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                            Library & Multimedia
 Spatial Regression Modeling (CrimeStat IV: A Spatial Statistics Program for the Analysis of Crime Incident Locations, Version 4.0)
NCJ Number
              242979

Author(s)
          Ned Levine;
                        Dominique Lord;
                        Byung-Jung Park;
                        Srinivas Geedipally;
                        Haiyan Teng;
                        Li Sheng
Date Published

June 2013
Length
              41 pages
Annotation
              This is the fifth of 10 chapters on "Spatial Modeling II" from the user manual for CrimeStat IV, a spatial statistics package that can analyze crime incident location data.
Abstract
              This chapter examines spatial regression models using the Markov Chain Monte Carlo (MCMC) method. It requires familiarity with the previous four chapters of the manual and a thorough knowledge of statistical analysis. Spatial regression involves adding a spatial component into a regression model. There are two major ways to express this component, either as an explicit spatial variable or as an internally estimated spatial parameter. There are advantages and disadvantages to each approach, and often they are included together. Examples of this are the area of the zone, the distance to the central city, the distance to a particular facility, or an average value of the dependent variable for nearby zones. An alternative approach is to use a spatial parameter within the model that is estimated within the calculations themselves. The advantage of this internally estimated spatial parameter is its simultaneous estimation with the coefficients and its inclusion of the reciprocal effects of nearby zones on the central zone and vice versa. As with a distance-based external variable, the user must make assumptions about the decay of the spatial autocorrelation effect. The remainder of the chapter profiles a number of spatial regression models that apply to normal, Poisson-distributed, and binomial logit models. The choice of any of these models will depend on the actual distribution of the dependent variable and the underlying assumptions for that model. Implications are drawn for an MCMC Normal-CAR or MCMC Normal-SAR model, the MCMC Poisson-Gamma-CAR/SAR and the MCMC Poisson-Lognormal-CAR/SAR models, as well as the MCMC Binomial Logit-CAR/SAR model. These models are applied to the prediction of burglaries in Houston, TX. Extensive tables and figures and 24 references
      
  
          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
    
          Downloads
        
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Related Datasets
        
                      https://doi.org/10.3886/ICPSR02824.v1
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