Title: Country crime analysis using the self-organising map, with special regard to economic factors
Authors: Xingan Li; Martti Juhola
Addresses: School of Information Sciences, University of Tampere, 33014, Finland ' School of Information Sciences, University of Tampere, 33014, Finland
Abstract: Data mining techniques have not been broadly applied in the study of crime. Criminologists and law enforcement need an instrument to efficiently analyse these data. We applied the self-organising map (SOM) to mapping countries with different economic situations of crime. The dataset was comprised of 50 countries and 30 variables. After initial processing of the data with the SOM, four clusters of countries were identified. Then the dataset was re-processed by ScatterCounter and four weak variables were removed. It was found that some roughly defined patterns of crime situation can be identified in traditionally economically homogeneous countries. Among different countries, positive correlation on crime in some countries may have negative correlation in other countries. Results of the study proved that, after the validation of ScatterCounter's separation power function, k-means clustering and nearest neighbour searching, the SOM can be a new tool for mapping criminal phenomena through processing of multivariate data.
Keywords: data mining; self-organising maps; SOM; ScatterCounter; k-means clustering; nearest neighbour search; country crime analysis; economic factors; law enforcement; criminal activity mapping; multivariate data.
International Journal of Data Mining, Modelling and Management, 2015 Vol.7 No.2, pp.130 - 153
Published online: 06 May 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article