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|Calculates a nearest neighbor index based on the average distance from each feature to its nearest neighboring feature.
|Calculates area values for each feature in a polygon feature class.
|Returns the minimum, the maximum, and the average distance to the specified Nth nearest neighbor (N is an input parameter) for a set of features.
|Identifies the most centrally located feature in a point, line, or polygon feature class.
|Given a set of weighted features, identifies statistically significant hot spots, cold spots, and spatial outliers using the Anselin Local Moran's I statistic.
|Given a set of weighted features, identifies hot spots, cold spots, and spatial outliers using the Anselin Local Moran's I statistic.
|Converts event data, such as crime or disease incidents, to weighted point data.
|Converts event data to weighted point data and then applies a graduated circle rendering to the resultant count field.
|Converts a binary spatial weights matrix file (.swm) to a table.
|Applies graduated circle rendering to a numeric field in a feature class.
|Creates standard deviational ellipses to summarize the spatial characteristics of geographic features: central tendency, dispersion, and directional trends.
|Identifies the mean direction, length, and geographic center for a set of lines.
|Exports feature class coordinates and attribute values to a space, comma, or semi-colon delimited ASCII text file.
|Constructs a spatial weights matrix file (.swm) using a Network dataset, defining feature spatial relationships in terms of the underlying network structure.
|Constructs a spatial weights matrix (.swm) file to represent the spatial relationships among features in a dataset.
|Performs Geographically Weighted Regression (GWR), a local form of linear regression used to model spatially varying relationships.
|Measures the degree of clustering for either high values or low values using the Getis-Ord General G statistic.
|The Hot Spot Analysis (Getis-Ord Gi*) tool is contained in the Spatial Statistics Tools tool box.
|Calculates the Getis-Ord Gi* statistic for hot spot analysis and then applies a cold-to-hot type of rendering to the output z-scores.
|Identifies the geographic center (or the center of concentration) for a set of features.
|Identifies the location that minimizes overall Euclidean distance to the features in a dataset.
|Determines whether features, or the values associated with features, exhibit statistically significant clustering or dispersion over a range of distances.
|Performs global Ordinary Least Squares (OLS) linear regression to generate predictions or to model a dependent variable in terms of its relationships to a set of explanatory variables.
|Measures spatial autocorrelation based on feature locations and attribute values using the Global Moran's I statistic.
|Measures the degree to which features are concentrated or dispersed around the geometric mean center.
|Applies a cold (blue) to hot (red) color rendering scheme for a field of z-scores.
The Spatial Statistics toolbox contains statistical tools for analyzing the distribution of geographic features: finding the geographic center, identifying statistically significant spatial clusters (hot spots) or outliers, assessing overall patterns of clustering or dispersion, and so on. Spatial statistics differ from traditional statistics in that space and spatial relationships are an integral and implicit component of their mathematics. The tools in the Spatial Statistics toolbox demonstrate a variety of statistical operations appropriate for analyzing geographic data. In addition, for those tools written with python the source code is available to encourage you to learn from, modify, extend and share these and other analysis tools. For more information about these tools and statistical analysis of geographic data in general, see The ESRI Guide to GIS Analysis, Volumes 1 and 2 (Volume 2 discusses the methods in the Spatial Statistics toolbox).
The following toolsets are provided with the Spatial Statistics toolbox at ArcGIS 9.
|Analyzing Patterns Toolset
|These tools evaluate if features or attribute values form a clustered, uniform, or random pattern across the region.
|Mapping Clusters Toolset
|These tools may be used to identify statistically significant hot spots, cold spots, or spatial outliers.
|Measuring Geographic Distributions Toolset
|These tools address questions such as: Where's the center? What's the shape and orientation? How dispersed are the features?
|These tools may be used to reformat data or to render analysis results.
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