GeoComputation and public health : a spatial approach / / Gouri Sankar Bhunia and Pravat Kumar Shit

• Autore: Bhunia Gouri Sankar
• Pubblicazione: Cham, Switzerland : , : Springer, , [2021]; ©2021
• Descrizione fisica: 1 online resource (320 pages)
• Disciplina: 362.1
• Soggetto topico: Public health - Geographic information systems; Communicable diseases - Prevention - Geographic information systems
• Persona (resp. second.): ShitPravat Kumar
• Nota di contenuto: Intro -- Foreword -- Preface -- Acknowledgements -- Disclaimer -- Contents -- About the Authors -- Abbreviation -- Chapter 1: Introduction to GeoComputation -- 1.1 Concept of GeoComputation -- 1.2 GeoComputation vs Geography -- 1.2.1 Geoposition -- 1.2.2 Geoprojection -- 1.2.3 Geodisplacement -- 1.2.4 Geodistance -- 1.2.5 Geonearest -- 1.2.6 Geoidentify -- 1.3 GeoComputation vs GIS -- 1.4 Advantages of GeoComputation -- 1.5 Future of GeoComputation -- 1.5.1 GeoQuantum Computation -- 1.5.2 Data Science -- 1.5.3 Data Science and GeoComputation -- 1.5.4 Internet of Things (IOT) and GeoComputation -- 1.5.5 Develop Project on Practical Interest -- 1.5.6 Must Develop Data Scientific Standards -- 1.5.7 Grab Bag of Problem-Solving Techniques of Varying Degrees of Practical Utility -- 1.5.8 Develop a Coherent Perspective on Geographical Space -- 1.5.9 Must Swap in Commands That Most Perceptively Analogus and Mutually Inspiring Computational Growths of the Information Age -- 1.6 Problems for GeoComputation -- 1.7 Conclusion -- References -- Chapter 2: GeoComputation and Spatial Data Operation -- 2.1 Spatial Data Operation -- 2.1.1 Spatial Subsetting -- 2.1.2 Topological Relations -- 2.1.3 Spatial Joining -- 2.1.4 Spatial Data Aggregation -- 2.1.5 Issues and Problems -- 2.2 Spatial Query in GeoComputation -- 2.3 Spatial Overlay -- 2.3.1 Types of Spatial Overlay -- 2.3.1.1 Spatial Overlay Operators -- 2.3.1.2 Buffer Operation -- 2.4 Voronoi Diagram -- 2.5 Map Algebra -- 2.5.1 Local Operations -- 2.5.2 Focal Operation -- 2.5.3 Zonal Operation -- 2.5.4 Global Operations -- 2.6 Data Formats -- 2.7 Web Services and GeoComputation -- References -- Chapter 3: E-Research and GeoComputation in Public Health -- 3.1 Introduction -- 3.2 Usefulness in E-Research in Public Health -- 3.2.1 Internet of Things (IoT) and Public Health -- 3.2.2 Cloud Computing for Healthcare. ; 3.2.3 Fog Computing for Healthcare -- 3.2.4 Internet of m-Health Things (mIoT) -- 3.2.5 Cognitive IoT (CioT) -- 3.2.6 Smartphone Solutions in Healthcare -- 3.3 Spatio-temporal Data Mining and Intelligent Service of Public Health -- 3.3.1 Spatial and Spatio-temporal Data -- 3.3.2 Data Attributes and Relationships -- 3.3.3 Spatial and Spatio-temporal Statistics for Public Health Data -- 3.3.4 Data and GeoComputational Analysis in Public Health -- 3.3.4.1 Clustering -- 3.3.4.2 Predictive Learning -- 3.3.4.3 Change Detection -- 3.3.4.4 Relationship Mining -- 3.4 Theories and Models of GeoComputation for Public Health -- 3.4.1 Spatial Microsimulation Model (SMM) -- 3.4.2 Agent-Based Model (ABM) -- 3.4.3 Local Intensity Estimation -- 3.4.4 Restricted and Unrestricted Monte Carlo Process -- 3.4.5 Unrestricted and Controlled Monte Carlo -- 3.4.6 Geographic Machine Analysis -- 3.4.6.1 Moran's Index -- 3.4.6.2 CUSUM Chart -- 3.4.6.3 Space-Time Accessibility -- 3.4.6.4 Spatio-temporal Conditional Autoregressive (STCAR) -- 3.5 Accuracy and Uncertainty of GeoComputational Models for Public Health -- 3.5.1 Data Error -- 3.5.2 Positional Error -- 3.5.3 Cartographic Confounding -- 3.5.4 Misclassification -- 3.5.5 Spatial Extent -- 3.5.6 Spatial Weights -- 3.5.7 Spatial Modelling -- 3.6 A Tiered Approach to Accuracy and Uncertainty -- 3.7 Conclusion -- References -- Chapter 4: GeoComputation and Geo-visualization in Public Health -- 4.1 Introduction -- 4.2 Exploratory Visual Analysis of Public Health Data -- 4.2.1 Visual Query -- 4.2.2 Temporal Pattern Mining -- 4.2.2.1 Frequent Pattern Miner -- 4.2.2.2 Statistical Pattern Analyser -- 4.2.3 Interactive Visualization -- 4.3 Exploration of Public Health Data on 2-D, 3-D and 4-D -- 4.4 Exploratory Analysis of Clustering of Public Health Data -- 4.4.1 Spatial Search Processes -- 4.4.2 Network-Based Cluster Detection. ; 4.4.3 Statistical Analysis and Modelling of Local Clusters -- 4.4.4 Space-Time Cluster Detection Methods -- 4.5 Mashups in Epidemiology -- 4.5.1 Basic Components of Mashups in Public Health Data -- 4.5.2 Geotagging -- 4.6 Visual Approaches to Data Exploration and Knowledge Construction -- 4.6.1 Population Mapping -- 4.6.2 Point Epi-units -- 4.6.3 Aggregated Mapping -- 4.6.4 Gridded Maps -- 4.7 Challenges of Geo-visualization and GeoComputational Analysis in Public Health -- 4.7.1 Data Containers -- 4.7.2 Data Privacy -- 4.7.3 Evolution Data Standards -- 4.7.4 Interface Challenges -- 4.7.5 Geography -- 4.7.6 Data Reporting -- 4.8 Conclusion -- References -- Chapter 5: GeoComputation and Disease Exploration -- 5.1 Introduction -- 5.2 Issues of Exploration in Public Health Through GeoComputation -- 5.3 Data Availability and Quality -- 5.4 Data Protection and Confidentiality -- 5.5 Exposure Assessment and Mapping -- 5.6 Application Services and Decision Modelling -- 5.7 The Ecologic Fallacy and the Atomistic Fallacy -- 5.8 Spatial Scale of Disease Exploration -- 5.8.1 Definitions of Spatial Scale -- 5.8.2 Cartographic Scale -- 5.8.3 Geographic Scale -- 5.8.4 Ecological Scale -- 5.8.5 Operational Scale -- 5.8.6 Modelling Scale -- 5.8.7 Policy Scale -- 5.9 GeoComputation in Epidemiological Analysis -- 5.10 GeoComputation and Spatio-temporal Trends -- 5.11 Spatio-temporal Analysis of Epidemic Phenomenon -- 5.11.1 Scan Statistics -- 5.11.2 Lattice Statistics -- 5.11.3 Topological Relationship Patterns -- 5.11.4 Spatio-temporal Neighbourhood -- 5.11.5 Collocation Pattern -- 5.12 Spatio-temporal Point Process -- 5.12.1 Gaussian Kernel Density Analysis (GKDA) -- 5.12.2 Average Nearest Neighbour (ANN) Distance -- 5.12.3 ST-DBSCAN -- 5.13 Spatio-temporal Outlier -- 5.13.1 Outliers in Spatial Time Series -- 5.13.2 Flow Anomalies. ; 5.13.3 Anomalous Moving Object Trajectories -- 5.14 Spatio-temporal Couplings and Tele-couplings -- 5.14.1 Spatio-temporal Sequential Pattern -- 5.14.2 Cascading Spatio-temporal Patterns -- 5.14.3 Spatial Time Series and Tele-connection -- 5.15 Spatio-temporal Prediction -- 5.15.1 Spatio-temporal Autoregressive Regression (STAR) -- 5.15.2 Spatio-temporal Kriging -- 5.15.3 Hierarchical Dynamic Spatio-temporal Models -- 5.16 Spatio-temporal Clustering -- 5.16.1 Spatio-temporal Event Partitioning -- 5.16.2 Spatial Time-Series Partitioning -- 5.16.3 Trajectory Data Partitioning -- 5.16.4 Spatio-temporal Summarization -- 5.17 Spatio-temporal Hotspots -- 5.17.1 Clustering-Based Approaches -- 5.17.1.1 Diagnostics for Spatio-temporal Clustering -- 5.17.1.2 Cylindrical Space-Time Analysis -- 5.17.2 Spatio-temporal Scan Statistics-Based Approaches -- 5.18 Spatio-temporal Analysis Tools -- 5.18.1 Softwares -- 5.18.2 Spatial Statistical Tools -- 5.18.3 Spatial Database Management Systems -- 5.18.4 Spatial Big Data Platform -- 5.19 Conclusion -- References -- Chapter 6: GeoComputation and Disease Ecology -- 6.1 Introduction -- 6.2 Basics of Host-Parasite Ecology -- 6.3 Emerging Infectious Diseases -- 6.4 History of Development -- 6.5 Role of GeoComputation in Disease Ecology -- 6.5.1 Progress and Improvement in Remote Sensing Satellites and Sensors for Environmental Assessment -- 6.5.2 Airborne Remote Sensing Era -- 6.5.3 Rudimentary Space-Borne Satellite Remote Sensing Era -- 6.5.4 Spy Satellite Remote Sensing Era -- 6.5.5 Meteorological Satellite Sensor Remote Sensing Era -- 6.5.6 Landsat Era -- 6.5.7 Earth Observing System Era -- 6.5.8 New Millennium Era -- 6.5.9 Private Industry Era -- 6.5.10 Analysis of Environmental Variables from Remote Sensing Data -- 6.6 Weather Variables and Disease Ecology: Case Study Through GeoComputation Technique. ; 6.6.1 Climate and Pathogens -- 6.6.1.1 Temperature -- 6.6.1.2 Precipitation -- 6.6.1.3 Relative Humidity -- 6.6.1.4 Sunshine -- 6.6.1.5 Wind -- 6.6.2 Climate and Vectors/Hosts -- 6.6.2.1 Temperature -- 6.6.2.2 Rainfall -- 6.6.2.3 Relative Humidity -- 6.6.2.4 Wind -- 6.6.2.5 Sunshine -- 6.6.3 Climate and the Spread of Diseases -- 6.6.3.1 Temperature -- 6.6.3.2 Wind and Dust Storms -- 6.6.4 Climate and Disease Transmission -- 6.6.5 Extreme Weather Events and Disease Transmission -- 6.6.6 Role of GeoComputation for Climatic Data Acquisition and Analysis -- 6.6.6.1 Precipitation -- 6.6.6.2 Air Temperature -- 6.7 Case Study: Association with Kala-Azar Incidence and Climate -- 6.7.1 Temperature and Kala-Azar Incidence -- 6.7.2 Relative Humidity and Kala-Azar Incidence -- 6.7.3 Association Between Rainfall and Kala-Azar Incidence -- 6.8 Topography and Disease Ecology: Case Study Through GeoComputation Technique -- 6.8.1 Vector-Borne Disease Transmission vs. Topography -- 6.8.2 Waterborne Disease vs. Topography -- 6.8.3 Neurological Infections vs. Topography -- 6.8.4 Respiratory Infections vs. Topography -- 6.8.5 Other Infections vs. Topography -- 6.8.6 Identification of Topographic Variables and GeoComputational Technology -- 6.9 Case Study: Spatial Association Between Kala-Azar Incidence and Absolute Relief -- 6.10 Surface Water Bodies and Disease Ecology: Case Study Through GeoComputation Technique -- 6.11 Identification of Surface Water Bodies Using GeoComputation -- 6.11.1 Case Study: Automatic Detection of Open Water Bodies to Map Malaria Incidence -- 6.12 Green Biomass and Disease Ecology: Case Study Through GeoComputation Technique -- 6.12.1 Malaria Transmission and Green Biomass Ecology -- 6.12.2 Filariasis and Green Biomass Ecology -- 6.13 Satellite-Derived Vegetation Indices and Disease Pattern. ; 6.13.1 Case Study: Acute Encephalitis Syndromes (AES) and Green Biomass.
• Titolo autorizzato: GeoComputation and public health
• ISBN: 3-030-71198-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910488697303321
• Lo trovi qui: Univ. Federico II
• Serie: Springer Geography