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                            Library & Multimedia
 Spatial Autocorrelation Statistics (CrimeStat IV: A Spatial Statistics Program for the Analysis of Crime Incident Locations, Version 4.0)
NCJ Number
              242965

Author(s)
          Ned	 Levine
Date Published

June 2013
Length
              49 pages
Annotation
              This is the second of three chapters on spatial description in CrimeStat IV, a spatial statistics package that can analyze crime incident location data.
Abstract
              This chapter on "Spatial Autocorrelation Statistics" discusses statistics relevant to spatial autocorrelation that are applicable to zonal data. The concept of "spatial autocorrelation" is one of the most important in spatial statistics, because it implies a lack of spatial independence. Spatial autocorrelation is a spatial arrangement of incidents such that the locations where incidents occur are related to each other; they are not statistically independent of one another. One section of this chapter contains six tests for global spatial autocorrelations: Moran's "I" statistic, Geary's "C" statistic, Getis-Ord "G" statistic, Moran Correlogram, Geary Correlogram, and Getis-Ord Correlogram. These indices would typically be applied to zonal data where an attribute value can be assigned to each zone. Six spatial autocorrelation indices are calculated. The chapter focuses on defining the indices and showing how they can be used. Instructions for running the routines are provided at the end of the chapter. Extensive figures of computer screens, 22 references, and attached preliminary statistical tests for hot spots, with examples from London, England; and Global Moran's "I" and small distance adjustment applied to the spatial pattern of crime in Tokyo
      
  
          Additional Details
Grant Number(s)
        2005-IJ-CX-K037


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Technical Assistance
                                Instructional Material (Programmed)


  
  
        
  Award
        The Expansion of the Crimestat Spatial Statistics Program to Incorporate Spatial Regression, Space-Time Clustering, Bayesian Journey to Crime Modeling, Incident Detection Methodology and Sparse Matrix
    
  
  
Date Published: June 1, 2013
    
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Related Datasets
        
                      https://doi.org/10.3886/ICPSR02824.v1
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                Automated crime analysis
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        Geographic distribution of crime



  
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