期刊
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
卷 115, 期 531, 页码 1111-1124出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/01621459.2019.1665526
关键词
Google Street View Air Quality Data; Kriging; Mobile sensors; Spatiotemporal models; Vecchia approximation
资金
- National Institutes of Health [R01ES027892]
- National Institutes of Environmental Health Sciences [K99ES029523]
- National Science Foundation [DMS-1638521, DMS-1654083]
People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. To make informed decisions on their day-to-day activities, they are interested in real-time information on a localized scale. Publicly available, fine-scale, high-quality air pollution measurements acquired using mobile monitors represent a paradigm shift in measurement technologies. A methodological framework utilizing these increasingly fine-scale measurements to provide real-time air pollution maps and short-term air quality forecasts on a fine-resolution spatial scale could prove to be instrumental in increasing public awareness and understanding. The Google Street View study provides a unique source of data with spatial and temporal complexities, with the potential to provide information about commuter exposure and hot spots within city streets with high traffic. We develop a computationally efficient spatiotemporal model for these data and use the model to make short-term forecasts and high-resolution maps of current air pollution levels. We also show via an experiment that mobile networks can provide more nuanced information than an equally sized fixed-location network. This modeling framework has important real-world implications in understanding citizens? personal environments, as data production and real-time availability continue to be driven by the ongoing development and improvement of mobile measurement technologies. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
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