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Application of Street View Images and GIS in Urban Studies

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Science and Engineering".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 7843

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Dr. Han Yue

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Center of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Interests: urban crime analysis; geospatial big data analysis

Special Issue Information

Dear Colleagues,

Traditional methods used to gather data about urban physical environments include questionnaire surveys, field surveys, human auditing, and so on. However, these methods are time-consuming and labor-intensive, and thus they are only suitable for small, scattered studies, and not applicable for large-scale research. The low accessibility of large-scale detailed data limits our ability to measure the urban environment in a systematic and quantitative way.

Street view images (SVIs) are an emerging form of big data. The most significant advantage of SVIs over other forms of data is that they are captured by cameras placed on top of cars, driving along streets. Therefore, SVIs can be utilized to extract environmental street features from a pedestrians’ point of view, and have the potential to help reveal the most direct connections between streetscape conditions and human activity. In addition, this type of data covers most major cities, and is usually open access. Geo-referenced big data and geographical information science (GIS) methods are usually used together with SVIs in relevant urban studies.

This Special Issue focuses on the application of SVIs and GIS in urban studies. We invite contributions that address the associations between urban street contexts and a broad spectrum of research objects (public safety, public health, human activity, transportation, etc.) using SVIs and various spatial analytics, including—but not limited to—spatial statistics, intelligent algorithms (i.e., machine learning and deep learning), and big data analytics. We also welcome studies that produce, design, or share original SVIs or information extracted from SVIs, reusable analytical tools, packages, or models.

Dr. Han Yue
Guest Editor

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Keywords

street view images
urban studies
geographical information science (GIS)
spatial analysis
big data

Published Papers (4 papers)

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Research

13 pages, 970 KiB

Open AccessArticle

Residents’ Location-Based Fear of Theft and Their Impact Factors in Guangzhou, China

by Guangwen Song, Jiaqi Li, Chunxia Zhang and Jie Gu

Int. J. Environ. Res. Public Health 2023, 20(1), 638; https://doi.org/10.3390/ijerph20010638 - 30 Dec 2022

Cited by 1 | Viewed by 1481

Abstract

While the fear of theft is common and is known to lead to lower satisfaction with life and subjective well-being, current literature regards the fear of theft as a stable psychological state and ignores discrepancies based on location and their influencing factors. To fill these gaps, we selected 74 typical communities and collected 1568 questionnaires throughout Guangzhou. The results show that: (1) the respondents demonstrated significant location-based differences in their fear of theft. Locations including a coach station, a railway station, a bus station, a subway station and a wholesale market had the highest associated levels of fear, whereas locations dedicated to leisure activities, especially those in high-end places, had a lower level of respondents’ fear of theft. (2) Vulnerability model, victimization model, community security and built environment can be applied to the analysis of fear of theft around different places, but interpretations of fear do vary widely from place to place. Full article

(This article belongs to the Special Issue Application of Street View Images and GIS in Urban Studies)

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10 pages, 1452 KiB

Open AccessArticle

Analyzing the Impact of COVID-19 Lockdowns on Violent Crime

by Lin Liu, Jiayu Chang, Dongping Long and Heng Liu

Int. J. Environ. Res. Public Health 2022, 19(23), 15525; https://doi.org/10.3390/ijerph192315525 - 23 Nov 2022

Cited by 3 | Viewed by 1641

Abstract

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. Full article

(This article belongs to the Special Issue Application of Street View Images and GIS in Urban Studies)

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15 pages, 1992 KiB

Open AccessArticle

Measurement and Prediction of Urban Land Traffic Accessibility and Economic Contact Based on GIS: A Case Study of Land Transportation in Shandong Province, China

by Zhiguo Shao, Li Zhang, Chuanfeng Han and Lingpeng Meng

Int. J. Environ. Res. Public Health 2022, 19(22), 14867; https://doi.org/10.3390/ijerph192214867 - 11 Nov 2022

Cited by 8 | Viewed by 2052

Abstract

As the basic support of regional economic and social development, land transportation is one of the important engines to promote regional development, and its construction and improvement will have an important impact on the regional economic pattern. Based on the road network of Shandong Province, China, in 2020, according to the Medium and Long-term Development Plan of Comprehensive Transportation Network of Shandong Province (2018–2035), this paper uses the GIS network analysis method, weighted average travel time, modified gravity model and other methods to study the land traffic accessibility and economic relation intensity of prefecture-level cities in Shandong Province, China, in 2020 and 2035. The results show that the distribution of land traffic accessibility in Shandong Province, China, shows a certain regional main road pointing characteristic in 2020, and the urban accessibility level gradually decreases along the Beijing–Shanghai high-speed railway and Jinan-Qingdao high-speed railway to the periphery. In 2035, the land traffic accessibility of Shandong Province, China, will be more spatially distributed as “concentric circles”. From 2020 to 2035, the urban land traffic accessibility and the balance of economic contact in Shandong Province, China, will be improved significantly. The research results can provide a theoretical reference for optimizing the traffic network pattern and promoting urban economic contact in Shandong Province, China. Full article

(This article belongs to the Special Issue Application of Street View Images and GIS in Urban Studies)

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22 pages, 45568 KiB

Open AccessArticle

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

by Huafang Xie, Lin Liu and Han Yue

Int. J. Environ. Res. Public Health 2022, 19(21), 13833; https://doi.org/10.3390/ijerph192113833 - 24 Oct 2022

Cited by 10 | Viewed by 2115

Abstract

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. Full article

(This article belongs to the Special Issue Application of Street View Images and GIS in Urban Studies)

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