Document Type
Article
Publication Date
8-2022
DOI
10.2196/37379
Publication Title
JMIR Public Health Surveillance
Volume
8
Issue
8
Pages
e37379 (1-19 pp.)
Abstract
Background: Adding additional bicycle and pedestrian paths to an area can lead to improved health outcomes for residents over time. However, quantitatively determining which areas benefit more from bicycle and pedestrian paths, how many miles of bicycle and pedestrian paths are needed, and the health outcomes that may be most improved remain open questions.
Objective: Our work provides and evaluates a methodology that offers actionable insight for city-level planners, public health officials, and decision makers tasked with the question “To what extent will adding specified bicycle and pedestrian path mileage to a census tract improve residents’ health outcomes over time?”
Methods: We conducted a factor analysis of data from the American Community Survey, Center for Disease Control 500 Cities project, Strava, and bicycle and pedestrian path location and use data from two different cities (Norfolk, Virginia, and San Francisco, California). We constructed 2 city-specific factor models and used an algorithm to predict the expected mean improvement that a specified number of bicycle and pedestrian path miles contributes to the identified health outcomes.
Results: We show that given a factor model constructed from data from 2011 to 2015, the number of additional bicycle and pedestrian path miles in 2016, and a specific census tract, our models forecast health outcome improvements in 2020 more accurately than 2 alternative approaches for both Norfolk, Virginia, and San Francisco, California. Furthermore, for each city, we show that the additional accuracy is a statistically significant improvement (P2 weeks of poor physical health days in the census tract within 1.83% (SD 0.57%). For San Francisco (n=49 census tracts), our approach estimates, on average, that the percentage of individuals who had a stroke in the census tract is within 1.81% (SD 0.52%), and the percentage of individuals with diabetes in the census tract is within 1.26% (SD 0.91%).
Conclusions: We propose and evaluate a methodology to enable decision makers to weigh the extent to which 2 bicycle and pedestrian paths of equal cost, which were proposed in different census tracts, improve residents’ health outcomes; identify areas where bicycle and pedestrian paths are unlikely to be effective interventions and other strategies should be used; and quantify the minimum amount of additional bicycle path miles needed to maximize health outcome improvements. Our methodology shows statistically significant improvements, compared with alternative approaches, in historical accuracy for 2 large cities (for 2016) within different geographic areas and with different demographics.
Original Publication Citation
Gore, R., Lynch, C.J., Jordan, C.A., Collins, A., Robinson, R.M., Fuller, G., Ames, P., Keerthi, P. & Kandukuri, Y. (2022). Estimating the health effects of adding bicycle and pedestrian paths at the census tract level: Multiple Model Comparison. JMIR Public Health and Surveillance, 8(8), Article e37379. https://doi.org/10.2196/37379
ORCID
0000-0003-4065-6146 (Gore), 0000-0002-4830-7488 (Lynch), 0000-0003-3892-7701 (Jordan), 0000-0002-8012-2272 (Collins),
Repository Citation
Gore, Ross J.; Lynch, Christopher; Jordan, Craig; Collins, Andrew; Robinson, R. Michael; Fuller, Gabrielle; Ames, Pearson; Keerthi, Prateek; and Kandukuri, Yash, "Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model Comparison" (2022). VMASC Publications. 70.
https://digitalcommons.odu.edu/vmasc_pubs/70
Included in
Community Health and Preventive Medicine Commons, Data Science Commons, Environmental Public Health Commons, Public Policy Commons
Comments
©Ross Gore, Christopher J Lynch, Craig A Jordan, Andrew Collins, R Michael Robinson, Gabrielle Fuller, Pearson Ames, Prateek Keerthi, Yash Kandukuri. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 24.08.2022.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.