Spatiotemporal Analysis of Air Pollution and Its Application in Public Health

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

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.