Tag: Police Crime Fighting

Anatomy of a Ferguson Cycle – by Charles Fain Lehman

Back in 2015, my Manhattan Institute colleague Heather MacDonald popularized the term “Ferguson effect” to refer to a dramatic increase in homicide which (the term and she implied) was caused by the wave of protests, in turn instigated by the killing of Michael Brown in Ferguson, Mo., the year before. The homicide rate rose 11 percent in 2015, and another 10 percent in 2016, before cresting and receding. This, MacDonald and others argued at the time, was the result of a reduction in police proactivity, itself caused by political attacks on and criticism of the police in the wake of Brown’s death (among other high-profile incidents).
— Read on thecausalfallacy.com/p/anatomy-of-a-ferguson-cycle

Recommended Readings | Situational Crime Prevention | ASU Center for Problem-Oriented Policing

Scroll down to the bottom for 6 very interesting articles. The articles are accessible by the link below. The magazine is members only.

The SCRAP Test: Identifying Common Fallacies About Effective Crime Prevention
— Read on popcenter.asu.edu/content/recommended-readings-situational-crime-prevention

Factors influencing the spatial distribution of police stops and their efficacy in crime prevention and control | in Nature

Abstract
Targeted police stops are frequently carried out by police in response to real-world needs. The effectiveness of various purpose-driven police stop tactics on crime prevention and control varies. However, existing research has neither identified the associated factors of police stops nor explored their impact on crime with different factors. Therefore, this study focuses on the main urban areas of megacities along the southeast coast of China. The space is partitioned using hierarchical clustering after applying the XGBoost and SHAP algorithms to determine the factors related to police stops. Lastly, this study explores the causal effects of police stops with different associated factors on crime, using causal forests within double machine learning. There are three conclusions. First, there is a strong correlation between police stops and four variables: alarm, visiting population, criminal, and government agencies. Second, by clustering based on different associated factors of police stops, existing police stops can be classified into five categories according to their purposes: (i) composite stops positively associated with “Alarm, Visiting Population, Criminals” (AVC-CPS); (ii) composite stops positively associated with “Alarm, Visiting Population, Bus Station” (AVB-CPS); (iii) random stops with no significant positive association (NA-RPS); (iv) single police stops positively associated with “Alarm” (A-SPS); and (v) single stops positively associated with “Visiting Population” (V-SPS).
— Read on www.nature.com/articles/s41599-025-05355-0