scholarly journals 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

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jesse Whitehead ◽  
Melody Smith ◽  
Yvonne Anderson ◽  
Yijun Zhang ◽  
Stephanie Wu ◽  
...  
Keyword(s):  
Physical Activity ◽  
Spatial Data ◽  
Practical Approach ◽  
Google Street View ◽  
Environmental Features ◽  
Temporal Specificity ◽  
Aotearoa New Zealand ◽  
Street View ◽  
District Council ◽  
Geospatial Datasets

Abstract 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.

scholarly journals Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States

2020 ◽  
Vol 17 (10) ◽  
pp. 3659
Author(s):  
Lynn Phan ◽  
Weijun Yu ◽  
Jessica M. Keralis ◽  
Krishay Mukhija ◽  
Pallavi Dwivedi ◽  
...  
Keyword(s):  
Physical Activity ◽  
Built Environment ◽  
Health Outcomes ◽  
State Level ◽  
Premature Mortality ◽  
The United States ◽  
Single Family ◽  
Google Street View ◽  
Street View ◽  
Neighborhood Built Environment

Previous studies have demonstrated that there is a high possibility that the presence of certain built environment characteristics can influence health outcomes, especially those related to obesity and physical activity. We examined the associations between select neighborhood built environment indicators (crosswalks, non-single family home buildings, single-lane roads, and visible wires), and health outcomes, including obesity, diabetes, cardiovascular disease, and premature mortality, at the state level. We utilized 31,247,167 images collected from Google Street View to create indicators for neighborhood built environment characteristics using deep learning techniques. Adjusted linear regression models were used to estimate the associations between aggregated built environment indicators and state-level health outcomes. Our results indicated that the presence of a crosswalk was associated with reductions in obesity and premature mortality. Visible wires were associated with increased obesity, decreased physical activity, and increases in premature mortality, diabetes mortality, and cardiovascular mortality (however, these results were not significant). Non-single family homes were associated with decreased diabetes and premature mortality, as well as increased physical activity and park and recreational access. Single-lane roads were associated with increased obesity and decreased park access. The findings of our study demonstrated that built environment features may be associated with a variety of adverse health outcomes.

scholarly journals ‘Sensor’ship and Spatial Data Quality

2016 ◽  
Vol 1 (2) ◽  
pp. 75-87 ◽  
Author(s):  
Elisabeth Sedano
Keyword(s):  
Los Angeles ◽  
Data Quality ◽  
Spatial Data ◽  
Important Lesson ◽  
Outdoor Advertising ◽  
Google Street View ◽  
Record Keeping ◽  
Spatial Data Quality ◽  
Street View ◽  
The City

This article describes a Los Angeles-based website that collects volunteered geographic information (VGI) on outdoor advertising using the Google Street View interface. The Billboard Map website was designed to help the city regulate signage. The Los Angeles landscape is thick with advertising, and the city efforts to count total of signs has been stymied by litigation and political pressure. Because outdoor advertising is designed to be seen, the community collectively knows how many and where signs exist. As such, outdoor advertising is a perfect subject for VGI. This paper analyzes the Los Angeles community's entries in the Billboard Map website both quantitatively and qualitatively. I find that members of the public are well able to map outdoor advertisements, successfully employing the Google Street View interface to pinpoint sign locations. However, the community proved unaware of the regulatory distinctions between types of signs, mapping many more signs than those the city technically designates as billboards. Though these findings might suggest spatial data quality issues in the use of VGI for municipal record-keeping, I argue that the Billboard Map teaches an important lesson about how the public's conceptualization of the urban landscape differs from that envisioned by city planners. In particular, I argue that community members see the landscape of advertising holistically, while city agents treat the landscape as a collection of individual categories. This is important because, while Los Angeles recently banned new off-site signs, it continues to approve similar signs under new planning categories, with more in the works.

scholarly journals Comparing the Normalized Difference Vegetation Index with the Google Street View Measure of Vegetation to Assess Associations between Greenness, Walkability, Recreational Physical Activity, and Health in Ottawa, Canada

2018 ◽  
Vol 15 (8) ◽  
pp. 1719 ◽  
Author(s):  
Paul J. Villeneuve ◽  
Renate L. Ysseldyk ◽  
Ariel Root ◽  
Sarah Ambrose ◽  
Jason DiMuzio ◽  
...  
Keyword(s):  
Physical Activity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Physical Activities ◽  
Living Environment ◽  
Active Living ◽  
Recreational Physical Activity ◽  
Recreational Activities ◽  
Google Street View ◽  
Street View

The manner in which features of the built environment, such as walkability and greenness, impact participation in recreational activities and health are complex. We analyzed survey data provided by 282 Ottawa adults in 2016. The survey collected information on participation in recreational physical activities by season, and whether these activities were performed within participants’ neighbourhoods. The SF-12 instrument was used to characterize their overall mental and physical health. Measures of active living environment, and the satellite derived Normalized Difference Vegetation Index (NDVI) and Google Street View (GSV) greenness indices were assigned to participants’ residential addresses. Logistic regression and least squares regression were used to characterize associations between these measures and recreational physical activity, and self-reported health. The NDVI was not associated with participation in recreational activities in either the winter or summer, or physical or mental health. In contrast, the GSV was positively associated with participation in recreational activities during the summer. Specifically, those in the highest quartile spent, on average, 5.4 more hours weekly on recreational physical activities relative to those in the lowest quartile (p = 0.01). Active living environments were associated with increased utilitarian walking, and reduced reliance on use of motor vehicles. Our findings provide support for the hypothesis that neighbourhood greenness may play an important role in promoting participation in recreational physical activity during the summer.

scholarly journals Developing and testing a street audit tool using Google Street View to measure environmental supportiveness for physical activity

2013 ◽  
Vol 10 (1) ◽  
pp. 103 ◽  
Author(s):  
Pippa Griew ◽  
Melvyn Hillsdon ◽  
Charlie Foster ◽  
Emma Coombes ◽  
Andy Jones ◽  
...  
Keyword(s):  
Physical Activity ◽  
Audit Tool ◽  
Google Street View ◽  
Street View

Deep learning-based visual crack detection using Google Street View images

2021 ◽  
Author(s):  
Mohsen Maniat ◽  
Charles V. Camp ◽  
Ali R. Kashani
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Google Street View ◽  
Street View

scholarly journals Feelings of safety during daytime walking: associations with mental health, physical activity and cardiometabolic health in high vacancy, low-income neighborhoods in Detroit, Michigan

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Amber L. Pearson ◽  
Kimberly A. Clevenger ◽  
Teresa H. Horton ◽  
Joseph C. Gardiner ◽  
Ventra Asana ◽  
...  
Keyword(s):  
Mental Health ◽  
Physical Activity ◽  
Low Income ◽  
Spatial Data ◽  
Vigorous Physical Activity ◽  
Future Research ◽  
Cardiometabolic Health ◽  
Multilevel Regression ◽  
Critical Pathway ◽  
Perceived Safety

Abstract Introduction Individuals living in low-income neighborhoods have disproportionately high rates of obesity, Type-2 diabetes, and cardiometabolic conditions. Perceived safety in one’s neighborhood may influence stress and physical activity, with cascading effects on cardiometabolic health. Methods In this study, we examined relationships among feelings of safety while walking during the day and mental health [perceived stress (PSS), depression score], moderate-to-vigorous physical activity (PA), Body Mass Index (BMI), and hemoglobin A1C (A1C) in low-income, high-vacancy neighborhoods in Detroit, Michigan. We recruited 69 adults who wore accelerometers for one week and completed a survey on demographics, mental health, and neighborhood perceptions. Anthropometrics were collected and A1C was measured using A1CNow test strips. We compiled spatial data on vacant buildings and lots across the city. We fitted conventional and multilevel regression models to predict each outcome, using perceived safety during daytime walking as the independent variable of interest and individual or both individual and neighborhood-level covariates (e.g., number of vacant lots). Last, we examined trends in neighborhood features according to perceived safety. Results In this predominantly African American sample (91%), 47% felt unsafe during daytime walking. Feelings of perceived safety significantly predicted PSS (β = − 2.34, p = 0.017), depression scores (β = − 4.22, p = 0.006), and BMI (β = − 2.87, p = 0.01), after full adjustment. For PA, we detected a significant association for sex only. For A1C we detected significant associations with blighted lots near the home. Those feeling unsafe lived in neighborhoods with higher park area and number of blighted lots. Conclusion Future research is needed to assess a critical pathway through which neighborhood features, including vacant or poor-quality green spaces, may affect obesity—via stress reduction and concomitant effects on cardiometabolic health.

scholarly journals Domain Adaptation Deep Attention Network for Automatic Logo Detection and Recognition in Google Street View

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ervin Yohannes ◽  
Chih-Yang Lin ◽  
Timothy K. Shih ◽  
Chen-Ya Hong ◽  
Avirmed Enkhbat ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Attention Network ◽  
Google Street View ◽  
Street View ◽  
Detection And Recognition

scholarly journals Facilitating fine-grained intra-urban dengue forecasting by integrating urban environments measured from street-view images

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Kang Liu ◽  
Ling Yin ◽  
Meng Zhang ◽  
Min Kang ◽  
Ai-Ping Deng ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Feature Vector ◽  
Urban Environments ◽  
Environmental Feature ◽  
Fine Grained ◽  
Environmental Features ◽  
Street View ◽  
The City ◽  
Dengue Epidemic

Abstract Background Dengue fever (DF) is a mosquito-borne infectious disease that has threatened tropical and subtropical regions in recent decades. An early and targeted warning of a dengue epidemic is important for vector control. Current studies have primarily determined weather conditions to be the main factor for dengue forecasting, thereby neglecting that environmental suitability for mosquito breeding is also an important factor, especially in fine-grained intra-urban settings. Considering that street-view images are promising for depicting physical environments, this study proposes a framework for facilitating fine-grained intra-urban dengue forecasting by integrating the urban environments measured from street-view images. Methods The dengue epidemic that occurred in 167 townships of Guangzhou City, China, between 2015 and 2019 was taken as a study case. First, feature vectors of street-view images acquired inside each township were extracted by a pre-trained convolutional neural network, and then aggregated as an environmental feature vector of the township. Thus, townships with similar physical settings would exhibit similar environmental features. Second, the environmental feature vector is combined with commonly used features (e.g., temperature, rainfall, and past case count) as inputs to machine-learning models for weekly dengue forecasting. Results The performance of machine-learning forecasting models (i.e., MLP and SVM) integrated with and without environmental features were compared. This indicates that models integrating environmental features can identify high-risk urban units across the city more precisely than those using common features alone. In addition, the top 30% of high-risk townships predicted by our proposed methods can capture approximately 50–60% of dengue cases across the city. Conclusions Incorporating local environments measured from street view images is effective in facilitating fine-grained intra-urban dengue forecasting, which is beneficial for conducting spatially precise dengue prevention and control.

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