How Many Ways Do Offenders Travel -- Evaluating the Activity Paths of Offenders

Frank, Richard; Kinney, Bryan

doi:10.1109/eisic.2012.18

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…al. [14] have proposed a tool to detect suspicious criminal activities based on Decision Tree classification technique. This is done by detecting suspicious e-mails about criminal activities by applying enhanced decision tree classifier.…”

Section: Literaturementioning

confidence: 99%

Geographical Information System Based Safe Path Recommender

Jain¹,

Kumar²,

Parashar³

et al. 2019

IJITEE

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The threat to women safety, especially on road, has become a major concern these days and the problem is increasing dramatically. Women are unsafe not just in the night but also in the day, they fear getting assaulted not only in public transport but also while travelling in cars and two-wheelers. People are harassed on road in various ways like robberies, killings, etc., but women go through even bigger traumas. Cases like, rape in moving cars, sexual assault and robberies by forcefully stopping the vehicle, teasing women on two-wheelers, etc. have increased to an alarming level. This study is an initiative to safe-guard the self-esteem of our women and prevent them from any ugly experience, while on road. It aims to find and suggest the safest path for women and tells the level of crime of the locations in that path, while travelling from one place to another. The system determines the various paths from source to destination, as entered by the user on the map interface, by calculating the danger index. This index is found on the basis of the crime rate of the grouped locations. These locations are grouped by clustering them on the basis of the crime history of that location. The app as a result suggests the best three paths in different colours (Green, Red, Yellow).

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Section: Literaturementioning

confidence: 99%

Geographical Information System Based Safe Path Recommender

Jain¹,

Kumar²,

Parashar³

et al. 2019

IJITEE

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“…Frank [19] proposed an approach to infer the activity paths of all offenders in a region based on their crime and home locations. Assuming the home location as the centre of an offender's movements, the orientation of activity paths of each individual offender were calculated so as to determine the major directions, relative to their home location, into which they tended to move to commit crimes.…”

Section: Crime Location Predictionmentioning

confidence: 99%

“…The method proposed in [36] and [13] discover offender home locations based on his crime locations. And finally, the output of the method proposed in [19] is locations which are centres of interest for committing crime. To provide a meaningful comparison, we compare CrimeTracer to different Collaborative Filtering methods which are used for location recommendation in location-based social networks [51,55].…”

Section: Crime Location Predictionmentioning

confidence: 99%

Personalized crime location prediction

et al. 2016

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Crime reduction and prevention strategies are vital for policymakers and law enforcement to face inevitable increases in urban crime rates as a side effect of the projected growth of urban population by the year 2030. Studies conclude that crime does not occur uniformly across urban landscapes but concentrates in certain areas. This phenomenon has drawn attention to spatial crime analysis, primarily focusing on crime hotspots, areas with disproportionally higher crime density. In this paper, we present CrimeTracer1, a personalized random walk-based approach to spatial crime analysis and crime location prediction outside of hotspots. We propose a probabilistic model of spatial behaviour of known offenders within their activity spaces. Crime Pattern Theory concludes that offenders, rather than venture into unknown territory, frequently select targets in or near places they are most familiar with as part of their activity space. Our experiments on a large real-world crime dataset show that CrimeTracer outperforms all other methods used for location recommendation we evaluate here.

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“…Frank [16] proposed an approach to infer the activity Paths of all offenders in a region based on their crime and home locations. Assuming the home location as the center of an offender's movements, the orientation of Activity Paths of each individual offender were calculated so as to determine the major directions, relative to their home location, into which they tended to move to cOlmnit crimes.…”

Section: Introductionmentioning

confidence: 99%

“…The method proposed in [5] and [15] discover offender home locations based on his crime locations. And finally the output of the method proposed in [16] is locations which are centers of interest for cOlmnitting crime. However we compare CRIME TRACER to different Collaborative Filtering methods which are used for location recommendation in location-based social networks [19], [20].…”

mentioning

confidence: 99%

CRIMETRACER: Activity space based crime location prediction

Tayebi

Ester

Glässer

et al. 2014

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)

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Crime reduction and prevention strategies are vital for policymakers and law enforcement to face inevitable increases in urban crime rates as a side ef1'ect of the projected growth of urban population by the year 2030. Studies conclude that crime does not occur uniformly across urban landscapes but concen trates in certain areas. This phenomenon has drawn attention to spatial crime analysis, primarily focusing on crime hotspots, areas with disproportionally higher crime density. In this paper we present CRIMETRACER, a personalized random walk based approach to spatial crime analysis and crime location prediction outside of hotspots. We propose a probabilistic model of spatial behavior of known ofl'enders within their activity space. Crime Pattern Theory concludes that offenders, rather than venture into unknown territory, frequently commit opportunistic crimes and serial violent crimes by taking advantage of opportunities they encounter in places they are most familiar with as part of their activity space. Our experiments on a large real-world crime dataset show that CRIMETRACER outperforms all other methods used for location recommendation we evaluate here.

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How Many Ways Do Offenders Travel -- Evaluating the Activity Paths of Offenders

Cited by 5 publications

References 7 publications

Geographical Information System Based Safe Path Recommender

Geographical Information System Based Safe Path Recommender

Personalized crime location prediction

CRIMETRACER: Activity space based crime location prediction

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