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Geospatial Environmental Data for Planetary Health Applications

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Earth Data Analytics for Planetary Health

Part of the book series: Atmosphere, Earth, Ocean & Space ((AEONS))

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Abstract

Planetary health research focused on vector-borne and zoonotic diseases often requires data on the environmental factors that influence vectors, hosts, and pathogens. We summarized major types of geospatial environmental data that are freely available and potentially useful for planetary health applications. There are many relevant geospatial data products that characterize weather, climate, vegetation, land surface temperature, land cover and land use, human population characteristics, and hydrology. However, these datasets differ greatly in their underlying measurement techniques and spatial and temporal resolutions. Although many datasets have global coverage, they vary considerably in their spatial accuracy and suitability for local applications. We recommend that researchers carefully consider the strengths and limitations of alternative data sources with a particular focus on the spatial and temporal scales of the data relative to the specific organisms and processes of interest. Research that addresses the sensitivities of analytical results and model predictions to alternative data sources can provide additional guidance to inform these decisions.

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References

  1. Whitmee S, Haines A, Beyrer C, Boltz F, Capon AG, de Souza Dias BF, Ezeh A, Frumkin H, Gong P, Head P (2015) Safeguarding human health in the Anthropocene epoch: report of The Rockefeller Foundation-Lancet Commission on planetary health. Lancet 386(10007):1973–2028

    Article  Google Scholar 

  2. Zinsstag J, Schelling E, Waltner-Toews D, Tanner M (2011) From “one medicine” to “one health” and systemic approaches to health and well-being. Prev Vet Med 101(3–4):148–156

    Article  Google Scholar 

  3. Charron DF (2012) Ecohealth: origins and approach. In: Charron DF (ed) Ecohealth research in practice. Springer, New York, pp 1–30

    Chapter  Google Scholar 

  4. Gorris ME, Anenberg SC, Goldberg DL, Kerr GH, Stowell JD, Tong D, Zaitchik BF (2021) Shaping the future of science: COVID‐19 highlighting the importance of GeoHealth. GeoHealth:e2021GH000412

    Google Scholar 

  5. Mordecai EA, Caldwell JM, Grossman MK, Lippi CA, Johnson LR, Neira M, Rohr JR, Ryan SJ, Savage V, Shocket MS (2019) Thermal biology of mosquito‐borne disease. Ecol Lett 22(10):1690–1708

    Google Scholar 

  6. Smith M, Willis T, Alfieri L, James W, Trigg M, Yamazaki D, Hardy A, Bisselink B, De Roo A, Macklin M (2020) Incorporating hydrology into climate suitability models changes projections of malaria transmission in Africa. Nat Commun 11(1):1–9

    Article  Google Scholar 

  7. Parham PE, Waldock J, Christophides GK, Hemming D, Agusto F, Evans KJ, Fefferman N, Gaff H, Gumel A, LaDeau S (2015) Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission. Philos T R Soc B 370(1665):20130551

    Article  Google Scholar 

  8. Thomson MC, Ukawuba I, Hershey CL, Bennett A, Ceccato P, Lyon B, Dinku T (2017) Using rainfall and temperature data in the evaluation of national malaria control programs in Africa. Am J Trop Med Hyg 97(Suppl 3):32–45

    Article  Google Scholar 

  9. Murdock CC, Evans MV, McClanahan TD, Miazgowicz KL, Tesla B (2017) Fine-scale variation in microclimate across an urban landscape shapes variation in mosquito population dynamics and the potential of Aedes albopictus to transmit arboviral disease. PLoS Negl Trop Dis 11(5):e0005640

    Article  Google Scholar 

  10. Wimberly MC, Davis JK, Evans MV, Hess A, Newberry PM, Solano-Asamoah N, Murdock CC (2020) Land cover affects microclimate and temperature suitability for arbovirus transmission in an urban landscape. PLoS Negl Trop Dis 14(9):e0008614

    Article  Google Scholar 

  11. Snyder RL, Spano D, Duce P (2013) Weather station siting: effects on phenological models. In: Schwartz MD (ed) Phenology: an integrative environmental science. Springer, New York, pp 367–382

    Chapter  Google Scholar 

  12. Fiebrich CA (2009) History of surface weather observations in the United States. Earth-Sci Rev 93(3–4):77–84

    Article  Google Scholar 

  13. Venter ZS, Brousse O, Esau I, Meier F (2020) Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data. Remote Sens Environ 242:111791

    Article  Google Scholar 

  14. Colston JM, Ahmed T, Mahopo C, Kang G, Kosek M, de Sousa JF, Shrestha PS, Svensen E, Turab A, Zaitchik B (2018) Evaluating meteorological data from weather stations, and from satellites and global models for a multi-site epidemiological study. Environ Res 165:91–109

    Article  Google Scholar 

  15. Dinku T (2019) Challenges with availability and quality of climate data in Africa. In: Melesse AM, Wossenu A, Senay G (eds) Extreme hydrology and climate variability. Elsevier, pp 71–80

    Chapter  Google Scholar 

  16. Hofstra N, New M, McSweeney C (2010) The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data. Clim Dyn 35(5):841–858

    Article  Google Scholar 

  17. Bostan P, Heuvelink GB, Akyurek S (2012) Comparison of regression and kriging techniques for mapping the average annual precipitation of Turkey. Int J Appl Earth Obs Geoinf 19:115–126

    Google Scholar 

  18. Herrera S, Fernández J, Gutiérrez JM (2016) Update of the Spain02 gridded observational dataset for EURO-CORDEX evaluation: assessing the effect of the interpolation methodology. Int J Climatol 36(2):900–908

    Article  Google Scholar 

  19. Fuka DR, Walter MT, MacAlister C, Degaetano AT, Steenhuis TS, Easton ZM (2014) Using the Climate Forecast System Reanalysis as weather input data for watershed models. Hydrol Process 28(22):5613–5623

    Article  Google Scholar 

  20. Behnke R, Vavrus S, Allstadt A, Albright T, Thogmartin WE, Radeloff VC (2016) Evaluation of downscaled, gridded climate data for the conterminous United States. Ecol Appl 26(5):1338–1351

    Article  Google Scholar 

  21. Zandler H, Haag I, Samimi C (2019) Evaluation needs and temporal performance differences of gridded precipitation products in peripheral mountain regions. Sci Rep 9(1):1–15

    Article  Google Scholar 

  22. Harris I, Jones PD, Osborn TJ, Lister DH (2014) Updated high‐resolution grids of monthly climatic observations–the CRU TS3. 10 Dataset. Int J Climatol 34(3):623–642

    Google Scholar 

  23. Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A (2015) The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci Data 2:150066

    Article  Google Scholar 

  24. Verdin A, Funk C, Peterson P, Landsfeld M, Tuholske C, Grace K (2020) Development and validation of the CHIRTS-daily quasi-global high-resolution daily temperature data set. Sci Data 7(1):1–14

    Article  Google Scholar 

  25. Fick SE, Hijmans RJ (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37(12):4302–4315

    Article  Google Scholar 

  26. Karger DN, Conrad O, Böhner J, Kawohl T, Kreft H, Soria-Auza RW, Zimmermann NE, Linder HP, Kessler M (2017) Climatologies at high resolution for the earth’s land surface areas. Sci Data 4(1):1–20

    Article  Google Scholar 

  27. Daly C, Neilson RP, Phillips DL (1994) A statistical topographic model for mapping climatological precipitation over mountainous terrain. J Appl Meteorol 33(2):140–158

    Article  Google Scholar 

  28. Xia Y, Mitchell K, Ek M, Sheffield J, Cosgrove B, Wood E, Luo L, Alonge C, Wei H, Meng J, Livneh B, Lettenmaier D, Koren V, Duan Q, Mo K, Fan Y, Mocko D (2012) Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. J Geophys Res 117(D3)

    Google Scholar 

  29. Abatzoglou JT (2013) Development of gridded surface meteorological data for ecological applications and modelling. Int J Climatol 33(1):121–131

    Article  Google Scholar 

  30. Ford TE, Colwell RR, Rose JB, Morse SS, Rogers DJ, Yates TL (2009) Using satellite images of environmental changes to predict infectious disease outbreaks. Emerg Infect Dis 15(9):1341–1346

    Article  Google Scholar 

  31. Parselia E, Kontoes C, Tsouni A, Hadjichristodoulou C, Kioutsioukis I, Magiorkinis G, Stilianakis NI (2019) Satellite earth observation data in epidemiological modeling of malaria, dengue and West Nile virus: a scoping review. Remote Sens 11(16):1862

    Google Scholar 

  32. Wimberly MC, de Beurs KM, Loboda TV, Pan WK (2021) Satellite observations and malaria: new opportunities for research and applications. Trends Parasitol 37(6):525–537

    Google Scholar 

  33. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150

    Article  Google Scholar 

  34. Pettorelli N, Vik JO, Mysterud A, Gaillard J-M, Tucker CJ, Stenseth NC (2005) Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol Evol 20(9):503–510

    Article  Google Scholar 

  35. Merkle JA, Cross PC, Scurlock BM, Cole EK, Courtemanch AB, Dewey SR, Kauffman MJ (2018) Linking spring phenology with mechanistic models of host movement to predict disease transmission risk. J Appl Ecol 55(2):810–819

    Article  Google Scholar 

  36. Chuang TW, Wimberly MC (2012) Remote sensing of climatic anomalies and West Nile virus incidence in the northern Great Plains of the United States. PLoS ONE 7(10):e46882

    Article  Google Scholar 

  37. Klisch A, Atzberger C (2016) Operational drought monitoring in Kenya using MODIS NDVI time series. Remote Sens 8(4):267

    Article  Google Scholar 

  38. Jiang Z, Huete A, Didan K, Miura T (2008) Development of a two-band enhanced vegetation index without a blue band. Remote Sens Environ 112(10):3833–3845

    Article  Google Scholar 

  39. Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25(3):295–309

    Article  Google Scholar 

  40. Thomson M, Connor S, Milligan P, Flasse S (1997) Mapping malaria risk in Africa: What can satellite data contribute? Parasitol Today 13(8):313–318

    Article  Google Scholar 

  41. Rogers D, Randolph S (1991) Mortality rates and population density of tsetse flies correlated with satellite imagery. Nature 351(6329):739–741

    Article  Google Scholar 

  42. Midekisa A, Senay G, Henebry GM, Semuniguse P, Wimberly MC (2012) Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia. Malar J 11:165

    Article  Google Scholar 

  43. Brown HE, Diuk-Wasser MA, Guan Y, Caskey S, Fish D (2008) Comparison of three satellite sensors at three spatial scales to predict larval mosquito presence in Connecticut wetlands. Remote Sens Environ 112(5):2301–2308

    Article  Google Scholar 

  44. Hilker T, Lyapustin AI, Tucker CJ, Sellers PJ, Hall FG, Wang Y (2012) Remote sensing of tropical ecosystems: atmospheric correction and cloud masking matter. Remote Sens Environ 127:370–384

    Article  Google Scholar 

  45. Li Z-L, Tang B-H, Wu H, Ren H, Yan G, Wan Z, Trigo IF, Sobrino JA (2013) Satellite-derived land surface temperature: Current status and perspectives. Remote Sens Environ 131:14–37

    Article  Google Scholar 

  46. Liu J, Hagan DFT, Liu Y (2020) Global land surface temperature change (2003–2017) and its relationship with climate drivers: airs, modis, and era5-land based analysis. Remote Sens 13(1):44

    Article  Google Scholar 

  47. Cao J, Zhou W, Zheng Z, Ren T, Wang W (2021) Within-city spatial and temporal heterogeneity of air temperature and its relationship with land surface temperature. Landsc Urban Plan 206:103979

    Article  Google Scholar 

  48. Vancutsem C, Ceccato P, Dinku T, Connor SJ (2010) Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sens Environ 114(2):449–465

    Article  Google Scholar 

  49. Wan Z (2014) New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens Environ 140:36–45

    Article  Google Scholar 

  50. Hulley GC, Malakar NK, Islam T, Freepartner RJ (2017) NASA’s MODIS and VIIRS land surface temperature and emissivity products: a long-term and consistent earth system data record. IEEE J Sel Top Appl Earth Obs Remote Sens 11(2):522–535

    Article  Google Scholar 

  51. Malakar NK, Hulley GC, Hook SJ, Laraby K, Cook M, Schott JR (2018) An operational land surface temperature product for Landsat thermal data: methodology and validation. IEEE Trans Geosci Remote Sens 56(10):5717–5735

    Article  Google Scholar 

  52. Hulley GC, Göttsche FM, Rivera G, Hook SJ, Freepartner RJ, Martin MA, Cawse-Nicholson K, Johnson WR (2021) Validation and quality assessment of the ECOSTRESS level-2 land surface temperature and emissivity product. IEEE Trans Geosci Remote Sens 60:1–23

    Article  Google Scholar 

  53. Chakraborty T, Lee X, Ermida S, Zhan W (2021) On the land emissivity assumption and Landsat-derived surface urban heat islands: a global analysis. Remote Sens Environ 265:112682

    Article  Google Scholar 

  54. Sun Q, Miao C, Duan Q, Ashouri H, Sorooshian S, Hsu KL (2018) A review of global precipitation data sets: Data sources, estimation, and intercomparisons. Rev Geophys 56(1):79–107

    Article  Google Scholar 

  55. Kirschbaum DB, Huffman GJ, Adler RF, Braun S, Garrett K, Jones E, McNally A, Skofronick-Jackson G, Stocker E, Wu H (2017) NASA’s remotely sensed precipitation: a reservoir for applications users. Bull Am Meteorol Soc 98(6):1169–1184

    Article  Google Scholar 

  56. Nguyen P, Ombadi M, Sorooshian S, Hsu K, AghaKouchak A, Braithwaite D, Ashouri H, Thorstensen AR (2018) The PERSIANN family of global satellite precipitation data: A review and evaluation of products. Hydrol Earth Syst Sci 22(11):5801–5816

    Article  Google Scholar 

  57. Xie P, Joyce R, Wu S, Yoo S-H, Yarosh Y, Sun F, Lin R (2017) Reprocessed, bias-corrected CMORPH global high-resolution precipitation estimates from 1998. J Hydrometeorol 18(6):1617–1641

    Article  Google Scholar 

  58. Adler RF, Sapiano MR, Huffman GJ, Wang J-J, Gu G, Bolvin D, Chiu L, Schneider U, Becker A, Nelkin E (2018) The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere 9 (4):138

    Google Scholar 

  59. Sulla-Menashe D, Gray JM, Abercrombie SP, Friedl MA (2019) Hierarchical mapping of annual global land cover 2001 to present: the MODIS collection 6 land cover product. Remote Sens Environ 222:183–194

    Article  Google Scholar 

  60. DiMiceli C, Townshend J, Carroll M, Sohlberg R (2021) Evolution of the representation of global vegetation by vegetation continuous fields. Remote Sens Environ 254:112271

    Article  Google Scholar 

  61. Buchhorn M, Lesiv M, Tsendbazar N-E, Herold M, Bertels L, Smets B (2020) Copernicus global land cover layers—collection 2. Remote Sens 12(6):1044

    Article  Google Scholar 

  62. Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JR (2013) High-resolution global maps of 21st-century forest cover change. Science 342(6160):850–853

    Article  Google Scholar 

  63. Potapov P, Turubanova S, Hansen MC, Tyukavina A, Zalles V, Khan A, Song X-P, Pickens A, Shen Q, Cortez J (2022) Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nat Food 3(1):19–28

    Article  Google Scholar 

  64. Gong P, Li X, Wang J, Bai Y, Chen B, Hu T, Liu X, Xu B, Yang J, Zhang W (2020) Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sens Environ 236:111510

    Article  Google Scholar 

  65. Tulbure MG, Hostert P, Kuemmerle T, Broich M (2021) Regional matters: on the usefulness of regional land‐cover datasets in times of global change. Remote Sens Ecol Conserv 8(3):272–283

    Google Scholar 

  66. Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PLoS ONE 10(2):e0107042

    Article  Google Scholar 

  67. Wardrop N, Jochem W, Bird T, Chamberlain H, Clarke D, Kerr D, Bengtsson L, Juran S, Seaman V, Tatem A (2018) Spatially disaggregated population estimates in the absence of national population and housing census data. Proc Natl Acad Sci USA 115(14):3529–3537

    Article  Google Scholar 

  68. Tatem AJ (2017) WorldPop, open data for spatial demography. Sci Data 4(1):1–4

    Article  Google Scholar 

  69. Dobson JE, Bright EA, Coleman PR, Durfee RC, Worley BA (2000) LandScan: a global population database for estimating populations at risk. Photogramm Eng Remote Sens 66(7):849–857

    Google Scholar 

  70. Yang K, LeJeune J, Alsdorf D, Lu B, Shum CK, Liang S (2012) Global distribution of outbreaks of water-associated infectious diseases. PLoS Negl Trop Dis 6(2):e1483

    Article  Google Scholar 

  71. Cann K, Thomas DR, Salmon R, Wyn-Jones A, Kay D (2013) Extreme water-related weather events and waterborne disease. Epidemiol Infect 141(4):671–686

    Article  Google Scholar 

  72. Stanke C, Kerac M, Prudhomme C, Medlock J, Murray V (2013) Health effects of drought: a systematic review of the evidence. PLoS Curr June 5

    Google Scholar 

  73. Cohen JM, Ernst KC, Lindblade KA, Vulule JM, John CC, Wilson ML (2008) Topography-derived wetness indices are associated with household-level malaria risk in two communities in the western Kenyan highlands. Malar J 7:40

    Article  Google Scholar 

  74. Beltrame L, Dunne T, Vineer HR, Walker JG, Morgan ER, Vickerman P, McCann CM, Williams DJ, Wagener T (2018) A mechanistic hydro-epidemiological model of liver fluke risk. J R Soc Interface 15(145):20180072

    Article  Google Scholar 

  75. Colston JM, Zaitchik B, Kang G, Yori PP, Ahmed T, Lima A, Turab A, Mduma E, Shrestha PS, Bessong P (2019) Use of earth observation-derived hydrometeorological variables to model and predict rotavirus infection (MAL-ED): a multisite cohort study. Lancet Planet Health 3(6):e248–e258

    Article  Google Scholar 

  76. Shaman J, Day JF, Komar N (2010) Hydrologic conditions describe West Nile virus risk in Colorado. Int J Environ Res Public Health 7(2):494–508

    Article  Google Scholar 

  77. Davis JK, Vincent GP, Hildreth MB, Kightlinger L, Carlson C, Wimberly MC (2018) Improving the prediction of arbovirus outbreaks: a comparison of climate-driven models for West Nile virus in an endemic region of the United States. Acta Trop 185:242–250

    Article  Google Scholar 

  78. Pekel J-F, Cottam A, Gorelick N, Belward AS (2016) High-resolution mapping of global surface water and its long-term changes. Nature 540(7633):418–422

    Article  Google Scholar 

  79. Pickens AH, Hansen MC, Hancher M, Stehman SV, Tyukavina A, Potapov P, Marroquin B, Sherani Z (2020) Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sens Environ 243:111792

    Article  Google Scholar 

  80. Worden J, de Beurs KM (2020) Surface water detection in the Caucasus. Int J Appl Earth Obs Geoinf 91:102159

    Google Scholar 

  81. Midekisa A, Senay GB, Wimberly MC (2014) Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia. Water Resour Res 50(11):8791–8806

    Article  Google Scholar 

  82. Catry T, Li Z, Roux E, Herbreteau V, Gurgel H, Mangeas M, Seyler F, Dessay N (2018) Wetlands and malaria in the Amazon: guidelines for the use of synthetic aperture radar remote-sensing. Int J Environ Res Public Health 15(3):468

    Article  Google Scholar 

  83. Odhiambo JN, Kalinda C, Macharia PM, Snow RW, Sartorius B (2020) Spatial and spatio-temporal methods for mapping malaria risk: a systematic review. BMJ Glob Health 5(10):e002919

    Article  Google Scholar 

  84. Senay G, Velpuri NM, Bohms S, Budde M, Young C, Rowland J, Verdin J (2015) Drought monitoring and assessment: remote sensing and modeling approaches for the famine early warning systems network. In: Parron P, Baldassarre GD (eds) Hydro-meteorological hazards, risks and disasters. Elsevier, pp 233–262

    Chapter  Google Scholar 

  85. Lelieveld J, Pozzer A, Pöschl U, Fnais M, Haines A, Münzel T (2020) Loss of life expectancy from air pollution compared to other risk factors: a worldwide perspective. Cardiovasc Res 116(11):1910–1917

    Article  Google Scholar 

  86. Anenberg SC, Bindl M, Brauer M, Castillo JJ, Cavalieri S, Duncan BN, Fiore AM, Fuller R, Goldberg DL, Henze DK (2020) Using satellites to track indicators of global air pollution and climate change impacts: lessons learned from a NASA‐supported science‐stakeholder collaborative. GeoHealth 4(7):e2020GH000270

    Google Scholar 

  87. Chu Y, Liu Y, Li X, Liu Z, Lu H, Lu Y, Mao Z, Chen X, Li N, Ren M (2016) A review on predicting ground PM2. 5 concentration using satellite aerosol optical depth. Atmosphere 7(10):129

    Google Scholar 

  88. Frumkin H, Bratman GN, Breslow SJ, Cochran B, Kahn PH Jr, Lawler JJ, Levin PS, Tandon PS, Varanasi U, Wolf KL (2017) Nature contact and human health: a research agenda. Environ Health Persp 125(7):075001–075001

    Article  Google Scholar 

  89. Labib S, Lindley S, Huck JJ (2020) Spatial dimensions of the influence of urban green-blue spaces on human health: a systematic review. Environ Res 180:108869

    Article  Google Scholar 

  90. Degefu MA, Bewket W, Amha Y (2022) Evaluating performance of 20 global and quasi-global precipitation products in representing drought events in Ethiopia I: visual and correlation analysis. Weather Clim Extrem 35:100416

    Article  Google Scholar 

  91. Hess A, Davis JK, Wimberly MC (2018) Identifying environmental risk factors and mapping the distribution of West Nile virus in an endemic region of North America. GeoHealth 2(12):395–409

    Article  Google Scholar 

  92. Estrada-Peña A, Estrada-Sánchez A, Estrada-Sánchez D, de la Fuente J (2013) Assessing the effects of variables and background selection on the capture of the tick climate niche. Int J Health Geogr 12(1):1–13

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK, 73061, USA

    Michael C. Wimberly

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  1. Michael C. Wimberly
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Correspondence to Michael C. Wimberly .

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Editors and Affiliations

  1. Department of Geography, College of Science, National Taiwan University, Taipei, Taiwan

    Tzai-Hung Wen

  2. Department of Molecular Parasitology and Tropical Diseases, College of Medicine, School of Medicine, Taipei Medical University, Taipei, Taiwan

    Ting-Wu Chuang

  3. Faculty of Public Health, Mahidol University, Bangkok, Thailand

    Mathuros Tipayamongkholgul

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Wimberly, M.C. (2023). Geospatial Environmental Data for Planetary Health Applications. In: Wen, TH., Chuang, TW., Tipayamongkholgul, M. (eds) Earth Data Analytics for Planetary Health. Atmosphere, Earth, Ocean & Space. Springer, Singapore. https://doi.org/10.1007/978-981-19-8765-6_7

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