Mining Mobile Phone Data to Investigate Urban Crime Theories at Scale

Traunmueller, Martin; Quattrone, Giovanni; Capra, Licia

doi:10.1007/978-3-319-13734-6_29

Cited by 53 publications

(33 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In data mining, newer types of data are used in the study. For example, there are studies using twitter to predict crime [41,20], and studies using cellphone data [38,7] to evaluate crime and social theories at scale. Overall, the existing work on crime prediction can be categorized into three paradigms.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Crime Rate Inference with Big Data

Wang

Kifer

Graif

et al. 2016

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

163

100

View full text Add to dashboard Cite

Crime is one of the most important social problems in the country, affecting public safety, children development, and adult socioeconomic status. Understanding what factors cause higher crime is critical for policy makers in their efforts to reduce crime and increase citizens' life quality. We tackle a fundamental problem in our paper: crime rate inference at the neighborhood level. Traditional approaches have used demographics and geographical influences to estimate crime rates in a region. With the fast development of positioning technology and prevalence of mobile devices, a large amount of modern urban data have been collected and such big data can provide new perspectives for understanding crime. In this paper, we used large-scale Point-Of-Interest data and taxi flow data in the city of Chicago, IL in the USA. We observed significantly improved performance in crime rate inference compared to using traditional features. Such an improvement is consistent over multiple years. We also show that these new features are significant in the feature importance analysis.

show abstract

Section: Related Workmentioning

confidence: 99%

“…The paper constructs association rules from population demographics in communities. In another paper [38], the authors use computational methods to validate various social theories at a large scale. They used mobile phone data in London, from which they mine the people dynamics as features to correlate with crime.…”

Section: Related Workmentioning

confidence: 99%

Crime Rate Inference with Big Data

Wang

Kifer

Graif

et al. 2016

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

163

100

View full text Add to dashboard Cite

show abstract

“…Mao et al used mobile phone records to predict economic indexes of ten areas of high economic activity in Cote d'Ivoire [27]. Traunmueller et al also used mobile phone records but did so to test existing urban theories (from, e.g., Jane Jacobs' work) at scale [41].…”

Section: Related Workmentioning

confidence: 99%

The Digital Life of Walkable Streets

Quercia

Aiello

Schifanella

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

View full text Add to dashboard Cite

Walkability has many health, environmental, and economic benefits. That is why web and mobile services have been offering ways of computing walkability scores of individual street segments. Those scores are generally computed from survey data and manual counting (of even trees). However, that is costly, owing to the high time, effort, and financial costs. To partly automate the computation of those scores, we explore the possibility of using the social media data of Flickr and Foursquare to automatically identify safe and walkable streets. We find that unsafe streets tend to be photographed during the day, while walkable streets are tagged with walkability-related keywords. These results open up practical opportunities (for, e.g., room booking services, urban route recommenders, and real-estate sites) and have theoretical implications for researchers who might resort to the use social media data to tackle previously unanswered questions in the area of walkability.

show abstract

“…In data mining, newer types of data are used. For example, studies use twitter to predict crime [30], [31], and cellphone data [32], [33] to evaluate crime and social theories at scale. Overall, existing work on crime prediction can be categorized into three paradigms.…”

Section: Related Workmentioning

confidence: 99%

Non-Stationary Model for Crime Rate Inference Using Modern Urban Data

Wang

Yao

Kifer

et al. 2019

IEEE Trans. Big Data

View full text Add to dashboard Cite

Crime is one of the most important social problems in the country, affecting public safety, children development, and adult socioeconomic status. Understanding what factors cause higher crime rate is critical for policy makers in their efforts to reduce crime and increase citizens’ life quality. We tackle a fundamental problem in our paper: crime rate inference at the neighborhood level. Traditional approaches have used demographics and geographical influences to estimate crime rates in a region. With the fast development of positioning technology and prevalence of mobile devices, a large amount of modern urban data have been collected and such big data can provide new perspectives for understanding crime. In this paper, we use large-scale Point-Of-Interest data and taxi flow data in the city of Chicago, IL in the USA. We observe significantly improved performance in crime rate inference compared to using traditional features. Such an improvement is consistent over multiple years. We also show that these new features are significant in the feature importance analysis. The correlations between crime and various observed features are not constant over the whole city. In order to address this geospatial non-stationary property, we further employ the geographically weighted regression on top of negative binomial model (GWNBR). Experiments have shown that GWNBR outperforms the negative binomial model.

show abstract

Mining Mobile Phone Data to Investigate Urban Crime Theories at Scale

Cited by 53 publications

References 17 publications

Crime Rate Inference with Big Data

Crime Rate Inference with Big Data

The Digital Life of Walkable Streets

Non-Stationary Model for Crime Rate Inference Using Modern Urban Data

Contact Info

Product

Resources

About