Reprint
Remote Sensing and Geospatial Technologies in Public Health
Edited by
September 2018
244 pages
- ISBN978-3-03897-172-6 (Paperback)
- ISBN978-3-03897-173-3 (PDF)
This book is a reprint of the Special Issue Remote Sensing and Geospatial Technologies in Public Health that was published in
Computer Science & Mathematics
Environmental & Earth Sciences
Summary
This book demonstrates the utilization of remote sensing and geospatial technologies for a wide range of public health studies.Although remote sensing and geospatial technologies have been successfully applied for more than 50 years, continuous advancements are essential to better understand the complex environment around us that impacts our health and well-being.Thankfully, the availability of spatial analytical tools and necessary data have enabled us to reveal multifaceted, obscured spatial relationships that would have been unexplored otherwise. Now, we are able to make more precise and effective public health-related decisions. However, without a proper understanding of the methodologies, applying these tools may result in inaccurate findings for decision-making.With 15 selected papers, this book covers diverse topics and discusses different methodologies that are fundamentals for spatial analysis in public health. Readers will have an opportunity to experience the advancements in spatial tools, data, and methodologies that are applicable to public health investigations.This book, Remote Sensing and Geospatial Technologies in Public Health, is expected to encourage academicians and professionals to further advance their knowledge in this sub-discipline.
Format
- Paperback
License
© 2019 by the authors; CC BY license
Keywords
health GIS; health atlas; development; geospatial technology; nighttime lights; satellite earth observation; remote sensing; modelling; public health; air pollution; hospitalizations; PM2.5; forest fires; global climate change; MODIS; TRMM; DEM; Aqua; remote sensing; elevation; mosquito; rainfall; temperature; influenza-like illness (ILI); peak event; properties of peak events; crossing theory; generalized Pareto distribution (GPD); risk mapping; mosquito-borne disease; dasymetric mapping; geospatial; machine learning; Big Data; health; remote sensing; Holistics 3.0; data-driven decisions; bayesian computation; chronic obstructive pulmonary disease; geographic epidemiology; prediction; random effects; spatial cluster detection; obesity prevalence; geographic scales; geographically weighted regression; chlorophyll-a; cyanobacteria biomass; empirical algorithms; remote sensing; dengue; remote-sensing; risk mapping; Aedes aegypti; medical entomology; remote sensing; climate; weather; public health; disease; environment; atmosphere; ocean; biosphere; precipitation; air pollution exposure; air pollutant intake; space-time path; time geography; personal health danger zone; malaria; Bangladesh; GIS; GPS; remote sensing; GIS; Lyme disease; habitat suitability; multi-criteria decision making; exposure to air pollutants; CALPUFF; ammonia; CAFO; environmental justice; hog industry; n/a