Visualization in law enforcement

Hsinchun Chen, Homa Atabakhsh, Chunju Tseng, Byron Marshall, Siddharth Kaza, Shauna Eggers, Hemanth Gowda, Ankit Shah, Tim Petersen, Chuck Violette

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publicationConference on Human Factors in Computing Systems - Proceedings
Pages1268-1271
Number of pages4
DOIs
StatePublished - 2005
EventConference on Human Factors in Computing Systems, CHI EA 2005 - Portland, OR, United States
Duration: Apr 2 2005Apr 7 2005

Other

OtherConference on Human Factors in Computing Systems, CHI EA 2005
CountryUnited States
CityPortland, OR
Period4/2/054/7/05

Fingerprint

Crime
Law enforcement
Visualization
Geographic information systems
Pattern recognition

Keywords

  • Association network
  • Crime analysis
  • Crime network
  • Lawenforcement
  • Social network analysis
  • Spatial and temporal visualization

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software

Cite this

Chen, H., Atabakhsh, H., Tseng, C., Marshall, B., Kaza, S., Eggers, S., ... Violette, C. (2005). Visualization in law enforcement. In Conference on Human Factors in Computing Systems - Proceedings (pp. 1268-1271) https://doi.org/10.1145/1056808.1056893

Visualization in law enforcement. / Chen, Hsinchun; Atabakhsh, Homa; Tseng, Chunju; Marshall, Byron; Kaza, Siddharth; Eggers, Shauna; Gowda, Hemanth; Shah, Ankit; Petersen, Tim; Violette, Chuck.

Conference on Human Factors in Computing Systems - Proceedings. 2005. p. 1268-1271.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chen, H, Atabakhsh, H, Tseng, C, Marshall, B, Kaza, S, Eggers, S, Gowda, H, Shah, A, Petersen, T & Violette, C 2005, Visualization in law enforcement. in Conference on Human Factors in Computing Systems - Proceedings. pp. 1268-1271, Conference on Human Factors in Computing Systems, CHI EA 2005, Portland, OR, United States, 4/2/05. https://doi.org/10.1145/1056808.1056893
Chen H, Atabakhsh H, Tseng C, Marshall B, Kaza S, Eggers S et al. Visualization in law enforcement. In Conference on Human Factors in Computing Systems - Proceedings. 2005. p. 1268-1271 https://doi.org/10.1145/1056808.1056893
Chen, Hsinchun ; Atabakhsh, Homa ; Tseng, Chunju ; Marshall, Byron ; Kaza, Siddharth ; Eggers, Shauna ; Gowda, Hemanth ; Shah, Ankit ; Petersen, Tim ; Violette, Chuck. / Visualization in law enforcement. Conference on Human Factors in Computing Systems - Proceedings. 2005. pp. 1268-1271
@inproceedings{189ca4f308ee4177be51a035427f4816,
title = "Visualization in law enforcement",
abstract = "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.",
keywords = "Association network, Crime analysis, Crime network, Lawenforcement, Social network analysis, Spatial and temporal visualization",
author = "Hsinchun Chen and Homa Atabakhsh and Chunju Tseng and Byron Marshall and Siddharth Kaza and Shauna Eggers and Hemanth Gowda and Ankit Shah and Tim Petersen and Chuck Violette",
year = "2005",
doi = "10.1145/1056808.1056893",
language = "English (US)",
isbn = "1595930027",
pages = "1268--1271",
booktitle = "Conference on Human Factors in Computing Systems - Proceedings",

}

TY - GEN

T1 - Visualization in law enforcement

AU - Chen, Hsinchun

AU - Atabakhsh, Homa

AU - Tseng, Chunju

AU - Marshall, Byron

AU - Kaza, Siddharth

AU - Eggers, Shauna

AU - Gowda, Hemanth

AU - Shah, Ankit

AU - Petersen, Tim

AU - Violette, Chuck

PY - 2005

Y1 - 2005

N2 - 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.

AB - 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.

KW - Association network

KW - Crime analysis

KW - Crime network

KW - Lawenforcement

KW - Social network analysis

KW - Spatial and temporal visualization

UR - http://www.scopus.com/inward/record.url?scp=84869100340&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84869100340&partnerID=8YFLogxK

U2 - 10.1145/1056808.1056893

DO - 10.1145/1056808.1056893

M3 - Conference contribution

AN - SCOPUS:84869100340

SN - 1595930027

SN - 9781595930026

SP - 1268

EP - 1271

BT - Conference on Human Factors in Computing Systems - Proceedings

ER -