Rethinking the association between green space and crime using spatial quantile regression modelling: Do vegetation type, crime type, and crime rates matter? Article Swipe

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Ruoyu Wang , Claire Cleland , Ruth Weir , Sally McManus , Agustina Martire , George Grekousis , Dominic Bryan , Ruth F. Hunter ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.1016/j.ufug.2024.128523

UN Sustainable Development Goals (e.g., Goal 16) have highlighted the importance of using policy tools (e.g., through urban planning) to prevent crimes. Existing evidence of the association between green space and crime is mixed. Some studies indicate that the inconsistencies may be due to the variance in types of vegetation and the rates of crime reported across regions and countries. This study aims to assess the conditional association between green space and crime by considering the influence of vegetation type (e.g., grassland, woodland), crime type (e.g., violence, theft) and rates of crime reported in Northern Ireland (NI), United Kingdom. Crime data were obtained from the Police Service NI and green space was determined by Land Cover Map at the Super Output Area (SOA) level provided by the UK Centre for Ecology & Hydrology. Spatial quantile regressions were used to model the adjusted association between green space and crime across areas with different rates of crime. The results showed that more grassland may be associated with lower crime rates, but only in areas with relatively low crime rates. More woodland may also be associated with lower crime rates, but only for areas with relatively high crime rates. Also, we found that associations between green space and crime varied by type of crime. In summary, policymakers and planners should consider green space as a potential crime reduction intervention, factoring in the heterogeneous effects of vegetation type, crime type and crime rate.

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Vegetation (pathology) Quantile regression Geography Space (punctuation) Association (psychology) Quantile Vegetation type Type (biology) Vegetation types Econometrics Statistics Mathematics Ecology Computer science Psychology Biology Habitat Psychotherapist Medicine Operating system Pathology Grassland

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Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.ufug.2024.128523
OA Status: hybrid
Cited By: 10
References: 104
Related Works: 10
OpenAlex ID: https://openalex.org/W4402667303

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DOI: https://doi.org/10.1016/j.ufug.2024.128523

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Title: Rethinking the association between green space and crime using spatial quantile regression modelling: Do vegetation type, crime type, and crime rates matter?

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Type: article

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Language: en

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Publication year: 2024

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Publication date: 2024-09-20

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Authors: Ruoyu Wang, Claire Cleland, Ruth Weir, Sally McManus, Agustina Martire, George Grekousis, Dominic Bryan, Ruth F. Hunter

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Landing page: https://doi.org/10.1016/j.ufug.2024.128523

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Concepts: Vegetation (pathology), Quantile regression, Geography, Space (punctuation), Association (psychology), Quantile, Vegetation type, Type (biology), Vegetation types, Econometrics, Statistics, Mathematics, Ecology, Computer science, Psychology, Biology, Habitat, Psychotherapist, Medicine, Operating system, Pathology, Grassland

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Cited by: 10

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Citations by year (recent): 2025: 8, 2024: 2

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