Hot spots policing of small geographic areas effects on crime
Bibliographic record
Abstract
Background: In recent years, crime scholars and practitioners have pointed to the potential benefits of focusing crime prevention efforts on crime places. A number of studies suggest that there is significant clustering of crime in small places, or "hot spots," that generate half of all criminal events. Researchers have argued that many crime problems can be reduced more efficiently if police officers focused their attention to these deviant places. The appeal of focusing limited resources on a small number of high-activity crime places is straightforward. If crime can be prevented at these hot spots, then citywide crime totals could be reduced. Objectives: To assess the effects of focused police crime prevention interventions at crime hot spots. The review also examined whether focused police actions at specific locations result in crime displacement (i.e., crime moving around the corner) or diffusion (i.e., crime reduction in surrounding areas) of crime control benefits. Search Methods: A keyword search was performed on 15 abstract databases. Bibliographies of past narrative and empirical reviews of literature that examined the effectiveness of police crime control programs were reviewed and forward searches for works that cited seminal hot spots policing studies were performed. Bibliographies of past completed Campbell systematic reviews of police crime prevention efforts were reviewed and hand searches of leading journals in the field were completed. Experts in the field were consulted and relevant citations were obtained. Selection Criteria: To be eligible for this review, interventions used to control crime hot spots were limited to police-led prevention efforts. Suitable police-led crime prevention efforts included traditional tactics such as directed patrol and heightened levels of traffic enforcement as well as alternative strategies such as aggressive disorder enforcement and problem-oriented policing. Studies that used randomized controlled experimental or quasiexperimental designs were selected. The units of analysis were limited to crime hot spots or high-activity crime "places" rather than larger areas such as neighborhoods. The control group in each study received routine levels of traditional police crime prevention tactics. Data Collection and Analysis: Sixty-five studies containing 78 tests of hot spots policing interventions were identified and full narratives of these studies were reported. Twenty-seven of the selected studies used randomized experimental designs and 38 used quasiexperimental designs. A formal meta-analysis was conducted to determine the crime prevention effects in the eligible studies. Random effects models were used to calculate mean effect sizes. Results: Sixty-two of 78 tests of hot spots policing interventions reported noteworthy crime and disorder reductions. The meta-analysis of key reported outcome measures revealed a small statistically significant mean effect size favoring the effects of hot spots policing in reducing crime outcomes at treatment places relative to control places. The effect was smaller for randomized designs but still statistically significant and positive. When displacement and diffusion effects were measured, a diffusion of crime prevention benefits was associated with hot spots policing. Authors' Conclusions: The extant evaluation research suggests that hot spots policing is an effective crime prevention strategy. The research also suggests that focusing police efforts on high-activity crime places does not inevitably lead to crime displacement; rather, crime control benefits may diffuse into the areas immediately surrounding the targeted locations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".