New Thinking in GIScience

This book is a collection of seminal position essays by leading researchers on new development in Geographic Information Sciences (GIScience), covering a wide range of topics and representing a variety of perspectives. The authors propose enrichments and extensions to the conceptual framework of GIScience; discuss a series of transformational methodologies and technologies for analysis and modeling; elaborate on key issues in innovative approaches to data acquisition and integration, across earth sensing to social sensing; and outline frontiers in application domains, spanning from natural science to humanities and social science, e.g., urban science, land use and planning, social governance, transportation, crime, and public health, just name a few. The book provides an overview of the strategic directions on GIScience research and development. It will benefit researchers and practitioners in the field who are seeking a high-level reference regarding those directions.

Presents the cutting edge research and development in GISci Offers the vision on the direction of future development of GISci from expert prospects Promotes GISci in the new era of big data, deep learning, and artificial intelligence

Auteur

Bin Li, Ph.D., is a full professor of Geography and Environmental Studies at Central Michigan University, USA. His academic interests include geographic information systems/science, cartography, spatial statistics, and economic geography. His research publications span high performance processing, geographic information services, environmental modeling, geovisualization, and spatial statistics. He serves in editorial boards of academic journals and is active in international collaborations. He was the past president of the International Association of Chinese Professionals in Geographic Information Science (CPGIS).

Xun Shi, Ph.D., is a full professor of Geography at Dartmouth College, USA. His primary research interest is in spatial analysis and spatial modeling, particularly their applications in health-related issues, including disease mapping, environmental health, epidemic modeling, and access to healthcare services. He served as the President of the International Association of Chinese Professionals in Geographic Information Science (CPGIS), Chair of the Specialty Group of Health and Medical Geography of the American Association of Geographers (AAG), Advisor of Guangdong Provincial Center for Disease Control and Prevention (Guangdong CDC).

A-Xing Zhu, Ph.D., is a full professor at the Department of Geography and the Manasse Chair Professor, the University of Wisconsin-Madison. He currently serves as the Editor-in-Chief of Annals of GIS. He was the Chairman of the Pedometrics Commission of the International Union of Soil Science, the Chair of the New Technology Transfer Committee of Natural Resources Conservation Services, USDA, the President of International Association of Geographic Information Sciences. His research interests focus on theoretical and methodological developments in GIS, including the Third Law of Geography, artificial intelligence, fuzzy logic, intelligent geocomputing, and their applications in environmental modeling and scenario analysis. His signature work on spatial prediction in soil mapping (digital soil mapping) was showcased at a 2003 U.S. congressional briefing on the role of geographic information science and technology in meeting the nation's scientific, economic, and social needs.

Cuizhen Wang, Ph.D., is a full professor of Geography at University of South Carolina, USA. Her primary research interests are bio-environmental remote sensing and Digital Earth. Specific training and expertise include optical and radar remote sensing, satellite time series, image-based deep learning, small Unmanned Aircraft Systems (sUAS) and a wide spectrum of environmental applications. Dr. Wang's research and education are embraced in Big Earth Data that harmonizes all remotely sensed data of Earth observations to promote environmental health and sustainability. Dr. Wang is the 2020-2021 President of the International Association of Chinese Professionals in Geographic Information Sciences (CPGIS). She is a Council Member of the International Society of Digital Earth, a U.S. Geospatial Intelligence Foundation (USGIF) Subject Matter Expert in Remote Sensing and Image Analysis, and on the Board of Directors of the South Carolina Interagency Drone Users Consortium (SCiDUC).

Hui Lin, Ph.D., Professor and Dean of the School of Geography and Environment, Jiangxi Normal University of China, and Emeritus Professor of The Chinese University of Hong Kong, Fellow of British Academy of Social Science, Founding President and Honorary President of the International Chinese Association of Geographic Information Science (CPGIS), Vice Chairman of China National Committee of International Society of Digital Earth, Co Editor-in-Chief of Computational Urban Science, and Honorary Editor-in-Chief of Annals of GIS. Professor Hui Lin's major research interests are in Virtual Geographic Environments (VGE) and radar satellite remote sensing. His academic achievement was recognized by the Miller Award from the Association of American Geographers (AAG), and the Outstanding Contribution Award from the Asian Association of Remote Sensing (AARS).

Contenu

From Representation to Geocomputation: Some Theoretical Accounts of Geographic Information Science.- On Holo-Spatial Information System.- The Virtual Geographic EnvironmentMore than the Digital Twin of the Physical Geographical Environments.- Big Remote Sensing Data as Curves.- GIScience from the Viewpoint of Information Science.- Towards Place-based GIS.- The Bottom-up Approach and De-mapping Direction of GIS.- The Geography of Geography.- Classification and Description of Geographic Information: a Comprehensive Expression Framework.- On the Third Law of Geography.- Human Mobility and the Neighborhood Effect Averaging Problem (NEAP).- How to Form and Answer the So What Question in GIScience.- Prospects on Causal Inferences in GIS.- Bayesian Methods for Geospatial Data Analysis.