Visualization techniques have proven to be critical in helping crime analysis. By interviewing and observing Criminal Intelligence Officers (CIO) and civilian crime analysts at the Tucson Police Department (TPD), we found that two types of tasks are important for crime analysis: crime pattern recognition and criminal association discovery. We developed two separate systems that provide automatic visual assistance on these tasks. To help identify crime patterns, a Spatial Temporal Visualization (STV) system was designed to integrate a synchronized view of three types of visualization techniques: a GIS view, a timeline view and a periodic pattern view. The Criminal Activities Network (CAN) system extracts, visualizes and analyzes criminal relationships using spring-embedded and blockmodeling algorithms. This paper discusses the design and functionality of these two systems and the lessons learned from the development process and interaction with law enforcement officers.

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Index Terms

Visualization in law enforcement
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Graphical user interfaces

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CHI EA '05: CHI '05 Extended Abstracts on Human Factors in Computing Systems

April 2005

1358 pages

ISBN:1595930027

DOI:10.1145/1056808

Conference Chair:
Gerrit van der Veer
Vrije Universiteit, The Netherlands
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Carolyn Gale
Stanford University, Stanford, CA

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Herrewijnen ELoerakker MVredenborg MWoźniak P(2024)Requirements and Attitudes towards Explainable AI in Law EnforcementProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661629(995-1009)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661629
Garcia-Zanabria GGomez-Nieto ESilveira JPoco JNery MAdorno SNonato L(2020)Mirante: A visualization tool for analyzing urban crimes2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)10.1109/SIBGRAPI51738.2020.00028(148-155)Online publication date: Nov-2020
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https://doi.org/10.1007/s11042-019-07950-1
Veale MVan Kleek MBinns RMandryk RHancock MPerry MCox A(2018)Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-MakingProceedings of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3173574.3174014(1-14)Online publication date: 21-Apr-2018
https://dl.acm.org/doi/10.1145/3173574.3174014
Del Fatto VDignoes AGamper J(2018)TIME°DIFF: A Visual Approach to Compare Period Data2018 22nd International Conference Information Visualisation (IV)10.1109/iV.2018.00017(38-43)Online publication date: Jul-2018
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Xu DWei Y(2018)Study on Visual Techniques of Potential Pattern Discovery for Time Series DataMATEC Web of Conferences10.1051/matecconf/201823202049232(02049)Online publication date: 19-Nov-2018
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Chen YChen QMingqian Zhao Boyer SVeeramachaneni KQu H(2016)DropoutSeer: Visualizing learning patterns in Massive Open Online Courses for dropout reasoning and prediction2016 IEEE Conference on Visual Analytics Science and Technology (VAST)10.1109/VAST.2016.7883517(111-120)Online publication date: Oct-2016
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Didimo WLiotta GMontecchiani F(2014)Network visualization for financial crime detectionJournal of Visual Languages and Computing10.1016/j.jvlc.2014.01.00225:4(433-451)Online publication date: 1-Aug-2014
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Didimo WLiotta GMontecchiani FPalladino P(2011)An advanced network visualization system for financial crime detection2011 IEEE Pacific Visualization Symposium10.1109/PACIFICVIS.2011.5742391(203-210)Online publication date: Mar-2011
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