Reference : Determining the Geographical Origin of a Serial Offender Considering the Temporal Unc...
Scientific congresses and symposiums : Paper published in a book
Law, criminology & political science : Multidisciplinary, general & others
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/2268/86868
Determining the Geographical Origin of a Serial Offender Considering the Temporal Uncertainty of the Recorded Crime Data
English
Trotta, Marie mailto [Université de Liège - ULg > Département de géographie > Unité de Géomatique - Cartographie et S.I.G. >]
Bidaine, Benoît mailto [Université de Liège - University of Liège - ULg / FNRS > Département de géographie - Department of Geography > Unité de Géomatique - Geomatics Unit > >]
Donnay, Jean-Paul mailto [Université de Liège - ULg > Département de géographie > Unité de Géomatique - Cartographie et S.I.G. >]
Feb-2011
GEOProcessing 2011 : The Third International Conference on Advanced Geographic Information Systems, Applications, and Services
40-45
Yes
No
International
978-1-61208-003-1
GEOProcessing 2011 : The Third International Conference on Advanced Geographic Information Systems, Applications, and Services
February 23-28, 2011
IARIA
Gosier
FRANCE
[en] crime mapping ; geographic profiling ; temporal data quality ; least-squares adjustments ; raster diffusion process ; error propagation
[fr] Cartographie et S.I.G.
[en] Since the days the investigating officers used ”pin maps” to locate and to think about crime events, crime mapping has become widespread thanks to spatial analysis mainly supplied by GIS-like software. In particular these methods suit well to geographic profiling devoted to crime series characterised by a single offender and hence limited space and time variability. Although spatial techniques are now regularly performed to delineate an offender’s area of residence, the temporal dimension is underemployed due to the wider uncertainty of time records. This paper proposes a methodology based on a least-squares adjustment in order to cope with this temporal issue for determining the most probable offender’s residence. Moreover, a chi-square test is described to check the significance of the solutions suggested by the method. The process is carried out on the real road network which has been discretised (rasterised) for computing convenience. Three simulations show the validity of the reasoning. Finally the main time and speed assumptions introduced in the model are discussed paving the way for further research.
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/86868
http://www.thinkmind.org/index.php?view=article&articleid=geoprocessing_2011_2_40_30096
Copyright (c) IARIA, 2011

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