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.

Using GIS for Law Enforcement

  November 1, 2005      Law Enforcement
Smith, R., A. Graettinger, K. Keith, M. Hudnall and A. Parrish, Journal of Safety Research, vol. 36, 2005, pp. 477-479.

The use of GIS for law enforcement and traffic safety within Alabama was reviewed as of the summer of 2005. Emphasis was placed on the integration of GIS with CARE, and the ability to generate a map of high crash locations for any type of crash. Additional applications involved a GIS comparison of crash locations with locations where electronic citations were being issued in order to move law enforcement resources to areas where they would be more effective.

Strategies to Improve Variable Selection Performance

  June 1, 2005      Analytics
Wang, H., A. Parrish, R. Smith, S. Vrbsky, Proceedings of the 2005 International Conference on Information and Knowledge Engineering, Las Vegas, June 2005

This paper compares a “row major order” data structure that is used standard relational databases against the transposed “column major order” data structure used by CARE. These data structures are described in detail, as were the various filtering methods that could be employed. Performance tradeoffs between the two data structures demonstrated a clear advantage of the column major order over the traditional storage approaches.