Spatiotemporal Analytics

Spatiotemporal analytics extends spatial statistics to analyze geographic events and objects over time. This book provides a comprehensive understanding of spatiotemporal data and statistics, theories, and methods with examples and software codes for various applications. It is useful for undergraduate and graduate students in GIS and related disciplines.

Format: Hardback
Length: 256 pages
Publication date: 17 March 2023
Publisher: Taylor & Francis Ltd

Spatiotemporal analytics, an extension of spatial statistics, is a powerful tool for recognizing and evaluating the spatial patterns and temporal trends of geographic events or objects. This comprehensive book introduces readers to the world of spatiotemporal analytics, providing a solid foundation for understanding and applying these techniques.

Chapter 1: Introduction to Spatiotemporal Analytics

In this chapter, the authors provide an overview of spatiotemporal data and statistics, explaining their importance in various fields. They introduce the concept of spatiotemporal patterns and discuss the theories and methods used to analyze them. The chapter also highlights the role of geographic information systems (GIS) in capturing, storing, and analyzing spatiotemporal data.

Chapter 2: Spatiotemporal Data and Statistics

This chapter delves into the basics of spatiotemporal data and statistics, including the types of data, their characteristics, and the statistical tools used to analyze them. The authors discuss the principles of spatial autocorrelation, spatial heterogeneity, and temporal dependence, which are fundamental to understanding spatiotemporal data. They also introduce the concept of spatiotemporal random variables and their applications in analyzing spatiotemporal data.

Chapter 3: Spatiotemporal Patterns

In this chapter, the authors explore the various types of spatiotemporal patterns, such as point patterns, line patterns, area patterns, and trajectory patterns. They discuss the methods used to detect and analyze these patterns, including spatial autocorrelation analysis, spatial clustering analysis, and trajectory analysis. The chapter also introduces the concept of spatiotemporal segmentation and its applications in understanding the spatial distribution of phenomena.

Chapter 4: Spatiotemporal Trends

This chapter focuses on analyzing the temporal trends of spatiotemporal data. The authors discuss the principles of temporal autocorrelation analysis, which is used to identify patterns in the temporal evolution of spatiotemporal data. They also introduce the concept of time series analysis and its applications in forecasting and modeling spatiotemporal trends. The chapter also includes examples of real-world applications of spatiotemporal trends in fields such as environmental science, economics, and public health.

Chapter 5: Case Studies

Each chapter includes a case study that demonstrates the application of spatiotemporal analytics in real-world scenarios. The case studies cover a wide range of applications, including urban planning, transportation analysis, disease surveillance, and environmental monitoring. The authors provide detailed explanations of the data used, the analytical methods employed, and the insights gained from the analysis.

Chapter 6: Software and Codes

The book includes software codes and instructions that readers can use to implement the analytical methods discussed in the book. The software is designed to be user-friendly and accessible to beginners, allowing them to apply spatiotemporal analytics in their projects. The authors also provide guidance on how to install and use the software, as well as tips on troubleshooting any potential issues.

Chapter 7: Conclusion

In this chapter, the authors summarize the key concepts and applications of spatiotemporal analytics. They emphasize the importance of understanding spatiotemporal patterns and trends in various fields and the potential benefits of using these techniques in decision-making and problem-solving. The book also provides a roadmap for further research in spatiotemporal analytics and encourages readers to explore the vast potential of this field.

Spatiotemporal analytics is a rapidly growing field with numerous applications across various disciplines. This book aims to provide a comprehensive understanding of spatiotemporal data and statistics, theories, and methods to readers with minimal knowledge in statistics. Written in simple, understandable language, the book includes numerous examples for all theories and methods explained, covering a wide range of applications from different fields.

The book is particularly useful for undergraduate and graduate students who use Geographic Information Systems or study Geographical Information Science. 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.

In conclusion, this book is a valuable resource for anyone interested in learning spatiotemporal analytics. It provides a solid foundation for understanding and applying these techniques, enabling readers to gain insights into complex spatial and temporal phenomena. With its comprehensive coverage, user-friendly software, and real-world case studies, the book is an essential tool for researchers, practitioners, and students in the fields of geography, environmental science, economics, and more.

Weight: 570g
Dimension: 162 x 241 x 20 (mm)
ISBN-13: 9781032303055