Assessing the Impact of Street-View Greenery on Fear of Neighborhood Crime in Guangzhou, China

Jing, Fengrui; Liu, Lin; Zhou, Suhong; Song, Jiangyu; Wang, Linsen; Zhou, Hanlin; Wang, Yiwen; Ruo-fei, MA

doi:10.3390/ijerph18010311

Cited by 43 publications

(18 citation statements)

References 62 publications

(72 reference statements)

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“…Both models suggest that violent crimes tend to occur more often in more populated block groups with lower median household incomes, higher concentrations of ethnic minorities, harmful product sales outlets, and dilapidated neighborhood environments. This result is not different from many of the previous studies’ findings [ 2 , 7 , 14 , 15 , 24 , 35 , 90 , 91 , 92 ]. The result is also one of the cornerstones for municipal governance and policing practices.…”

Section: Discussioncontrasting

confidence: 60%

How Neighborhood Characteristics Influence Neighborhood Crimes: A Bayesian Hierarchical Spatial Analysis

Fang

2022

IJERPH

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Urban crimes are a severe threat to livable and sustainable urban environments. Many studies have investigated the patterns, causes, and strategies for curbing the occurrence of urban crimes. It is found that neighborhood socioeconomic status, physical environment, and ethnic composition all might play a role in the occurrence of urban crimes. Inspired by the recent interest in exploring urban crime patterns with spatial data analysis techniques and the development of Bayesian hierarchical analytical approaches, we attempt to explore the inherently intricate relationships between urban assaultive violent crimes and the neighborhood socioeconomic status, physical environment, and ethnic composition in Paterson, NJ, using census data of the American Community Survey, alcohol and tobacco sales outlet data, and abandoned property listing data from 2013. Analyses are set at the census block group level. Urban crime data are obtained from the Paterson Police Department. Instead of examining relationships at a global level with both non-spatial and spatial analyses, we examine in depth the potential locally varying relationships at the local level through a Bayesian hierarchical spatially varying coefficient model. At both the global and local analysis levels, it is found that median household income is decisively negatively related to urban crime occurrence. Percentage of African Americans and Hispanics, number of tobacco sales outlets, and number of abandoned properties are all positively related with urban crimes. At the local level of analysis, however, the different factors have varying influence on crime occurrence throughout the city of Paterson, with median household income having the broadest influence across the city. The practice of applying a Bayesian hierarchical spatial analysis framework to understand urban crime occurrence and urban neighborhood characteristics enables urban planners, stakeholders, and public safety officials to engage in more active and targeted crime-reduction strategies.

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Section: Discussioncontrasting

confidence: 60%

How Neighborhood Characteristics Influence Neighborhood Crimes: A Bayesian Hierarchical Spatial Analysis

Fang

2022

IJERPH

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“…Recently, Yi et al (2019) and Yiyong et al (2020) uncovered the inconsistency by proving that eye-level greenness extracted from street view image data with deep learning methods was positively associated with cycling activities and dockless bike-sharing usage, respectively, rather than overhead view greenness measured by the normalized difference vegetation index (NDVI). Additionally, some studies have shown that eye-level greenness might better measure greenness out of cyclists' subjective feelings and sights than overhead view greenness [3][4][5]30,[35][36][37][38]. Hence, street view image data and deep learning segmentation methods have become efficient data sources and assessment methods for measuring urban greenness, which has been applied in transport geography, psychology, and crime studies [3,35,37,38].…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, some studies have shown that eye-level greenness might better measure greenness out of cyclists' subjective feelings and sights than overhead view greenness [3][4][5]30,[35][36][37][38]. Hence, street view image data and deep learning segmentation methods have become efficient data sources and assessment methods for measuring urban greenness, which has been applied in transport geography, psychology, and crime studies [3,35,37,38].…”

Section: Related Workmentioning

confidence: 99%

How Is Urban Greenness Spatially Associated with Dockless Bike Sharing Usage on Weekdays, Weekends, and Holidays?

Gao

Tan

et al. 2021

IJGI

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Dockless bike sharing plays an important role in residents’ daily travel, traffic congestion, and air pollution. Recently, urban greenness has been proven to be associated with bike sharing usage around metro stations using a global model. However, their spatial associations and bike sharing usage on public holidays have seldom been explored in previous studies. In this study, urban greenness was obtained objectively using eye-level greenness with street-view images by deep learning segmentation and overhead view greenness from the normalized difference vegetation index (NDVI). Geographically weighted regression (GWR) was applied to fill the research gap by exploring the spatially varying association between dockless bike sharing usage on weekdays, weekends, and holidays, and urban greenness indicators as well as other built environment factors. The results showed that eye-level greenness was positively associated with bike sharing usage on weekdays, weekends, and holidays. Overhead-view greenness was found to be negatively related to bike usage on weekends and holidays, and insignificant on weekdays. Therefore, to promote bike sharing usage and build a cycling-friendly environment, the study suggests that the relevant urban planner should pay more attention to eye-level greenness exposure along secondary roads rather than the NDVI. Most importantly, planning implications varying across the study area during different days were proposed based on GWR results. For example, the improvement of eye-level greenness might effectively promote bike usage in northeastern and southern Futian districts and western Nanshan on weekdays. It also helps promote bike usage in Futian and Luohu districts on weekends, and in southern Futian and southeastern Nanshan districts on holidays.

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“…Green elements such as trees and grass can facilitate people’s outdoor activities and thus promote informal monitoring, thereby restraining the occurrence of crimes. However, thick shrubs may obstruct people’s lines of sight and provide hiding places for offenders; therefore, areas with shrubs have potential crime risks [ 1 , 23 , 24 , 25 ]. A holistic investigation of these features and their possible interactions remains unanswered.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Effect of Streetscape Environment on Crime Using Street View Images and Interpretable Machine-Learning Technique

Xie

Liu

Han

2022

IJERPH

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Street crime is a common social problem that threatens the security of people’s lives and property. Understanding the influencing mechanisms of street crime is an essential precondition for formulating crime prevention strategies. Widespread concern has contributed to the development of streetscape environment features as they can significantly affect the occurrence of street crime. Emerging street view images are a low-cost and highly accessible data source. On the other hand, machine-learning models such as XGBoost (eXtreme Gradient Boosting) usually have higher fitting accuracies than those of linear regression models. Therefore, they are popular for modeling the relationships between crime and related impact factors. However, due to the “black box” characteristic, researchers are unable to understand how each variable contributes to the occurrence of crime. Existing research mainly focuses on the independent impacts of streetscape environment features on street crime, but not on the interaction effects between these features and the community socioeconomic conditions and their local variations. In order to address the above limitations, this study first combines street view images, an objective detection network, and a semantic segmentation network to extract a systematic measurement of the streetscape environment. Then, controlling for socioeconomic factors, we adopted the XGBoost model to fit the relationships between streetscape environment features and street crime at the street segment level. Moreover, we used the SHAP (Shapley additive explanation) framework, a post-hoc machine-learning explainer, to explain the results of the XGBoost model. The results demonstrate that, from a global perspective, the number of people on the street, extracted from street view images, has the most significant impact on street property crime among all the street view variables. The local interpretability of the SHAP explainer demonstrates that a particular variable has different effects on street crime at different street segments. The nonlinear associations between streetscape environment features and street crime, as well as the interaction effects of different streetscape environment features are discussed. The positive effect of the number of pedestrians on street crime increases with the length of the street segment and the number of crime generators. The combination of street view images and interpretable machine-learning techniques is helpful in better accurately understanding the complex relationships between the streetscape environment and street crime. Furthermore, the readily comprehensible results can offer a reference for formulating crime prevention strategies.

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Assessing the Impact of Street-View Greenery on Fear of Neighborhood Crime in Guangzhou, China

Cited by 43 publications

References 62 publications

How Neighborhood Characteristics Influence Neighborhood Crimes: A Bayesian Hierarchical Spatial Analysis

How Neighborhood Characteristics Influence Neighborhood Crimes: A Bayesian Hierarchical Spatial Analysis

How Is Urban Greenness Spatially Associated with Dockless Bike Sharing Usage on Weekdays, Weekends, and Holidays?

Modeling the Effect of Streetscape Environment on Crime Using Street View Images and Interpretable Machine-Learning Technique

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