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Spatial Point Patterns (spatial + point_pattern)
Selected AbstractsLocal Indicators of Network-Constrained Clusters in Spatial Point PatternsGEOGRAPHICAL ANALYSIS, Issue 3 2007Ikuho Yamada The detection of clustering in a spatial phenomenon of interest is an important issue in spatial pattern analysis. While traditional methods mostly rely on the planar space assumption, many spatial phenomena defy the logic of this assumption. For instance, certain spatial phenomena related to human activities are inherently constrained by a transportation network because of our strong dependence on the transportation system. This article thus introduces an exploratory spatial data analysis method named local indicators of network-constrained clusters (LINCS), for detecting local-scale clustering in a spatial phenomenon that is constrained by a network space. The LINCS method presented here applies to a set of point events distributed over the network space. It is based on the network K -function, which is designed to determine whether an event distribution has a significant clustering tendency with respect to the network space. First, an incremental K -function is developed so as to identify cluster size more explicitly than the original K -function does. Second, to enable identification of cluster locations, a local K -function is derived by decomposing and modifying the original network K -function. The local K -function LINCS, which is referred to as KLINCS, is tested on the distribution of 1997 highway vehicle crashes in the Buffalo, NY area. Also discussed is an adjustment of the KLINCS method for the nonuniformity of the population at risk over the network. As traffic volume can be seen as a surrogate of the population exposed to a risk of vehicle crashes, the spatial distribution of vehicle crashes is examined in relation to that of traffic volumes on the network. The results of the KLINCS analysis are validated through a comparison with priority investigation locations (PILs) designated by the New York State Department of Transportation. [source] Book Review: Statistical Analysis of Spatial Point Patterns.BIOMETRICAL JOURNAL, Issue 3 2005By Peter J. Diggle No abstract is available for this article. [source] Nonparametric One-way Analysis of Variance of Replicated Bivariate Spatial Point PatternsBIOMETRICAL JOURNAL, Issue 1 2004Sabine Landau Abstract A common problem in neuropathological studies is to assess the spatial patterning of cells on tissue sections and to compare spatial patterning between disorder groups. For a single cell type, the cell positions constitute a univariate point process and interest focuses on the degree of spatial aggregation. For two different cell types, the cell positions constitute a bivariate point process and the degree of spatial interaction between the cell types is of interest. We discuss the problem of analysing univariate and bivariate spatial point patterns in the one-way design where cell patterns have been obtained for groups of subjects. A bootstrapping procedure to perform a nonparametric one-way analysis of variance of the spatial aggregation of a univariate point process has been suggested by Diggle, Lange and Bene, (1991). We extend their replication-based approach to allow the comparison of the spatial interaction of two cell types between groups, to include planned comparisons (contrasts) and to assess whole groups against complete spatial randomness and spatial independence. We also accommodate several replicate tissue sections per subject. An advantage of our approach is that it can be applied when processes are not stationary, a common problem in brain tissue sections since neurons are arranged in cortical layers. We illustrate our methods by applying them to a neuropathological study to investigate abnormalities in the functional relationship between neurons and astrocytes in HIV associated dementia. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Statistical Analysis and Modelling of Spatial Point Patterns by ILLIAN, J., PENTTINEN, A., STOYAN, H., and STOYAN, D.BIOMETRICS, Issue 3 2009Jesper MøllerArticle first published online: 14 SEP 200 No abstract is available for this article. [source] Spatial point-process statistics: concepts and application to the analysis of lead contamination in urban soil,ENVIRONMETRICS, Issue 4 2005Christian Walter Abstract This article explores the use of spatial point-process analysis as an aid to describe topsoil lead distribution in urban environments. The data used were collected in Glebe, an inner suburb of Sydney. The approach focuses on the locations of punctual events defining a point pattern, which can be statistically described through local intensity estimates and between-point distance functions. F -, G - and K -surfaces of a marked spatial point pattern were described and used to estimate nearest distance functions over a sliding band of quantiles belonging to the marking variable. This provided a continuous view of the point pattern properties as a function of the marking variable. Several random fields were simulated by selecting points from random, clustered or regular point processes and diffusing them. Recognition of the underlying point process using variograms derived from dense sampling was difficult because, structurally, the variograms were very similar. Point-event distance functions were useful complimentary tools that, in most cases, enabled clear recognition of the clustered processes. Spatial sampling quantile point pattern analysis was defined and applied to the Glebe data set. The analysis showed that the highest lead concentrations were strongly clustered. The comparison of this data set with the simulation confidence limits of a Poisson process, a short-radius clustered point process and a geostatistical simulation showed a random process for the third quartile of lead concentrations but strong clustering for the data in the upper quartile. Thus the distribution of topsoil lead concentrations over Glebe may have resulted from several contamination processes, mainly from regular or random processes with large diffusion ranges and short-range clustered processes for the hot spots. Point patterns with the same characteristics as the Glebe experimental pattern could be generated by separate additive geostatistical simulation. Spatial sampling quantile point patterns statistics can, in an easy and accurate way, be used complementarily with geostatistical methods. Copyright © 2005 John Wiley & Sons, Ltd. [source] Multigenerational analysis of spatial structure in the terrestrial, food-deceptive orchid Orchis masculaJOURNAL OF ECOLOGY, Issue 2 2009Hans Jacquemyn Summary 1In long-lived, terrestrial orchids, strong aggregation of adults and recruits within populations and pronounced spatial association between recruits and adults can be expected when seed dispersal is limited, probabilities of seed germination decrease with increasing distance from mother plants and/or not all mother plants contribute to future generations. When individuals are distributed evenly across life-history stages, these processes can also be expected to result in a significant fine-scale spatial genetic structure in recruits that will persist into the adult-stage class. 2We combined detailed spatial genetic and point pattern analyses across different generations with parentage analyses to elucidate the role of the diverse processes that might determine spatial structure in Orchis mascula. 3Analyses of spatial point patterns showed a significant association between adults and recruits and similar clustering patterns for both. Weak, but highly significant spatial genetic structure was observed in adults and recruits, but no significant differences were observed across life stages, indicating that the spatial genetic structure present in recruits persists into the adult stage. 4Parentage analyses highlighted relatively short seed dispersal distances (median offspring-recruitment distance: 1.55 and 1.70 m) and differential contribution of mother plants to future generations. 5Persistence of fine-scale spatial genetic structure from seedlings into the adult stage class is consistent with the life history of O. mascula, whereas relatively large dispersal distances of both pollen and seeds compared to the fine-scale clustering of adults and seedlings suggest overlapping seed shadows and mixing of genotypes within populations as the major factors explaining the observed weak spatial genetic structure. 6Nonetheless, comparison of the spatial association between recruits and adults with the genetic analysis of offspring-parent distances suggests that the tight clustering of recruits around adults was probably caused by decreasing probabilities of seed germination with increasing distance from mother plants. 7Synthesis. This study shows that the approach presented here, which combines spatial genetic and spatial pattern analyses with parentage analyses, may be broadly applied to other plant species to elucidate the processes that determine spatial structure within their populations. [source] Nonparametric One-way Analysis of Variance of Replicated Bivariate Spatial Point PatternsBIOMETRICAL JOURNAL, Issue 1 2004Sabine Landau Abstract A common problem in neuropathological studies is to assess the spatial patterning of cells on tissue sections and to compare spatial patterning between disorder groups. For a single cell type, the cell positions constitute a univariate point process and interest focuses on the degree of spatial aggregation. For two different cell types, the cell positions constitute a bivariate point process and the degree of spatial interaction between the cell types is of interest. We discuss the problem of analysing univariate and bivariate spatial point patterns in the one-way design where cell patterns have been obtained for groups of subjects. A bootstrapping procedure to perform a nonparametric one-way analysis of variance of the spatial aggregation of a univariate point process has been suggested by Diggle, Lange and Bene, (1991). We extend their replication-based approach to allow the comparison of the spatial interaction of two cell types between groups, to include planned comparisons (contrasts) and to assess whole groups against complete spatial randomness and spatial independence. We also accommodate several replicate tissue sections per subject. An advantage of our approach is that it can be applied when processes are not stationary, a common problem in brain tissue sections since neurons are arranged in cortical layers. We illustrate our methods by applying them to a neuropathological study to investigate abnormalities in the functional relationship between neurons and astrocytes in HIV associated dementia. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] | ||||||||||