CERIAS - Center for Education and Research in Information Assurance and Security

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Purdue University
Center for Education and Research in Information Assurance and Security

Visual Analytics Law Enforcement Toolkit (VALET)

Principal Investigator: David Ebert

The Visual Analytics Law Enforcement Toolkit (VALET) integrates large volumes of criminal, traffic and civil incident data into a single, interactive user interface. VALET helps law enforcement decision makers, analysts, and officers identify crime trends and patterns, discover crime anomalies, and perform predictive crime analytics to assist in allocating law enforcement resources.

Developed by the Center for Visualization and Data Analytics, a Department of Homeland Security (DHS) Science and Technology (S&T) Center of Excellence, VALET provides users with a visual display of multiple integrated crime datasets and a variety of crime analysis capabilities.

Benefits of Using VALET

  • Support for multiple platforms including desktop or laptop computers, iPhones, or iPads.
  • Can be used for a variety of functions and activities including predictive analytics, training, investigation, response, recovery.
  • Integrates multiple datasets onto one visual display (for example, social media, street light locations, law enforcement records, weather reports, civil court data, and bus routes).
  • Allows users to choose what types of data they want to look at or analyze
  • Personnel

    Students: Guizhen Wang

    Representative Publications

    • Malik, A., Maciejewski, R., Elmqvist, N., Jang, Y., Ebert, D. S., & Huang, W. (2012, October). A correlative analysis process in a visual analytics environment. In 2012 IEEE Conference on Visual Analytics Science and Technology (VAST),  (pp. 33-42). IEEE.

    • Malik, A., Maciejewski, R., Towers, S., McCullough, S., & Ebert, D. S. (2014). Proactive spatiotemporal resource allocation and predictive visual analytics for community policing and law enforcement. IEEE transactions on visualization and computer graphics20(12), 1863-1872.

    • Razip, A. M., Malik, A., Afzal, S., Potrawski, M., Maciejewski, R., Jang, Y., ... & Ebert, D. S. (2014, March). A mobile visual analytics approach for law enforcement situation awareness. In 2014 IEEE Pacific Visualization Symposium (pp. 169-176). IEEE.