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Full Description
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.
Contents
1. Introduction to Spatiotemporal Analytics. 2. Spatiotemporal Centrography and Dispersion. 3. Spatiotemporal Quadrat Analytics. 4. Spatiotemporal Nearest Neighbor Analytics. 5. Spatiotemporal Ripley's 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.
