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Date: April 29, 2024 Mon
Time: 10:18 pm
Time: 10:18 pm
Results for crime clusters
5 results foundAuthor: Police Executive Research Forum Title: Violent Crime in America: What We Know About Hot Spots Enforcement Summary: This report is the fourth in a series in which the Police Executive Research Forum focuses on violence in the United States and what local police agencies are doing to prevent homicides, robberies, assaults, and other violent crimes. Once again, PERF has been able to call on our member police chiefs, sheriffs, and other local police officials as well as federal agency leaders and other experts to provide answers to these questions: Are violent crime levels going up or down in your jurisdiction? What kinds of strategies and tactics are you using to fight violent crime? In particular, most of you have told us that “hot spots” enforcement is high on your list of violent crime countermeasures. Please give us all of the details you can about this. Tell us stories that illustrate what hot spots enforcement means to you. A bit of background: In 2005, police chiefs began telling PERF that violent crime seemed to be making an unwelcome comeback in the United States, following a decade in which levels of violence fell dramatically. PERF began tracking this development by conducting surveys of our member police agencies in which we asked them for their most up-to-date statistics on their violent crime levels. We also began convening Violent Crime Summits, where police officials gathered to discuss the survey findings and talk about the latest tactics that seemed effective in pushing violent crime back down. To date, we have conducted four violent crime surveys and organized three Violent Crime Summits. Here’s where we stand in the spring of 2008: Violent crime spiked dramatically in 2005 and 2006, with many jurisdictions showing double-digit percentage increases in homicides and other crimes; PERF’s surveys, while much smaller than the FBI’s massive Uniform Crime Reporting (UCR) system, seem to be a good sample of jurisdictions, because when the FBI released its UCR figures, they confirmed PERF’s finding of significantly higher violence in 2005 and 2006; Police agencies have responded to the higher crime levels quickly, implementing many types of programs designed to bring violent crime back down. The most common type of violence reduction strategy reported is hot-spots enforcement; It appears that the police anti-violence strategies are having an impact in many jurisdictions. PERF’s latest figures for all of 2007 show that in the same sample of 56 jurisdictions that proved accurate in 2005 and 2006, violent crime fell approximately 4 to 8 percent in all four categories tracked by PERF: homicide, robbery, aggravated assault, and aggravated assault with a firearm. Violent crime does remain volatile, however. Even though the total numbers of violent crimes in PERF’s sample of jurisdictions are down, many cities and counties are still reporting increases in violence. In fact, depending on the type of crime, our most recent numbers for all of 2007 show that 42 to 48 percent of the reporting jurisdictions reported increases in violence. Details: Washington, DC: Police Executive Research Forum, 2008. 43p. Source: Internet Resource: Critical Issues in Policing Series: Accessed April 15, 2011 at: http://www.policeforum.org/library/critical-issues-in-policing-series/HotSpots_v4.pdf Year: 2008 Country: United States URL: http://www.policeforum.org/library/critical-issues-in-policing-series/HotSpots_v4.pdf Shelf Number: 121365 Keywords: Crime ClustersCrime RatesCrime SurveysHot SpotsPolicingViolenceViolent Crime |
Author: Emeno, Karla Title: Space-Time Clustering and Prospective Hot-Spotting of Canadian crime Summary: Previous research has consistently shown that repeat crime victimization is common. More recently, research has shown that near repeat victimization is also common, whereby targets located in close proximity to previously victimized dwellings/people/vehicles (depending on the crime) are at an increased risk of also being victimized. However, this elevated risk is only temporary and subsides over time. This near repeat space-time clustering has been found across various crime types (e.g., burglary, theft from motor vehicle (TFMV), gun crime, etc.) as well as across jurisdictions. However, the precise space-time patterning of crimes is location-specific. To date, no published research exists that has examined near repeat victimization using Canadian data; the current study fills this gap. This dissertation consisted of 4 phases of analyses. Phase 1 determined the exact space-time clustering of three crime types (burglary, TFMV, common assault) across three Canadian cities (Edmonton, AB, Moose Jaw, MB, Saint John, NB). Phase 1 results found significant near repeat space-time clustering for Edmonton burglary, Edmonton TFMV, and Saint John TFMV, with the exact near repeat space-time pattern varying from one data file to the next. Phase 2 analyses used the time and distance over which crime clusters (as found in Phase 1) to generate prospective risk surfaces. Risk surfaces were also generated using two traditional hot-spotting methods. Overall, the various hotspot mapping techniques examined were found to be comparable in their accuracy at predicting future crime. Phase 3 examined whether it was possible to improve the accuracy of prospective hot-spotting by considering three different strategies. Although Phase 3 results suggested that the three strategies examined were not effective at improving predictive accuracy of the maps, some interesting trends did emerge, which may have practical implications. Finally, Phase 4 investigated whether near repeat burglaries in one Canadian city (Edmonton, AB) were more likely to be committed by the same offender than more distant burglaries. Phase 4 results suggested that serial offending by the same offender offers a viable explanation for near repeat crime. The theoretical and practical implications of these results, as well as some limitations and directions for future research, are also discussed. Details: Ottawa, ONT: Carleton University, 2014. 2013p. Source: Internet Resource: Dissertation: Accessed July 20, 2015 at: https://curve.carleton.ca/system/files/etd/a6e73756-e339-4660-8112-daf4149fd585/etd_pdf/efb739e7e175a2b0b7aabde71546e822/emeno-spacetimeclusteringandprospectivehotspotting.pdf Year: 2014 Country: Canada URL: https://curve.carleton.ca/system/files/etd/a6e73756-e339-4660-8112-daf4149fd585/etd_pdf/efb739e7e175a2b0b7aabde71546e822/emeno-spacetimeclusteringandprospectivehotspotting.pdf Shelf Number: 136114 Keywords: Burglary Crime ClustersCrime Hotspots Crime Mapping Repeat Victimization |
Author: Harrell, Kim Title: The Predictive Accuracy of Hotspot Mapping of Robbery over Time and Space Summary: Police forces use hotspot mapping to provide a targeted approach to resource allocation, ensuring police officers are despatched to areas of high crime where their presence will have the most impact. Hotspot intelligence products are reliant on crime data sourced from police databases, and positional errors in this data will have an impact on the accuracy of the hotspot maps produced. The location of crime hotspots varies across both space and time. Despite this the use of temporal information is still rare because of the difficulties in pinpointing crime to an exact point in time, though crimes involving attended property provide the opportunity to record time more accurately. This research aimed to evaluate both the impact positional errors and the addition of temporal information have on the predictive accuracy of hotspot mapping of crime that inherently occurs in outdoor or public places through the utilisation of robbery data. Using robbery data recorded during a 24 month period (1st April 2011 - 31st March 2013) in the West Midlands Police Local Policing Unit of Birmingham South, the number and magnitude of positional errors present in the raw data was measured based on the Euclidean distance between recorded and actual locations of robbery offences. Positional errors ranging between 1 - 3766 metres were responsible for the suppression of a number of high intensity hotspots in the study area, and only 31% of all robberies had been allocated to the correct geographical location. To determine the influence of temporal information a mid-point measurement date was employed, and the ability of the retrospective robbery hotspots to predict the location of prospective robbery events measured, based on police shift periods, days of the week and spatial data alone. The results suggest that shift periods provide the best prospect for police forces utilising temporal information to improve the predictive ability of hotspot maps. Care needs to be taken to select a large enough dataset that will ensure sufficient clustering of crime points, and further research could be extended to incorporate different crime types. Details: Manchester, UK: University of Salford, Manchester, 2014. 159p. Source: Internet Resource: Dissertation: Accessed November 14, 2015 Year: 2014 Country: United Kingdom URL: Shelf Number: 137769 Keywords: Crime AnalysisCrime ClustersCrime ForecastingCrime HotspotsCrime MappingRobbery |
Author: Engel, Robin S. Title: Cincinnati Police Department 15-Minute Hotspot Policing Experiment Summary: Hotspot policing is an intensified, intermittent patrol in specified crime clusters. This approach is not a constant, security guard-style presence, but rather approximates a crackdown-backoff approach where police are present at a hotspot for an intermittent yet brief period of time; typically fifteen minutes every two hours (see Koper, 1995 for more detail). Importantly, a sizable body of experimental research on hotspots policing led the National Research Council (NRC) Committee to Review Research on Police Policy and Practices (2004, p. 250) to conclude that studies of "focused police resources on crime hotspots provided the strongest collective evidence of police effectiveness that is now available." In an effort to promote evidence based practices to address specific types of crime problems, the Cincinnati Police Department (CPD) partnered with researchers from the Institute of Crime Sciences (ICS) at the University of Cincinnati. The CPD has been using crime analysis for deployment purposes to address serious, violent, and persistent street crimes since 2007. The purpose of the CPD's 15-Minute Hotspot Patrol Experiment was to further reduce the likelihood of victimization associated in high-risk areas throughout the city. The CPD was interested in implementing a hotspot policing experiment as a way to police more efficiently and to potentially build upon data-driven policing approaches already being used in the department (e.g., Statistical and Tactical Analytic Review for Solutions (STARS) is an oversight mechanism used to enhance strategic deployment for crime reduction). Of particular interest to CPD administrators was the ability to determine whether different types of policing practices within hotspot locations could lead to discernible differences in crime incidents. To identify Cincinnati's crime hotspots, Uniform Crime Report (UCR) Part I crime data collected by the CPD, ranging from November 2010 - November 2012 (N=48,568) were geocoded in ArcGIS and merged with Cincinnati street segments (N=13,550). This data merger provided information regarding how many serious crimes were committed on individual street segments within the city. Recent studies have indicated that it is important to focus on crime trends at micro-units of analysis due to street-to-street variability in crime patterns (Groff, Weisburd, & Yang, 2010). As a result, the most recent hotspot experiments focus police efforts at these micro-places, including individual street segments, to address patterns in crime variability by place and focus police resources more efficiently (Telep et al., 2012). To be consistent with these most recent research developments, the Cincinnati strategy focused police attention at specific street segments. Given the CPD's focus on reducing violence, a weighting system was designed where violent crimes were weighted proportionally more than property crimes based on their level of seriousness. Using this weighting system, crime counts for each street segment were calculated. When determining whether a street segment was considered "hot," both persistent and emerging crime trends were identified. A persistent hotspot was one identified based on reported crimes over the past three years, while an emerging hotspot was one identified based only on reported crimes over the last 12 months (Jan 1 - Dec 31, 2012). After determining hot street segments based on the process above, CPD District Commanders were consulted to verify if the selected street segments were appropriate hotspots based on their direct experiences. Ultimately, 54 individual street segments were identified for inclusion in the experiment. Each identified hot street segment was then individually paired with another hot street segment (with a similar amount and type of crime), creating 27 matched hotspot pairs. These 27 matched hotspot pairs were then randomly assigned to either treatment or control conditions. Note, that a street was considered a "treated street segment" if it received additional patrols. A "non-treated street segment" was a street that was matched to a treated street segment but did not receive additional patrols. Those assigned to treatment were further randomly assigned to one of three types of treatments: 1) stationary - sit in parked patrol car, 2) stationary with lights - sit in parked patrol car with emergency lights activated, or 3) proactive - park car and walk. Each crime hotspot selected for treatment received an additional "dose" of directed patrol seven times per day. Specifically, these treatment conditions were applied on the same streets for 15 minutes every two hours, during the hours of 12:00 pm - 2:00 am for a 5-month period. The matched control street segments were patrolled as they normally would be, absent the experiment. In the most general terms, we determine the impact of the additional patrols in three ways. Analysis 1 compares the treated street segments directly to their non-treated matched street segments during the intervention period (Feb 1- Jun 30, 2013). Analysis 2 compares the crimes that occurred on the treated street segments during the intervention period to the average number of crimes occurring during the seasonal pre-intervention period on those same treated street segments. Then the crimes that occurred on the non-treated street segments during the intervention period are compared to the number of crimes on those same non-treated street segments during the seasonal pre-intervention period. These differences are ultimately compared to one another to determine an overall effect. Analysis 3 compares the differences within the treated street segments by the type of treatment: stationary, lights, or foot. Details: Cincinnati: Institute of Crime Science, University of Cincinnati, 2014. 28p. Source: Internet Resource: Accessed June 11, 2016 at: https://ext.dps.state.oh.us/OCCS/Pages/Public/Reports/ICS_CPD%2015%20Minute%20Hotspot%20Policing%20Experiment_FINAL.pdf Year: 2014 Country: United States URL: https://ext.dps.state.oh.us/OCCS/Pages/Public/Reports/ICS_CPD%2015%20Minute%20Hotspot%20Policing%20Experiment_FINAL.pdf Shelf Number: 139391 Keywords: Crime AnalysisCrime ClustersEvidence-Based PracticesHotspot PolicingPolice Patrol |
Author: Ingram, Matthew C. Title: Targeting Violence Reduction in Brazil: Policy Implications from a Spatial Analysis of Homicide Summary: Violence in Latin America generates heavy economic, social and political costs for individuals, communities and societies. A particularly pernicious effect of violence is that it undermines citizen confidence in democracy and in their own government. Responding to public fear, politicians across the region have hastily adopted a wide range of policy responses to violence, ranging from militarizing public security, to 'mano dura' crack downs, to negotiating truces with organized crime, to decriminalizing illicit economic activity. Although many of these policies are politically expedient, few are based on evidence of how public policy actually affects rates of violence. By contrast, this paper examines how violence clusters within a country-Brazil-to study how public policies affect homicide rates and how these policies might be further tailored geographically to have greater impact. Brazil provides a particularly useful case for examining the effectiveness of violence-reduction strategies because of the availability of comparable data collected systematically across 5562 municipal units. This allows for an explicitly spatial approach to examining geographic patterns of violence-how violence in one municipality is related to violence in neighboring municipalities, and how predictors of violence are also conditioned by geography. The key added value of the spatial perspective is that it addresses the dependent structure of the data, accounting for the fact that units of analysis (here, municipalities) are connected to each other geographically. In this way, the spatial perspective accounts for the fact that what happens in nearby units may have a meaningful impact on the outcome of interest in a home, focal unit. Thus, the spatial approach is better able to examine compelling phenomena like the spread of violence across units. We visualize data on six types of homicide-aggregate homicides, homicides of men, homicides of women (i.e., "femicides"), firearm-related homicides, youth homicides (ages 15-29) and homicides of victims identified by race as either black or brown (mulatto), i.e., non-white victims-all for 2011, presenting these data in maps. We adopt a municipal level of analysis, and include homicide data from 2011 for the entire country, i.e., on all 5562 municipalities across 27 states (including the Federal District). This allows us to develop maps that identify specific municipalities that constitute cores of statistically significant clusters of violence for each type of homicide. These clusters offer a useful tool for targeting policies aimed at reducing violence. We then develop an analysis based on a spatial regression model, using predictors from the 2010 census and other official sources in Brazil. This paper finds that areas with higher rates of marginalization and of households headed by women who also work and have young children experience higher rates of homicide, which suggests increased support for policies aimed at reducing both marginalization and family disruption. More specifically, the paper finds that policies that expand local coverage of the Bolsa Familia poverty reduction program and reduce the environmental footprint of large, industrial development projects tend to reduce homicide rates, but primarily for certain types of homicide. Thus, violence-reduction policies need to be targeted by type of violence. In addition, the spatial analysis presented in the paper suggests that violence-reduction policies should be targeted regionally rather than at individual communities - informed by the cluster analysis and the spatial regression. Finally, this paper argues that policies aimed at femicides, gun-related homicides, youth homicides and homicides of non-whites should be especially sensitive to geographic patterns, and be built around territorially-targeted policies over and above national policies aimed at homicide more generally. Details: Washington, DC: Latin America Initiative Foreign Policy at BROOKINGS, 2014. 18p. Source: Internet Resource: Policy Brief: Accessed April 28, 2017 at: https://www.brookings.edu/wp-content/uploads/2016/06/Ingram-Policy-Brief.pdf Year: 2014 Country: Brazil URL: https://www.brookings.edu/wp-content/uploads/2016/06/Ingram-Policy-Brief.pdf Shelf Number: 145192 Keywords: Crime AnalysisCrime ClustersCrime HotspotsFemicidesGun-Related ViolenceHomicidesViolenceViolence PreventionViolent Crime |