Detecting Clusters in Spatially Repetitive Point Event Data Sets

The analysis of point event patterns has a long tradition. Of particular interest are patterns of clustering or ‘hot spots’ and such cluster detection lies at the heart of spatial data mining. Certain classes of point event patterns have a significant proportion of the data having a tendency towards exact spatial repetitiveness. Examples are crime and traffic accidents. Spatial superimposition of point events challenges many existing approaches to cluster detection. In this paper a variable resolution approach, Geo-ProZones, is applied to residential burglary data exhibiting a high level of repeat victimisation. This is coupled with robust normalisation as a means of consistently defining and visualising the ‘hot spots’.

[1]  Allan J. Brimicombe,et al.  A variable resolution, geocomputational approach to the analysis of point patterns. , 2000 .

[2]  P. Diggle,et al.  Spatial point pattern analysis and its application in geographical epidemiology , 1996 .

[3]  Allan J. Brimicombe,et al.  A Variable Resolution Approach to Cluster Discovery in Spatial Data Mining , 2003, ICCSA.

[4]  J. Snow.  On the Mode of Communication of Cholera , 1856, Edinburgh medical journal.

[5]  Alan T. Murray,et al.  Cluster Discovery Techniques for Exploratory Spatial Data Analysis , 1998, Int. J. Geogr. Inf. Sci..

[6]  Doug Williamson,et al.  Identifying Crime Hot Spots Using Kernel Smoothing , 2000 .

[7]  Martin Charlton,et al.  A Mark 1 Geographical Analysis Machine for the automated analysis of point data sets , 1987, Int. J. Geogr. Inf. Sci..

[8]  Allan Brimicombe,et al.  Constructing and Evaluating Contextual Indices Using GIS: A Case of Primary School Performance Tables , 2000 .

[9]  A. Brimicombe.  GIS, environmental modelling and engineering , 2003 .

[10]  Chris Brunsdon,et al.  Estimating probability surfaces for geographical point data: an adaptive kernel algorithm , 1995 .

[11]  K. Clayton,et al.  Transactions of the Institute of British Geographers , 1959 .

[12]  M. Aldenderfer.  Cluster Analysis , 1984 .

[13]  J. Ord,et al.  Spatial Processes: Models and Applications , 1984 .

[14]  Jie Mi,et al.  Robust Nonparametric Statistical Methods , 1999, Technometrics.

[15]  Allan J. Brimicombe,et al.  The hierarchical tessellation model and its use in spatial analysis , 1997, Trans. GIS.

[16]  K. Pease,et al.  WHAT IS DIFFERENT ABOUT HIGH CRIME AREAS , 1992 .

[17]  Jiawei Han,et al.  Geographic Data Mining and Knowledge Discovery , 2001 .

[18]  David J. Unwin,et al.  Density and local attribute estimation of an infectious disease using MapInfo , 2002 .

[19]  D. W. Harvey,et al.  Geographical Processes and the Analysis of Point Patterns: Testing Models of Diffusion by Quadrat Sampling , 1966 .

[20]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[21]  Kent A. Harries,et al.  Mapping Crime: Principle And Practice , 1999 .

[22]  Allan J. Brimicombe,et al.  Adaptive Recursive Tessellations (ART) for Geographical Information Systems , 1997, Int. J. Geogr. Inf. Sci..

[23]  J. H. Ratcliffe,et al.  Hotbeds of crime and the search for spatial accuracy , 1999, J. Geogr. Syst..

[24]  A C Gatrell,et al.  Modelling exposure opportunities: estimating relative risk for motor neurone disease in Finland. , 2000, Social science & medicine.

[25]  N. Mantel.  The detection of disease clustering and a generalized regression approach. , 1967, Cancer research.

[26]  M. Townsley,et al.  Infectious Burglaries. A Test of the Near Repeat Hypothesis , 2003 .

[27]  Peter J. Halls,et al.  Dirichlet neighbours: revisiting Dirichlet tessellation for neighbourhood analysis , 2001 .

[28]  H. Ross.  Principles of Numerical Taxonomy , 1964 .

[29]  V. Estivill-Castro,et al.  Argument free clustering for large spatial point-data sets via boundary extraction from Delaunay Diagram , 2002 .

[30]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[31]  M. Charlton,et al.  Quantitative geography : perspectives on spatial data analysis by , 2001 .