Package com.esri.arcgis.geoprocessing.tools.spatialstatisticstools

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.

See:
Description

Class Summary

AverageNearestNeighbor	Calculates a nearest neighbor index based on the average distance from each feature to its nearest neighboring feature.
CalculateAreas	Calculates area values for each feature in a polygon feature class.
CalculateDistanceBand	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.
CentralFeature	Identifies the most centrally located feature in a point, line, or polygon feature class.
ClustersOutliers	Given a set of weighted features, identifies statistically significant hot spots, cold spots, and spatial outliers using the Anselin Local Moran's I statistic.
ClustersOutliersRendered	Given a set of weighted features, identifies hot spots, cold spots, and spatial outliers using the Anselin Local Moran's I statistic.
CollectEvents	Converts event data, such as crime or disease incidents, to weighted point data.
CollectEventsRendered	Converts event data to weighted point data and then applies a graduated circle rendering to the resultant count field.
ConvertSpatialWeightsMatrixtoTable	Converts a binary spatial weights matrix file (.swm) to a table.
CountRenderer	Applies graduated circle rendering to a numeric field in a feature class.
DirectionalDistribution	Creates standard deviational ellipses to summarize the spatial characteristics of geographic features: central tendency, dispersion, and directional trends.
DirectionalMean	Identifies the mean direction, length, and geographic center for a set of lines.
ExportXYv	Exports feature class coordinates and attribute values to a space, comma, or semi-colon delimited ASCII text file.
GenerateNetworkSpatialWeights	Constructs a spatial weights matrix file (.swm) using a Network dataset, defining feature spatial relationships in terms of the underlying network structure.
GenerateSpatialWeightsMatrix	Constructs a spatial weights matrix (.swm) file to represent the spatial relationships among features in a dataset.
GeographicallyWeightedRegression	Performs Geographically Weighted Regression (GWR), a local form of linear regression used to model spatially varying relationships.
HighLowClustering	Measures the degree of clustering for either high values or low values using the Getis-Ord General G statistic.
HotSpots	The Hot Spot Analysis (Getis-Ord Gi*) tool is contained in the Spatial Statistics Tools tool box.
HotSpotsRendered	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.
MeanCenter	Identifies the geographic center (or the center of concentration) for a set of features.
MedianCenter	Identifies the location that minimizes overall Euclidean distance to the features in a dataset.
MultiDistanceSpatialClustering	Determines whether features, or the values associated with features, exhibit statistically significant clustering or dispersion over a range of distances.
OrdinaryLeastSquares	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.
SpatialAutocorrelation	Measures spatial autocorrelation based on feature locations and attribute values using the Global Moran's I statistic.
StandardDistance	Measures the degree to which features are concentrated or dispersed around the geometric mean center.
ZRenderer	Applies a cold (blue) to hot (red) color rendering scheme for a field of z-scores.

Package com.esri.arcgis.geoprocessing.tools.spatialstatisticstools Description

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.

Name	Description
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?
Utilities Toolset	These tools may be used to reformat data or to render analysis results.