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Cited by 151Policing Practices and PerceptionsCrime Patterns and InterventionsCriminal Justice and Penology

The DOI matches its Crossref record, with a small difference in title or year.

The Crime Numbers Game

Eterno, John A., Silverman, Eli B.·2017·Routledge eBooks

Abstract

In the mid-1990s, the NYPD created a performance management strategy known as Compstat. It consisted of computerized data, crime analysis, and advanced crime mapping coupled with middle management accountability and crime strategy meetings with high-ranking decision makers. While initially credited with a dramatic reduction in crime, questions quic

Overview

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Crossref · 10.1201/b11477

Cites

Statistics

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Verification

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Citations

Metadata

Authors
Eterno, J. A., & Silverman, E. B.
Venue
Routledge eBooks
Publisher
Routledge
Published
2017-07-27
OpenAlex
W4251259286
Cited by
151
References
0
Citations by year
2026: 4 · 2025: 17 · 2024: 6 · 2023: 6 · 2022: 3 · 2021: 11 · 2020: 9 · 2019: 13 · 2018: 15 · 2017: 12
FWCI
20.54
Type
book
Language
en
Open access
closed
Locations
Routledge eBooks (publishedVersion)
Topics
  • Social Sciences › Political Science and International Relations › Policing Practices and Perceptions
  • Social Sciences › Sociology and Political Science › Crime Patterns and Interventions
  • Social Sciences › Law › Criminal Justice and Penology
Keywords
Compstat, crime statistics, crime mapping, crime analysis, police performance management, New York City Police Department, crime reduction, police accountability

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How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: The Crime Numbers Game. https://app.cochat.ai/papers/10.1201/b11477
The paper
Eterno, J. A., & Silverman, E. B. (2017). The Crime Numbers Game. Routledge eBooks. https://doi.org/10.1201/b11477

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.