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Date: April 29, 2024 Mon
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Results for predictive policing
8 results foundAuthor: Hunt, Priscillia Title: Evaluation of the Shreveport Predictive Policing Experiment Summary: Predictive policing is the application of statistical methods to identify likely targets for police intervention (the predictions) to prevent crimes or solve past crimes, followed by conducting interventions against those targets. The concept has been of high interest in recent years as evidenced by the growth of academic, policy, and editorial reports; however, there have been few formal evaluations of predictive policing efforts to date. In response, the National Institute of Justice (NIJ) funded the Shreveport Police Department (SPD) in Louisiana to conduct a predictive policing experiment in 2012. SPD staff developed and estimated a statistical model of the likelihood of property crimes occurring within block-sized areas. Then, using a blocked randomized approach to identify treatment and control district pairs, districts assigned to the treatment group were given maps that highlighted blocks predicted to be at higher risk of property crime. These districts were also provided with overtime resources to conduct special operations. Control districts conducted property crime-related special operations using overtime resources as well, just targeting areas that had recently seen property crimes (hot spots). This study presents results of an evaluation of the processes in addition to the impacts and costs of the SPD predictive policing experiment. It should be of interest to those considering predictive policing and directed law enforcement systems and operations, and to analysts conducting experiments and evaluations of public safety strategies. This evaluation is part of a larger project funded by the NIJ, composed of two phases. Phase I focuses on the development and estimation of predictive models, and Phase II involves implementation of a prevention model using the predictive model. For Phase II, RAND is evaluating predictive policing strategies conducted by the SPD and the Chicago Police Department (contract #2009-IJ-CX-K114). This report is one product from Phase II. Details: Santa Monica, CA: RAND, 2014. 88p. Source: Internet Resource: Accessed August 4, 2014 at: http://www.rand.org/content/dam/rand/pubs/research_reports/RR500/RR531/RAND_RR531.pdf Year: 2014 Country: United States URL: http://www.rand.org/content/dam/rand/pubs/research_reports/RR500/RR531/RAND_RR531.pdf Shelf Number: 132885 Keywords: Crime AnalysisCrime PredictionCrime PreventionHot-Spots PolicingPredictive PolicingProperty Crimes |
Author: Bachner, Jennifer Title: Predictive Policing: Preventing Crime with Data and Analytics Summary: In this report, Dr. Bachner tells compelling stories of how new policing approaches in communities are turning traditional police officers into "data detectives." Police departments across the country have adapted business techniques -- initially developed by retailers, such as Netflix and WalMart, to predict consumer behavior -- to predict criminal behavior. The report presents case studies of the experiences of Santa Cruz, CA; Baltimore County, MD; and Richmond, VA, in using predictive policing as a new and effective tool to combat crime. While this report focuses on the use of predictive techniques and tools for preventing crime in local communities, these techniques and tools can also be applied to other policy arenas, as well, such as the efforts by the Department of Housing and Urban Development to predict and prevent homelessness, or the Federal Emergency Management Agency's efforts to identify and mitigate communities vulnerable to natural disasters. Details: Washington, DC: IBM Center for The Business of Government, 2013. 40p. Source: Internet Resource: Accessed August 12, 2014 at: http://www.businessofgovernment.org/sites/default/files/Predictive%20Policing.pdf Year: 2013 Country: United States URL: http://www.businessofgovernment.org/sites/default/files/Predictive%20Policing.pdf Shelf Number: 133020 Keywords: Crime AnalysisCrime PreventionData MiningPredictive Policing |
Author: Vagle, Jeffrey L. Title: Tightening the OODA Loop: Police Militarization, Race, and Algorithmic Surveillance Summary: This Article examines the role military automated surveillance and intelligence systems and techniques have supported a self-reinforcing racial bias when used by civilian police departments to enhance predictive policing programs. I will focus on two facets of this problem. First, my research will take an inside-out perspective, studying the role played by advanced military technologies and methods within civilian police departments, and how they have enabled a new focus on deterrence and crime prevention by creating a system of structural surveillance where decision support relies increasingly upon algorithms and automated data analysis tools, and which automates de facto penalization and containment based on race. Second, I will explore these systems - and their effects - from an outside-in perspective, paying particular attention to racial, societal, economic, and geographic factors that play into the public perception of these policing regimes. I will conclude by proposing potential solutions to this problem, which incorporate tests for racial bias to create an alternative system that follows a true community policing model. Details: Philadelphia: University of Pennsylvania, 2016. 54p. Source: Internet Resource: U of Penn Law School, Public Law Research Paper No. 16-9 : Accessed March 14, 2016 at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2737451 Year: 2016 Country: United States URL: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2737451 Shelf Number: 138214 Keywords: Broken Windows TheoryCommunity PolicingLaw EnforcementPolice MilitarizationPredictive PolicingRacial BiasRacial Profiling in Law Enforcement |
Author: Baker, Angie Title: Evaluation of the Oklahoma City SAFE Oklahoma Grant Summary: Using grant funds, OCPD developed a program model to address violent crime in the target area. According to the program narrative, the purpose of this program is to "reduce the occurrence of violent crime through both proactive and reactive efforts while using directed patrols, "hot spot" policing, and intelligence-led policing tactics in conjunction with code enforcement strategies." The program also included a community outreach strategy to address the perception of law enforcement and the community among citizens and business owners in the target area. Details: Oklahoma City, OK: Oklahoma State Bureau of Investigation, Office of Criminal Justice Statistics, 2015. 39p. Source: Internet Resource: Accessed April 8, 2016 at: https://www.ok.gov/osbi/documents/SAC%20Oklahoma%20City%20SAFE%20Oklahoma%20Year%201%20Report.pdf Year: 2015 Country: United States URL: https://www.ok.gov/osbi/documents/SAC%20Oklahoma%20City%20SAFE%20Oklahoma%20Year%201%20Report.pdf Shelf Number: 138602 Keywords: Hot Spots Intelligence-Led Policing Predictive PolicingViolence Prevention Violent Crime |
Author: Mohler, George O. Title: Randomized controlled field trials of predictive policing Summary: The concentration of police resources in stable crime hotspots has proven effective in reducing crime, but the extent to which police can disrupt dynamically changing crime hotspots is unknown. Police must be able to anticipate the future location of dynamic hotspots to disrupt them. Here we report results of two randomized controlled trials of near real-time Epidemic Type Aftershock Sequence (ETAS) crime forecasting, one trial within three divisions of the Los Angeles Police Department and the other trial within two divisions of the Kent Police Department (UK). We investigate the extent to which i) ETAS models of short term crime risk outperform existing best practice of hotspot maps produced by dedicated crime analysts, ii) police officers in the field can dynamically patrol predicted hotspots given limited resources, and iii) crime can be reduced by predictive policing algorithms under realistic law enforcement resource constraints. While previous hotspot policing experiments fix treatment and control hotspots throughout the experimental period, we use a novel experimental design to allow treatment and control hotspots to change dynamically over the course of the experiment. Our results show that ETAS models predict 1.4-2.2 times as much crime compared to a dedicated crime analyst using existing criminal intelligence and hotspot mapping practice. Police patrols using ETAS forecasts led to a average 7.4% reduction in crime volume as a function of patrol time, whereas patrols based upon analyst predictions showed no significant effect. Dynamic police patrol in response to ETAS crime forecasts can disrupt opportunities for crime and lead to real crime reductions. Details: Unpublished paper, 2015. 30p. Source: Internet Resource: Accessed September 23, 2016 at: http://paleo.sscnet.ucla.edu/MohlerEtAl-2015-JASA-Predictive-InPress.pdf Year: 2015 Country: United States URL: http://paleo.sscnet.ucla.edu/MohlerEtAl-2015-JASA-Predictive-InPress.pdf Shelf Number: 146052 Keywords: Crime ForecastingCrime MappingCriminal IntelligenceHot SpotsPredictive Policing |
Author: Gerstner, Dominik Title: Predictive Policing in the Context of Residential Burglary: An Empirical Illustration on the Basis of a Pilot Project in Baden-Wurttemberg, Germany Summary: Predictive policing has become an important issue in recent times, and different applications have been implemented in different countries. With a remarkable increase in residential burglaries in Germany during the last years, several place-based predictive policing strategies have been applied for this type of offence. In the federal state of Baden-Wurttemberg, the "pilot project predictive policing" (P4) was started in October 2015. The project was designed to produce open-ended and unbiased results and therefore included an external scientific evaluation. The article describes how predictive policing was applied in the P4 pilot and summarizes the main findings of the evaluation study. As predictive policing is more than making predictions, the article sheds light on different aspects of a "prediction-led policing business process" (Perry et al., Predictive policing: the role of crime forecasting in law enforcement operations, Rand Corporation, Santa Monica, 2013). Despite some positive findings, the impact on crime remains unclear and the size of crime reducing effects appears to be moderate. Within the police force, the acceptance of predictive policing is a divisive issue. Future research is recommended. Details: In: European Journal for Security Research (2018) 3:115-138. (Open Access) Source: Internet Resource: Accessed November 3, 2018 at: https://link.springer.com/content/pdf/10.1007%2Fs41125-018-0033-0.pdf Year: 2018 Country: Germany URL: https://link.springer.com/content/pdf/10.1007%2Fs41125-018-0033-0.pdf Shelf Number: 153241 Keywords: BurglaryPredictive PolicingResidential Burglary |
Author: Richardson, Rashida Title: Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice Summary: Law enforcement agencies are increasingly using algorithmic predictive policing systems to forecast criminal activity and allocate police resources. Yet in numerous jurisdictions, these systems are built on data produced within the context of flawed, racially fraught and sometimes unlawful practices ('dirty policing'). This can include systemic data manipulation, falsifying police reports, unlawful use of force, planted evidence, and unconstitutional searches. These policing practices shape the environment and the methodology by which data is created, which leads to inaccuracies, skews, and forms of systemic bias embedded in the data ('dirty data'). Predictive policing systems informed by such data cannot escape the legacy of unlawful or biased policing practices that they are built on. Nor do claims by predictive policing vendors that these systems provide greater objectivity, transparency, or accountability hold up. While some systems offer the ability to see the algorithms used and even occasionally access to the data itself, there is no evidence to suggest that vendors independently or adequately assess the impact that unlawful and bias policing practices have on their systems, or otherwise assess how broader societal biases may affect their systems. In our research, we examine the implications of using dirty data with predictive policing, and look at jurisdictions that (1) have utilized predictive policing systems and (2) have done so while under government commission investigations or federal court monitored settlements, consent decrees, or memoranda of agreement stemming from corrupt, racially biased, or otherwise illegal policing practices. In particular, we examine the link between unlawful and biased police practices and the data used to train or implement these systems across thirteen case studies. We highlight three of these: (1) Chicago, an example of where dirty data was ingested directly into the city's predictive system; (2) New Orleans, an example where the extensive evidence of dirty policing practices suggests an extremely high risk that dirty data was or will be used in any predictive policing application, and (3) Maricopa County where despite extensive evidence of dirty policing practices, lack of transparency and public accountability surrounding predictive policing inhibits the public from assessing the risks of dirty data within such systems. The implications of these findings have widespread ramifications for predictive policing writ large. Deploying predictive policing systems in jurisdictions with extensive histories of unlawful police practices presents elevated risks that dirty data will lead to flawed, biased, and unlawful predictions which in turn risk perpetuating additional harm via feedback loops throughout the criminal justice system. Thus, for any jurisdiction where police have been found to engage in such practices, the use of predictive policing in any context must be treated with skepticism and mechanisms for the public to examine and reject such systems are imperative. Details: Unpublished paper, 2019. 30p. Source: Internet Resource: Accessed February 19, 2019 at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3333423 Year: 2019 Country: United States URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3333423 Shelf Number: 154662 Keywords: Civil RightsPolice DataPolice MisconductPolicingPredictive PolicingRacial Bias |
Author: Los Angeles Police Commission. Office of the Inspector General Title: Review of Selected Los Angeles Police Department Data-Driven Policing Strategies Summary: Data-driven policing strategies and artificial intelligence-driven technologies utilized by the Los Angeles Police Department lacked oversight in their implementation and often strayed from their stated goals, an internal audit found Friday. Some of the largest law enforcement agencies in the country use so-called predictive policing programs and technologies to forecast where and when crime will occur in their communities. Those technologies, while seen by police as objective tools, have come under scrutiny by advocates who claim the tools disproportionately target those who are low-income or people of color and that they collect data on individuals without consent. Under the auspices of two LAPD programs - Predpol and Operation Los Angeles Strategic Extraction and Restoration program, or LASER - officers scan license plates across the city, conduct in-person interviews with so-called chronic offenders and analyze crime data to determine which individuals are most likely to commit or recommit crimes. LASER draws on technology developed by data giant Palantir, which mines government and private company databases to build extensive profiles of individuals. Predpol uses historical data from both property and violent crime reports to identify which city blocks are most likely to be the site of crimes. Privacy rights advocates crowded an August 2018 Board of Police Commissioners hearing on the programs and demanded a thorough review of new policing tools utilized by LAPD. Commissioners agreed and ordered the resulting 48-page audit by Inspector General Mark Smith, though a third data-driven policing tool called the Suspicious Activity Program was not analyzed in the report. The audit found that training on how to use the programs was "informal" and that different departments across city adapted the programs "for their own use," which led to inconsistencies in how the programs were utilized. The LAPD's Chronic Offender Program - the in-person interview component of LASER, which was first introduced in the city in 2011- utilized a department database of so-called chronic offenders who had few, if any, actual contact with officers. Of the more than 230 "active" individuals listed on 637-person chronic offender list - which is not available to the public - almost 80 percent are black and Latino men, the audit found. The arrests and stops of people listed on the database could also not be clearly tied to LASER-relative activities, the audit found. "These inconsistencies appeared to be related to a lack of centralized oversight, as well as a lack of formalized and detailed protocols and procedures," the audit said. "To the extent the Department continues to deploy a person-based strategy, more rigorous parameters about the selection of people, as well as the tracking of data, should allow for a better assessment of these issues." A more formal, standardized training was recommended for officers using the programs going forward. Various inconsistencies with LASER data troubled auditors, with more than a third coming from department vehicles that were scanned as squad cars with license plate readers entered police stations and department parking lots. A department trend towards using LASER as a crime-deterrence strategy was endorsed in the audit, rather than one that uses it to arrest and remove residents from communities listed as having high crime rates. "While the overall goal might be the general reduction of violent crime, a program focused on extraction may naturally count an arrest of a particular person as a measure of success, while one focused on deterrence might ostensibly look for the absence of a crime and/or an arrest involving the person," the audit said. LAPD forecasts and analysis of crime trends - collected by using GPS data to track the amount of time officers spent in certain areas of the city – found that crime rates decreased with increased officer presence, but the audit found that a region-by-region breakdown of crime data found "more mixed" results. The audit noted that the LAPD said it intends to introduce a "precision policing" strategy that "combines intensive crime analysis - and a focused response that values precision over high levels of enforcement - with neighborhood engagement and collaboration." An LAPD spokesperson did not immediately to respond to a request for comment on the audit, which noted that officials have begun making changes to the programs under review. Details: Los Angeles: Author, 2019. 51p. Source: Internet Resource: Accessed April 15, 2019 at: https://docs.wixstatic.com/ugd/b2dd23_21f6fe20f1b84c179abf440d4c049219.pdf Year: 2019 Country: United States URL: https://docs.wixstatic.com/ugd/b2dd23_21f6fe20f1b84c179abf440d4c049219.pdf Shelf Number: 155404 Keywords: Chronic OffendersData-Driven PolicingLicense Plate ScanningPolice PerformancePolice PoliciesPolice TechnologyPredictive Policing |