Spatiotemporal Analytics [Paperback]

1. Introduction to Spatiotemporal Analytics.  2. Spatiotemporal Centrography and Dispersion.  3. Spatiotemporal Quadrat Analytics.  4. Spatiotemporal Nearest Neighbor Analytics.  5. Spatiotemporal Ripleys K and L Functions.  6. Spatiotemporal Autocorrelation Analytics.  7. Spatiotemporal G Statistical Analytics.  8. Spatiotemporal Kernel Density Estimation.  9. Spatiotemporally Weighted Regression.  10. Spatiotemporal Bayesian Regression.  11. Spatiotemporal Process Analytics and Simulations.  12. Spatiotemporal Analytical Unit Problems.     
   

This book explains in very simple terms the concepts of spatiotemporal analytics 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 to learn statistics and use them effectively in their projects.

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. Lees research grants include funding supports from NSF, USGS, EPA, NIJ, HUD, NASA, NOAA, and other state and local agencies.

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 tl³B