TY - GEN
T1 - Data clustering and rule abduction to facilitate crime hot spot prediction
AU - Wilson, Ian D.
AU - Corcoran, Jonathan
AU - Lewis, Owen M.
AU - Ware, Andrew
PY - 2001/1/1
Y1 - 2001/1/1
N2 - Crime rates differ between types of urban district, and these disparities are best explained by the variation in use of urban sites by differing populations. A database of violent incidents is rich in spatial information and studies have, to date, provided a statistical analysis of the variables within this data. However, a much richer survey can be undertaken by linking this database with other spatial databases, such as the Census of Population, weather and police databases. Coupling Geographical Information Systems (GIS) with Artificial Neural Networks (ANN) offers a means of uncovering hidden relationships and trends within these disparate databases. Therefore, this paper outlines the first stage in the development of such a system, designed to facilitate the prediction of crime hot spots. For this stage, a series of Kohonen Self-Organising Maps (KSOM) will be used to cluster the data in a way that should allow common features to be extracted.
AB - Crime rates differ between types of urban district, and these disparities are best explained by the variation in use of urban sites by differing populations. A database of violent incidents is rich in spatial information and studies have, to date, provided a statistical analysis of the variables within this data. However, a much richer survey can be undertaken by linking this database with other spatial databases, such as the Census of Population, weather and police databases. Coupling Geographical Information Systems (GIS) with Artificial Neural Networks (ANN) offers a means of uncovering hidden relationships and trends within these disparate databases. Therefore, this paper outlines the first stage in the development of such a system, designed to facilitate the prediction of crime hot spots. For this stage, a series of Kohonen Self-Organising Maps (KSOM) will be used to cluster the data in a way that should allow common features to be extracted.
U2 - 10.1007/3-540-45493-4_80
DO - 10.1007/3-540-45493-4_80
M3 - Conference contribution
AN - SCOPUS:33846649283
SN - 3540427325
SN - 9783540427322
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 807
EP - 821
BT - Computational Intelligence
A2 - Reusch, Bernd
PB - Springer
T2 - 7th International Conference on Computational Intelligence: Theory and Applications, Fuzzy Days 2001
Y2 - 1 October 2001 through 3 October 2001
ER -
