Crime analysts often use an area’s historical record to visualize past crimes (e.g., using hot-spot mapping) and to predict locations of future criminal activity. Models for the latter task use geographic and demographic factors to characterize the appeal of potential crime sites, demonstrating promising performance on real-world prediction tasks (Fox et al., 2012; Wang and Brown, 2012). However, these models often ignore the vast repository of unstructured text that is freely available through, for example, news and social media outlets. Such information sources contain detailed descriptions of past, present, and future events, and recent work has shown that these descriptions can improve crime prediction performance (Wang et al., 2012). Despite this encouraging result, textual information remains largely unexploited due to its vast size and unstructured format. This special issue of Security Informatics will focus on fusing text processing outputs (e.g., events, facts, and opinions) with historical criminal incident data (e.g., spatio-temporal criminal incident locations). Such work will help bridge the current gap between unstructured text and crime analytics (e.g., predictive policing).

In particular, we welcome high-quality submissions on the following topics:

* Extraction and geocoding (address resolution) of event locations within unstructured text
* Extraction and normalization of event times within unstructured text
* Extraction of person/group names and sentiment from unstructured text
* Processing of “noisy” sources of unstructured text (e.g., Twitter and weblogs)
* Fusion of the above (or other) textual information with criminal incident data

Submission Instructions

Before submission authors should carefully read over the Instructions for Authors, which are located at http://security-informatics.com/authors/instructions. Prospective authors should submit an electronic copy of their complete manuscript through the SpringerOpen submission system at http://security-informatics.com/manuscript according to the submission schedule. They should specify the manuscript as a submission to the “Special Issue on Fusing Automatic Text Processing with Criminal Incident Data” in the cover letter. All submissions will undergo initial screening by the Guest Editors for fit to the theme of the Special Issue and prospects for successfully negotiating the review process.

Lead Guest Editor

Matthew Gerber, Dept. of Systems and Information Engineering, University of Virginia, Email: msg8u@virginia.edu

Guest Editor

Donald Brown, Dept. of Systems and Information Engineering, University of Virginia, Email: deb@virginia.edu