Abstract
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.
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2010 IEEE International Conference on Intelligence and Security Informatics. |
| Editors | C.C. Yang, D. Zeng, K. Wang, A. Sanfillippo, H.H. Tsang, M-Y Day, U. Glasser, P.L. Brantingham, H. Chen |
| Place of Publication | Vancouver, BC Canada |
| Publisher | CPS/Elsevier |
| Pages | 13-18 |
| Volume | 10.1109/ISI.2010.5484782 |
| Edition | Vancouver |
| ISBN (Print) | 978 1 4244 6460 9 |
| Publication status | Published - 2010 |
| Event | 2010 IEEE International Conference on Intelligence and Security Informatics - Vancouver, Canada Duration: 23 May 2010 → 26 May 2010 |
Conference
| Conference | 2010 IEEE International Conference on Intelligence and Security Informatics |
|---|---|
| Country/Territory | Canada |
| City | Vancouver |
| Period | 23/05/10 → 26/05/10 |
