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Local Spatial Autocorrelation (local + spatial_autocorrelation)
Selected AbstractsTesting for Local Spatial Autocorrelation in the Presence of Global AutocorrelationJOURNAL OF REGIONAL SCIENCE, Issue 3 2001J. Keith Ord A fundamental concern of spatial analysts is to find patterns in spatial data that lead to the identification of spatial autocorrelation or association. Further, they seek to identify peculiarities in the data set that signify that something out of the ordinary has occurred in one or more regions. In this paper we provide a statistic that tests for local spatial autocorrelation in the presence of the global autocorrelation that is characteristic of heterogeneous spatial data. After identifying the structure of global autocorrelation, we introduce a new measure that may be used to test for local structure. This new statistic Oi is asymptotically normally distributed and allows for straightforward tests of hypotheses. We provide several numerical examples that illustrate the performance of this statistic and compare it with another measure that does not account for global structure. [source] GeoDa: An Introduction to Spatial Data AnalysisGEOGRAPHICAL ANALYSIS, Issue 1 2006Luc Anselin This article presents an overview of GeoDaÔ, a free software program intended to serve as a user-friendly and graphical introduction to spatial analysis for non-geographic information systems (GIS) specialists. It includes functionality ranging from simple mapping to exploratory data analysis, the visualization of global and local spatial autocorrelation, and spatial regression. A key feature of GeoDa is an interactive environment that combines maps with statistical graphics, using the technology of dynamically linked windows. A brief review of the software design is given, as well as some illustrative examples that highlight distinctive features of the program in applications dealing with public health, economic development, real estate analysis, and criminology. [source] Testing for Local Spatial Autocorrelation in the Presence of Global AutocorrelationJOURNAL OF REGIONAL SCIENCE, Issue 3 2001J. Keith Ord A fundamental concern of spatial analysts is to find patterns in spatial data that lead to the identification of spatial autocorrelation or association. Further, they seek to identify peculiarities in the data set that signify that something out of the ordinary has occurred in one or more regions. In this paper we provide a statistic that tests for local spatial autocorrelation in the presence of the global autocorrelation that is characteristic of heterogeneous spatial data. After identifying the structure of global autocorrelation, we introduce a new measure that may be used to test for local structure. This new statistic Oi is asymptotically normally distributed and allows for straightforward tests of hypotheses. We provide several numerical examples that illustrate the performance of this statistic and compare it with another measure that does not account for global structure. [source] Exploratory spatial data analysis of the distribution of regional per capita GDP in Europe, 1980,1995PAPERS IN REGIONAL SCIENCE, Issue 2 2003Julie Le Gallo European Union; exploratory spatial data analysis; regional disparities; spatial autocorrelation; spatial heterogeneity Abstract. The aim of this paper is to study the space-time dynamics of European regional per capital GDP. A sample of 138 European regions over the 1980,1995 period provides clear evidence of global and local spatial autocorrelation as well as spatial heterogeneity in the distribution of regional per capita GDP. The detection of spatial clusters of high and low per capita GDP throughout the period is an indication of the persistence of spatial disparities among European regions. The dynamism of European regions is investigated by exploring the spatial pattern of regional growth. Implications for applied econometric work on the convergence of European regions are then suggested. [source] Confidence Intervals for Relative Risks in Disease MappingBIOMETRICAL JOURNAL, Issue 4 2003M.D. Ugarte Abstract Several analysis of the geographic variation of mortality rates in space have been proposed in the literature. Poisson models allowing the incorporation of random effects to model extra-variability are widely used. The typical modelling approach uses normal random effects to accommodate local spatial autocorrelation. When spatial autocorrelation is absent but overdispersion persists, a discrete mixture model is an alternative approach. However, a technique for identifying regions which have significant high or low risk in any given area has not been developed yet when using the discrete mixture model. Taking into account the importance that this information provides to the epidemiologists to formulate hypothesis related to the potential risk factors affecting the population, different procedures for obtaining confidence intervals for relative risks are derived in this paper. These methods are the standard information-based method and other four, all based on bootstrap techniques, namely the asymptotic-bootstrap, the percentile-bootstrap, the BC-bootstrap and the modified information-based method. All of them are compared empirically by their application to mortality data due to cardiovascular diseases in women from Navarra, Spain, during the period 1988,1994. In the small area example considered here, we find that the information-based method is sensible at estimating standard errors of the component means in the discrete mixture model but it is not appropriate for providing standard errors of the estimated relative risks and hence, for constructing confidence intervals for the relative risk associated to each region. Therefore, the bootstrap-based methods are recommended for this matter. More specifically, the BC method seems to provide better coverage probabilities in the case studied, according to a small scale simulation study that has been carried out using a scenario as encountered in the analysis of the real data. [source] | ||||||||||