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
1. Introduction 2. Deterministic Models for Spatiotemporal Interpolation 3. Statistical Models for Spatiotemporal Interpolation 4. Machine Learning for Geo-Spatiotemporal Big Data in Air Pollution 5. Bayesian Modeling for Linkage between Air Pollution and Population Health 6. Historical Population Exposure to Air Pollution Extracted by Spatiotemporal Interpolation across the Contiguous U.S. 7. Real-time Visualization to Air Pollution 8. Concentrating Risk? The Geographic Concentration of Health Risk from Industrial Air Toxins Across America 9. High Resolution Mapping of Ground-level PM2.5 Concentrations Using Remote Sensing 10. Analysis of the Relationship between Environmental Exposure and Maternal Health 11. Visual analytics approaches in analyzing air pollution data 12. Individual based sensors in quantifying air pollution exposure 13. Conclusion
Lixin Li is a tenured Professor in the Department of Computer Sciences at Georgia Southern University. She received her B.S. and M.S. degrees in Computer Science from Southwest Jiaotong University, Chengdu, China. She received her Ph.D. in Computer Science from the University of Nebraska-Lincoln. Dr. Li's research focuses on spatiotemporal interpolation methods, air pollution and GIS (Geographic Information System) applications. She has more than 50 peer-reviewed journal articles, book chapters and conference proceeding publications. Through her publications, she has a growing reputation as a researcher in the area of Geographic Information Systems and applications.
Xiaolu Zhou is a tenure-track assistant professor in Geographic Information Science (GIS). He received his B.S. degree from Wuhan University, China, M.S. degree from National University of Singapore, and Ph.D. degree from University of Illinois at Urbana-Champaign. His research interests include geospatial analytics in spatial big data and human mobility analysis based on user generated content and smartphone sensing.
Weitian Tong is a tenure-track assistant professor in the Department of Computer Sciences at Georgia Southern University. He received his B.S. degree in Math and Applied Mathematics from Zhejiang University, Hangzhou, China, in 2010, a Ph.D. degree in Computer Science from the University of Alberta (UofA), Edmonton, Canada, in 2015. His research interests include big data processing in data science, data privacy, approximation algorithm design and smoothed analysis in theoretical computer science.