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                            Library & Multimedia
 Understanding the spatial distribution of crime based on its related variables using geospatial discriminative patterns
NCJ Number
              306190
Journal

*Computers, Environment and Urban Systems* Volume: 39 Dated: May 2013 Pages: 93-106
Author(s)
          Dawei Wang;
                        Wei Ding;
                        Henry Lo;
                        Melissa Morabito;
                        Ping Chen;
                        Josue Salazar;
                        Tomasz Stepinski
Date Published

2013
Length
              14 pages
Annotation
              Since existing mapping methods usually identify hotspots without considering the underlying correlates of crime, this study introduces a spatial data mining framework to study crime hotspots through their related variables.
Abstract
              The authors used Geospatial Discriminative Patterns (GDPatterns) to capture the significant difference between two classes (hotspots and normal areas) in a geo-spatial dataset. Utilizing GDPatterns, the authors developed a novel model—Hotspot Optimization Tool (HOT)—to improve the identification of crime hotspots. Finally, based on a similarity measure, we group GDPattern clusters and visualize the distribution and characteristics of crime related variables. The approach was evaluated using a real-world dataset collected from a northeast city in the United States. (Published abstract provided)
      
  
          Additional Details
Grant Number(s)
        2009-DE-BX-K219


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Award
        Boston Police Department Predictive Policing Project
    
  
  
Date Published: January 1, 2013
    
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