Exploration of unstructured narrative crime reports: An unsupervised neural network and point pattern analysis approach

Abstract

Crime intelligence analysis and criminal investigations are increasingly making use of geospatial methodologies to improve tactical and strategic decision-making. However, the full potential of geospatial technologies is yet to be exploited. In particular, geospatial technology currently applied by law enforcement is somewhat limited in handling the increasing volume of police recorded and relatively unstructured narrative crime reports, such as observations and interviews of eyewitnesses, the general public, or other relevant persons. The main objective of this research is to promote text mining, particularly the self-organizing map algorithm and its visualization capabilities, in combination with point pattern analysis, to explore the value of otherwise hidden information in a geographical context and to gain further insight into the complex behavior of the geography of crime. This methodological approach is applied to a high-profile and still unsolved homicide series in the city of Jennings, Louisiana. In a collaborative effort with the Jennings Police Task Force, the analysis is based upon a range of information sources, including email correspondence, transcribed face-to-face interviews, and phone calls that have been stored as “Information Packages” in the Orion database, which is maintained by the Federal Bureau of Investigation. Close to 200 individual information packages related to Necole Guillory, the eighth and last victim whose dead and dumped body was discovered in August 2009, are analyzed and resulted in new geographic patterns and relationships previously unknown to the Task Force.