Earth Data Analytics for Planetary Health

Description

Planetary health involves complex spatial–temporal interactions among agents, hosts, and earth environment. Due to rapid technical development of geomatics, including geographic information systems (GIS) and remote sensing (RS) in the era of big data analytics, therefore, earth data analytics has become one of the important approaches for monitoring earth surface process and measuring of the effects of environment changes on all humans and other living organisms on earth. Various methods in earth data analytics, including spatial–temporal statistics, spatial evolutionary algorithms, remote sensing image analysis, wireless geo-sensors, and location-based analytics, are an emerging discipline in understanding complex interactions in planetary health. This edited book provides a broad focus on methodological theories of earth data analytics and their applications to measuring the process of planetary health, with the goal to build scientific understanding on how geospatial analytics can provide valuable insights in measuring environmental risks in Southeast Asian regions. It is collection of selected papers covering both theoretical and empirical studies focusing on topics relevant to spatial perspectives on planetary health and environmental exposure studies. The book is written for senior undergraduates, graduate students, lecturers, and researchers in applications of geospatial technologies for public health and environmental studies. Contents Editors and Contributors Part I Environmental Quality Monitoring 1 Applications of Remote Sensing for Air Pollution Monitoring in Thailand: An Early Warning for Public Health 1.1 Introduction 1.2 Overview Earth Observing Satellites and Current Remote Sensing Technology for Air Pollution Observations 1.2.1 Earth Observing Satellites 1.2.2 Ground-Based Remote Sensing 1.3 Assessing Magnitude and Extents of Atmospheric Pollutants and Human Exposure Level from Remote Sensing 1.3.1 Tropospheric Ozone O3 and Its Precursors 1.3.2 Aerosol Loading 1.3.3 Biomass Burning Smoke 1.4 Air Pollution Source Identification Using Remote Sensing Technology 1.4.1 Aerosol Optical Properties 1.4.2 Local Biomass Burning 1.4.3 Urban Pollution 1.4.4 Long-Range Transport Air Mass 1.5 Satellite Retrievals for Forecasting Haze Episode as the Early Warning Tool 1.6 Summary References 2 A Novel Evaluation of Air Pollution Impact from Stationary Emission Sources to Ambient Air Quality via Time-Series Granger Causality 2.1 Introduction 2.2 Characteristics of the Study Area 2.3 Data and Methodology 2.3.1 Data Collection 2.3.2 Data Processing 2.3.3 Time-Series Granger Causality 2.3.4 The Evaluation of CEMS Impact 2.4 Results and Discussions 2.4.1 Seasonal Comparison 2.4.2 Diurnal Variation of Spatial Characteristics of GC Event 2.4.3 Industrial Comparison 2.4.4 Comparison with Emissions and Impacts 2.5 Conclusion References 3 Groundwater Recharge, Monitoring and Finding Suitable Areas for Groundwater Recharge in Northeast Thailand 3.1 Introduction 3.2 Groundwater 3.3 Groundwater Recharge 3.4 Groundwater Recharge Monitoring 3.5 Current Methodologies for Groundwater Recharge Monitoring 3.6 Conclusion References Part II Environmental Changes and Health 4 Health Benefits of Air Pollution Reduction During the COVID-19 Lockdown Period in Thailand Using a Machine Learning Algorithm 4.1 Introduction 4.2 The Situation of Air Pollution Concentration in Thailand 4.3 Short-Term Effect of Ambient Air Pollution on Mortality in Thailand 4.4 Business-as-Usual Concentration of Air Pollution During the COVID-19 Lockdown Period 4.5 Health Benefits of Air Pollution Reduction During the COVID-19 Lockdown Period 4.6 Discussion and Conclusions References 5 Satellite-Derived Vegetation Indices as a Criterion for Assessing Green Exposure that is Related to Human Health Burdens 5.1 Introduction 5.2 Methodology 5.2.1 Definitions of Vegetation Indices 5.2.2 Calculating Green Exposure Using Satellite-Based Vegetation Indices 5.2.3 Assessing Green Exposure in Relation to Health Burden 5.3 Example Cases: Green Exposure Related to Health Burdens 5.3.1 The Relationship Between Greenness and IHD and Stroke: Global Ecological Study 5.3.2 The Relationship Between Residential Green Space and Ischemic Stroke: A Cohort Study 5.3.3 The Relationship Between Residential Greenness and Mortality in the Elderly: A Cohort Study 5.4 Conclusions References 6 Five Common Myths About Land Use Change and Infectious Disease Emergence 6.1 Pervasive Social Constructs in Inferences About Land Use Change and Disease Emergence 6.2 The Five Myths 6.2.1 Everything is Driven by Population Growth 6.2.2 Deforestation is Due to Landless Peasant Groups 6.2.3 All Agricultural Land Use Change is Detrimental to Biodiversity—Intensification of Agriculture and Land Sparing are the Solution 6.2.4 Spillover Occurs Because of Wet Markets and People that Eat Wildlife 6.2.5 Models Tell “The Truth” 6.3 Inferences About Land Use Change and Disease Emergence and the Society We Can Build References Part III Data and Methodological Issues for Health Studies 7 Geospatial Environmental Data for Planetary Health Applications 7.1 Introduction 7.2 Meteorological Data 7.3 Satellite Vegetation Indices 7.4 Satellite Land Surface Temperature 7.5 Satellite Precipitation Estimates 7.6 Land Cover and Land Use Change 7.7 Human Populations 7.8 Surface Water and Hydrology 7.9 Synthesis and Conclusions References 8 Delineating Zones of Disease Diffusion from the Amenity-Sharing Network in Peninsular Malaysia 8.1 Introduction 8.2 Materials and Methods 8.2.1 Study Site 8.2.2 Datasets 8.2.3 Study Framework and Analyses 8.3 Analyses and Results 8.3.1 Part I: Delineate Neighbourhoods from Road Network 8.3.2 Part II: Explore Reachability of Neighbourhood Amenities 8.3.3 Part III: Identify Zones of Disease Diffusion 8.3.4 Part IV: Explore the Zone Centres of the Neighbourhoods Interactions 8.4 Discussions 8.5 Conclusion References 9 Approaches for Spatial and Temporal-Spatial Clustering Analysis in Avian Influenza Outbreaks 9.1 Introduction 9.2 Factors Associated with Zoonotic Transmission of Avian Influenza Virus 9.3 Spatial Clustering Analysis of Avian Influenza Viruses Transmission 9.3.1 Cluster Analysis 9.3.2 Hotspot Analysis 9.4 Temporal Spatial Clustering Identification of Avian Influenza Viruses Transmission 9.4.1 Scan Statistics or Space–Time Permutation Model 9.4.2 Knox Test 9.4.3 Standard Deviational Ellipse (SDE) Method 9.4.4 Regression Modeling 9.5 Future Perspectives References 10 Detecting Urban form Using Remote Sensing: Spatiotemporal Research Gaps for Sustainable Environment and Human Health 10.1 Introduction 10.2 Background of Conceptualizing Urban Form 10.3 Methods 10.4 Results 10.4.1 Synopsis of the Reviewed Study Cases 10.4.2 Remote Sensing Data Used to Detect Urban Form 10.4.3 Methods Used to Detect Urban Form 10.4.4 Spatiotemporal Characteristics of the Urban Form Studies 10.5 Discussion: Research Gaps and the Way Forward 10.5.1 Dilemma of the Spatial and Temporal Needs 10.5.2 Strategies Forward 10.6 Conclusions Appendix References