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

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. • There is not enough evidence on the association between green space and crime. • This study aims to assess the conditional association between green space and crime. • More grassland may be associated with lower crime rates, but only in areas with relatively low crime rates. • More woodland may be associated with lower crime rates, but only for areas with relatively high crime rates. • Associations between green space and crime varied by type of crime.

Authors

Institutions

Publication Details

Journal
Urban forestry & urban greening
Published
2024-09-20
DOI
https://doi.org/10.1016/j.ufug.2024.128523
Citations
12
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
3.39

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

George Grekousis, Ruth Fiona Hunter, Claire Cleland, Dominic Bryan et al.
12 citations
Urban forestry & urban greening
Land Use and Ecosystem Services
3.39
article

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

George Grekousis, Ruth Fiona Hunter, Claire Cleland, Dominic Bryan, Agustina Martire, Sally McManus, Ruth Weir, Ruoyu Wang
article en
12 citations

Abstract

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. • There is not enough evidence on the association between green space and crime. • This study aims to assess the conditional association between green space and crime. • More grassland may be associated with lower crime rates, but only in areas with relatively low crime rates. • More woodland may be associated with lower crime rates, but only for areas with relatively high crime rates. • Associations between green space and crime varied by type of crime.

Urban forestry & urban greeningVol. 101
University of Essex (GB), Queen's University Belfast (GB), Sun Yat-sen University (CN), St George's, University of London (GB), Key Laboratory of Guangdong Province (CN), Zhejiang Provincial Public Security Department (CN), National Centre for Social research (GR)
Cancer Research UK, National Institute for Health and Care Research, British Heart Foundation, University of Exeter, University of Glasgow, Cranfield University, Queen's University Belfast, Public Health Agency, Queen's University, Liverpool John Moores University, Albert Einstein College of Medicine, Yeshiva University, Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Natural Environment Research Council, Health and Social Care Research and Development Division
Openalex Percentile: Top 7%
Land Use and Ecosystem Services
3.39
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.