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Applying Geostatistical Analysis to Crime Data: Car-Related Thefts in the Baltic States
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment.ORCID iD: 0000-0001-5302-1698
2010 (English)In: Geographical Analysis, ISSN 0016-7363, E-ISSN 1538-4632, Vol. 42, no 1, 53-77 p.Article in journal (Refereed) Published
Abstract [en]

Geostatistical methods have rarely been applied to area-level offense data. This article demonstrates their potential for improving the interpretation and understanding of crime patterns using previously analyzed data about car-related thefts for Estonia, Latvia, and Lithuania in 2000. The variogram is used to inform about the scales of variation in offense, social, and economic data. Area-to-area and area-to-point Poisson kriging are used to filter the noise caused by the small number problem. The latter is also used to produce continuous maps of the estimated crime risk (expected number of crimes per 10,000 habitants), thereby reducing the visual bias of large spatial units. In seeking to detect the most likely crime clusters, the uncertainty attached to crime risk estimates is handled through a local cluster analysis using stochastic simulation. Factorial kriging analysis is used to estimate the local- and regional-scale spatial components of the crime risk and explanatory variables. Then regression modeling is used to determine which factors are associated with the risk of car-related theft at different scales.

Place, publisher, year, edition, pages
2010. Vol. 42, no 1, 53-77 p.
Keyword [en]
institutional-anomie, autocorrelation, homicide, rates
National Category
Other Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-19147ISI: 000273942200005Scopus ID: 2-s2.0-75249086325OAI: oai:DiVA.org:kth-19147DiVA: diva2:337194
Note
QC 20100525Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2017-12-12Bibliographically approved

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Ceccato, Vania

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