Urban crime analysis through areal categorized multivariate associations mining

Abstract

As geospatial data grows explosively, there is a great demand for the incorporation of data mining techniques into a geospatial context. Association rules mining is a core technique in data mining and is a solid candidate for the associative analysis of large geospatial databases. In this article, we propose a geospatial knowledge discovery framework for automating the detection of multivariate associations based on a given areal base map. We investigate a series of geospatial preprocessing steps involving data conversion and classification so that the traditional Boolean and quantitative association rules mining can be applied. Our framework has been integrated into GISs using a dynamic link library to allow the automation of both the preprocessing and data mining phases to provide greater ease of use for users. Experiments with real-crime datasets quickly reveal interesting frequent patterns and multivariate associations, which demonstrate the robustness and efficiency of our approach.