This book introduces readers to spatiotemporal analytics that are extended from spatial statistics. Spatiotemporal analytics help analysts to quantitatively recognize and evaluate the spatial patterns and their temporal trends of a set of geographic events or objects. Spatiotemporal analyses are very important in geography, environmental sciences, economy, and many other domains. Spatiotemporal Analytics explains in very simple terms the concepts of spatiotemporal data and statistics, theories, and methods used. Each chapter introduces a case study as an example application for an in-depth learning process. The software used and the codes provided enable readers not only to learn statistics but also to use them effectively in their projects.
• Provides a comprehensive understanding of spatiotemporal analytics to readers with minimum knowledge in statistics.
• Written in simple, understandable language with step-by-step instructions.
• Includes numerous examples for all theories and methods explained in the book covering a wide range of applications from different disciplines.
• Each application includes a software code needed to follow the instructions.
• Each chapter also has a set of prepared PowerPoint slides to help spatiotemporal analytics instructors explain the content.
Undergraduate and graduate students who use Geographic Information Systems or study Geographical Information Science will find this book useful. The subject matter is also pertinent to an array of disciplines such as agriculture, anthropology, archaeology, architecture, biology, business administration and management, civic engineering, criminal justice, epidemiology, geography, geology, marketing, political science, and public health.
Jay Lee
2. Spatiotemporal Centrography and Dispersion.
Lang Xue Dang, Huiyu Lin and Jay Lee
3. Spatiotemporal Quadrat Analytics.
Zhuo Chen
4. Spatiotemporal Nearest Neighbor Analytics.
Qingsong Liu and Jay Lee
5. Spatiotemporal Ripley’s K and L Functions.
Jay Lee
6. Spatiotemporal Autocorrelation Analytics.
Shengwen Li, Xuyang Cheng, Bo Wan, Junfang Gong and Jay Lee
7. Spatiotemporal G Statistical Analytics.
Zhuo Chen and Huiyu Lin
8. Spatiotemporal Kernel Density Estimation.
Junfang Gong, Zhuang Zeng, Bo Wan, Shengwen Li, and Jay Lee
9. Spatiotemporally Weighted Regression.
Bo Huang and Sensen Wu
10. Spatiotemporal Bayesian Regression.
Ortis Yankey, Tao Hu, Han Yue, Peixiao Wang, and Xiao Xu
11. Spatiotemporal Process Analytics and Simulations.
Moira O’Neill and Jay Lee
12. Spatiotemporal Analytical Unit Problems.
Lang Xue Dang, Huiyu Lin and Jay Lee
Biography
Jay Lee received his doctoral degree in Geography from the University of Western Ontario in 1989. Since then, he has tagut GIS and related courses at Kent State University. His research in applied geography aims at solving practical problems that the society faces. Dr. Lee applied spatial and spatiotemporal analysis of geographic information in his works of over 120 published journal articles, book chapters and books. Among others, he has co-authored two widely read books on statistical analysis of geographic information with GIS. Dr. Lee’s research grants include funding supports from NSF, USGS, EPA, NIJ, HUD, NASA, NOAA, and other state and local agencies.
