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A statistical property of geographical data whereby similar values occur near each other. It is used in GIS to identify patterns of clustering or dispersion of spatial attributes.

A class of statistics used to analyze and predict the values associated with spatial or spatiotemporal phenomena. It's applied in GIS for surface modeling and generating interpolated surfaces through kriging.

A tool in geostatistics representing the difference in data value variance as a function of spatial distance. Variograms are crucial in GIS for understanding spatial dependence and for modeling in kriging.

An analytical tool in GIS that creates a zone of a specified distance around a feature. It is used to evaluate impacts on nearby areas, conduct proximity analysis, and define zones of influence.

A measure of spatial autocorrelation, quantifying the degree to which similar values are clustered in space. In GIS, Moran's I is used to detect spatial patterns, identify clusters, and test for randomness.

A spatial statistical technique to identify areas that have a statistically significant higher concentration of a particular attribute, typically using Getis-Ord Gi*. It is applied in GIS to visualize and analyze clustering of events or attributes.

The process of estimating unknown spatial variables using a model derived from known sample data points. GIS uses spatial interpolation for creating continuous surfaces, such as elevation or pollution levels, from point data.

An advanced geostatistical method for spatial interpolation that takes into account spatial autocorrelation. Kriging provides predictions with minimized variance in GIS, often used for creating surfaces like elevation or precipitation.

A type of deterministic spatial interpolation method in GIS where values are estimated based on the weighted average of nearby known values, with weights inversely related to the distance from the prediction location.

Statistical techniques used in GIS to model spatially dependent relationships between variables, accounting for spatial autocorrelation and spatial heterogeneity, often leading to better predictive models.

A local indicator of spatial association (LISA) used to identify spatial clusters of high or low values. It is incorporated in GIS hot spot analysis to discern locations of statistically significant spatial clusters.

A set of techniques in GIS used to study the spatial arrangement of points, which can reveal underlying spatial structures, processes, or trends. Applicable to crime mapping, epidemiology, and urban planning.

GIS operations that calculate statistics on values of a raster within the zones of another dataset, typically a vector polygon layer. Useful for summarizing environmental conditions within administrative boundaries or habitats.

A form of spatial regression that allows local rather than global parameters to be estimated. This GIS method is valuable in analyzing spatial non-stationarity and providing locally adaptive model fits.

The process in GIS of grouping spatial entities based on their similarities in both space and attribute dimensions. It's employed to identify areas with high concentrations of similar values, aiding in decision-making.

A fundamental tool in geostatistics representing the degree of spatial variation, showing how data similarity decreases with increasing distance. In GIS, it supports kriging by modeling spatial autocorrelation.

A spatial statistic property where direction affects spatial data variability. In GIS, it is recognized when variograms differ by direction, informing analysts about directional influences on spatial phenomena.