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Country crime analysis using the self-organising map, with special regard to economic factors

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  • Xingan Li
  • Martti Juhola

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.

Suggested Citation

  • Xingan Li & Martti Juhola, 2015. "Country crime analysis using the self-organising map, with special regard to economic factors," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 7(2), pages 130-153.
    • Handle: RePEc:ids:ijdmmm:v:7:y:2015:i:2:p:130-153
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    Cited by:

    1. Félix J. López-Iturriaga & Iván Pastor Sanz, 2018. "Predicting Public Corruption with Neural Networks: An Analysis of Spanish Provinces," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 140(3), pages 975-998, December.

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