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Characterizing community-wide housing attributes using georeferenced street-level photography

Abstract

New methods are needed to efficiently characterize built environment attributes and residential behaviors to improve exposure assessment in epidemiologic research, given limitations of available databases and approaches. Window-opening and presence of air conditioning (AC) units predict indoor air quality and thermal comfort, but data are not widely available. In this study, we tested the utility of a GIS-based tool for rapidly assessing open windows and window/wall AC units in the city of Chelsea, Massachusetts using georeferenced street-level photographs and crowdsourced online surveys. We characterized open windows and window/wall AC units for 969 parcels in the winter and 1213 parcels in the summer, requiring ~40 person hours per season. In the winter, 21% of parcels surveyed had a window or wall AC unit and 19% had an open window. In the summer, 69% had a window or wall AC unit and 53% had an open window. We demonstrated an efficient method for rapidly characterizing open windows and window/wall AC units across an entire city. This tool can help to characterize exposures for epidemiological research, engage community members, and inform local land use planning and decision-making.

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Acknowledgements

This work was supported by the National Institute of Environmental Health Sciences (grant number T32 ES014562), the National Institute on Minority Health and Health Disparities (grant number P50 MD010428), and the U.S. Environmental Protection Agency (grant number RD-836156). The funding sources had no involvement in the conduct of this research or preparation of this manuscript. There were no other sources of financial support, sponsorship, or materials. The authors thank GreenRoots, the Chelsea Beautification Committee, and the Chelsea Department of Public Works for their input and ideas related to applications of this data collection method. In addition, we would like to acknowledge Raquel Jimenez Celsi, Daniel Nguyen, Leila Heidari, Felipe Jordán Colzani, Iman Ali, Bailey Alvarado, and Baderha Bujiriri for their important contributions to photograph collection and survey testing. Finally, we wish to thank Chris Buscaglia for his help in setting up the surveys and everyone who completed the surveys online.

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Correspondence to Zoe E. Petropoulos.

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Petropoulos, Z.E., Levy, J.I., Scammell, M.K. et al. Characterizing community-wide housing attributes using georeferenced street-level photography. J Expo Sci Environ Epidemiol 30, 299–308 (2020). https://doi.org/10.1038/s41370-019-0167-9

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Keywords

  • Exposure assessment
  • Residential behavior
  • Neighborhood
  • Photo survey
  • Window opening
  • Geographic information system

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