Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases
Abstract
:1. Introduction
2. Materials and Methods
Statistical Analyses
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Characteristic | Number of Images | Number of Zip Codes | Mean (Standard Deviation) |
---|---|---|---|
Google Street View | |||
Non-single family home | 164,443,190 | 30,556 | 25.62% (21.10) |
Sidewalks | 164,443,190 | 30,556 | 19.50% (24.31) |
Crosswalks | 164,443,190 | 30,556 | 1.56% (3.17) |
Visible wires | 164,443,190 | 30,556 | 44.14% (16.81) |
Dilapidated building | 164,443,190 | 30,556 | 18.04% (11.40) |
Single lane road | 164,443,190 | 30,556 | 65.47% (14.31) |
Green street | 164,443,190 | 30,556 | 87.08% (15.70) |
COVID-19 outcomes | |||
Cases per 100,000 | 8171 | 545.86 (1353.86) |
Characteristic | Rate Ratio (95% CI) | Rate Ratio (95% CI) | Rate Ratio (95% CI) | Rate Ratio (95% CI) | Rate Ratio (95% CI) | Rate Ratio (95% CI) | Rate Ratio (95% CI) |
---|---|---|---|---|---|---|---|
GSV indicators | |||||||
Non-single family home | 1.21 (1.16, 1.25) | ||||||
Sidewalks | 1.40 (1.34, 1.46) | ||||||
Crosswalks | 1.14 (1.10, 1.18) | ||||||
Visible wires | 1.08 (1.03, 1.13) | ||||||
Dilapidated building | 1.03 (0.99, 1.08) | ||||||
Single lane roads | 0.90 (0.86, 0.94) | ||||||
Green streets | 0.96 (0.92, 1.00) | ||||||
Covariates | |||||||
Household size | 1.03 (0.99, 1.07) | 1.02 (0.99, 1.06) | 1.03 (0.99, 1.07) | 0.99 (0.95, 1.03) | 0.98 (0.94, 1.02) | 1.00 (0.96, 1.04) | 0.98 (0.94, 1.02) |
Median household income | 1.17 (1.13, 1.22) | 1.12 (1.08, 1.17) | 1.15 (1.10, 1.20) | 1.18 (1.13, 1.23) | 1.17 (1.12, 1.21) | 1.16 (1.11, 1.20) | 1.17 (1.12, 1.22) |
Poverty rate | 1.11 (1.05, 1.18) | 1.09 (1.03, 1.16) | 1.16 (1.09, 1.23) | 1.20 (1.13, 1.27) | 1.21 (1.14, 1.28) | 1.16 (1.09, 1.23) | 1.20 (1.13, 1.27) |
% Less than H.S. education | 1.42 (1.32, 1.52) | 1.54 (1.43, 1.65) | 1.47 (1.37, 1.57) | 1.46 (1.36, 1.57) | 1.49 (1.39, 1.61) | 1.43 (1.32, 1.53) | 1.47 (1.36, 1.58) |
Civilian employment | 1.07 (0.99, 1.16) | 1.12 (1.04, 1.20) | 1.07 (0.99, 1.15) | 1.05 (0.97, 1.14) | 1.05 (0.97, 1.14) | 1.03 (0.96, 1.12) | 1.05 (0.97, 1.14) |
% Asian | 1.04 (1.02, 1.07) | 0.98 (0.96, 1.01) | 1.05 (1.02, 1.08) | 1.07 (1.04, 1.10) | 1.07 (1.04, 1.10) | 1.07 (1.04, 1.10) | 1.07 (1.04, 1.10) |
% Black | 1.25 (1.22, 1.29) | 1.17 (1.13, 1.20) | 1.26 (1.22, 1.30) | 1.29 (1.25, 1.32) | 1.29 (1.26, 1.33) | 1.29 (1.25, 1.32) | 1.29 (1.26, 1.33) |
% Hispanic | 1.13 (1.08, 1.18) | 1.02 (0.98, 1.07) | 1.13 (1.08, 1.18) | 1.19 (1.14, 1.24) | 1.19 (1.14, 1.25) | 1.19 (1.15, 1.24) | 1.20 (1.15, 1.25) |
Population density | 1.01 (1.00, 1.02) | 1.01 (1.00, 1.02) | 1.02 (1.01, 1.03) | 1.04 (1.03, 1.05) | 1.04 (1.03, 1.05) | 1.03 (1.02, 1.04) | 1.04 (1.03, 1.05) |
Median age | 1.07 (1.00, 1.16) | 1.01 (0.94, 1.09) | 1.05 (0.97, 1.13) | 1.04 (0.96, 1.12) | 1.03 (0.96, 1.11) | 1.06 (0.98, 1.14) | 1.04 (0.96, 1.12) |
Adjusted R-square | 0.4416 | 0.4818 | 0.4370 | 0.4223 | 0.4202 | 0.4253 | 0.4207 |
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Share and Cite
Nguyen, Q.C.; Huang, Y.; Kumar, A.; Duan, H.; Keralis, J.M.; Dwivedi, P.; Meng, H.-W.; Brunisholz, K.D.; Jay, J.; Javanmardi, M.; et al. Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases. Int. J. Environ. Res. Public Health 2020, 17, 6359. https://doi.org/10.3390/ijerph17176359
Nguyen QC, Huang Y, Kumar A, Duan H, Keralis JM, Dwivedi P, Meng H-W, Brunisholz KD, Jay J, Javanmardi M, et al. Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases. International Journal of Environmental Research and Public Health. 2020; 17(17):6359. https://doi.org/10.3390/ijerph17176359
Chicago/Turabian StyleNguyen, Quynh C., Yuru Huang, Abhinav Kumar, Haoshu Duan, Jessica M. Keralis, Pallavi Dwivedi, Hsien-Wen Meng, Kimberly D. Brunisholz, Jonathan Jay, Mehran Javanmardi, and et al. 2020. "Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases" International Journal of Environmental Research and Public Health 17, no. 17: 6359. https://doi.org/10.3390/ijerph17176359