Spatiotemporal Analytics
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Spatiotemporal Analytics
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.
Spatiotemporal Data Analytics and Modeling
With the growing advances in technology and transformation to digital services, the world is becoming more connected and more complex. Huge heterogeneous data are generated at rapid speed from various types of sensors. Augmented with artificial intelligence and machine learning and internet of things, latent relations, and new insights can be captured helping in optimizing plans and resource utilization, improving infrastructure, and enhancing quality of services. A “spatial data management system” is a way to take care of data that has something to do with space. This could include data such as maps, satellite images, and GPS data. A temporal data management system is a system designed to manage data that has a temporal component. This could include data such as weather data, financial data, and social media data. Some advanced techniques used in spatial and temporal data management systems include geospatial indexing for efficient querying and retrieval of location-based data, time-series analysis for understanding and predicting temporal patterns in datasets like weather or financial trends, machine learning algorithms for uncovering hidden patterns and correlations in large and complex datasets, and integration with Internet of Things (IoT) technologies for real-time data collection and analysis. These techniques, augmented with artificial intelligence, enable the extraction of latent relations and insights, thereby optimizing plans, improving infrastructure, and enhancing the quality of services. This book provides essential technical knowledge, best practices, and case studies on the state-of-the-art techniques of artificial intelligence and machine learning for spatiotemporal data analysis and modeling. The book is composed of several chapters written by experts in their fields and focusing on several applications including recommendation systems, big data analytics, supply chains and e-commerce, energy consumption and demand forecasting,and traffic and environmental monitoring. It can be used as academic reference at graduate level or by professionals in science and engineering related fields such as data science and engineering, big data analytics and mining, artificial intelligence, machine learning and deep learning, cloud computing, and internet of things.
Spatiotemporal Data Analysis
How do we study the storm's mutation into a deadly twister? Avian flu cases are reported in China.