Papers

Alcohol Age Crash Analysis – Projections of the Effect of Lowering the Legal Drinking Age in Alabama (pdf)

  August 16, 2008      Analytics, Health and Human Services, Law Enforcement
Brown, D., CAPS Research Report, Aug. 16, 2008.

This report, and the Op Ed that follows it, is in response to a number of prominent college presidents who have recently come out in favor of lowering the drinking age to 18 years. The goal of the report is to project how many additional fatalities will be caused in Alabama should the law be change in Alabama.

A Host Architecture for Automobile License Plate Recognition

  May 1, 2008      Law Enforcement, Motor Vehicles
Mitchell, M, M. Hudnall, D. Brown, D. Cordes, R. Smith, A. Parrish, Proceedings of the IEEE International Conference on Intelligence and Security Informatics 2008 (ISI 2008), New Brunswick, NJ, May 2008.

The objective of this paper was to present an architecture that supports data transmission and data sharing among applications related to commodity tag recognition systems. These systems are used extensively in Great Britain and other countries to recognize automobile license plates and to notify security officials of suspicious activity or trafficking by known suspects. This architecture was tested in a successful pilot project.

Using an Edge-dual Graph and k-connectivity to Identify Strong Connections in Social Networks

  March 1, 2008      Analytics, Law Enforcement
Li Ding, and Brandon. Dixon, in Proc. ACM Southeast Regional Conference 2008, Auburn, Alabama, US 2008

The goal of this paper is to use edge-dual graph transformation techniques to improve the accuracy of social network analysis (SNA). SNA is used in law enforcement to determine if relationships exist among potential suspects, and to identify just what those relationships might be. Relationships can be family, friends, past associates, cell mates and even prison enemies. The paper presented results that showed that this transformation had a very high potential for increasing the accuracy of relationship search routines.

Identifying High Frequency Crash Locations: Empowering End-Users with GIS Capabilities

  January 1, 2007      Emergency Medical Services, Law Enforcement, Motor Vehicles, Traffic Safety
Smith, R., A. Graettinger, K. Keith and A. Parrish, ITE Journal, January 2007, vol. 77, no. 1, pp. 22-27.

This paper presented the status of CARE with emphasis on its newly created GIS capabilities as of January, 2007. Emphasis is placed on the following aspects of the CARE/GIS system:

• The timeliness of the data and the use of temporal analysis to track changes over time;
• The ability to create filters using GIS and use them within the CARE analytical engines;
• The integration of demographic GIS layers for schools, hospitals, bridges, census data; roadway inventory, railroads, etc.;
• The extension of the desktop system to a Web-based system.

Several additional innovations have been made to CARE since early 2007, and those interested are urged to review the CARE pages within this site.

Improved Variable and Value Ranking Techniques for Mining Categorical Traffic Accident Data

  December 1, 2005      Analytics, Law Enforcement, Motor Vehicles, Traffic Safety
Wang, H., A. Parrish, R. Smith and S. Vrbsky, Expert Systems with Applications, Volume 29, 2005, pp. 795-806.

This paper reviews the use of two new metrics for the process of assessing the significance of attributes in a database when two subsets of the data are compared. Traditional statistical techniques are useful, and the sample size in public safety databases usually allows the normal approximation to the binomial distribution to be used in comparing proportionate values. For example, the comparison of the proportion of alcohol related crashes on Saturdays would show an very highly significantly higher proportion than that for non-alcohol related crashes. However the new metrics go a step further than this in that they provide a clear intuitive grasp to the user as to exactly how much more is occurring, not in terms of proportions but in terms of number of crashes (for the traffic safety example). The metric is called Maximum Gain, and it measures directly the number of crashes over and above that which is typically expected. This provides a clear indication to the user of just what the potential gain is by applying a countermeasure related to the attribute (e.g., applying selective enforcement on Saturdays). It is not realistic to think that this gain would include all of the crashes for the attribute value; rather, it is realistic to view the maximum gain to be the total over-represented amount.