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Record W2912419496 · doi:10.1177/0306624x19828586

The Spatial Effect of Police Foot Patrol on Crime Patterns: A Local Analysis

2019· article· en· W2912419496 on OpenAlexaff
Martin A. Andresen, Jen-Li Shen

Bibliographic record

VenueInternational Journal of Offender Therapy and Comparative Criminology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFoot (prosody)GeographyNeighbourhood (mathematics)Similarity (geometry)Common spatial patternDisplacement (psychology)CartographyComputer sciencePsychologyArtificial intelligenceStatisticsMathematics

Abstract

fetched live from OpenAlex

A foot patrol program was implemented in Lower Lonsdale, British Columbia, in the summer of 2010 and continues today. As a part of assessing the foot patrol's effect on crime in the neighbourhood, the spatial similarity was examined by comparing the crime pattern before the foot patrol initiative (2007-2009) with the crime pattern during the foot patrol program (2010-2012). Considering these baseline and treatment data sets and a spatial point pattern test, the spatial similarity between two data sets is analyzed. In general, the continued presence of foot patrol appears to have created a concentration of crime in specific areas, rather than a diffusion effect. The areas that continued to experience increased crime during foot patrol presence were often in the catchment area, suggesting displacement does occur, or along the border between the catchment and primary patrol area.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.624

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.156
GPT teacher head0.402
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2019
Admission routes1
Has abstractyes

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