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Downs NJ, Amar A, Dearnaley J, Butler H, Dekeyser S, Igoe D, Parisi AV, Raj N, Deo R, Turner J. The mitigating effect of street trees, urban flora, and the suburban environment on seasonal peak UV indices: A case study from Brisbane, Australia. Photochem Photobiol 2024. [PMID: 38926941 DOI: 10.1111/php.13988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/29/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024]
Abstract
Tree shade, particularly shade that obscures direct sunlight near peak periods of midday solar exposure can have a pronounced effect on potentially harmful ultraviolet radiation, and in turn, strongly influence the maximum daily UV index (UVI). In this study, the seasonal influence of tree shade on the UVI is evaluated from 210 hemispherical sky view images collected alongside public walkways and footpaths from 10 residential Brisbane suburbs. The effective sidewalk UV index is calculated underneath planted tree canopies, adjacent residential gardens, buildings and background tree species. Results are presented with respect to seasonal variations in the diurnal solar elevation for each month of the year at Brisbane's latitude. The research also examines the total reduction in UVI due to the presence of individual tree species, showing reductions in the midday UVI of up to 91% of an equivalent unimpeded sky hemisphere when overhead tree canopies are present. Important footpath tree species for peak midday UVI mitigation include Pongamia pinnata, Xanthostemon chrysanthus, Senna siamea, and Libidibia ferrea. The planting and maintenance of existing tree species already growing alongside residential Brisbane streets will improve the shade characteristics of suburbs and enhance UV protection for local residents.
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Affiliation(s)
- Nathan James Downs
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Queensland, Australia
| | - Abdurazaq Amar
- UniSQ College, University of Southern Queensland, Toowoomba, Queensland, Australia
| | - John Dearnaley
- School of Agriculture and Environmental Science, University of Southern Queensland, Toowoomba, Queensland, Australia
| | - Harry Butler
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Queensland, Australia
| | - Stijn Dekeyser
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Queensland, Australia
| | - Damien Igoe
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Queensland, Australia
| | - Alfio V Parisi
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Queensland, Australia
| | - Nawin Raj
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Queensland, Australia
| | - Ravinesh Deo
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Queensland, Australia
| | - Joanna Turner
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Queensland, Australia
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Alirezaei M, Nguyen QC, Whitaker R, Tasdizen T. Multi-Task Classification for Improved Health Outcome Prediction Based on Environmental Indicators. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2023; 11:73330-73339. [PMID: 38405414 PMCID: PMC10888441 DOI: 10.1109/access.2023.3295777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
This paper aims to address the challenges associated with evaluating the impact of neighborhood environments on health outcomes. Google street view (GSV) images provide a valuable tool for assessing neighborhood environments on a large scale. By annotating the GSV images with labels indicating the presence or absence of specific neighborhood features, we can develop classifiers capable of automatically analyzing and evaluating the environment. However, the process of labeling GSV images to analyze and evaluate the environment is a time-consuming and labor-intensive task. To overcome these challenges, we propose using a multi-task classifier to enhance the training of classifiers with limited supervised GSV data. Our multi-task classifier utilizes readily available, inexpensive online images collected from Flickr as a related classification task. The hypothesis is that a classifier trained on multiple related tasks is less likely to overfit to small amounts of training data and generalizes better to unseen data. We leverage the power of multiple related tasks to improve the classifier's overall performance and generalization capability. Here we show that, with the proposed learning paradigm, predicted labels for GSV test images are more accurate. Across different environment indicators, the accuracy, F1 score and balanced accuracy increase up to 6 % in the multi-task learning framework compared to its single-task learning counterpart. The enhanced accuracy of the predicted labels obtained through the multi-task classifier contributes to a more reliable and precise regression analysis determining the correlation between predicted built environment indicators and health outcomes. The R2 values calculated for different health outcomes improve by up to 4 % using multi-task learning detected indicators.
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Affiliation(s)
- Mitra Alirezaei
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT 84112, USA
| | - Quynh C Nguyen
- Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA
| | - Ross Whitaker
- School of Computing, University of Utah, Salt Lake City, UT 84112, USA
| | - Tolga Tasdizen
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT 84112, USA
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Wende ME, Lohman MC, Friedman DB, McLain AC, LaMonte MJ, Whitsel EA, Shadyab AH, Garcia L, Chrisinger BW, Pan K, Bird CE, Sarto GE, Kaczynski AT. Neighborhood Socioeconomic Status, Green Space, and Walkability and Risk for Falls Among Postmenopausal Women: The Women's Health Initiative. Womens Health Issues 2023; 33:443-458. [PMID: 37149415 PMCID: PMC10330171 DOI: 10.1016/j.whi.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 03/01/2023] [Accepted: 03/24/2023] [Indexed: 05/08/2023]
Abstract
PURPOSE This study estimated associations between neighborhood socioeconomic status (NSES), walkability, green space, and incident falls among postmenopausal women and evaluated modifiers of these associations, including study arm, race and ethnicity, baseline household income, baseline walking, age at enrollment, baseline low physical functioning, baseline fall history, climate region, and urban-rural residence. METHODS The Women's Health Initiative recruited a national sample of postmenopausal women (50-79 years) across 40 U.S. clinical centers and conducted yearly assessments from 1993 to 2005 (n = 161,808). Women reporting a history of hip fracture or walking limitations were excluded, yielding a final sample of 157,583 participants. Falling was reported annually. NSES (income/wealth, education, occupation), walkability (population density, diversity of land cover, nearby high-traffic roadways), and green space (exposure to vegetation) were calculated annually and categorized into tertiles (low, intermediate, high). Generalized estimating equations assessed longitudinal relationships. RESULTS NSES was associated with falling before adjustment (high vs. low, odds ratio, 1.01; 95% confidence interval, 1.00-1.01). Walkability was significantly associated with falls after adjustment (high vs. low, odds ratio, 0.99; 95% confidence interval, 0.98-0.99). Green space was not associated with falling before or after adjustment. Study arm, race and ethnicity, household income, age, low physical functioning, fall history, and climate region modified the relationship between NSES and falling. Race and ethnicity, age, fall history, and climate region modified relationships between walkability and green space and falling. CONCLUSIONS Our results did not show strong associations of NSES, walkability, or green space with falling. Future research should incorporate granular environmental measures that may directly relate to physical activity and outdoor engagement.
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Affiliation(s)
- Marilyn E Wende
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina.
| | - Matthew C Lohman
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina
| | - Daniela B Friedman
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina
| | - Alexander C McLain
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina
| | - Michael J LaMonte
- Deparment of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, New York
| | - Eric A Whitsel
- Department of Epidemiology, Gillings School of Global Public Health and Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Aladdin H Shadyab
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California
| | - Lorena Garcia
- Department of Public Health Sciences, School of Medicine, University of California Davis, Davis, California
| | - Benjamin W Chrisinger
- Department of Social Policy and Intervention, Division of Social Sciences, University of Oxford, Oxford, UK
| | - Kathy Pan
- Department of Medical Oncology and Hematology, Downey Medical Center, Kaiser Permanente, Downey, California
| | - Chloe E Bird
- RAND Corporation, Santa Monica, California; Center for Health Equity Research, Tufts Medical Center, Boston, Massachusetts; Tufts University School of Medicine, Boston, Massachusetts
| | - Gloria E Sarto
- Department of Obstetrics and Gynecology, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Andrew T Kaczynski
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina; Prevention Research Center, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina
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Yue X, Antonietti A, Alirezaei M, Tasdizen T, Li D, Nguyen L, Mane H, Sun A, Hu M, Whitaker RT, Nguyen QC. Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:12095. [PMID: 36231394 PMCID: PMC9564970 DOI: 10.3390/ijerph191912095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/14/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
Built environment neighborhood characteristics are difficult to measure and assess on a large scale. Consequently, there is a lack of sufficient data that can help us investigate neighborhood characteristics as structural determinants of health on a national level. The objective of this study is to utilize publicly available Google Street View images as a data source for characterizing built environments and to examine the influence of built environments on chronic diseases and health behaviors in the United States. Data were collected by processing 164 million Google Street View images from November 2019 across the United States. Convolutional Neural Networks, a class of multi-layer deep neural networks, were used to extract features of the built environment. Validation analyses found accuracies of 82% or higher across neighborhood characteristics. In regression analyses controlling for census tract sociodemographics, we find that single-lane roads (an indicator of lower urban development) were linked with chronic conditions and worse mental health. Walkability and urbanicity indicators such as crosswalks, sidewalks, and two or more cars were associated with better health, including reduction in depression, obesity, high blood pressure, and high cholesterol. Street signs and streetlights were also found to be associated with decreased chronic conditions. Chain link fence (physical disorder indicator) was generally associated with poorer mental health. Living in neighborhoods with a built environment that supports social interaction and physical activity can lead to positive health outcomes. Computer vision models using manually annotated Google Street View images as a training dataset were able to accurately identify neighborhood built environment characteristics. These methods increases the feasibility, scale, and efficiency of neighborhood studies on health.
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Affiliation(s)
- Xiaohe Yue
- Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA
| | | | - Mitra Alirezaei
- Department of Electrical and Computer Engineering, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Tolga Tasdizen
- Department of Electrical and Computer Engineering, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Dapeng Li
- Department of Geography and Geospatial Sciences, South Dakota State University, Brookings, SD 57007, USA
| | - Leah Nguyen
- Department of Health Policy and Management, University of Maryland School, College Park, MD 20742, USA
| | - Heran Mane
- Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA
| | - Abby Sun
- Public Health Science Program, University of Maryland School, College Park, MD 20742, USA
| | - Ming Hu
- School of Architecture, Planning & Preservation, University of Maryland School, College Park, MD 20742, USA
| | - Ross T. Whitaker
- School of Computing, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Quynh C. Nguyen
- Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20742, USA
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