دانلود کتاب Handbook of Big Geospatial Data

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Preface Overview of the Book Contents Part I Spatial Computing Systems and Applications 1 IBM PAIRS: Scalable Big Geospatial-Temporal Data and Analytics As-a-Service 1.1 Introduction 1.2 PAIRS Architecture Overview 1.3 Key-Value Store Design and Performance 1.4 PAIRS User Experience 1.4.1 Data Service 1.4.2 Search or Discovery Service 1.4.3 Analytics Platform Service 1.5 Selected Industry Applications 1.5.1 PAIRS Enabled Improvements in Weather Forecasting 1.5.2 Vegetation Management 1.6 Conclusion and PAIRS Resources References 2 Big Geospatial Data Processing Made Easy: A Working Guide to GeoSpark 2.1 Introduction 2.2 Background 2.2.1 Cluster Computing Systems 2.2.2 Spatial Queries 2.3 Overview 2.4 Spatial RDD Layer 2.4.1 Supported Spatial Data Sources 2.4.2 Spatial RDD Built-In Geometrical Library 2.4.3 Spatial RDD Partitioning 2.4.4 Spatial RDD Index 2.4.5 Spatial RDD Customized Serializer 2.5 Spatial Query Processing Layer 2.5.1 Spatial Range Query 2.5.2 Spatial K Nearest Neighbors (KNN) Query 2.5.3 Spatial Join Query 2.6 Perform Spatial Data Analytics in GeoSpark 2.6.1 Run Queries Using RDD APIs 2.6.2 Run Queries Using SQL APIs 2.6.3 Interact with GeoSpark via Zeppelin Notebook References 3 Indoor 3D: Overview on Scanning and Reconstruction Methods 3.1 Introduction 3.1.1 Terminology 3.2 Properties of Indoor Environments and Identification of Scanning and Reconstruction Problems 3.3 Map Representations 3.4 Development of Indoor Scanning Systems 3.4.1 Single Sensor Methods and Multi-sensor Systems 3.4.1.1 Carriable Systems 3.4.1.2 Mobile Platforms 3.4.1.3 Micro Aerial Vehicles 3.5 Iterative Closest Point SLAM 3.5.1 The ICP Algorithm 3.5.2 Computing Optimal Poses 3.5.3 Marker and Feature-Based Registration 3.5.4 ICP-Based SLAM 3.5.5 Assessing the SLAM Errors 3.6 Indoor 3D Reconstruction 3.6.1 Space Subdivision and Room Segmentation 3.6.2 Reconstruction of Walls 3.6.3 Grammar Approach 3.6.4 Detection and Reconstruction of Openings 3.6.5 Reconstructing Occluded Data by Machine Learning 3.7 Applications 3.8 Future Trends 3.9 Exercises for Students References 4 Big Earth Observation Data Processing for Disaster Damage Mapping 4.1 Monitoring Disasters from Space 4.2 Earth Observation Satellites 4.2.1 Optical Satellite Missions 4.2.2 SAR Satellite Missions 4.3 Land Cover Mapping 4.4 Disaster Mapping 4.4.1 Flood Mapping 4.4.2 Landslide Mapping 4.4.3 Building Damage Mapping 4.5 Conclusion and Future Lines References 5 Spatial Data Reduction Through Element-of-Interest (EOI) Extraction 5.1 Introduction 5.2 Methods to Obtain EOI from Georeferenced Big Data 5.2.1 Methods Commonly Used in the Remote Sensing and Mapping Fields 5.2.1.1 Pixel-Based Methods 5.2.1.2 Object-Based Methods 5.2.1.3 Machine Learning 5.2.2 Methods to Analyze Social Media and Location-Based Data 5.2.2.1 Data Mining 5.2.2.2 Data Analytics 5.2.2.3 Machine Learning 5.3 Use Cases in the Active and Passive Big Data Spatial Realms 5.3.1 Active Use Cases 5.3.2 Passive Use Cases 5.4 Conclusion References 6 Semantic Graphs to Reflect the Evolution of GeographicDivisions 6.1 Introduction 6.2 Context 6.2.1 Not Fully Interconnected Data 6.2.2 Broken Time-Series 6.2.3 Removal of Changes 6.3 Towards a Change in Representation with the Semantic Web 6.3.1 Open Data 6.3.2 Linked Data 6.3.3 Semantic Data 6.3.4 Linked Open Geospatial Data 6.4 Modeling Geospatial Changes in the Semantic Web 6.4.1 Identity and Changes 6.4.2 Modeling Changes 6.4.2.1 Standard Space and Time Ontologies 6.4.2.2 Fundamentals for the Modeling of Evolving Geospatial Entities 6.4.3 Ontological Approaches for the Modeling of Evolving Entities 6.4.3.1 Versioning Approach 6.4.3.2 SNAP and SPAN Approach 6.4.3.3 Ontologies for Fluents Approach 6.4.4 Ontological Approaches for the Modeling of Evolving Geospatial Entities 6.5 Contributions 6.6 Conclusion and Perspectives References Part II Trajectories, Event and Movement Data 7 Big Spatial Flow Data Analytics 7.1 Introduction 7.2 Flow Mapping & Geovisualization 7.2.1 Flow Aggregation 7.2.2 Edge Bundling 7.2.3 Visual Analytics and Tools 7.3 Spatial Data Mining Methods 7.3.1 Spatial Outlier Detection 7.3.2 Flow Clustering 7.4 Spatial Statistical Methods 7.4.1 Spatial Patterns Detection 7.4.2 From Patterns to Spatial Interaction Models 7.5 Conclusion References 8 Semantic Trajectories Data Models 8.1 Introduction 8.2 Preliminaries 8.2.1 Historical Perspective 8.2.2 Spatial vs. Semantic Trajectories 8.3 A Semantic Trajectory Meta-model 8.4 Semantic Trajectory Data Models: A Purpose-Driven Taxonomy 8.4.1 Conceptual Representation 8.4.2 Database Logical Models 8.4.3 Query Processing 8.4.4 Data Analytics 8.5 Final Remarks and Research Directions References 9 Multi-attribute Trajectory Data Management 9.1 Introduction 9.2 Related Work 9.2.1 Enriching Spatio-Temporal Trajectories 9.2.2 Indexing Spatio-Temporal Trajectories 9.3 Problem Definition 9.3.1 Data Representation 9.3.2 Queries 9.4 Indexing Multi-attribute Trajectories 9.4.1 An Overview of the Structure 9.4.2 Packing Trajectories 9.4.3 Partitioning Trajectories 9.4.4 BAR 9.4.5 Updating the Index 9.4.6 The Generality 9.5 Query Algorithms 9.5.1 An Outline 9.5.2 Processing RQMAT 9.5.3 Processing CRQMAT 9.5.4 Processing CkNN_MAT 9.6 The System Development 9.6.1 The Architecture 9.6.2 A Tool for GPS Data Clean 9.6.3 The Generation of Multi-attribute Values and Query Interface 9.6.4 MDBF: A Tool for Monitoring Database Files 9.7 Performance Evaluation 9.7.1 Evaluation of RQMAT 9.7.2 Evaluation of CRQMAT 9.7.3 Evaluation of CkNN_MAT 9.8 Future Directions 9.8.1 Data Analytics 9.8.2 Intelligent Trajectory Data Management 9.9 Conclusions References 10 Mining Colocation from Big Geo-Spatial Event Data on GPU 10.1 Introduction 10.2 GPU Computing 10.3 Related Work 10.4 Problem Statement 10.4.1 Basic Concepts 10.4.2 Problem Definition 10.5 Approach 10.5.1 Algorithm Overview 10.5.2 Cell-Aggregate-Based Upper Bound Filter 10.5.3 Refinement Algorithms 10.6 Evaluation 10.6.1 Results on Synthetic Data 10.6.1.1 Effect of the Number of Instances 10.6.1.2 Effect of Clumpiness 10.6.1.3 Comparison on Filter and Refinement 10.6.2 Results on Real World Dataset 10.6.2.1 Effect of Minimum Participation Index Threshold 10.6.2.2 Comparison of Filter and Refinement 10.7 Discussion and Conclusion References 11 Automatic Urban Road Network Extraction From Massive GPS Trajectories of Taxis 11.1 Introduction 11.2 Literature Review 11.2.1 Density-Based Approaches 11.2.2 Cluster-Based Approaches 11.3 Methodology 11.3.1 Trajectory Compression 11.3.2 Identification of the Trajectory Points Along the Road 11.3.2.1 Density-Based Spatial Clustering of Applications with Noise (DBSCAN) 11.3.2.2 Anisotropic Perspective on Local Point Density 11.3.2.3 Anisotropic Density-Based Clusters with Noise (ADCN) Algorithm 11.3.2.4 ADCN Algorithm in Road Network Extraction 11.3.3 Road Network Generation 11.3.3.1 Road Density Surface Generation 11.3.3.2 Collapse Surface to Centerline 11.4 Case Study 11.4.1 Data 11.4.2 Experiment 11.4.2.1 Evaluation Metrics 11.4.2.2 Results 11.5 Conclusion and Future Work References 12 Exploratory Analysis of Massive Movement Data 12.1 Introduction 12.2 Movement Data Characteristics & Their Relation to Big Data Vs 12.2.1 Variety 12.2.2 Velocity & Volume 12.3 Exploratory Data Analysis (EDA) 12.4 EDA Tasks for Massive Movement Data 12.4.1 Task 1: Spatio-Temporal Lookup or Range Queries 12.4.1.1 Challenge 1: Trajectory Indexing 12.4.1.2 Challenge 2: Spatio-Temporal Visualizations of Massive Movement Data 12.4.2 Task 2: Similar Trajectory Search and Join 12.4.2.1 Challenge 3: Building and Segmenting Trajectories 12.4.2.2 Challenge 4: Moving Object Identifiers 12.4.3 Task 3: Density Mapping and Other Grid-Based Summarizations 12.4.3.1 Challenge 5: Representativeness & Bias 12.4.4 Task 4: Extracting Events & Places 12.4.4.1 Challenge 6: Data Quality or Veracity 12.4.5 Task 5: Detection of Outliers and Anomalies 12.4.5.1 Challenge 7: Anomaly Detection Performance 12.5 Privacy 12.5.1 k-Anonymity 12.5.2 Differential Privacy 12.5.3 Privacy by Design 12.6 Recommended EDA Workflow for Massive Movement Data 12.6.1 Establishing an Overview 12.6.2 Putting Movement Records in Context 12.6.3 Extracting Trajectories & Events 12.6.4 Exploring Patterns in Trajectory and Event Data 12.6.5 Analyzing Outliers 12.7 Conclusions References Part III Statistics, Uncertainty and Data Quality 13 Spatio-Temporal Data Quality: Experience from Provision of DOT Traveler Information 13.1 Introduction 13.2 Example Data Quality Problems 13.3 Data Quality Attributes 13.4 Data Quality Assessment Methods 13.5 Enhanced Methods 13.5.1 General Definitions 13.5.2 General Approach 13.5.3 Interpolation to Model Ground Truth 13.5.4 Our SMART Approach 13.5.5 Artificial Data Set 13.5.6 Evaluation 13.5.7 Evaluation Using an Artificial Data Set 13.5.8 December 2015 MADIS California Data 13.5.9 December 2017 MADIS Montana Data 13.5.10 December 2015–2017 USGS Streamflow Data 13.5.11 Evaluation Summary 13.6 Further Research and Development Topics Bibliography 14 Uncertain Spatial Data Management: An Overview 14.1 Introduction 14.2 Discrete and Continuous Models for Uncertain Data 14.2.1 Existing Models for Uncertain Data 14.2.2 Discrete Models 14.2.3 Continuous Models 14.3 Possible World Semantics 14.4 Existing Uncertain Spatial Database Management Systems 14.5 Probabilistic Result Semantics 14.5.1 Object Based Probabilistic Result Semantics 14.5.2 Result Based Probabilistic Result Semantics 14.6 Probabilistic Query Predicates 14.6.1 Probabilistic Threshold Queries 14.6.2 Probabilistic Topk Queries 14.6.3 Discussion 14.7 The Paradigm of Equivalent Worlds 14.7.1 Equivalent Worlds 14.7.2 Exploiting Equivalent Worlds for Efficient Algorithms 14.8 Case Study: Range Queries and the Sum of Independent Bernoulli Trials 14.8.1 Poisson-Binomial Recurrence 14.8.1.1 Complexity Analysis 14.8.2 Generating Functions 14.8.2.1 Complexity Analysis 14.9 Advanced Techniques for Managing Uncertain Spatial Data 14.10 Summary References 15 Spatial Statistics, or How to Extract Knowledge from Data 15.1 Introduction 15.2 Spatial Data 15.3 Geostatistical Models 15.3.1 Covariogram Estimation 15.3.2 Modeling Approaches 15.3.3 Dimensionality Reduction of the Spatial Covariance Matrix 15.4 Spatial Regression Models 15.4.1 Specification of Spatial Weighting Matrices 15.4.2 Inferences on Parameter Estimates 15.4.3 Estimation Procedures 15.5 Case Study 15.6 Conclusion 15.7 Further Reading References Part IV Information Retrieval from Multimedia Spatial Datasets 16 A Survey of Textual Data & Geospatial Technology 16.1 Introduction 16.2 Research Questions & Different Notions of ``Where'' 16.3 Spatial Indexing 16.3.1 Spatial Data Structures 16.3.2 Spatially Enabled Database Management Systems 16.4 Address Geocoding 16.5 Geoparsing and Spatial Resolution 16.5.1 Toponym Resolution 16.5.2 Geospatial Expression Resolution 16.6 Content Enrichment with Geospatial Metadata 16.7 Hybrid Textual/Spatial Document Retrieval 16.8 Geofencing 16.9 Applications 16.9.1 Location Search 16.9.2 Crime Mapping, Hotspot Analysis and Forecasting 16.9.3 Political Anaysis and Intelligence Applications 16.9.4 Healthcare Applications 16.9.5 Location-Based Services and Location-Aware Advertising 16.9.6 Other Applications 16.10 Summary, Conclusion and Future Work Appendix: Ancillary Tasks Augmenting Gazetteers via Web Mining Curating Gold Standard Data for Evaluation and Training Bibliography References 17 Harnessing Heterogeneous Big Geospatial Data 17.1 Introduction 17.2 Geospatial Data Conflation 17.3 Geospatial Data Integration 17.4 Geospatial Data Enrichment 17.5 Summary References 18 Big Historical Geodata for Urban and Environmental Research 18.1 Introduction 18.2 Data Sources and Time Spans 18.3 From the Data Source to Big Geospatial Data 18.4 Potentials of Big Historical Geodata 18.4.1 Human-Environment Interactions 18.4.2 Land Change Model Calibration 18.4.3 Data-Driven Geoscience and Geodata Science 18.4.4 Digital Humanities and Cultural Heritage 18.4.5 Urban Research and Spatial Planning 18.5 Conclusion References 19 Harvesting Big Geospatial Data from Natural Language Texts 19.1 Introduction and Motivation 19.2 Methods and Tools 19.2.1 Toponym Recognition 19.2.2 Toponym Resolution 19.2.3 Developed Geoparsers and Tools 19.2.4 Location Inference from Language Modeling 19.2.5 Summary 19.3 Applications of Geospatial Data Harvested from Texts 19.3.1 Understanding Places and Human Experiences 19.3.2 Situation Awareness for Emergency Response 19.3.3 Place Relations in Virtual or Cognitive Space 19.4 Summary and Future Directions References 20 Automating Information Extraction from Large Historical Topographic Map Archives: New Opportunities and Challenges 20.1 Introduction 20.2 Digital Historical Map Archives 20.3 Preprocessing Methods 20.3.1 Automated Georeferencing 20.3.2 Spatial Data Alignment 20.3.3 Exploratory Methods 20.4 Automated Map Content Recognition and Extraction 20.4.1 Training Data Collection 20.4.2 Recognition and Extraction Methods 20.5 Conclusions and Outlook References Part V Governance, Infrastructures and Society 21 The Integration of Decision Maker's Requirements to Develop a Spatial Data Warehouse 21.1 Introduction 21.2 Overview of the Existing Approaches 21.3 Overview of the Proposal 21.4 GeoCIM Definition 21.5 Classification of the GeoCIMs Models 21.6 K == Random Number of the Clusters Containing Adjacent Objects 21.7 From GeoCIM to GeoPIM 21.7.1 GeoPIM Definition 21.7.2 Formal Transformations from GeoCIM to GeoPIM 21.8 Using Topological Relationships to Enrich Dimension Hierarchies 21.8.1 Geo SM Definition 21.9 Transformations from GeoPIM to GeoPSM 21.10 Experimentation 21.10.1 Transition from the Requirements Model to the Implementation Model of a SDW 21.11 Case Study 21.12 Evaluation of the Proposal 21.13 Conclusion References 22 Smart Cities 22.1 Introduction 22.2 History and Background: A Brief Review 22.3 Defining Smart Cities in Practice 22.4 Context Variables Affecting Smart Cities 22.4.1 Structural Factors 22.4.2 Economic Development 22.4.3 Technology 22.4.4 Effective Environmentally-Progressive Governance 22.5 The Role of Data 22.5.1 Smart City and Big Data 22.5.2 Real-Time Data 22.5.3 Open Government Data 22.5.4 The Semantic Web and Linked Open Data 22.5.4.1 OpenStreetMap 22.5.4.2 GeoNames 22.6 Examples of Smart Cities 22.7 Future Directions 22.8 Conclusion References 23 The 4th Paradigm in Multiscale Data Representation: Modernizing the National Geospatial Data Infrastructure 23.1 Access to Nationally Managed Spatial Data in the United States 23.2 Chronology and Current Status of NSDI in the United States 23.2.1 Geospatial Interoperability Reference Architecture (GIRA) 23.2.2 Geospatial Platform 23.2.3 Cloud Computing 23.2.4 Multiagency Geospatial Acquisition 23.3 The Role of the Fourth Paradigm 23.4 Activities for Short- and Longer-Term NSDI Implementation 23.4.1 Short-Term Goals: Integrate NSDI Across Spatial and Temporal Scales 23.4.2 Longer-Term Goals: Aligning NSDI with User Needs and Demands 23.5 Implications and Prospects of the Fourth Paradigm for the NSDI References 24 INSPIRE: The Entry Point to Europe's Big Geospatial Data Infrastructure 24.1 Introduction 24.2 Big Data in the EU 24.3 INSPIRE State of Play 24.3.1 Legal, Technical and Organisational Framework 24.3.2 INSPIRE Geoportal 24.4 Inspire as a Big Data Infrastructure 24.4.1 Characteristics of INSPIRE in Terms of Big Data 24.4.2 Challenges from the User Perspective 24.4.2.1 Discoverability of Datasets 24.4.2.2 Combining National Datasets to Create Pan-European Products 24.4.2.3 Data Access and Consumption by Clients 24.4.2.4 Cloud Infrastructures 24.5 Conclusions and Outlook References