Analyzing Urban Crime Through Street View Imagery: Insights from Urban Micro Built Environment and Perceptions DOI Creative Commons
Devin Yongzhao Wu, Jue Wang

Urban Science, Journal Year: 2024, Volume and Issue: 8(4), P. 247 - 247

Published: Dec. 7, 2024

Understanding the relationship between urban crime and built environment is crucial for developing effective prevention strategies, particularly in context of rapid development city planning. As cities grow, urbanization leads to environments that either promote or inhibit criminal activity, making it essential explore interactions design crime. This study investigates impact micro (MBE) elements place perceptions on occurrences Toronto using street view imagery (SVI) data machine learning models. We used logistic regression models an XGBoost (Version 1.7.5) classifier assess significance MBE perception variables classifying non-crime intersections. Our findings reveal intersections with activity tend be related more mobility-related features, such as roads vehicles, fewer natural elements, vegetation. The “beautiful” “depressing” emerged most significant explaining events, surpassing commonly studied “safety” perception. model achieved 86% accuracy, indicating are strong predictors risk. These suggest enhancing vegetation improving aesthetics could serve measures environments. However, limitations include general nature reliance aggregated data. Future research should incorporate local fine-scale provide tailored insights planning

Language: Английский

The moderating influence of safety on green space’s health benefits in Chinese urban communities DOI
Jia Chen, Longfeng Wu, Han Ma

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 375, P. 124232 - 124232

Published: Jan. 25, 2025

Language: Английский

Citations

1
Comparing XAI techniques for interpreting short-term burglary predictions at micro-places DOI Creative Commons
Robin Khalfa,

Naomi Theinert,

Wim Hardyns

et al.

Computational Urban Science, Journal Year: 2025, Volume and Issue: 5(1)

Published: May 9, 2025

Abstract This study empirically compares multiple eXplainable Artificial Intelligence (XAI) techniques to interpret short-term (weekly) machine learning-based burglary predictions at the micro-place level in Ghent, Belgium. While previous research predominantly relies on SHAP spatiotemporal crime predictions, this is first systematically evaluate alongside other XAI techniques, offering both global and local model interpretability within context of prediction. Using data from 2014 2018 residential burglary, repeat near-repeat victimization, environmental features, socio-demographic indicators, seasonal effects, we trained an XGBoost with 76 features predict weekly hot spots. serves as a basis for comparing interpretative power different techniques. Our results show that built environment land use characteristics are most consistent predictors risk. However, their influence varies substantially level, revealing importance spatial context. feature rankings broadly aligned across explanations, especially between LIME, often diverge. These discrepancies highlight need careful method selection when translating into prevention strategies. In addition, demonstrates risks influenced by complex interactions threshold effects social disorganization features. We these findings through lens criminological theory, argue more integrated approaches go beyond examining isolated specific predictors. Finally, call greater attention methodological implications arise applying particularly learning outputs used inform policy decisions.

Language: Английский

Citations

0
Neighborhood social environment and mental health of older adults in China: the mediating role of subjective well-being and the moderating role of green space DOI Creative Commons

Tzu-Ming Lin,

Qianhui Wang,

Zixuan Tan

et al.

Frontiers in Public Health, Journal Year: 2024, Volume and Issue: 12

Published: Dec. 6, 2024

With the continuous development of global aging trend, mental health older adults has been a concern by world. The living space is limited due to decline their activity function. Neighborhood environment, especially neighborhood social become an important factor affecting adults. Therefore, this study explores mechanism that influences environment and adults, mediating effect subjective well-being (SWB), moderating green space.

Language: Английский

Citations

1
Analyzing Urban Crime Through Street View Imagery: Insights from Urban Micro Built Environment and Perceptions DOI Creative Commons
Devin Yongzhao Wu, Jue Wang

Urban Science, Journal Year: 2024, Volume and Issue: 8(4), P. 247 - 247

Published: Dec. 7, 2024

Understanding the relationship between urban crime and built environment is crucial for developing effective prevention strategies, particularly in context of rapid development city planning. As cities grow, urbanization leads to environments that either promote or inhibit criminal activity, making it essential explore interactions design crime. This study investigates impact micro (MBE) elements place perceptions on occurrences Toronto using street view imagery (SVI) data machine learning models. We used logistic regression models an XGBoost (Version 1.7.5) classifier assess significance MBE perception variables classifying non-crime intersections. Our findings reveal intersections with activity tend be related more mobility-related features, such as roads vehicles, fewer natural elements, vegetation. The “beautiful” “depressing” emerged most significant explaining events, surpassing commonly studied “safety” perception. model achieved 86% accuracy, indicating are strong predictors risk. These suggest enhancing vegetation improving aesthetics could serve measures environments. However, limitations include general nature reliance aggregated data. Future research should incorporate local fine-scale provide tailored insights planning

Language: Английский

Citations

1