کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
384629 | 660852 | 2012 | 8 صفحه PDF | دانلود رایگان |

Crime activities are geospatial phenomena and as such are geospatially, thematically and temporally correlated. We analyze crime datasets in conjunction with socio-economic and socio-demographic factors to discover co-distribution patterns that may contribute to the formulation of crime. We propose a graph based dataset representation that allows us to extract patterns from heterogeneous areal aggregated datasets and visualize the resulting patterns efficiently. We demonstrate our approach with real crime datasets and provide a comparison with other techniques.
► Propose a co-distribution pattern mining approach for large crime data.
► Discover co-patterning crime incidents.
► Propose visualization approaches for co-patterning crime data.
Journal: Expert Systems with Applications - Volume 39, Issue 14, 15 October 2012, Pages 11556–11563