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Bowers, Kate; Johnson, Shane D.

doi:10.1023/a:1024909009240

Cited by 151 publications

(33 citation statements)

References 6 publications

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“…Environmental features include weather and temperature, or physical characteristics such as Natural Surveillance [53]. In addition, displacement features like police actions, controls, and buffer zones can be considered to simulate displacement effects more precisely [54].…”

Section: Discussionmentioning

confidence: 99%

Grid-Based Crime Prediction Using Geographical Features

Lin

Yen

2018

IJGI

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Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design's baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline.

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Section: Discussionmentioning

confidence: 99%

Grid-Based Crime Prediction Using Geographical Features

Lin

Yen

2018

IJGI

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“…The result of this dynamic response by motivated offenders may be higher overall crime rates farther from the arrest location in subsequent time periods, revealing how law enforcement actions could displace crime in space-time (e.g. Eck 1993;Bowers and Johnson 2003;Guerette and Bowers 2009;Bowers et al 2011). Displacement caused by an arrest would be revealed by, all other things being equal, higher numbers of intentional fires in subsequent time periods in areas more distant from the arrest location.…”

Section: Capable Guardiansmentioning

confidence: 99%

Net reductions or spatiotemporal displacement of intentional wildfires in response to arrests? Evidence from Spain

Prestemon

Butry

Amil

et al. 2019

Int. J. Wildland Fire

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Research to date has not examined how the impacts of arrests manifest across space and time in environmental crimes. We evaluate whether arrests reduce or merely spatiotemporally displace intentional illegal outdoor firesetting. Using municipality-level daily wildfire count data from Galicia, Spain, from 1999 to 2014, we develop daily spatiotemporal ignition count models of agricultural, non-agricultural and total intentional illegal wildfires as functions of spatiotemporally lagged arrests, the election cycle, seasonal and day indicators, meteorological factors and socioeconomic variables. We find evidence that arrests reduce future intentional illegal fires across space in subsequent time periods.

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“…What is not clear is what degree of concentration of crime is preferable. Crime prevention strategies might result in some displacement of crime (Guerette and Bowers 2009;Johnson et al 2014) from one place to the other, to an alternative victim (which is referred to as target displacement), to different times of the day, to a different tactic or to a different type of crime (Bowers and Johnson 2003), which has an effect on the levels of concentration of crime, but then this promotes the question: is it desirable to have less concentrated crime? Clearly, a population with overall less crime is desirable, but let us compare two populations with the same number of crimes.…”

Section: Less or More Concentrated?mentioning

confidence: 99%

“…On the other hand, a low degree of concentration of crime means more victims, which makes policies less efficient, and it might deteriorate the perception of security in a particular region. Suffering a crime when there is a low degree of concentration of crime becomes a matter of bad luck and not a matter of being socially deprived, a minority, a female or any other attribute which perhaps increases the chances of suffering a crime, therefore it could be considered as a more fair distribution of crime (Bowers and Johnson 2003) as opposed to a population with a high degree of concentration of crime.…”

Section: Less or More Concentrated?mentioning

confidence: 99%

Measuring the Distribution of Crime and Its Concentration

2017

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Objectives Generally speaking, crime is, fortunately, a rare event. As far as modelling is concerned, this sparsity of data means that traditional measures to quantify concentration are not appropriate when applied to crime suffered by a population. Our objective is to develop a new technique to measure the concentration of crime which takes into account its low frequency of occurrence and its high degree of concentration in such a way that this measure is comparable over time and over different populations.Methods This article derives an estimate of the distribution of crime suffered by a population based on a mixture model and then evaluates a new and standardised measurement of the concentration of the rates of suffering a crime based on that distribution. ResultsThe new measure is successfully applied to the incidence of robbery of a person in Mexico and is able to correctly quantify the concentration crime in such a way that is comparable between different regions and can be tracked over different time periods. ConclusionsThe risk of suffering a crime is not uniformly distributed across a population. There are certain groups which are statistically immune to suffering crime but there are also groups which suffer chronic victimisation. This measure improves our understanding of how patterns of crime can be quantified allowing us to determine if a Electronic supplementary material The online version of this article (doi:10.1007/s10940-017-9354-9) contains supplementary material, which is available to authorized users. prevention policy results in a crime reduction rather than target displacement. The method may have applications beyond crime science.

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Untitled

Cited by 151 publications

References 6 publications

Grid-Based Crime Prediction Using Geographical Features

Grid-Based Crime Prediction Using Geographical Features

Net reductions or spatiotemporal displacement of intentional wildfires in response to arrests? Evidence from Spain

Measuring the Distribution of Crime and Its Concentration

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