Stephen J Mooney1, Charles J DiMaggio1, Gina S Lovasi1, Kathryn M Neckerman1, Michael D M Bader1, Julien O Teitler1, Daniel M Sheehan1, Darby W Jack1, Andrew G Rundle1. 1. Stephen J. Mooney, Charles J. DiMaggio, Gina S. Lovasi, Daniel M. Sheehan, and Andrew G. Rundle are with Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY. Kathryn M. Neckerman is with Columbia Population Research Center, Columbia University. Michael D. M. Bader is with Department of Sociology, American University, Washington, DC. Julien O. Teitler is with School of Social Work, Columbia University. Darby W. Jack is with Department of Environmental Health Sciences, Mailman School of Public Health.
Abstract
OBJECTIVES: To demonstrate an information technology-based approach to assess characteristics of streets and intersections associated with injuries that is less costly and time-consuming than location-based studies of pedestrian injury. METHODS: We used imagery captured by Google Street View from 2007 to 2011 to assess 9 characteristics of 532 intersections within New York City. We controlled for estimated pedestrian count and estimated the relation between intersections' characteristics and frequency of injurious collisions. RESULTS: The count of pedestrian injuries at intersections was associated with the presence of marked crosswalks (80% increase; 95% confidence interval [CI] = 2%, 218%), pedestrian signals (156% increase; 95% CI = 69%, 259%), nearby billboards (42% increase; 95% CI = 7%, 90%), and bus stops (120% increase; 95% CI = 51%, 220%). Injury incidence per pedestrian was lower at intersections with higher estimated pedestrian volumes. CONCLUSIONS: Consistent with in-person study observations, the information-technology approach found traffic islands, visual advertising, bus stops, and crosswalk infrastructures to be associated with elevated counts of pedestrian injury in New York City. Virtual site visits for pedestrian injury control studies are a viable and informative methodology.
OBJECTIVES: To demonstrate an information technology-based approach to assess characteristics of streets and intersections associated with injuries that is less costly and time-consuming than location-based studies of pedestrian injury. METHODS: We used imagery captured by Google Street View from 2007 to 2011 to assess 9 characteristics of 532 intersections within New York City. We controlled for estimated pedestrian count and estimated the relation between intersections' characteristics and frequency of injurious collisions. RESULTS: The count of pedestrian injuries at intersections was associated with the presence of marked crosswalks (80% increase; 95% confidence interval [CI] = 2%, 218%), pedestrian signals (156% increase; 95% CI = 69%, 259%), nearby billboards (42% increase; 95% CI = 7%, 90%), and bus stops (120% increase; 95% CI = 51%, 220%). Injury incidence per pedestrian was lower at intersections with higher estimated pedestrian volumes. CONCLUSIONS: Consistent with in-person study observations, the information-technology approach found traffic islands, visual advertising, bus stops, and crosswalk infrastructures to be associated with elevated counts of pedestrian injury in New York City. Virtual site visits for pedestrian injury control studies are a viable and informative methodology.
Authors: Jeffrey S Wilson; Cheryl M Kelly; Mario Schootman; Elizabeth A Baker; Aniruddha Banerjee; Morgan Clennin; Douglas K Miller Journal: Am J Prev Med Date: 2012-02 Impact factor: 5.043
Authors: Christopher N Morrison; Andrew G Rundle; Charles C Branas; Stanford Chihuri; Christina Mehranbod; Guohua Li Journal: Spat Spatiotemporal Epidemiol Date: 2020-07-18
Authors: Stephen J Mooney; Michael D M Bader; Gina S Lovasi; Julien O Teitler; Karestan C Koenen; Allison E Aiello; Sandro Galea; Emily Goldmann; Daniel M Sheehan; Andrew G Rundle Journal: Am J Epidemiol Date: 2017-08-01 Impact factor: 4.897
Authors: Elizabeth D Nesoff; Adam J Milam; Keshia M Pollack; Frank C Curriero; Janice V Bowie; Andrea C Gielen; Debra M Furr-Holden Journal: J Urban Health Date: 2018-04 Impact factor: 3.671
Authors: Stephen J Mooney; Caroline Magee; Kolena Dang; Julie C Leonard; Jingzhen Yang; Frederick P Rivara; Beth E Ebel; Ali Rowhani-Rahbar; D Alex Quistberg Journal: Am J Epidemiol Date: 2018-09-01 Impact factor: 4.897
Authors: Elizabeth D Nesoff; Adam J Milam; Keshia M Pollack; Frank C Curriero; Janice V Bowie; Amy R Knowlton; Andrea C Gielen; Debra M Furr-Holden Journal: Inj Prev Date: 2018-03-27 Impact factor: 2.399
Authors: Elizabeth D Nesoff; Keshia M Pollack; Amy R Knowlton; Janice V Bowie; Andrea C Gielen Journal: Traffic Inj Prev Date: 2018-04-11 Impact factor: 1.491
Authors: Mitzi Morris; Katherine Wheeler-Martin; Dan Simpson; Stephen J Mooney; Andrew Gelman; Charles DiMaggio Journal: Spat Spatiotemporal Epidemiol Date: 2019-08-12