Mapping harmspots: An exploration of the spatial distribution of crime harm

Fenimore, Danielle M.

doi:10.1016/j.apgeog.2019.06.002

Cited by 23 publications

(27 citation statements)

References 43 publications

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“…The results of the analysis demonstrated what we believed at the onset – that Dallas PD could identify much smaller hot spots than the current TAAG areas in place and capture a much larger density of cost of crime per area. Like prior analysis of concentration of crime harm (Curtis-Ham & Walton, 2017; Fenimore, 2019; Norton et al., 2018; Weinborn et al., 2018), we find micro places that have high values of crime cost in clusters. Additionally, we show that such micro places have long term stability in the cost of crime, by showing that hot spots generated with historical data also capture high values of crime cost in the future (Norton et al., 2018).…”

Section: Discussionsupporting

confidence: 72%

“…Mitchell (2019) created the California Crime Harm Index using a measure of potential maximum prison days to evaluate the Sacramento Hot Spot Experiment in comparison with crime counts for the area. Fenimore (2019) used weighted kernel density estimates to show that while crime harm tended to be as concentrated as crime counts, crime clusters were in different areas than clusters based on crime counts. This was due to violent crimes contributing more to the weights, which were based on sentencing guidelines or the prior severity scores based on the Wolfgang survey.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The third novel contribution of the study is to evaluate the predictive accuracy of our identified cost of crime weighted hot spots using historical data to predict future crimes. While prior work has mapped harm spots (Curtis-Ham & Walton, 2017;Fenimore, 2019;Norton et al, 2018;Weinborn et al, 2017), the majority of that work has simply focused on crime concentration, and has not assessed the predictive accuracy of a technique to create such harmspots (for an exception, see Macbeth & Ariel, 2019). Additionally, most of that work has mapped harm spots at a specific pre-chosen unit of analysis (Curtis-Ham & Walton, 2017;Mitchell, 2019;Norton et al, 2018;Weinborn et al, 2018, for an exception see Fenimore, 2019 who used weighted kernel density estimates).…”

Section: Introductionmentioning

confidence: 99%

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Redrawing Hot Spots of Crime in Dallas, Texas

Wheeler¹,

Reuter

2020

Police Quarterly

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In this work we evaluate the predictive capability of identifying long term, micro place hot spots in Dallas, Texas. We create hot spots using a clustering algorithm, using law enforcement cost of responding to crime estimates as weights. Relative to the much larger current hot spot areas defined by the Dallas Police Department, our identified hot spots are much smaller (under 3 square miles), and capture crime cost at a higher density. We also show that the clustering algorithm captures a wide array of hot spot types; some one or two addresses, some street segments, and others an agglomeration of larger areas. This suggests identifying hot spots based on a specific unit of aggregation (e.g. addresses, street segments), may be less efficient than using a clustering technique in practice.

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Section: Discussionsupporting

confidence: 72%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Redrawing Hot Spots of Crime in Dallas, Texas

Wheeler¹,

Reuter

2020

Police Quarterly

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“…Other sources of data though, including estimates of ambient population and data from social media and smart devices can help moving forward in generating more precise estimates of risk (see Malleson & Andresen, 2015). We also suggest the value of looking at the harm generated by violent incidents (Fenimore, 2019; Weinborn et al, 2017), whether they be recorded by police or EMS. There is generally more harm associated with violence, but using crime harm indices for police data and considering how to adapt those tools for use with EMS data would be useful in future work.…”

Section: Discussionmentioning

confidence: 99%

Going Beyond the Blue: The Utility of Emergency Medical Services Data in Understanding Violent Crime

Hibdon

Telep

Huff

2021

Criminal Justice Review

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National victimization data suggest less than 50% of violent crime incidents are reported to the police. Official reports of crime to police, however, are often the only type of data used for the analysis of violence problems, the identification of geographic concentrations of violent crime, and the selection of targets for police and prevention resources. Yet, the question remains, are estimates of violent crime prevalence and location distorted from a unilateral reliance on police data? Here, we examine whether emergency medical service (EMS) data collected by the fire department are spatially concentrated in the same way as police data and whether these data can help identify instances of violence unreported to police in the city of Seattle between 2009 and 2011. We find high levels of concentration in both police and EMS data and evidence that new information is learned about the location of violence problems from utilizing multiple data sources. Overall, these findings contribute to a small but growing body of work that demonstrates the utility of nonconventional data in the identification of crime and harm concentrations of interest.

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“…Mapping the area involving most of the areas mentioned, different areas also have different potential [11]. In this case, the K-algorithm means a grouping that is used to group crime areas based on the standard of crime that occurred in the region.…”

Section: Introductionmentioning

confidence: 99%

Implementation of K-Means Clustering and Weighted Products in Determining Crime-Prone Locations

Rahmatika

Sediyono

Widodo

2020

KINETIK

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Crime is an act that violates the law. The number of criminal acts that occur becomes a social problem that makes the community and the police uneasy. Increasing the number of crimes is a problem in the social aspect. This research aims to build an information system to provide information on areas prone to a crime that can help the police to speed up the crime resolution process. The grouping process uses the k-means method used to classify based on the level of vulnerability of the area, grouping crime is a good strategy in improving crime prevention planning. In addition to the k-means clustering method, we also use the weighted product method which functions as a recommendation ranking for crime selection. The grouping results obtained from this study are that there is 1 very vulnerable area, 5 areas in the vulnerable category and 3 safe areas. While the weighted product method produces melatilor kudus city 'as a prone to beating areas with a score of 0.182093.

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Mapping harmspots: An exploration of the spatial distribution of crime harm

Cited by 23 publications

References 43 publications

Redrawing Hot Spots of Crime in Dallas, Texas

Redrawing Hot Spots of Crime in Dallas, Texas

Going Beyond the Blue: The Utility of Emergency Medical Services Data in Understanding Violent Crime

Implementation of K-Means Clustering and Weighted Products in Determining Crime-Prone Locations

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