Using Social Media to Measure Temporal Ambient Population: Does it Help Explain Local Crime Rates?

Hipp, John R.; Bates, Christopher J.; Lichman, Moshe; Smyth, Padhraic

doi:10.1080/07418825.2018.1445276

Cited by 77 publications

(96 citation statements)

References 55 publications

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“…Previous studies have used various data sources to explore the role of ambient populations upon crime rates (Andresen 2006, Malleson and Andresen 2015a, b, Boivin 2018, Kounadi et al 2018, Hipp et al 2019). Due to the recency of the period under study, these data were unavailable.…”

Section: Mobility Datamentioning

confidence: 99%

Crime and coronavirus: social distancing, lockdown, and the mobility elasticity of crime

et al. 2020

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Governments around the world restricted movement of people, using social distancing and lockdowns, to help stem the global coronavirus (COVID-19) pandemic. We examine crime effects for one UK police force area in comparison to 5-year averages. There is variation in the onset of change by crime type, some declining from the WHO 'global pandemic' announcement of 11 March, others later. By 1 week after the 23 March lockdown, all recorded crime had declined 41%, with variation: shoplifting (− 62%), theft (− 52%), domestic abuse (− 45%), theft from vehicle (− 43%), assault (− 36%), burglary dwelling (− 25%) and burglary non-dwelling (− 25%). We use Google Covid-19 Community Mobility Reports to calculate the mobility elasticity of crime for four crime types, finding shoplifting and other theft inelastic but responsive to reduced retail sector mobility (MEC = 0.84, 0.71 respectively), burglary dwelling elastic to increases in residential area mobility (− 1), with assault inelastic but responsive to reduced workplace mobility (0.56). We theorise that crime rate changes were primarily caused by those in mobility, suggesting a mobility theory of crime change in the pandemic. We identify implications for crime theory, policy and future research.

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Section: Mobility Datamentioning

confidence: 99%

Crime and coronavirus: social distancing, lockdown, and the mobility elasticity of crime

et al. 2020

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“…Another is to attempt to measure the ambient population of individuals using a particular space, which tends to diverge from residential census estimates (Andresen 2006). Studies have estimated the ambient population via journey-to-work estimates (Boivin and Felson 2018;Stults and Hasbrouck 2015), twitter postings (Hipp et al 2018;Malleson and Andresen 2015), measures based on cell phone usage (Song et al 2019), or estimates derived from satellite imagery (Andresen and Jenion 2008).…”

Section: Explaining Places At High Risk Of Crimementioning

confidence: 99%

Mapping the Risk Terrain for Crime Using Machine Learning

Wheeler

Steenbeek

2020

J Quant Criminol

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Objectives: We illustrate how a machine learning algorithm, Random Forests, can provide accurate long-term predictions of crime at micro places relative to other popular techniques. We also show how recent advances in model summaries can help to open the 'black box' of Random Forests, considerably improving their interpretability. Methods: We generate long-term crime forecasts for robberies in Dallas at 200 by 200 feet grid cells that allow spatially varying associations of crime generators and demographic factors across the study area. We then show how using interpretable model summaries facilitate understanding the model's inner workings. Results: We find that Random Forests greatly outperform Risk Terrain Models and Kernel Density Estimation in terms of forecasting future crimes using different measures of predictive accuracy, but only slightly outperform using prior counts of crime. We find different factors that predict crime are highly non-linear and vary over space. Conclusions: We show how using black-box machine learning models can provide accurate micro placed based crime predictions, but still be interpreted in a manner that fosters understanding of why a place is predicted to be risky.

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“…Instead, population estimates are considered to represent victims, offenders, and guardians in one crowd. Hipp, Bates, Lichman and Smyth (2018) have engaged with this issue by conceptualising guardians as a homogeneous subset of the ambient population. However this is not a direct measure of guardians, and does not account for availability, capability, and willingness to intervene of the individual guardians.…”

Section: Guardianship and Crime Preventionmentioning

confidence: 99%

Exploring Spatial Patterns of Guardianship Through Civic Technology Platforms

Solymosi

2018

Criminal Justice Review

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Civic technologies levy advances in digital tools to promote civic engagement, giving people a voice to participate in public decision making. While democratising participation, the use of such civic tech also leaves behind a digital trace of the behaviour of its users. This paper uses such a digital trace to explore spatial patterns in active guardianship of public space. Through mapping people's participation in a platform for reporting neighbourhood concerns (a form of digitally enabled guardianship), the spatial range of guardianship is unpacked using exploratory spatial data analysis. Typologies for guardianship behaviour are then created using k-means clustering. Results provide an insight into the heterogeneity of spatial behaviour of different groups of guardians outside the home environment. Guardians appear to not be limited to activity within a neighbourhood, and instead cover a larger awareness space with nodes and paths, and also show distinct patterns, indicating heterogeneity in guardianship patterns. Recommendations are made to consider operationalising guardians as heterogeneous, and active in their entire activity space, rather than homogeneous groups assigned as crime prevention forces to a residential area.

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Using Social Media to Measure Temporal Ambient Population: Does it Help Explain Local Crime Rates?

Cited by 77 publications

References 55 publications

Crime and coronavirus: social distancing, lockdown, and the mobility elasticity of crime

Crime and coronavirus: social distancing, lockdown, and the mobility elasticity of crime

Mapping the Risk Terrain for Crime Using Machine Learning

Exploring Spatial Patterns of Guardianship Through Civic Technology Platforms

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