Crime analysis mapping in the UK: a GIS analysis of burglaries in Leicestershire

Paper


Burnham, Jahnavi. 2014. "Crime analysis mapping in the UK: a GIS analysis of burglaries in Leicestershire." Moore, Antoni, Whyte, Brendan and Drecki, Igor (ed.) 42nd Australian and New Zealand Map Society Conference (GeoCart 2014): Cartographic Journaeys through Space and Time. Auckland, New Zealand 03 - 05 Sep 2014 Auckland, New Zealand.
Paper/Presentation Title

Crime analysis mapping in the UK: a GIS analysis of burglaries in Leicestershire

Presentation TypePaper
Authors
AuthorBurnham, Jahnavi
EditorsMoore, Antoni, Whyte, Brendan and Drecki, Igor
Journal or Proceedings TitleProceedings of the 42nd Australian and New Zealand Map Society Conference (GeoCart 2014)
Number of Pages8
Year2014
Place of PublicationAuckland, New Zealand
ISBN9780473298722
Conference/Event42nd Australian and New Zealand Map Society Conference (GeoCart 2014): Cartographic Journaeys through Space and Time
Event Details
42nd Australian and New Zealand Map Society Conference (GeoCart 2014): Cartographic Journaeys through Space and Time
Event Date
03 to end of 05 Sep 2014
Event Location
Auckland, New Zealand
Abstract

Crime mapping studies have a history of more than 150 years. However, in the past, studies on Crime Analysis mapping have advanced due to the advent of GIS. GIS gives an array of suitable capabilities for crime analysis mapping. The present study utilises GIS to examine the relationships between burglaries and the socio-demographic characteristics in the context of Leicestershire County, UK, with a hypothesis that the socio-economic status can influence the crime patterns and crime rate of particular area. The factors such as population density, unemployment, and single households are considered for this study. The other factors such as types of houses, occupancy and tenure/ownership are also investigated. Using the regression analysis models - Geographically Weighted Regression (GWR) and Ordinary Least-Squares (OLS), the variations in the relationships are examined. The local and global significant variables are successfully identified and mapped. These variables are both locally and globally significant. The practical implication of the regression analysis techniques is that, by exploring the local processes that drive crime levels it is possible to implement crime prevention policies in different locations.

Keywordscrime mapping; GIS; geographically weighted regression; ordinary least squares regression; socio-economic characteristics
ANZSRC Field of Research 2020490501. Applied statistics
440211. Police administration, procedures and practice
401302. Geospatial information systems and geospatial data modelling
Public Notes

© New Zealand Cartographic Society Inc 2014. Permanent restricted access to published version due to publisher copyright policy.

Byline AffiliationsFaculty of Health, Engineering and Sciences
Institution of OriginUniversity of Southern Queensland
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