Measuring the Temporal Stability of Near-Repeat Crime Patterns

A Longitudinal Analysis

Bibliographic Data

ID21308644
AuthorsDavid N Hatten (0000-0002-0925-0538, John Jay College, The City University of New York, Graduate Center, New York City, USA, corresponding author), Eric L Piza (0000-0002-3753-5004, John Jay College, The City University of New York, New York City, USA)
Year2021
Volume67
Issue4
Pages498-522
Publication date2021-05-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueCrime & Delinquency (JOURNAL)
Journal identifiersISSN: 0011-1287 • E-ISSN: 1552-387X
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/0011128720922545
OpenAlexW3027710495
LanguageEN
Citations received3
References cited51

This study investigates the temporal stability of identified near-repeat robbery patterns in Newark, New Jersey. With one noteworthy exception, scholars have yet to explore the temporal stability of identified spatiotemporal crime clusters. Furthermore, researchers have yet to measure the near-repeat phenomenon longitudinally. To fill this gap, this study employs a longitudinal design to measure variation in effect size and significance of identified near-repeat crime patterns across 13 “rolling” one-year time periods within a 2-year study period (January 2015–December 2016). Temporal instability was found within two out of six spatiotemporal crime clusters. Results are reported in the form of formalized descriptive statistics and visualizations of temporal trends

Biology · Data mining · Descriptive statistics · Geography · Instability · Machine learning · Measure (data warehouse) · Phenomenon · Physics · Sociology · Stability (learning theory) · Statistics · Temporal scales · Variation (astronomy) · Computer Science · Crime Patterns and Interventions · Crime, Illicit Activities, and Governance · Demography · Ecology · Mathematics · Substance Abuse Treatment and Outcomes

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