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| RESEARCH ON CLASSIFICATION OF CRIMES IN CHANGCHUN USING FACTOR ANALYSIS |
| LIU Da-qian1, XIU Chun-liang2, SONG Wei3 |
1. Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China;
2. School of Geographical Sciences, Northeast Normal University, Changchun 130024, China;
3. Department of Geography and Geosciences, University of Louisville, Lousville 40292 KY, USA |
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Abstract This study chose Changchun city as the study area to classify the crime areas using the factor analysis based on principal component method, which is conducted in the software of SPSS 16.0. The crime data were collected at the level of police precincts (the number is 74 in total) in Changchun, which consist of Occasional Violent Crimes (including arson, homicide, rape and kidnap), intentional assault, robbery, burglary,auto theft, forcible seizure, pick-pocketing, and fraud. 3 underlying factors are identified as the principal factors which account for more than 70% of the total variance. In order to improve the interpretability of the factors, the solutions are rotated using the varimax orthogonal rotation. The scores of each principal factor is computed and visualized in the maps made by the Arc GIS 9.3, showing the general pictures of criminal situation in Changchun in 2008. The interpretation and connotation of each factor and the reason why the spatial structure and pattern of the principal factors formed are made based on the demographic, socio-economic and land use characteristics for each factors as well as the features of the crimes they have the highest loadings on respectively. The results indicated that the first factor explain 26.99% of the variance, with the largest loadings on intentional assault, fraud, and Occasional Violent Crimes. The second factor account for 25.31% of the variance and has the highest loadings on robbery, forcible seizure and pick-pocketing. The third factor explains 17.93% of the variance with the largest loadings on burglary and the auto theft.
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Received: 20 September 2014
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2015, Vol. 30 
