This awardee has received supplemental funding. This award detail page includes information about both the original award and supplemental awards.
Description of original award (Fiscal Year 2010, $233,802)
The goal of this project is to address some of the issues facing the crime analyst through research and development of statistical methods and tools for linkage analysis. SPADIC Inc. use Bayesian statistical methods to explicitly connect the theoretical assumptions of offender behavior with the components of the model, and formally incorporate expert prior knowledge into the analysis. The Bayesian analysis produces interpretable results and offers the potential to improve linkage accuracy, properly quantify linkage uncertainty, put linkage analysis on a more reliable and scientific basis, reduce the cognitive workload placed on the analyst, and improve the prospect of using linkage analysis as evidence in legal proceedings. Specifically, the applicants will address the tasks of case linkage, cluster analysis, suspect identification, and future site selection prediction. The resulting algorithms will be integrated into a linkage analysis decision tool that allows practitioners the ability to quickly and accurately perform various linkage tasks, gaining access to sophisticated statistical methodology without having to become an expert in statistics. In addition, the applicants will evaluate and test the models on real world crime data and scenarios common to practice.
This award completes funding for Cooperative Agreement 2010-DE-BX-K255. This award was competitively made in response to a proposal submitted by SPADAC Inc. to the NIJ FY2010 "Geospatial Technology" solicitation. The award was only partially funded.
This research and development effort seeks to develop a software tool to enable practitioners to quickly and accurately perform link analyses without having to become expert statisticians. Development of this tool will enable law enforcement agencies across the United States to make more accurate linkage assessments more efficiently. ca/ncf