This book demystifies the core principles and architectural design of spatiotemporal big data systems, offering incisive analysis of the unique attributes of spatiotemporal data and its broad applicability in real-world scenarios, aiming to impart a profound understanding of the intrinsic value of spatiotemporal big data and its far-reaching impact across various fields.
In the core technology section, particular emphasis is placed on the pivotal role of GIS (Geographic Information Systems) technology in the processing of spatiotemporal big data, comprehensively covering the entire process from data collection, pre-processing, in-depth analysis to final visualization. Through carefully selected real-world cases and detailed technical explanations, readers will gain proficiency in leveraging GIS technology to uncover the latent value of spatiotemporal big data.
The book also delves into the essential techniques and algorithms required for building efficient spatiotemporal big data systems, such as efficient data storage and management, intelligent data mining and analysis, alongside specific application cases under the smart city framework, including advanced practices in urban planning optimization, traffic management innovation, and environmental monitoring upgrades. Rich in content and blending theoretical depth with practical guidance, it serves as a valuable resource and reference guide for both academic experts and practitioners in the field, as well as a quality read for those intrigued by spatiotemporal big data systems.
The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.
Chapter 1: The Emergence of Spatiotemporal Big Data Systems.
Chapter 2:
Basic Knowledge of Spatiotemporal Data.
Chapter 3: Data Perception and
Access.
Chapter 4: Data Storage and Indexing.
Chapter 5: Spatiotemporal Big
Data Analysis and Mining.
Chapter 6: Data Services and Sharing.
Chapter 7:
Data Visualization.
Chapter 8: Application of Spatiotemporal Data in Online
Shopping.
Chapter 9: Application of Spatiotemporal Data in Logistics
Services.
Chapter 10: Hazardous Materials Vehicle Supervision.
Yuan Sui, graduated from Beijing Normal University, is currently the head of the Spatio-Temporal Data Team and a senior architect at JD Technology Group. He is a committee member of CCFs Database, Big Data, and Distributed Computing Special Interest Groups. Sui has 15 years of research and work experience in spatiotemporal data, covering areas such as GIS, quantitative remote sensing, and smart cities. He led the team in developing JD Citys Spatio-Temporal Data Engine JUST. Under his leadership, the team has published over 20 papers in top international journals, applied for more than 60 patents, and successfully served over 20 major smart city projects involving urban governance, monitoring and early warning, smart parks, intelligent public security, and government-citizen interaction.
Zisheng Yu, graduated from Xidian University, is a Corresponding Executive Committee member of CCFs Database Professional Committee. His research focus is on urban computing and spatiotemporal data management and analysis. He is the author of the book "GeoMesa Spatiotemporal Data Management".
Rubin Wang, holding a Masters degree in Computer Science and Technology from Southwest Jiaotong University, primarily researches urban computing and spatiotemporal data management.
