By Topic

Identifying high risk crime areas using topology

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Richard Frank ; Institute for Canadian, Urban Research Studies, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada ; Andrew J. Park ; Patricia L. Brantingham ; Joseph Clare
more authors

Computational criminology is an area of research that joins advanced theories in criminology with theories and methods in mathematics, computing science, geography and behavioural psychology. It is a multidisciplinary approach that takes the strengths of several disciplines and, with semantic challenges, builds new methods for the analysis of crime and crime patterns. This paper presents a developing algorithm for linking the geographic and cognitive psychology sides of criminology research with a prototype topology algorithm that joins local urban areas together using rules that define similarity between adjacent small units of analysis. The approach produces irregular shapes when mapped in a Euclidean space, but which follow expectations in a non-Euclidean topological sense. There are high local concentrations or hot spots of crime but frequently there is a sharp break on one side of the hot spot and with a gradual diffusion on the other. These shapes follow the cognitive psychological way of moving from one location to another without noticing gradual changes or conversely being aware of sharp changes from one location to the next. This article presents a pattern modeling approach that uses topology to spatially identify the concentrations of crime and their crisp breaks and gradual blending into adjacent areas using the basic components: interior, boundary and exterior. This topology algorithm is used to analyze crimes in a moderate sized city in British Columbia.

Published in:

Intelligence and Security Informatics (ISI), 2010 IEEE International Conference on

Date of Conference:

23-26 May 2010