Do Mobile Phone Data Provide a Better Denominator in Crime Rates and Improve Spatiotemporal Predictions of Crime?

Rummens, Anneleen; Snaphaan, Thom; Weghe, Nico Van de; Poel, Dirk Van den; Pauwels, Lieven; Hardyns, Wim

doi:10.3390/ijgi10060369

Cited by 13 publications

(7 citation statements)

References 81 publications

(115 reference statements)

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“…The most important task is to track the cause of crime in a specific space for an extended time and to find a general application strategy of environmental criminology. Using big data ( [72,73]) and new techniques, such as machine learning [74], should be actively accepted to enhance the reliability of environmental criminology.…”

Section: Discussionmentioning

confidence: 99%

Pattern and Explanation of Inter-City Crime Variation in South Korea

Kim

Seong²

2022

Sustainability

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The primary purpose of this paper is to test the applicability of environmental criminology in South Korea. Moreover, it explores effective strategies from a spatial planning perspective by taking control of diverse spatial planning factors. The study area is South Korea, and the base year is 2016. A spatial econometric model is built to analyze the relationship between the built environment and three crimes (theft, violence, and sexual assault). As a result, the best spatial regression models for violent crime rate and sexual assault rate are a spatial error model (SEM) and a spatial autoregressive model (SAC), respectively. The most prominent finding is that the regression results in the three crimes are slightly different. The broken windows effect was negligible for significant crimes in South Korea. The influence of regional disorders on the incidence of crimes was marginal. In the three crime types, mixed land use affected rising crime rates, which aligns with some previous studies that mixed land use increases the likelihood of crime incidences. In contrast with a series of relevant works, brighter nighttime light has not effectively decreased crimes in South Korea. In South Korea, closed-circuit television (CCTV) did not play a role in deterring crimes. Lastly, socio-economic characteristics were closely connected with crime rates in South Korea. The theft rate, violent crime rate, and sexual assault rate confirm the reliability of environmental criminology. Although this study has examined the likelihood of applying environmental criminology, further research and discussions are followed for concrete plans.

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Section: Discussionmentioning

confidence: 99%

Pattern and Explanation of Inter-City Crime Variation in South Korea

Kim

Seong²

2022

Sustainability

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“…To depict these mobility behaviors, the cited authors focused on spatiotemporal features, such as the total numbers of calls or mobile phone devices for each cell tower every hour. Other researchers [ 27 , 28 , 31 , 32 , 33 ] have similarly relied on defining specific populations’ spatiotemporal patterns to investigate the relationships between human dynamics and spatiotemporal crime patterns. These studies have tracked features of ambient populations that indicate specific areas are at risk of criminal activities or that individuals are in danger of becoming victims of theft or assault, thereby confirming that ambient populations’ configurations have a significant impact on crime patterns and rates.…”

Section: Human Mobility Patternsmentioning

confidence: 99%

Mobile Phone Data: A Survey of Techniques, Features, and Applications

Okmi

Por

Ang

et al. 2023

Sensors

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Due to the rapid growth in the use of smartphones, the digital traces (e.g., mobile phone data, call detail records) left by the use of these devices have been widely employed to assess and predict human communication behaviors and mobility patterns in various disciplines and domains, such as urban sensing, epidemiology, public transportation, data protection, and criminology. These digital traces provide significant spatiotemporal (geospatial and time-related) data, revealing people’s mobility patterns as well as communication (incoming and outgoing calls) data, revealing people’s social networks and interactions. Thus, service providers collect smartphone data by recording the details of every user activity or interaction (e.g., making a phone call, sending a text message, or accessing the internet) done using a smartphone and storing these details on their databases. This paper surveys different methods and approaches for assessing and predicting human communication behaviors and mobility patterns from mobile phone data and differentiates them in terms of their strengths and weaknesses. It also gives information about spatial, temporal, and call characteristics that have been extracted from mobile phone data and used to model how people communicate and move. We survey mobile phone data research published between 2013 and 2021 from eight main databases, namely, the ACM Digital Library, IEEE Xplore, MDPI, SAGE, Science Direct, Scopus, SpringerLink, and Web of Science. Based on our inclusion and exclusion criteria, 148 studies were selected.

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“…This measure is a proxy indicator of the ambient population in a given spatial unit: how many potential targets are there, on average, for work, education, shopping, or recreation purposes (Andresen 2011). Ambient population predicts personal robbery and other offences against mobile targets (such as sexual assaults) better than residential population, thus forming the better single indicator of target distribution (Andresen 2011;Andresen and Jenion 2010;Rummens et al 2021). We thus adopted the same measure of opportunity for sex offences.…”

Section: Opportunity Variablesmentioning

confidence: 99%

Relationships Between Offenders’ Crime Locations and Different Prior Activity Locations as Recorded in Police Data

Curtis‐Ham

Bernasco

Medvedev

et al. 2022

J Police Crim Psych

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Understanding the relationships between individual offenders’ crime locations and their prior activity locations is important to enable individual level predictions to support crime prevention and investigation strategies. This study examined a wider range of crimes and activity locations than included in previous studies, to determine whether offenders are more likely to commit crime near some types of activity locations than others. Using discrete spatial choice models, we identified relationships between proximity to pre-crime activity locations recorded in a police database (e.g., offenders’ homes, family members’ homes, schools, prior crimes, and other police interactions) and the locations of 17,054 residential burglaries, 10,353 non-residential burglaries, 1,977 commercial robberies, 4,315 personal robberies, and 4,421 extra-familial sex offences in New Zealand. Offenders were generally more likely to commit crime closer to their activity locations than farther away, and closer to those visited more frequently (e.g., home versus family homes) or more likely to impart relevant knowledge about crime opportunities (e.g., prior crimes versus prior victim or witness locations). The observed patterns for different activity locations and crime types broadly support a recently proposed extension to crime pattern theory and illustrate the benefits of differentiating activity location and crime types when examining criminal spatial behaviour. The results have implications for offender risk assessment and management, and geographic profiling in police investigations.

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Do Mobile Phone Data Provide a Better Denominator in Crime Rates and Improve Spatiotemporal Predictions of Crime?

Cited by 13 publications

References 81 publications

Pattern and Explanation of Inter-City Crime Variation in South Korea

Pattern and Explanation of Inter-City Crime Variation in South Korea

Mobile Phone Data: A Survey of Techniques, Features, and Applications

Relationships Between Offenders’ Crime Locations and Different Prior Activity Locations as Recorded in Police Data

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