Multiscale analysis of the influence of street built environment on crime occurrence using street-view images

He, Zhanjun; Wang, Zhipeng; Xie, Zhen; Wu, Liang; Chen, Zhanlong

doi:10.1016/j.compenvurbsys.2022.101865

Cited by 30 publications

(11 citation statements)

References 61 publications

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“…Despite the fact that hotspot detection is mainly based on retrospective crime data, crime hotpot detection often serves as an effective tool for crime prediction because crime hotspots are often stable for a period of time. The reason behind that is that the existence of crime hotspots is often related to a collection of influential factors (e.g., the social–economic condition, building environment and human mobility), and these factors will not change drastically in the short term [ 61 , 62 , 63 ]. In this study, we mainly focused on the performance of various methods in crime spatial hotspot detection; the research can be extended to crime prediction by considering both retrospective and future occurrence of crime.…”

Section: Discussionmentioning

confidence: 99%

Comparative Study of Approaches for Detecting Crime Hotspots with Considering Concentration and Shape Characteristics

Lai

Wang

et al. 2022

IJERPH

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Hotspot detection is an important exploratory technique to identify areas with high concentrations of crime and help deploy crime-reduction resources. Although a variety of methods have been developed to detect crime hotspots, few studies have systematically evaluated the performance of various methods, especially in terms of the ability to detect complex-shaped crime hotspots. Therefore, in this study, a comparative study of hotspot detection approaches while simultaneously considering the concentration and shape characteristics was conducted. Firstly, we established a framework for quantitatively evaluating the performance of hotspot detection for cases with or without the ”ground truth”. Secondly, accounting for the concentration and shape characteristics of the hotspot, we additionally defined two evaluation indicators, which can be used as a supplement to existing evaluation indicators. Finally, four classical hotspot-detection methods were quantitatively compared on the synthetic and real crime data. Results show that the proposed evaluation framework and indicators can describe the size, concentration and shape characteristics of the detected hotspots, thus supporting the quantitative comparison of different methods. From the selected methods, the AMOEBA (A Multidirectional Optimal Ecotope-Based Algorithm) method was more accurate in describing the concentration and shape characteristics and was powerful in discovering complex hotspots.

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Section: Discussionmentioning

confidence: 99%

Comparative Study of Approaches for Detecting Crime Hotspots with Considering Concentration and Shape Characteristics

Lai

Wang

et al. 2022

IJERPH

Self Cite

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“…First, it used the nearest distance to crime from an isolation location as the main measurement for the analysis. Other measurements can be developed to capture a more comprehensive picture [ 39 , 40 , 41 , 42 , 43 ]. Second, this study only explored the effect of risk ratings and lockdowns on crime.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Analyzing the Impact of COVID-19 Lockdowns on Violent Crime

Liu

Chang

Long

et al. 2022

IJERPH

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Existing research suggests that COVID-19 lockdowns tend to contribute to a decrease in overall urban crime rates. Most studies have compared pre-lockdown and post-lockdown periods to lockdown periods in Western cities. Few have touched on the fine variations during lockdowns. Equally rare are intracity studies conducted in China. This study tested the relationship between violent crime and COVID-19 lockdown policies in ZG City in southern China. The distance from the isolation location to the nearest violent crime site, called “the nearest crime distance”, is a key variable in this study. Kernel density mapping and the Wilcoxon signed-rank test are used to compare the pre-lockdown and post-lockdown periods to the lockdown period. Panel logistic regression is used to test the fine variations among different stages during the lockdown. The result found an overall decline in violent crime during the lockdown and a bounce-back post-lockdown. Violent crime moved away from the isolation location during the lockdown. This outward spread continued for the first two months after the lifting of the lockdown, suggesting a lasting effect of the lockdown policy. During the lockdown, weekly changes in COVID-19 risk ratings at the district level in ZG City also affected changes in the nearest crime distance. In particular, an increase in the risk rating increased that distance, and a drop in the risk rating decreased that distance. These findings add new results to the literature and could have policy implications for joint crime and pandemic prevention and control.

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“…Factors like the secondary industry proportion, per capita fiscal expenditure, night light index, healthcare facilities, and phone access ratios aided in poverty measurement [65]. Population data and investigating population changes [61] elucidated the correlation between street-built environments and crime occurrence. Mobile phone data revealed patterns in population demographics, exploring urban spatial features related to COVID-19 transmission [42].…”

Section: Methodological Approachesmentioning

confidence: 99%

Advancing Urban Life: A Systematic Review of Emerging Technologies and Artificial Intelligence in Urban Design and Planning

He,

Chen

2024

Buildings

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The advancement of cutting-edge technologies significantly transforms urban lifestyles and is indispensable in sustainable urban design and planning. This systematic review focuses on the critical role of innovative technologies and digitalization, particularly artificial intelligence (AI), in urban planning through geo-design, aiming to enhance urban life. It begins with exploring the importance of AI and digital tools in revolutionizing contemporary urban planning practices. Through the methodology based on the Systematic Reviews and Meta-Analyses (PRISMA) protocol, this review sifts through relevant literature over the past two decades by categorizing artificial intelligence technologies based on their functionalities. These technologies are examined for their utility in urban planning, environmental modeling, and infrastructure development, highlighting how they contribute to creating smarter and more livable cities. For instance, machine learning techniques like supervised learning excel in forecasting urban trends, whereas artificial neural networks and deep learning are superior in pattern recognition and vital for environmental modeling. This analysis, which refers to the comprehensive evaluation conducted in this Systematic Review, encompasses studies based on diverse data inputs and domains of application, revealing a trend toward leveraging AI for predictive analytics, decision-making improvements, and the automation of complex geospatial tasks in urban areas. The paper also addresses the challenges encountered, including data privacy, ethical issues, and the demand for cross-disciplinary knowledge. The concluding remarks emphasize the transformative potential of innovative technologies and digitalization in urban planning, advocating for their role in fostering better urban life. It also identifies future research avenues and development opportunities. In light of our review findings, this study concludes that AI technologies indeed hold transformative promise for the field of geo-design and urban planning. They have proven instrumental in advancing predictive analytics, refining decision-making, and streamlining complex geospatial tasks. The AI’s capacity to process expansive datasets and improve urban planning accuracy has facilitated more sustainable urban development and enhanced the resilience of urban environments.

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Multiscale analysis of the influence of street built environment on crime occurrence using street-view images

Cited by 30 publications

References 61 publications

Comparative Study of Approaches for Detecting Crime Hotspots with Considering Concentration and Shape Characteristics

Comparative Study of Approaches for Detecting Crime Hotspots with Considering Concentration and Shape Characteristics

Analyzing the Impact of COVID-19 Lockdowns on Violent Crime

Advancing Urban Life: A Systematic Review of Emerging Technologies and Artificial Intelligence in Urban Design and Planning

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