New thinking in GIScience / / Bin Li [and four others], editors

• Pubblicazione: Singapore : , : Springer : , : Higher Education Press, , [2022]; ©2022
• Descrizione fisica: 1 online resource (379 pages)
• Disciplina: 910.285
• Soggetto topico: Geographic information systems; Geographic information systems - Research; Public health
• Persona (resp. second.): LiBin
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Intro -- Preface -- Contents -- 1 From Representation to Geocomputation: Some Theoretical Accounts of Geographic Information Science -- 1.1 Introduction -- 1.2 Geographic Representation -- 1.3 Geocomputation -- 1.4 Concluding Remarks -- References -- 2 On Holo-spatial Information System -- 2.1 Introduction -- 2.2 The Concept of Holo-spatial Information System -- 2.3 Object-Oriented Modeling for HSIS -- 2.4 Information Management Framework of HSIS -- 2.5 Conclusion and Discussion -- References -- 3 The Virtual Geographic Environments: More than the Digital Twin of the Physical Geographical Environments -- 3.1 Introduction -- 3.2 Virtual Geographic Environments -- 3.2.1 The Definition and Concepts of Virtual Geographic Environments -- 3.2.2 The Evolution of Virtual Geographic Environments -- 3.2.3 Features of Virtual Geographic Environments -- 3.3 Digital Twins -- 3.3.1 Concepts and Definitions of Digital Twin -- 3.3.2 Characteristics of Digital Twins -- 3.4 Discussion -- 3.5 Conclusions -- References -- 4 Big Remote Sensing Data as Curves -- 4.1 Introduction -- 4.2 Traditional Perceptions of Big Remote Sensing Data -- 4.3 Novel Perceptions of Big Remote Sensing Data -- 4.4 New Thinking of Big Remote Sensing Data and New Theoretic Frame for Data Processing and Fusion -- 4.5 Conclusions -- References -- 5 GIScience from Viewpoint of Information Science -- 5.1 Introduction -- 5.2 GIScience in Its Current Definitions -- 5.3 GIScience from the Viewpoint of Information Science -- 5.4 GIScience as a Branch of Information Science -- 5.5 Outlook -- References -- 6 Towards Place-Based GIS -- 6.1 Introduction -- 6.2 Building Blocks Towards Place-Based GIS -- 6.2.1 Platial Data and Characteristics -- 6.2.2 Representation and Computational Models of Place -- 6.2.3 Platial Analysis and Visualization -- 6.3 Conclusion -- References. ; 7 The Bottom-Up Approach and De-mapping Direction of GIS -- 7.1 Introduction -- 7.2 Motivation and Facilitation for GIS to Incorporate Bottom-Up Methods -- 7.3 Examples of Bottom-Up Methods -- 7.4 Concluding Remarks -- References -- 8 The Geography of Geography -- 8.1 The Questions -- 8.2 The Exploration -- 8.2.1 The Data -- 8.2.2 The Findings -- 8.3 The Future -- References -- 9 Classification and Description of Geographic Information: A Comprehensive Expression Framework -- 9.1 Introduction -- 9.2 The Connotation of Geographic Information -- 9.2.1 Overall Framework -- 9.2.2 Information Elements for Ternary Space -- 9.2.3 Seven Dimensions for Geographical Information Description -- 9.3 Example of the New Geographic Information Description -- 9.4 Conclusion -- References -- 10 On the Third Law of Geography -- 10.1 About Laws of Geography -- 10.2 The Third Law of Geography -- 10.3 Issues to Address -- 10.4 Summary -- References -- 11 Human Mobility and the Neighborhood Effect Averaging Problem (NEAP) -- 11.1 Introduction -- 11.2 The Neighborhood Effect Averaging Problem -- 11.3 Recent Studies on the NEAP -- 11.4 Implications of the NEAP -- References -- 12 How to Form and Answer the So What Question in GIScience -- 12.1 Introduction -- 12.2 The "So What" Question in Education, Medical Research and Geography -- 12.2.1 The Relevance in Technology Education -- 12.2.2 The PICOT Format in Medical Research -- 12.2.3 The WWO Format in Geography -- 12.3 The WWHO or the "Gazing on the Peak" Format in GIScience -- 12.4 Conclusion -- References -- 13 Prospects on Causal Inferences in GIS -- 13.1 Introduction -- 13.2 Causal Inference Is Not New -- 13.3 Spatial Statistical Causal Inference Is New -- 13.4 Relevance to GIS -- 13.5 Conclusions -- References -- 14 Bayesian Methods for Geospatial Data Analysis -- 14.1 Introduction -- 14.2 Bayesian Inference. ; 14.3 Applications of Bayesian Models in Geospatial Problems -- 14.3.1 Bayesian Spatial Interpolation -- 14.3.2 Bayesian Models for Disease Mapping, Risk Estimate, and Prediction -- 14.3.3 Bayesian Hierarchical Models -- 14.3.4 Bayesian Spatial Autoregressive Models -- 14.4 Bayesian Implementation -- 14.5 Some Concluding Thoughts -- References -- 15 GIS Software Product Development Challenges in the Era of Cloud Computing -- 15.1 Introduction -- 15.2 Challenges to Developing GIS Software as SaaS -- 15.2.1 Agile Development Philosophy and Microservice Architecture -- 15.2.2 Security -- 15.2.3 Continuous Integration/Continuous Delivery (CI/CD) -- 15.2.4 Shift-Left Testing, Testing Automation and Chaos Engineering -- 15.2.5 Integration with Existing Systems -- 15.2.6 Big Data Stores -- 15.2.7 Big Data Processing and GPU Database -- 15.2.8 Production System Monitoring -- 15.2.9 Integration of GeoAI and Machine Learning -- 15.2.10 Open-Source Strategy -- 15.2.11 Geospatial Functionality Development -- 15.2.12 Development Team Building -- 15.3 Concluding Remarks -- References -- 16 Spatial Thinking of Computational Intensity in the Era of CyberGIS -- 16.1 Introduction -- 16.2 Computational Intensity Map -- 16.3 Summary -- References -- 17 GeoAI and the Future of Spatial Analytics -- 17.1 Challenges in Spatial Analytics -- 17.1.1 The Size Challenge of Big Data -- 17.1.2 Navigating Through the Messiness of Big Data -- 17.1.3 Hypothesis Test Versus Knowledge Mining -- 17.2 GeoAI: A New Form of Spatial Analytics -- 17.3 Concluding Remarks -- References -- 18 Deep Learning of Big Geospatial Data: Challenges and Opportunities -- 18.1 Introduction -- 18.2 Challenges in Geospatial Analysis of Big Geospatial Data -- 18.2.1 Complex Geospatial Patterns -- 18.2.2 Heterogeneous Data Sources -- 18.2.3 Geospatial Uncertainty -- 18.3 The Promises of Deep Learning. ; 18.4 Discussions -- References -- 19 Towards Domain-Knowledge-Based Intelligent Geographical Modeling -- 19.1 Complexity in Geographical Modeling -- 19.2 Intelligent Geographical Modeling -- 19.3 Domain Knowledge and Operation of Intelligent Geographical Modeling -- 19.4 How to Realize Intelligent Geographical Modeling? -- 19.5 Potential Contributions to AI -- 19.6 Concluding Remarks -- References -- 20 Mitigating Spatial Bias in Volunteered Geographic Information for Spatial Modeling and Prediction -- 20.1 Introduction -- 20.2 Spatial Bias in VGI -- 20.3 A Representativeness-Directed Approach to Bias Mitigation -- 20.3.1 Measuring Sample Representativeness -- 20.3.2 Representativeness-Directed Bias Mitigation -- 20.4 Applications -- 20.5 Outlook on Future Research -- References -- 21 Dealing with Unstructured Geospatial Data -- 21.1 Introduction -- 21.2 Characteristics of the Unstructured Geospatial Data -- 21.3 Technologies and Challenges of Unstructured Geospatial Data -- 21.4 Conclusion -- References -- 22 Green Cartography and Energy-Aware Maps: Possible Research Opportunities -- 22.1 Introduction -- 22.2 Should Digital Maps Be Energy-Aware? -- 22.2.1 Map Content with Energy Consumption -- 22.2.2 Map Form with Energy Consumption -- 22.3 Possible Research Opportunities of Digital Maps Being Energy-Aware -- 22.3.1 Making Energy-Aware Maps -- 22.3.2 Using Energy-Aware Maps -- 22.4 Summary -- References -- 23 Next Step in Vegetation Remote Sensing: Synergetic Retrievals of Canopy Structural and Leaf Biochemical Parameters -- 23.1 Introduction -- 23.2 Synergetic Retrievals of Both Canopy Structural and Leaf Biochemical Parameters -- 23.2.1 Major Issues in LAI Retrieval -- 23.2.2 Major Issues in LCC Retrieval -- 23.2.3 Synergetic Retrievals of LAI and LCC. ; 23.3 Tradeoff of Canopy Structural and Leaf Biochemical Parameters in Terrestrial Ecosystem Models -- 23.4 Summary -- References -- 24 LiDAR Remote Sensing of Forest Ecosystems: Applications and Prospects -- 24.1 Introduction -- 24.2 Evolution of 3D Forest Observation -- 24.3 Beyond 3D: New Spectrum of LiDAR Applications in Forest Ecosystem Studies -- 24.3.1 Application of LiDAR Structural, Temporal, and Spectral Information in Forest Ecosystem Studies -- 24.3.2 Linking the Forest Structure Information with Radiative Transfer Models and Ecological Processes -- 24.4 Prospects for LiDAR Remote Sensing of Forest Ecosystems -- 24.5 Conclusions -- References -- 25 Dense Satellite Image Time Series Analysis: Opportunities, Challenges, and Future Directions -- 25.1 Introduction -- 25.2 Opportunities for Developing Dense Time-Series Remote Sensing -- 25.2.1 New Data Sources -- 25.2.2 Stronger Capability of Data Processing -- 25.2.3 New Applications -- 25.3 Challenges of Dense SITS Analysis -- 25.3.1 Data Quality Control -- 25.3.2 Data Analysis Techniques -- 25.3.3 Cloud Impact -- 25.4 Future Directions -- 25.4.1 Data Fusion to Reconstruct High-Quality Time Series -- 25.4.2 Modeling Spatial-Temporal Information -- 25.4.3 Development of Analysis-Ready Data and User-Friendly Tools -- 25.5 Conclusion -- References -- 26 Digital Earth: From Earth Observations to Analytical Solutions -- 26.1 Introduction -- 26.2 Remote Sensing: A Long Path of Earth Observations -- 26.3 Social Sensing: VGI Collection and Dissemination -- 26.4 Digital Earth: An Integrated Analytical Solution -- 26.5 Conclusion -- References -- 27 Spatial-Temporal Big Data Enables Social Governance -- 27.1 Introduction -- 27.2 Current Situation of Social Governance -- 27.2.1 Why Social Governance Needs GIS? -- 27.2.2 Problems and Challenges in Social Governance. ; 27.2.3 New Ways and Exploration of GIS for Social Governance.
• Titolo autorizzato: New thinking in GIScience
• ISBN: 981-19-3816-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910741137603321
• Lo trovi qui: Univ. Federico II