Innovative Crime Mapping Techniques and Spatial Analysis

• NCJ Number: 204432
• Author(s): John Mollenkopf; Victor Goldsmith
• Date Published: 1997
• Length: 17 pages
• Grant Number(s): 97-LB-VX-K013
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical)

Annotation

This report reviews the status of innovative crime mapping techniques and spatial analysis in the cooperative efforts of the City University of New York and the New York Police Department, with a focus on solving the problems of overlaying police crime data with social/demographic data.

Abstract

New developments in Geographic Information Systems (GIS) and spatial analyses have also complicated the analysis of crime data in many ways. This is particularly the case in undertaking the next stage in these analyses, i.e., the overlaying of crime data with other datasets in order to compare the two based on their spatial and temporal relationships. The ability to perform such analyses still has many obstacles, including data input and verification. Issues to be addressed are how the data can best be used, where they can be used, and how they can be interpreted. One goal of the New York Police Department is to overlay demographic data from the U.S. Bureau of the Census with their street files and geocoded crime data. This is typically done by joining tabular data to geographic (map) files, both of which are distributed by the Census Bureau. Another goal is to overlay the 76 precinct maps, as well as sector maps within each precinct, with their street files and geocoded crime data. This report identifies the overlap problems and then describes possible solutions. The solutions are not all-inclusive, and there are many approaches that can be used. The general advice offered in this paper is that in comparing data sets, it is critical to use the same map projections, datums, and coordinate systems; this implies having the same base maps for the data sets. Where there are differences, they can be overcome by changing the pertinent parameters through options in the software, geocoding the data on the same base map as the other datasets, or by importing the data into ArcInfo or equivalent software and then "snapping" the arcs and nodes of one dataset to the arcs/nodes of the other mapped dataset. Also, attention should be given to the street files, their accuracy, commonality, and their role in the accuracy of the geocoding process. Finally, "metadata dictionaries" should be developed throughout the data acquisition, modification, and analysis processes. 7 figures and 1 reference