The Indianapolis harmspot policing experiment

Carter, Jeremy; Mohler, George; Raje, Rajeev; Chowdhury, Nahida Sultana; Pandey, Saurabh

doi:10.1016/j.jcrimjus.2021.101814

Cited by 6 publications

(5 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach primarily involves the retrospective analysis, description, and visualization of areas with high crime concentrations based on spatial intelligence in order to allocate police resources and implement specific tactics Ariel & Partridge, 2017;Collazos et al, 2020;Corsaro et al, 2021). Qualitative analyses of crime hot spots based on specific criteria or journey-after-crime analyses, as well as quantitative methods such as the Local Moran's I statistic or point-pattern analyses, were commonly employed (spatial and temporal analysis of crime or STAC) (Carter et al, 2021;Gerell, 2016;Lum et al, 2011;Mazeika, 2014). In some studies, prospective hot spot methods such as kernel density estimation (KDE) and risk terrain modeling (RTM) were used to analyze and map crime concentrations (Carter et al, 2021;Gerell, 2016;Lum et al, 2011;Mazeika, 2014).…”

Section: Interpreting the Criminal Environmentmentioning

confidence: 99%

“…Qualitative analyses of crime hot spots based on specific criteria or journey-after-crime analyses, as well as quantitative methods such as the Local Moran's I statistic or point-pattern analyses, were commonly employed (spatial and temporal analysis of crime or STAC) (Carter et al, 2021;Gerell, 2016;Lum et al, 2011;Mazeika, 2014). In some studies, prospective hot spot methods such as kernel density estimation (KDE) and risk terrain modeling (RTM) were used to analyze and map crime concentrations (Carter et al, 2021;Gerell, 2016;Lum et al, 2011;Mazeika, 2014). These methods differ from retrospective hot spot methods as they create a dynamic aggregation of crime risk zones centered around each criminal incident, providing a more short-term and forward-looking perspective (Caplan et al, 2021;Kennedy et al, 2022).…”

Section: Interpreting the Criminal Environmentmentioning

confidence: 99%

“…Crime and harm reduction. Appendix 1 clearly shows that most of the studies focused on establishing crime reduction effects, whereas only one explicitly focused on measuring and reducing social harm in so-called social harm spots (Carter et al, 2021). As discussed earlier, establishing statistically significant reduction effects is inherently challenging and influenced by various methodological factors.…”

Section: Primary Effectsmentioning

confidence: 99%

See 2 more Smart Citations

‘Led by Intelligence': A Scoping Review on the Experimental Evaluation of Intelligence-Led Policing

Khalfa,

Hardyns

2023

Eval Rev

View full text Add to dashboard Cite

Intelligence-led policing (ILP) was introduced in the 1990s as a proactive approach to policing, but to date, there is a lack of studies that have synthesized and summarized the central characteristics and insights of (quasi-)experimental studies related to ILP. This study aims to address this gap by synthesizing and characterizing the central characteristics of 38 quasi-experimental and experimental studies related to ILP. In this study, a scoping review is conducted on different quasi-experimental and experimental studies that relate to the framework of ILP. It was found that most studies within the domain of ILP focus on testing the crime reduction effects of using spatio-temporal crime intelligence to deploy police resources more efficiently and effectively. However, some studies have combined different types of crime intelligence or used solely offender-related intelligence. Several statistical-methodological challenges were also identified that should be considered when designing experimental research within the domain of ILP. Additionally, most studies focused solely on measuring crime reduction, with few focusing on secondary effects of interventions. The review concludes that future evaluation studies should consider evaluating the use of different types of crime intelligence and establish specific, objective, and realistic criteria for measuring specific performance measures such as crime disruption. Future experimental research within the domain of ILP should consider applying the 3-i model, evaluating each leg of ILP thoroughly. The limitations of the study are also discussed. This review provides valuable insights for future research and development of ILP-related approaches.

show abstract

Section: Interpreting the Criminal Environmentmentioning

confidence: 99%

Section: Interpreting the Criminal Environmentmentioning

confidence: 99%

Section: Primary Effectsmentioning

confidence: 99%

See 1 more Smart Citation

‘Led by Intelligence': A Scoping Review on the Experimental Evaluation of Intelligence-Led Policing

Khalfa,

Hardyns

2023

Eval Rev

View full text Add to dashboard Cite

show abstract

“…Despite the growing popularity of crime harm indices (e.g., Andersen & Mueller-Johnson, 2018;Bland & Ariel, 2020;Carter et al, 2021;Frydensberg et al, 2019;Link & Losel, 2022;Simon & Kichova, 2020; see also review in Ashby, 2018, but cf. critique in Sarnecki, 2021, the list of studies examining crime harm remains short.…”

Section: Concentration Of Harm In Micro-placesmentioning

confidence: 99%

Spatial distribution and developmental trajectories of crime versus crime severity: do not abandon the count-based model just yet

2022

View full text Add to dashboard Cite

Purpose/background A new body of research that focuses on crime harm scores rather than counts of crime incidents has emerged. Specifically in the context of spatial analysis of crime, focusing on crime harm suggests that harm is more concentrated than counts, at the level of crime hot spots. It remains presently unclear what drives the concentration distributions, and whether the count-based model should be abandoned. Methods Cross-sectional and longitudinal analysis of 6 year of spatiotemporal crime data in Toronto, Canada, to compare patterns and concentration of crime harm (measured in terms of the Crime Severity Index (CSI) against crime counts. Conditional probabilities, trajectory analyses, power few concentrations, and spatial Global Moran’s I are used to infer generalised trends from the data. Findings Overall CSI and crime counts tend to exhibit similar concentrations at the spatial micro levels, except against-the-body crimes such as violence which seems to drive nearly all the variations between the two measurement types. Violence harm spots tend to be more dispersed citywide and often do not remain constant year-to-year, whereas overall crime hotspots are more stable over time. Nevertheless, variations in disproportionally high crime hot spots are associated with total variations in crime, with as little as 1% increase in crime levels in these hot spots translating into substantial overall gains in recorded crime citywide. Conclusions Abandoning count-based models in spatial analysis of crime can lead to an incomplete picture of crime concentrations. Both models are needed not just for understanding spatial crime distributions but also for cost-effective allocation of policing resources.

show abstract

“…Hence, algorithms used by police organisations can have severe consequences for the power relations between governments and citizens (Van Brakel 2020). Furthermore, law enforcement agencies have access to tremendous amounts of sensitive public and policing data -such as personal records of suspects, victims and offenders -which private organisations do not have access to (Van Brakel and De Hert 2011, Perry 2013, Sheehey 2019, Brayne 2020, Carter 2021). This paper is set out to answer the following research question:…”

Section: Introductionmentioning

confidence: 99%

Algorithmic policing accountability: eight sociotechnical challenges

Wessels

2023

Policing and Society

View full text Add to dashboard Cite

Law enforcement agencies increasingly adopt algorithms for their policing practices, leading to new accountability challenges. However, academic literature tends to regard algorithmic applications as a single entity, resulting in broad and general statements on these risks. By introducing a sociotechnical model of algorithmic policing, this paper identifies eight accountability challenges that originate from technical, organisational and human components. It is illuminated how algorithmic policing accountability is a process that consists of multiple steps that should be considered separately to scrutinise the accountability challenges. The present study showcases how the sociotechnical context determines whether and how accountability issues might arise in algorithmic policing practices. The accountability challenges are presented as propositions that can direct future research in assessing the accountability risks of algorithmic policing applications.

show abstract

The Indianapolis harmspot policing experiment

Cited by 6 publications

References 75 publications

‘Led by Intelligence': A Scoping Review on the Experimental Evaluation of Intelligence-Led Policing

‘Led by Intelligence': A Scoping Review on the Experimental Evaluation of Intelligence-Led Policing

Spatial distribution and developmental trajectories of crime versus crime severity: do not abandon the count-based model just yet

Algorithmic policing accountability: eight sociotechnical challenges

Contact Info

Product

Resources

About