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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 semicolon 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 GetisOrd General G statistic. 
HotSpots  The Hot Spot Analysis (GetisOrd Gi*) tool is contained in the Spatial Statistics Tools tool box. 
HotSpotsRendered  Calculates the GetisOrd Gi* statistic for hot spot analysis and then applies a coldtohot type of rendering to the output zscores. 
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 zscores. 
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. 


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