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Reassessing Urban Health Interventions: Back to the Future with Google Street View Time Machine
Ronaldo L Cândido1, Madeleine Steinmetz-Wood2, Patrick Morency3
1Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada.
American Journal of Preventive Medicine
|September 19, 2018
Summary
Google Street View Time Machine effectively tracks urban changes like traffic-calming features. This method validates administrative data, improving built environment research for public health interventions.
Area of Science:
- Urban planning and public health research.
- Geospatial analysis and environmental monitoring.
Background:
- Reliable data on built environment changes is crucial for urban health research.
- Tracking urban transformations is essential for effective public health interventions.
Purpose of the Study:
- To present an online observation method using Google Street View Time Machine.
- To validate administrative data on traffic-calming measures using this novel method.
Main Methods:
- Online observation of traffic-calming features (curb extensions, speed bumps) in Montreal.
- Utilizing Google Street View Time Machine for historical image visualization (2007-2016).
- Comparing online observations with administrative data for validation (sensitivity, specificity, PPV).
Main Results:
- Google Street View Time Machine provided a median of seven dates per intersection and six per segment.
- Analysis covered 3,973 time periods across 708 locations.
- Validation showed 99% sensitivity, 58% specificity, and 77% positive predictive value for administrative data.
Conclusions:
- Google Street View Time Machine enables historical documentation of microscale urban interventions.
- This method offers a valuable tool for studying built environment changes in urban health research.
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