Applying Geovisual Analytics to Volunteered Crime Data

This article gives insight in a running dissertation at the University in Potsdam. Point of discussion is the spatial and temporal distribution of emergencies of German fire brigades that have not sufficiently been scientifically examined. The challenge is seen in Big Data: enormous amounts of data that exist now (or can be collected in the future) and whose variables are linked to one another. These analyses and visualizations can form a basis for strategic, operational and tactical planning, as well as prevention measures. The user-centered (geo-) visualization of fire brigade data accessible to the general public is a scientific contribution to the research topic 'geovisual analytics and geographical profiling'. It may supplement antiquated methods such as the so-called pinmaps as well as the areas of engagement that are freehand constructions in GIS. Considering police work, there are already numerous scientific projects, publications, and software solutions designed to meet the specific requirements of Crime Analysis and Crime Mapping. By adapting and extending these methods and techniques, civil security research can be tailored to the needs of fire departments. In this paper, a selection of appropriate visualization methods will be presented and discussed.

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Social Production of Crime Data: A Critical Examination of Chinese Crime Statistics

International Criminal Justice Review ◽

10.1177/105756779700700103 ◽

1997 ◽

Vol 7 (1) ◽

pp. 46-64 ◽

Cited By ~ 12

Author(s):

Ni He ◽

Ineke H. Marshall

Keyword(s):

Critical Examination ◽

Crime Statistics ◽

Social Production ◽

Crime Data

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Utilizing Incident-Based Crime Data to Inform Strategic Interventions

Justice Research and Policy ◽

10.1177/1525107116674925 ◽

2016 ◽

Vol 17 (1) ◽

pp. 3-27

Author(s):

Jason Rydberg ◽

Rebecca Stone ◽

Edmund F. McGarrell

Keyword(s):

Reporting System ◽

Excess Risk ◽

Public Health Approach ◽

Least Squares Regression ◽

Crime Data ◽

Targeted Interventions ◽

Aggravated Assault ◽

System A ◽

High Crime ◽

Demographic Subgroups

A public health approach to violence prevention involves the empirical identification of groups and communities at the highest risk for violence to inform targeted interventions. We demonstrate the utility of complete incident-level crime data toward this end. Data for 32,056 unique incidents involving homicide, aggravated assault, and robbery were extracted from the 2013 Michigan Incident Crime Reporting system, a statewide National Incident-Based Reporting System (NIBRS) data system. Differential victimization rates were calculated across demographic subgroups and jurisdictions to identify patterns in risk. Two-stage least squares regression models were estimated to examine correlates of variation in excess risk. Analyses identified young Black males and females at relatively high risk for violent victimization, and that this risk was amplified within cities with disproportionately high crime rates. Multivariate models suggested concentrated disadvantage as the most stable correlate of variation in excess risk across Michigan cities and towns. The results highlight the importance of expanding NIBRS adoption and the deployment of focused interventions involving both short-term enforcement and long-term social reinvestment.

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