Spatiotemporal Analysis of Air Pollution and Its Application in Public Health

Häftad, Engelska, 2019

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Beskrivning

Spatiotemporal Analysis of Air Pollution and Its Application in Public Health reviews, in detail, the tools needed to understand the spatial temporal distribution and trends of air pollution in the atmosphere, including how this information can be tied into the diverse amount of public health data available using accurate GIS techniques. By utilizing GIS to monitor, analyze and visualize air pollution problems, it has proven to not only be the most powerful, accurate and flexible way to understand the atmosphere, but also a great way to understand the impact air pollution has in diverse populations.

This book is essential reading for novices and experts in atmospheric science, geography and any allied fields investigating air pollution.

Introduces readers to the benefits and uses of geo-spatiotemporal analyses of big data to reveal new and greater understanding of the intersection of air pollution and health
Ties in machine learning to improve speed and efficacy of data models
Includes developing visualizations, historical data, and real-time air pollution in large geographic areas

Produktinformation

Utgivningsdatum:2019-11-13
Mått:191 x 235 x 22 mm
Vikt:660 g
Format:Häftad
Språk:Engelska
Antal sidor:328
Förlag:Elsevier Science
ISBN:9780128158227

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Mer om författaren

Lixin Li is a tenured Professor in the Department of Computer Science at Georgia Southern University. She received her Ph.D. degree in Computer Science from the University of Nebraska-Lincoln in 2003. Her research focuses on spatiotemporal interpolation methods, air pollution, and GIS applications Xiaolu Zhou received his Ph.D. degree from University of Illinois at Urbana-Champaign in 2014. His research interests include geospatial analytics in spatial big data and human mobility analysis based on user generated content and smartphone sensing. Weitian Tong received his Ph.D. degree in Computer Science from the University of Alberta in 2015. His research interests include data science and optimization algorithm design.

Innehållsförteckning

1. Introduction to Spatiotemporal Variations of Air Pollutants and related Public Health Impacts2. Illustrations of Statistical Analysis for Air Pollution Data3. Case Study: Does PM2.5 Contribute to the Incidence of Lung and Bronchial Cancers in the U.S.? 4. Bayesian Modeling for Linkage between Air Pollution and Population Health5. Machine Learning for Spatiotemporal Big Data in Air Pollution6. Integrate Machine Learning and Geostatistics for High Resolution Mapping of Gound-level PM2.5 Concentrations 7. Spatiotemporal Interpolation Methods 　for Air Pollution8. Sensing Air Quality: Spatiotemporal Interpolation and Visualization of Real-time Air Pollution Data in the Contiguous U.S. 9. Assessment Methods for Air Pollution Exposure10. Applying LUR Model to Estimate Spatial Variation of PM2.5 in Greater Bay Area, China11. Analysis of Exposure to Ambient Air Pollution: the Link Between Environmental Exposure and Children’s School Performance in Memphis, TN12. Concentrating Risk? The Geographic Concentration of Health Risk from Industrial Air Toxins Across America13. Travel-related exposure to air pollution and its socio-environmental inequalities: Evidence from a week-long GPS-based travel diary dataset