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Titlebook: New Thinking in GIScience; Bin Li,Xun Shi,Hui Lin Book 2022 Higher Education Press 2022 GIS (Geographical Information System).GISci.GISc.R

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发表于 2025-3-21 19:21:24 | 显示全部楼层 |阅读模式
书目名称New Thinking in GIScience
编辑Bin Li,Xun Shi,Hui Lin
视频video
概述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 lear
图书封面Titlebook: New Thinking in GIScience;  Bin Li,Xun Shi,Hui Lin Book 2022 Higher Education Press 2022 GIS (Geographical Information System).GISci.GISc.R
描述.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..
出版日期Book 2022
关键词GIS (Geographical Information System); GISci; GISc; Remote Sensing; AI in GIS; Deep Learning in GIS; Geovi
版次1
doihttps://doi.org/10.1007/978-981-19-3816-0
isbn_softcover978-981-19-3818-4
isbn_ebook978-981-19-3816-0
copyrightHigher Education Press 2022
The information of publication is updating

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发表于 2025-3-22 00:00:18 | 显示全部楼层
From Representation to Geocomputation: Some Theoretical Accounts of Geographic Information Science,pply existing ones from other disciplines or surround conceptual, logical, or ontological arguments. The lack of a well-defined theory for geographic information presents an excellent research opportunity. Theories for statistics and machine learning are exemplars.
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Prospects on Causal Inferences in GIS,mising concepts. Spatially explicit causal models can be developed by integrating spatial statistical models with existing computational and statistical models for causal analysis. There are limits to quantitative approaches to causal inferences; a comprehensive causal analysis should include qualitative analysis.
发表于 2025-3-23 00:24:28 | 显示全部楼层
Deep Learning of Big Geospatial Data: Challenges and Opportunities,tiotemporal data and discusses the recent progresses in addressing them with a particular focus on the promises of deep learning and GeoAI methods. The chapter is then concluded with a discussion on possible future directions.
发表于 2025-3-23 05:16:47 | 显示全部楼层
GIScience from Viewpoint of Information Science,al information is a special type of information. Then, it is pointed out that the foundation of developing GIScience as a branch of information science has been laid down already and it is time to develop theories behind such a science. This article provides an insight into the future development of GIScience.
发表于 2025-3-23 06:35:01 | 显示全部楼层
The Bottom-Up Approach and De-mapping Direction of GIS,ms of classification, assessment, estimation, and prediction to illustrate the distinction between the top-down and bottom-up approaches. We also point out that an outcome of this new expansion of GIS is that mapping is receding from its center-stage position in GIS.
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