New Thinking in GIScience - Li, Bin.; Shi, Xun.; Zhu, A-Xing.; Wang, Cuizhen; Lin, Hui. (Singapore : Springer Nature Singapore : Imprint: Springer, 2022)
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.
From Representation to Geocomputation: Some Theoretical Accounts of Geographic Information Science -- On Holo-Spatial Information System -- The Virtual Geographic Environment-More 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.
Keywords: Geographic information systems; Artificial intelligence; Machine learning; Geographical Information System; Artificial Intelligence; Machine Learning