm and for humans by means of a faceted geo-data browser.

Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression

Abstract: A major focus of geographical ecology and macroecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regression, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modeling is used. However, the extent to which coefficients may shift and why shifts occur are unclear. Here, we analyze the relationship between environmental predictors and the geographical distribution of species richness, body size, range size and abundance in 97 multi-factorial data sets. Our goal was to compare standardized partial regression coefficients of non-spatial ordinary least squares regressions (i.e. models fitted using ordinary least squares without taking autocorrelation into account; "OLS models" hereafter) and eight spatial methods to evaluate the frequency of coefficient shifts and identify characteristics of data that might predict when shifts are likely. We generated three metrics of coefficient shifts and eight characteristics of the data sets as predictors of shifts. Typical of ecological data, spatial autocorrelation in the residuals of OLS models was found in most data sets. The spatial models varied in the extent to which they minimized residual spatial autocorrelation. Patterns of coefficient shifts also varied among methods and datasets, although the magnitudes of shifts tended to be small in all cases. We were unable to identify strong predictors of shifts, including the levels of autocorrelation in either explanatory variables or model residuals. Thus, changes in coefficients between spatial and non-spatial methods depend on the method used and are largely idiosyncratic, making it difficult to predict when or why shifts occur. We conclude that the ecological importance of regression coefficients cannot be evaluated with confidence irrespective of whether spatially explicit modelling is used or not. Researchers may have little choice but to be more explicit about the uncertainty of models and more cautious in their interpretation.

Modeling Spatial Variations of Urban Growth Patterns in Chinese Cities: The Case of Nanjing

Abstract: Revealing spatially varying relationships between urban growth patterns and underlying determinants is important for better understanding local dimensions of urban development. Through a case study of Nanjing, China, we employ both global and local logistic regressions to model the probability of urban land expansion against a set of spatial variables. We found that compared with other fast growing coastal cities, Nanjing remains a relatively compact city. The orthodox logistic regression found the significance of proximity, neighborhood conditions, and urban agglomeration in urban land change. The logistic GWR significantly improves the global logistic regression model in terms of better model goodness-of-fit and lower level of spatial autocorrelation of residuals. More importantly, the local estimates of parameters of spatial variables enable us to investigate spatial variations of the influences of spatial variables on urban growth. We have found distinctive local patterns and effects of urban growth in Nanjing, shaped by local urban spatial and institutional structures. A probability surface of urban growth, which is generated from raster calculations among the parameter and variable surfaces, provides a clear scenario of urban growth patterns and can be useful for decision making. This study also shows the importance of policy studies and fieldwork in the interpretation of results generated from statistical and GIS modeling.

Spatial Preprocessing for Endmember Extraction

Abstract: Endmember extraction is the process of selecting a collection of pure signature spectra of the materials present in a remotely sensed hyperspectral scene. These pure signatures are then used to decompose the scene into abundance fractions by means of a spectral unmixing algorithm. Most techniques available in the endmember extraction literature rely on exploiting the spectral properties of the data alone. As a result, the search for endmembers in a scene is conducted by treating the data as a collection of spectral measurements with no spatial arrangement. In this paper, we propose a novel strategy to incorporate spatial information into the traditional spectral-based endmember search process. Specifically, we propose to estimate, for each pixel vector, a scalar spatially derived factor that relates to the spectral similarity of pixels lying within a certain spatial neighborhood. This scalar value is then used to weigh the importance of the spectral information associated to each pixel in terms of its spatial context. Two key aspects of the proposed methodology are given as follows: 1) No modification of existing image spectral-based endmember extraction methods is necessary in order to apply the proposed approach. 2) The proposed preprocessing method enhances the search for image spectral endmembers in spatially homogeneous areas. Our experimental results, which were obtained using both synthetic and real hyperspectral data sets, indicate that the spectral endmembers obtained after spatial preprocessing can be used to accurately model the original hyperspectral scene using a linear mixture model. The proposed approach is suitable for jointly combining spectral and spatial information when searching for image-derived endmembers in highly representative hyperspectral image data sets.

Spatial variance and spatial skewness: leading indicators of regime shifts in spatial ecological systems.

Abstract: Ecosystems can undergo large-scale changes in their states, known as catastrophic regime shifts, leading to substantial losses to services they provide to humans. These shifts occur rapidly and are difficult to predict. Several early warning signals of such transitions have recently been developed using simple models. These studies typically ignore spatial interactions, and the signal provided by these indicators may be ambiguous. We employ a simple model of collapse of vegetation in one and two spatial dimensions and show, using analytic and numerical studies, that increases in spatial variance and changes in spatial skewness occur as one approaches the threshold of vegetation collapse. We identify a novel feature, an increasing spatial variance in conjunction with a peaking of spatial skewness, as an unambiguous indicator of an impending regime shift. Once a signal has been detected, we show that a quick management action reducing the grazing activity is needed to prevent the collapse of vegetated state. Our results show that the difficulties in obtaining the accurate estimates of indicators arising due to lack of long temporal data can be alleviated when high-resolution spatially extended data are available. These results are shown to hold true independent of various details of model or different spatial dispersal kernels such as Gaussian or heavily fat tailed. This study suggests that spatial data and monitoring multiple indicators of regime shifts can play a key role in making reliable predictions on ecosystem stability and resilience.

Statistics for spatial functional data: some recent contributions

Abstract: Functional data analysis (FDA) is a relatively new branch in statistics. Experiments where a complete function is observed for each individual give rise to functional data. In this work we focus on the case of functional data presenting spatial dependence. The three classic types of spatial data structures (geostatistical data, point patterns, and areal data) can be combined with functional data as it is shown in the examples of each situation provided here. We also review some contributions in the literature on spatial functional data. Copyright © 2009 John Wiley & Sons, Ltd.

Experiences on Processing Spatial Data with MapReduce

Abstract: The amount of information in spatial databases is growing as more data is made available. Spatial databases mainly store two types of data: raster data (satellite/aerial digital images), and vector data (points, lines, polygons). The complexity and nature of spatial databases makes them ideal for applying parallel processing. MapReduce is an emerging massively parallel computing model, proposed by Google. In this work, we present our experiences in applying the MapReduce model to solve two important spatial problems: (a) bulk-construction of R-Trees and (b) aerial image quality computation, which involve vector and raster data, respectively. We present our results on the scalability of MapReduce, and the effect of parallelism on the quality of the results. Our algorithms were executed on a Google&IBM cluster, which became available to us through an NSF-supported program. The cluster supports the Hadoop framework --- an open source implementation of MapReduce. Our results confirm the excellent scalability of the MapReduce framework in processing parallelizable problems.

Forest Type Mapping using Object-specific Texture Measures from Multispectral Ikonos Imagery: Segmentation Quality and Image Classification Issues

Abstract: This study investigated the use of a geographic object-based image analysis (GEOBIA) approach with the incorporation of object-specific grey-level co-occurrence matrix (GLCM) texture measures from a multispectral Ikonos image for delineation of deciduous, evergreen, and mixed forest types in Guilford Courthouse National Military Park, North Carolina. A series of automated segmentations was produced at a range of scales, each resulting in an associated range of number and size of objects (or segments). Prior to classification, the spatial autocorrelation of each segmentation was evaluated by calculating Moran’s I using the average image digital numbers (DNs) per segment. An initial assumption was made that the optimal segmentation scales would have the lowest spatial autocorrelation, and conversely, that over- and under-segmentation would result in higher autocorrelation between segments. At these optimal segmentation scales, the automated segmentation was found to yield information comparable to manually interpreted stand-level forest maps in terms of the size and number of segments. A series of object-based classifications was carried out on the image at the entire range of segmentation scales. The results demonstrated that the scale of segmentation directly influenced the object-based forest type classification results. The accuracies were higher for classification of images identified from a spatial autocorrelation analysis to have an optimal segmentation, compared to those determined to have over- and under-segmentation. An overall accuracy of 79 percent with a Kappa of 0.65 was obtained at the optimal segmentation scale of 19. The addition of object-specific GLCM multiple texture analysis improved classification accuracies up to a value of 83 percent overall accuracy and a Kappa of 0.71 by reducing the confusion between evergreen and mixed forest types. Although some misclassification still remained because of local segmentation quality, a visual assessment of the texture-enhanced GEOBIA classification generally agreeable with manually interpreted forest types.

Spatiotemporal analysis of rural-urban land conversion

Abstract: Understanding the complexity of urban expansion requires an analysis of the factors influencing the spatial and temporal processes of rural-urban land conversion. This study aims at building a statistical land conversion model to assist in understanding land use change patterns. Specifically, GIS coupled with a logistic regression model and exponential smoothing techniques is used for exploring the effects of various factors on land use change. These factors include population density, slope, proximity to roads, and surrounding land use, and their influence on land use change is studied for generating a predictive model. Methods to reduce spatial autocorrelation in a logistic regression framework are also discussed. Primarily, an optimal sampling scheme that can eliminate spatial autocorrelation while maintaining adequate samples to allow the model to achieve the comparable accuracy as the spatial autoregressive model is developed. Since many of the previous studies on modeling the spatial complexity of urban growth ignored temporal complexity, a modified exponential smoothing technique is employed to produce a smoothed model from a series of bi-temporal models obtained from different time periods. The proposed model is validated using the multi-temporal land use data in New Castle County, DE, USA. It is demonstrated that our approach provides an effective option for multi-temporal land use change modeling and the modeling results help interpret the land use change patterns.

Hierarchical Gaussianization for image classification

Abstract: In this paper, we propose a new image representation to capture both the appearance and spatial information for image classification applications First, we model the feature vectors, from the whole corpus, from each image and at each individual patch, in a Bayesian hierarchical framework using mixtures of Gaussians After such a hierarchical Gaussianization, each image is represented by a Gaussian mixture model (GMM) for its appearance, and several Gaussian maps for its spatial layout Then we extract the appearance information from the GMM parameters, and the spatial information from global and local statistics over Gaussian maps Finally, we employ a supervised dimension reduction technique called DAP (discriminant attribute projection) to remove noise directions and to further enhance the discriminating power of our representation We justify that the traditional histogram representation and the spatial pyramid matching are special cases of our hierarchical Gaussianization We compare our new representation with other approaches in scene classification, object recognition and face recognition, and our performance ranks among the top in all three tasks

Machine Learning for Spatial Environmental Data: Theory, Applications, and Software

Abstract: PREFACE LEARNING FROM GEOSPATIAL DATA Problems and important concepts of machine learning Machine learning algorithms for geospatial data Contents of the book Software description Short review of the literature EXPLORATORY SPATIAL DATA ANALYSIS PRESENTATION OF DATA AND CASE STUDIES Exploratory spatial data analysis Data pre-processing Spatial correlations: Variography Presentation of data k-Nearest neighbours algorithm: a benchmark model for regression and classification Conclusions to chapter GEOSTATISTICS Spatial predictions Geostatistical conditional simulations Spatial classification Software Conclusions ARTIFICIAL NEURAL NETWORKS Introduction Radial basis function neural networks General regression neural networks Probabilistic neural networks Self-organising maps Gaussian mixture models and mixture density network Conclusions SUPPORT VECTOR MACHINES AND KERNEL METHODS Introduction to statistical learning theory Support vector classification Spatial data classification with SVM Support vector regression Advanced topics in kernel methods REFERENCES INDEX

Spatial Weights Matrices

Abstract: The impetus for Cliff and Ord's landmark article, “The Problem of Spatial Autocorrelation,” was to avoid the problem of topological invariance in the spatial weights matrix. The problem and three points of view on it – theoretical, topological, and empirical – are discussed.

Using choice experiments to explore the spatial distribution of willingness to pay for rural landscape improvements

Abstract: We report findings from a choice experiment survey designed to estimate the economic benefits of policy measures to improve the rural landscape in the Republic of Ireland. Using a panel mixed logit specification to account for unobserved taste heterogeneity we derived individual-specific willingness-to-pay (WTP) estimates for each respondent in the sample. We subsequently investigated the spatial dependence of these estimates. Results suggest the existence of positive spatial autocorrelation for all rural landscape attributes. As a means of benefit transfer, kriging methods were employed to interpolate WTP estimates across the whole of the Republic of Ireland. The kriged WTP surfaces confirm the existence of spatial dependence and illustrate the implied spatial variation and regional disparities in WTP for all the rural landscape improvements investigated.

Assessing the scale-specific importance of niches and other spatial processes on beta diversity: a case study from a temperate forest

Abstract: Niche processes and other spatial processes, such as dispersal, may simultaneously control beta diversity, yet their relative importance may shift across spatial and temporal scales. Although disentangling the relative importance of these processes has been a continuing methodological challenge, recent developments in multi-scale spatial and temporal modeling can now help ecologists estimate their scale-specific contributions. Here we present a statistical approach to (1) detect the presence of a space–time interaction on community composition and (2) estimate the scale-specific importance of environmental and spatial factors on beta diversity. To illustrate the applicability of this approach, we use a case study from a temperate forest understory where tree seedling abundances were monitored during a 9-year period at 40 permanent plots. We found no significant space–time interaction on tree seedling composition, which means that the spatial abundance patterns did not vary over the study period. However, for a given year the relative importance of niche processes and other spatial processes was found to be scale-specific. Tree seedling abundances were primarily controlled by a broad-scale environmental gradient, but within the confines of this gradient the finer scale patchiness was largely due to other spatial processes. This case study illustrates that these two sets of processes are not mutually exclusive and can affect abundance patterns in a scale-dependent manner. More importantly, the use of our methodology for future empirical studies should help in the merging of niche and neutral perspectives on beta diversity, an obvious next step for community ecology.

Integrated water resource development plan for sustainable management of Mayurakshi watershed, India using remote sensing and GIS.

Abstract: Integrated watershed management requires a host of inter-related information to be generated and studied in relation to each other. Remote sensing technique provides valuable and up-to-date spatial information on natural resources and physical terrain parameters. Geographical Information System (GIS) with its capability of integration and analysis of spatial, aspatial, multi-layered information obtained in a wide variety of formats both from remote sensing and other conventional sources has proved to be an effective tool in planning for watershed development. In this study, area and locale specific watershed development plans were generated for Mayurakshi watershed, India using remote sensing and GIS techniques. Adopting Integrated Mission for Sustainable Development (IMSD) guidelines, decision rules were framed. Using the overlay and decision tree concepts water resource development plan was generated. Indian Remote Sensing Satellite (IRS-1C), Linear Imaging Self Scanner (LISS-III) satellite data along with other field and collateral data on lithology, soil, slope, well inventory, fracture have been utilized for generating land use/land cover and hydro geomorphology of the study area, which are an essential prerequisites for water resources planning and development. Spatial data integration and analyses are carried out in GIS environment.

Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006

Abstract: Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions These analytical approaches can be used to not only identify the location of such hotspots, but also their spatial patterns In this study, we utilize spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters, and areas in which these are situated, for the 20 leading causes of death in Taiwan In addition, we use the fit to a logistic regression model to test the characteristics of similarity and dissimilarity by gender Gender is compared in efforts to formulate the common spatial risk The mean found by local spatial autocorrelation analysis is utilized to identify spatial cluster patterns There is naturally great interest in discovering the relationship between the leading causes of death and well-documented spatial risk factors For example, in Taiwan, we found the geographical distribution of clusters where there is a prevalence of tuberculosis to closely correspond to the location of aboriginal townships Cluster mapping helps to clarify issues such as the spatial aspects of both internal and external correlations for leading health care events This is of great aid in assessing spatial risk factors, which in turn facilitates the planning of the most advantageous types of health care policies and implementation of effective health care services

A Composite Semisupervised SVM for Classification of Hyperspectral Images

Abstract: This letter presents a novel composite semisupervised support vector machine (SVM) for the spectral-spatial classification of hyperspectral images. In particular, the proposed technique exploits the following: 1) unlabeled data for increasing the reliability of the training phase when few training samples are available and 2) composite kernel functions for simultaneously taking into account spectral and spatial information included in the considered image. Experiments carried out on a hyperspectral image pointed out the effectiveness of the presented technique, which resulted in a significant increase of the classification accuracy with respect to both supervised SVMs and progressive semisupervised SVMs with single kernels, as well as supervised SVMs with composite kernels.