Spatiotemporal Analytics

✍ Scribed by Jay Lee

• Publisher: CRC Press
• Year: 2023
• Tongue: English
• Leaves: 267
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Editor
Contributors
Chapter 1 Introduction to Spatiotemporal Analytics
1.1 From Spatial Analytics to Spatiotemporal Analytics
1.2 Spatial Dependency and Spatiotemporal Dependency Among Geographic Events or Objects
1.3 Space–Time Dependency
1.4 Concluding Remarks
References
Chapter 2 Spatiotemporal Centrography and Dispersion
2.1 Introduction
2.2 Review of Relevant Literature
2.3 Analytical Methods
2.3.1 Centrography of Spatiotemporal Points
2.3.1.1 Spatiotemporal Mean Center
2.3.1.2 Weighted Spatiotemporal Mean Center
2.3.1.3 Changes in Spatiotemporal Mean Centers
2.3.2 Dispersion of Spatiotemporal Points
2.3.2.1 Standard Spatiotemporal Distance
2.3.2.2 Standard Spherical Volume
2.4 Application Example
2.5 Software and Usage
2.5.1 Hardware/Software Requirements
2.5.2 Software Usage for Spatiotemporal Mean Centers
2.5.3 Software Usage for Standard Spatiotemporal Distance
2.6 Concluding Remarks
References
Chapter 3 Spatiotemporal Quadrat Analytics
3.1 Introduction
3.2 Review of Relevant Literature
3.3 Analytical Methods
3.4 Application Example
3.5 Software and Usage
3.5.1 Hardware/Software Requirements
3.5.2 Software Usage for Spatiotemporal Quadrat Analysis
3.6 Concluding Remarks
References
Chapter 4 Spatiotemporal Nearest Neighbor Analytics
4.1 Introduction
4.2 Nearest Neighbor Index
4.3 Spatiotemporal Nearest Neighbor Index
4.3.1 The Time Dimension
4.3.2 Space–Time Nearest Neighbor Index
4.3.3 STNNI Application
4.3.4 Some Final Remarks
4.4 Software and Usage
4.4.1 Installation and Uninstallation
4.4.1.1 Install QGIS and NNI Plugin
4.4.1.2 Uninstall
4.4.2 Run STNNI and NNI Scripts
4.4.2.1 Space–Time Nearest Neighborhood Index
4.4.2.2 Spatial Nearest Neighborhood Index
4.5 Concluding Remarks
References
Appendix
Chapter 5 Spatiotemporal Ripley's K and L Functions
5.1 Introduction
5.2 Concept and Methods
5.2.1 Spatial Ripley's K Function
5.2.2 Spatiotemporal Ripley's K Function
5.3 An Example Application
References
Chapter 6 Spatiotemporal Autocorrelation Analytics
6.1 Introduction
6.2 Methodology
6.2.1 Spatial Autocorrelation Moran's I
6.2.2 Temporal Autocorrelation
6.2.2.1 Global Temporal Moran's I[sub(t)]
6.2.2.2 Localized Temporal Moran's I
6.2.3 Spatiotemporal Autocorrelation (Temporal and Spatial Moran's I)
6.2.3.1 Global Spatiotemporal Moran's Index
6.2.3.2 Localized Spatiotemporal Moran's Index
6.3 Example Application
6.3.1 Disease Patterns
6.3.2 Simulation Experiments
6.3.2.1 Monte Carlo Simulation Process
6.3.2.2 Sensitivity and Temporal and Spatial Trend Analysis
6.4 Software and User Manual
6.4.1 Moran's I Tool User Manual
6.4.2 Demonstration of Software Results
6.4.3 Supplementary Explanation
References
Chapter 7 Spatiotemporal G Statistical Analytics
7.1 Introduction
7.2 The Getis – Ord G[sub(i)] and G[sub(i)] Statistics
7.2.1 Space–Time Weight Matrix
7.2.2 Space–Time G[sub(i)] and G[sub(i)]
7.3 Space–Time Crime Pattern in Chicago
7.3.1 Software and Usage
7.3.2 Hardware/Software Requirements
7.3.3 Software Usage for ST G[sub(i)] and G[sub(i)]* Analysis
7.4 Concluding Remarks
References
Chapter 8 Spatiotemporal Kernel Density Estimation
8.1 Introduction
8.2 Methods
8.2.1 Classic Spatiotemporal Kernel Density Estimation (CL_STKDE)
8.2.2 Conditional Spatiotemporal Kernel Density Estimation (CN_STKDE)
8.2.3 Integrative Spatiotemporal Kernel Density Estimation (IN_STKDE)
8.2.4 Validation Measurement
8.2.4.1 Hit Rate
8.2.4.2 Compactness Index
8.3 Example Application
8.4 Software and User Manual
References
Chapter 9 Spatiotemporally Weighted Regression
9.1 Introduction
9.2 Methodology
9.2.1 OLS Model
9.2.2 GWR Model
9.2.3 GTWR Model
9.3 Application Examples
9.3.1 House Price Estimation
9.3.2 Environmental Pollution Monitoring
9.3.3 Transportation Management
9.3.4 Crime Analysis – Based Urban Planning
9.4 Software and Usage
9.4.1 Installation and Uninstallation
9.4.1.1 How to Install GTWR Add-in
9.4.1.2 Uninstall
9.4.2 Run GTWR
9.4.2.1 Data Input
9.4.2.2 Setting
9.4.2.3 Output
9.4.2.4 Error
9.4.3 Some Notes
9.4.3.1 Data Requirements
9.4.3.2 Model Test
9.4.3.3 Spatiotemporal Distance
9.5 Concluding Remarks
References
Chapter 10 Spatiotemporal Bayesian Regression
10.1 Introduction to Bayesian Inference
10.1.1 Disease Mapping
10.1.2 Adding a Temporal Component
10.1.3 Parametric Time Trend
10.1.4 Exceedance Probabilities and Hotspot Identification
10.2 Example Applications
10.2.1 Example 1: Modeling Drug Overdose Incident
10.2.1.1 Defining Spatial Adjacency
10.2.1.2 Mapping the Relative Risk
10.2.1.3 Spatial Risk
10.2.1.4 Spatiotemporal Trend and Exceedance Probabilities
10.2.2 Example Application 2: Predictive Distribution of Spatiotemporal Bayesian Model
10.2.2.1 Predictive Distribution of Spatiotemporal Bayesian Model
10.2.2.2 Parameter Estimation via MCMC
10.2.2.3 Application Example 2
10.3 Concluding Remarks
References
Chapter 11 Spatiotemporal Process Analytics and Simulations
11.1 Introduction to Space–Time Network Simulations
11.2 Network Complexity
11.3 Classifying Network Diffusion Processes
11.4 Spatiotemporal Simulation with Agent-Based Modeling (ABM)
11.5 Application Example
11.5.1 Modeling the Spatiotemporal Network of a Dengue Fever Outbreak
11.6 Concluding Remarks
References
Chapter 12 Spatiotemporal Analytical Unit Problems
12.1 Introduction
12.2 Review of Relevant Literature
12.3 Analytical Methods
12.3.1 Modifiable Areal-Temporal Unit Problem (MATUP)
12.3.1.1 Spatiotemporal Scale
12.3.1.2 Spatiotemporal Divisions
12.3.1.3 Spatiotemporal Boundaries
12.3.2 Research Method
12.4 Application Example
12.4.1 Data
12.4.2 The Scale Effect of Space–Time Unit
12.4.3 Effects by Different Division Schemes
12.4.4 Effects of the Spatiotemporal Boundary
12.5 Software and Usage
12.6 Concluding Remarks
References
Index