Time-varying relationships between land use and crime: A spatio-temporal analysis of small-area seasonal property crime trends

Quick, Matthew; Law, Jane; Li, Guangquan

doi:10.1177/2399808317744779

Cited by 27 publications

(30 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For small‐area data, the most common prior distribution used to model residual spatial structure is the intrinsic conditional autoregressive distribution (ICAR), which borrows information from nearby areas to estimate a spatially smoothed risk surface (Besag et al ; see Section “Prior distributions”). With a temporal adjacency matrix, the ICAR prior distribution has also been applied to model nonlinear time trends (Richardson et al ; Quick et al ). Past studies applying Bayesian spatiotemporal models to small‐area crime data have analyzed violent crime and property crime over two years (Law et al ; ), burglary over 4‐ and 8‐year time periods (Li et al ; ), and police confidence over 36 quarters (Williams et al ).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Modeling of Correlated Small‐Area Outcomes: Analyzing the Shared and Type‐Specific Patterns of Crime and Disorder

Quick

Law

2018

Geographical Analysis

Self Cite

View full text Add to dashboard Cite

This research applies a Bayesian multivariate modeling approach to analyze the spatiotemporal patterns of physical disorder, social disorder, property crime, and violent crime at the small‐area scale. Despite crime and disorder exhibiting similar spatiotemporal patterns, as hypothesized by broken windows and collective efficacy theories, past studies often analyze a single outcome and overlook the correlation structures between multiple crime and disorder types. Accounting for five covariates, the best‐fitting model partitions the residual risk of each crime and disorder type into one spatial shared component, one temporal shared component, and type‐specific spatial, temporal, and space–time components. The shared components capture the underlying spatial pattern and time trend common to all types of crime and disorder. Results show that population size, residential mobility, and the central business district are positively associated with all outcomes. The spatial shared component is found to explain the largest proportion of residual variability for all types of crime and disorder. Spatiotemporal hotspots of crime and disorder are examined to contextualize broken windows theory. Applications of multivariate spatiotemporal modeling with shared components to ecological crime theories and crime prevention policy are discussed.

show abstract

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Modeling of Correlated Small‐Area Outcomes: Analyzing the Shared and Type‐Specific Patterns of Crime and Disorder

Quick

Law

2018

Geographical Analysis

Self Cite

View full text Add to dashboard Cite

show abstract

“…A park is an appealing meeting place on a warm summer evening, but less so on a cold and windy winter night. In support of this, Quick et al (2017) found that, in warm seasons, crime rates are higher in areas dominated by parks, whereas in colder seasons, crime rates are higher in areas with nightlife. Corcoran, Higgs, Rohde and Chhetri (2011) found some evidence that the increase in city fires on warm days is greater in poor neighborhoods.…”

Section: The Effect Of Weather On the Spatial Distribution Of Crimementioning

confidence: 73%

“…Weather may affect the number of people who experience opportunities to commit crimes and the number who will be exposed to such crimes. However, changing weather does not necessarily impact overall crime rates; instead, it may rather lead to crimes being committed at alternative locations (Quick, Law, and Li, 2017).…”

Section: Introductionmentioning

confidence: 99%

Blowing in the wind? The effect of weather on the intensity and spatial distribution of crime

Hart¹,

Pedersen²,

Skarðhamar³

2019

Preprint

View full text Add to dashboard Cite

Despite an extensive literature on weather and crime, the magnitude of weather effects on crime and their implications for practical policing remain unclear. Similarly, the effects of weather on the location of crime have barely been explored empirically. We investigated how weather influences the intensity and spatial distribution of crime in Oslo, the capital of Norway. Geocoded locations of criminal offences were combined with data on temperature, wind, and rain. We used negative binomial count models to assess the effect of weather on the intensity of crime and generalized additive models (GAMs) to test for spatial variations. The intensity and spatial distribution of crime were not very sensitive to weather in Oslo. The largest effect was for drug crimes, for which maximum relative to minimum temperature was related to a single incident increase every six days. No effects were found for dislocation in the spatial models. In Oslo, Norway, weather conditions are of little importance for practical policing. The effects of weather on the intensity of crime are miniscule, and effects on the location of crime even smaller.

show abstract

“…A few studies have incorporated areal characteristics in the model to explain the varying levels of crime in space and time. They have linked changes of crime levels in relatively small areas to the characteristics of areal units such as predefined geographical neighborhood (Harries, Stadler, & Zdorkowski, 1984) census tract (Sorg & Taylor, 2011), or census block (Haberman, Sorg, & Ratcliffe, 2018; Quick, Law, & Li, 2017). Harries and colleagues (1984) tracked the level of assault by the economic status of the neighborhood in the city and found that low-status neighborhoods show a more distinct peak of assault in the summer.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Harries and colleagues (1984) tracked the level of assault by the economic status of the neighborhood in the city and found that low-status neighborhoods show a more distinct peak of assault in the summer. Quick et al (2017) studied how specific land use or the presence of certain facilities is related to the seasonal change of property crime and suggested that parks and eating and drinking establishments are related to additional seasonal variation of crime. Haberman et al (2018) also focused on how the effect of specific facilities on street robbery changes across seasons.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Spatiotemporal Association Between Temperature and Assaults: A Generalized Linear Mixed-Model Approach

Jung

Kim

2019

Crime & Delinquency

View full text Add to dashboard Cite

We aim to analyze the association between temperature and assault at highly disaggregated spatial units with great temporal resolution to investigate their spatiotemporal dynamics. We applied generalized linear mixed models (GLMMs) to assault and weather data from 2015, aggregated weekly at 424 subdistricts in Seoul, South Korea, controlling for various socioeconomic and environmental variables. Analyses revealed a positive and significant linear association between temperature and assaults and a few small but significant interaction effects that relate to an increase in assaults. A more enhanced understanding of the spatiotemporal relationship between temperature and crime would provide useful implications for targeted crime prevention and resource allocations.

show abstract

Time-varying relationships between land use and crime: A spatio-temporal analysis of small-area seasonal property crime trends

Abstract: Acknowledgements:We thank the Waterloo Regional Police Service for providing crime data.

Cited by 27 publications

References 55 publications

Spatiotemporal Modeling of Correlated Small‐Area Outcomes: Analyzing the Shared and Type‐Specific Patterns of Crime and Disorder

Spatiotemporal Modeling of Correlated Small‐Area Outcomes: Analyzing the Shared and Type‐Specific Patterns of Crime and Disorder

Blowing in the wind? The effect of weather on the intensity and spatial distribution of crime

Spatiotemporal Association Between Temperature and Assaults: A Generalized Linear Mixed-Model Approach

Contact Info

Product

Resources

About