Date
Spring 2014
Document Type
Poster Session
Department
Geographic Information Systems
Advisor
Matthew Bampton
Keywords
gis, crime mapping, spatial analysis
Abstract
Crime incident locations and trends are examined spatially using GIS to produce maps that pinpoint high crime areas or Hot Spots Crime mapping aids police departments by identifying areas to allocate limited resources where and when they are most needed. This project introduces the availability of GIS technology to smaller police departments as a tool to assist in the development of crime prevention strategies. In this model crime incident reports for Windham, Maine are geocoded and patterns of motor vehicle and structure burglaries analyzed for date, time and location of incident. An addressing protocol is followed to protect victim privacy by masking the actual incident locations. The data is analyzed using the following four methods: point density; Getis-Ord Gi*; Kriging and Anselin Local Morans I. Getis-Ord Gi* is the method usually associated with hot spot analysis, however, the results of this project favor the point density method which displays data in a raster format. The raster image has the greatest visual impact by clearly distinguishing degrees of high crime areas with a progressive coloring scheme ranging from blue to red Seven hot spot neighborhoods were identified, prioritized from high to low and overlayed on E911 roads. The outcome this analysis resulted in 94 high priority and 54 medium priority roads being recommended for increased police patrol. This process can easily be replicated to measure the success of strategic policing plans.
Start Date
April 2014
Recommended Citation
Trepanier, Elisa, "Identifying High Crime Areas Using Spatial Analysis" (2014). Thinking Matters Symposium Archive. 24.
https://digitalcommons.usm.maine.edu/thinking_matters/24
Included in
Categorical Data Analysis Commons, Environmental Monitoring Commons, Other Public Affairs, Public Policy and Public Administration Commons, Urban Studies and Planning Commons