A review of urban physical environment sensing using street view imagery in public health studies

Kang, Yuhao; Zhang, Fan; Gao, Song; Lin, Hui; Liu, Yu

doi:10.1080/19475683.2020.1791954

Cited by 167 publications

(60 citation statements)

References 138 publications

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“…Since GSV images have spatial coordinates associated with them, and can be accessed through an API, this is a potentially important approach for researchers to test and improve secondary GIS datasets. Although GSV images can be automatically downloaded, the process we have described is still reliant on manual analysis of environmental features by RAs meaning that it would be time consuming for larger projects, and potentially prone to human error [ 80 ]. While our approach is currently practical for projects and studies undertaken in a small geographic area, there is potential for scalability and replication of this approach with different datasets and different spatial contexts, or for use validating a subset of spatial data to gauge the estimated completeness and temporal validity of an overall dataset.…”

Section: Discussionmentioning

confidence: 99%

Improving spatial data in health geographics: a practical approach for testing data to measure children’s physical activity and food environments using Google Street View

et al. 2021

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Background Geographic information systems (GIS) are often used to examine the association between both physical activity and nutrition environments, and children’s health. It is often assumed that geospatial datasets are accurate and complete. Furthermore, GIS datasets regularly lack metadata on the temporal specificity. Data is usually provided ‘as is’, and therefore may be unsuitable for retrospective or longitudinal studies of health outcomes. In this paper we outline a practical approach to both fill gaps in geospatial datasets, and to test their temporal validity. This approach is applied to both district council and open-source datasets in the Taranaki region of Aotearoa New Zealand. Methods We used the ‘streetview’ python script to download historic Google Street View (GSV) images taken between 2012 and 2016 across specific locations in the Taranaki region. Images were reviewed and relevant features were incorporated into GIS datasets. Results A total of 5166 coordinates with environmental features missing from council datasets were identified. The temporal validity of 402 (49%) environmental features was able to be confirmed from council dataset considered to be ‘complete’. A total of 664 (55%) food outlets were identified and temporally validated. Conclusions Our research indicates that geospatial datasets are not always complete or temporally valid. We have outlined an approach to test the sensitivity and specificity of GIS datasets using GSV images. A substantial number of features were identified, highlighting the limitations of many GIS datasets.

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Section: Discussionmentioning

confidence: 99%

Improving spatial data in health geographics: a practical approach for testing data to measure children’s physical activity and food environments using Google Street View

et al. 2021

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“…Chen et al recorded the underlying interface variables and walking activities of commercial streets through on-the-spot observation and taking pictures, in order to explore the influence of street visual appearance on walking activities [17]; Xiong et al took people as the core object of street evaluation, and made quantitative observation and evaluation based on people-oriented observation method on people's composition, distribution, behavior and color, so as to improve street quality [18]; Brown et al learned the influence of the building environment on physical activities through telephone interviews, questionnaires and systematic observation [20]. These studies have certain limitations in objectivity of evaluation results, large-scale refined evaluation and humanistic perspective [9,[21][22][23].…”

Section: Literature Reviewmentioning

confidence: 99%

Heterogeneity Study of the Visual Features Based on Geographically Weighted Principal Components Analysis Applied to an Urban Community

2021

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Communities in urban space are the most basic living units. Community visual features directly reflect the local living quality and influence the perception of residents and visitors. The evaluation of the community visual features is of great significance to the space design under the guidance of urban landscape recognition and urban space perception. Based on the street view image data, this paper analyzes the composition of visual features in the community space scale by using the geographically weighted principal components analysis. GWPCA can not only reflect the global characteristics, but also analyze the local components, thus describing the visual features of the community in a comprehensive manner. The results show that: (1) community visual features have significant spatial heterogeneity at different statistical scales, and the spatial heterogeneity of community visual features can provide a basis for urban landscape planning and design; (2) the combination mode of dominant visual elements can reflect different community landscapes. The analysis of this paper further illustrates the effectiveness and application prospect of street view images in identifying the landscape composition mode of urban space from the medium-micro perspective. This conclusion is helpful for planners to learn the dominant visual features of the community through street view images, and, further, use the classification of elements of street view images to guide the planning and design of cityscape.

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“…As roadsides are important public spaces in cities and important components of the urban fabric [9], many studies have constructed analytical models related to public health [10], air quality [11] and noise pollution [12] at the roadside scale. These environmental conditions play important roles in urban planning, as traffic heat emissions and carbon emissions are two of the main contributors to climate change [13].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Effects of Roadside Vegetation on the Urban Thermal Environment Using Street View Images

Xing

Cao

et al. 2022

IJERPH

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Roadsides are important urban public spaces where residents are in direct contact with the thermal environment. Understanding the effects of different vegetation types on the roadside thermal environment has been an important aspect of recent urban research. Although previous studies have shown that the thermal environment is related to the type and configuration of vegetation, remote sensing-based technology is not applicable for extracting different vegetation types at the roadside scale. The rapid development and usage of street view data provide a way to solve this problem, as street view data have a unique pedestrian perspective. In this study, we explored the effects of different roadside vegetation types on land surface temperatures (LSTs) using street view images. First, the grasses–shrubs–trees (GST) ratios were extracted from 19,596 street view images using semantic segmentation technology, while LST and normalized difference vegetation index (NDVI) values were extracted from Landsat-8 images using the radiation transfer equation algorithm. Second, the effects of different vegetation types on roadside LSTs were explored based on geographically weighted regression (GWR), and the different performances of the analyses using remotely sensed images and street view images were discussed. The results indicate that GST vegetation has different cooling effects in different spaces, with a fitting value of 0.835 determined using GWR. Among these spaces, the areas with a significant cooling effect provided by grass are mainly located in the core commercial area of Futian District, which is densely populated by people and vehicles; the areas with a significant cooling effect provided by shrubs are mainly located in the industrial park in the south, which has the highest industrial heat emissions; the areas with a significant cooling effect provided by trees are mainly located in the core area of Futian, which is densely populated by roads and buildings. These are also the areas with the most severe heat island effect in Futian. This study expands our understanding of the relationship between roadside vegetation and the urban thermal environment, and has scientific significance for the planning and guiding of urban thermal environment regulation.

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A review of urban physical environment sensing using street view imagery in public health studies

Cited by 167 publications

References 138 publications

Improving spatial data in health geographics: a practical approach for testing data to measure children’s physical activity and food environments using Google Street View

Improving spatial data in health geographics: a practical approach for testing data to measure children’s physical activity and food environments using Google Street View

Heterogeneity Study of the Visual Features Based on Geographically Weighted Principal Components Analysis Applied to an Urban Community

Exploring the Effects of Roadside Vegetation on the Urban Thermal Environment Using Street View Images

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