Geospatial Technologies for Surveillance of Heat Related Health Disasters

  • Daniel P. Johnson
Part of the Geotechnologies and the Environment book series (GEOTECH, volume 1)

Heat Related Health Disasters (HRHD) and Extreme Heat Events (EHE) are currently a major public health and climate change concern. EHEs are the number one cause of death in relation to environmental disasters; precipitated as an HRHD. Thought to exacerbate this phenomenon in urban settings is the Urban Heat Island (UHI) effect. Moreover, over 50% of the current worldwide population resides in an urban setting. Therefore the need of a system to specify spatially the areas of increased risk due to an EHE is apparent. The conceptualization of such a system is presented in a parsimonious fashion involving the description of geostatistical methods and thermal remote sensing platforms. Socioeconomic indicators of risk, to extreme heat, are discussed with how they potentially blend with neighborhood level thermal characteristics obtained from remotely sensed assets. Modeling such relationships is discussed with logistic regression and artificial neural networks. The primary proposed outputs are cartographic products elucidating risk from HRHDs. Such geospatial techniques have intrinsic abilities to both plan for and mitigate urban disasters. This conceptualization should assist medical geographers, public health practitioners and researchers in planning for the surveillance of HRHDs.

Medical geography GIS Heat related deaths Spatial analysis 


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  1. Aniello, C., Morgan, K., Busbey, A., & Newland, L. (1995). “Mapping micro-urban heat islands using Landsat TM and A GIS.” Computers & Geosciences 21[s](8): 965–969.CrossRefGoogle Scholar
  2. Basu, R., & Samet, J. (2002). “An exposure assessment study of ambient heat exposrue in an elderly population in Baltimore, Maryland.” Environmental Health Perspectives 110[s](12): 1219–1224.Google Scholar
  3. Beniston, M. (2004). “The 2003 heat wave in Europe: A shape of things to come?” Geophysical Research Letters 31.Google Scholar
  4. CDC (1994). “Heat-related deaths-Philadelphia and United States, 1993–1994.” 44[s](25): 465–468.Google Scholar
  5. Coll, C., V. Caselles, et al. (2007). “Temperature and emissivity separation from ASTER data for low spectral Contrast surfaces.” Remote Sensing of Environment 110[s](2): 162–175.CrossRefGoogle Scholar
  6. Cutter, S. L. (2003). “Social vulnerability to environmental hazards.” Social Science Quarterly 84: 242–261.CrossRefGoogle Scholar
  7. Ebi, K., T. Teisberg, et al. (2003). “Heat watch/warning systems save lives: Estimated costs and benefits for Philadelphia 1995–1998.” Epidemiology 14[s](5): S35–S35.CrossRefGoogle Scholar
  8. Ebi, K. L. and M. O’Neill (2008). “Climate change and human health: Risks and responses.” American Journal of Epidemiology 167[s](11): S135–S135.Google Scholar
  9. Fouillet, A., G. Rey, et al. (2006). “Excess mortality related to the August 2003 heat wave in France.” International Archives of Occupational and Environmental Health 80(1): 16–24.CrossRefGoogle Scholar
  10. Fouillet, A., G. Rey, et al. (2008). “Has the impact of heat waves on mortality changed in France since the European heat wave of summer 2003? A study of the 2006 heat wave.” International Journal of Epidemiology 37[s](2): 309–317.CrossRefGoogle Scholar
  11. Furfey, P. (1927). A Note on Lefever’s “Standand Deviational Ellipse”. American Journal of Sociology. 94(33), DOI: 10.1086/214336.Google Scholar
  12. Gahegan, M. (2003). “Is inductive machine learning just another wild goose (or might it lay the golden egg)?” International Journal of Geographical Information Science 17[s](1): 69–92.CrossRefGoogle Scholar
  13. Getis, A. and J. K. Ord (1992). “The analysis of spatial association by use of distance statistics.” Geographical Analysis 24[s](3): 189–206.Google Scholar
  14. Glass, G. E., B. S. Schwartz, et al. (1995). “Environmental risk-factors for Lyme-disease identified with geographic information-systems.” American Journal of Public Health 85[s](7): 944–948.CrossRefGoogle Scholar
  15. Golden, J. S., D. Hartz, et al. (2008). “A biometeorology study of climate and heat-related morbidity in Phoenix from 2001 to 2006.” International Journal of Biometeorology 52(6): 471–480.CrossRefGoogle Scholar
  16. Harlan S.L., B., A.J., Prashad, L., Stefanov, W.L., & Larson, L. (2006). “Neighborhood microclimates and vulnerability to heat stress.” Social Science and Medicine 63: 2847–2863.Google Scholar
  17. Jemal, A., M. Kulldorff, et al. (2002). “A geographic analysis of prostate cancer mortality in the United States, 1970–89.” International Journal of Cancer 101(2): 168–174.CrossRefGoogle Scholar
  18. Jin, M. L., J. M. Shepherd, et al. (2007). “Development of a parameterization for simulating the urban temperature hazard using satellite observations in climate model.” Natural Hazards 43[s](2): 257–271.CrossRefGoogle Scholar
  19. Jones, T. S., A. P. Liang, et al. (1982). “Morbidity and mortality associated with the July 1980 heat wave in St Louis and Kansas City, Mo.” JAMA 247[s](24): 3327–3331.CrossRefGoogle Scholar
  20. Kalkstein, A. J. and S. C. Sheridan (2007). “The social impacts of the heat-health watch/warning system in Phoenix, Arizona: assessing the perceived risk and response of the public.” International Journal of Biometeorology 52[s](1): 43–55.CrossRefGoogle Scholar
  21. Kalkstein, L. S. (1991). “A new approach to evaluate the impact of climate on human mortality.” Environmental Health Perspectives 96: 145–150.CrossRefGoogle Scholar
  22. Kalkstein, L. S., P. F. Jamason, et al. (1996). “The Philadelphia hot weather-health watch warning system: Development and application, summer 1995.” Bulletin of the American Meteorological Society 77[s](7): 1519–1528.CrossRefGoogle Scholar
  23. Katz, R. W. and B. G. Brown (1994). “Sensitivity of extreme events to climate-change – the case of autocorrelated time-series.” Environmetrics 5[s](4): 451–462.CrossRefGoogle Scholar
  24. Kimes, D., A. Ullah, et al. (2004). “Relationships between pediatric asthma and socioeconomic/urban variables in Baltimore, Maryland.” Health & Place 10[s](2): 141–152.CrossRefGoogle Scholar
  25. Klinenberg, E. (2001). “Dying alone: The social production of urban isolation.” Ethnography 2[s](4): 501–531.CrossRefGoogle Scholar
  26. Klinenberg, E. (2002). HeatWave : A Social Autopsy of Disaster in Chicago. Chicago, University of Chicago Press.Google Scholar
  27. Koffi, B. and E. Koffi (2008). “Heat waves across Europe by the end of the 21st century: Multiregional climate simulations.” Climate Research 36[s](2): 153–168.CrossRefGoogle Scholar
  28. Kovats, R. S. and K. L. Ebi (2006). “Heatwaves and public health in Europe.” European Journal of Public Health 16[s](6): 592–599.CrossRefGoogle Scholar
  29. Kovats, S. and R. Akhtar (2008). “Climate, climate change and human health in Asian cities.” Environment and Urbanization 20[s](1): 165–175.CrossRefGoogle Scholar
  30. Kulldorff, M. (2001). “Prospective time periodic geographical disease surveillance using a scan statistic.” Journal of the Royal Statistical Society Series a-Statistics in Society 164: 61–72.Google Scholar
  31. Kulldorff, M., T. Tango, et al. (2003). “Power comparisons for disease clustering tests.” Computational Statistics & Data Analysis 42[s](4): 665–684.CrossRefGoogle Scholar
  32. Liu, Y. B., Y. Yamaguchi, et al. (2007). “Reducing the discrepancy between ASTER and MODIS land surface temperature products.” Sensors 7[s](12): 3043–3057.CrossRefGoogle Scholar
  33. Lo, C. P. and D. A. Quattrochi (2003). “Land-use and land-cover change, urban heat island phenomenon, and health implications: A remote sensing approach.” Photogrammetric Engineering and Remote Sensing 69[s](9): 1053–1063.Google Scholar
  34. Lorentzen, P., J. McMillan, et al. (2008). “Death and development.” Journal of Economic Growth 13[s](2): 81–124.CrossRefGoogle Scholar
  35. Mastrangelo, G., S. Hajat, et al. (2006). “Contrasting patterns of hospital admissions and mortality during heat waves: Are deaths from circulatory disease a real excess or an artifact?” Medical Hypotheses 66[s](5): 1025–1028.CrossRefGoogle Scholar
  36. McGeehin, M. A. and M. Mirabelli (2001). “The potential impacts of climate variability and change on temperature-related morbidity and mortality in the United States.” Environmental Health Perspectives 109: 185–189.CrossRefGoogle Scholar
  37. Meehl, G. A., F. Zwiers, et al. (2000). “Trends in extreme weather and climate events: Issues related to modeling extremes in projections of future climate change.” Bulletin of the American Meteorological Society 81[s](3): 427–436.CrossRefGoogle Scholar
  38. Michie, D., D. J. Spiegelhalter, et al. (1994). Machine Learning, Neural and Statistical Classification. New York; London, Ellis Horwood.Google Scholar
  39. Mirchandani, H. G., G. McDonald, et al. (1996). “Heat-related deaths in Philadelphia – 1993.” American Journal of Forensic Medicine and Pathology 17[s](2): 106–108.CrossRefGoogle Scholar
  40. Ned Levine, K. E. K. a. L. H. N. (1995). “Spatial analysis of Honolulu motor vehicle crashes: I. Spatial patterns.” Accident Analysis and Prevention 27[s](5): 663–674.Google Scholar
  41. Newbern, C. (2008). Epidemiologist. D. Johnson. Philadelphia, PA.Google Scholar
  42. NOAA. 2008. NWS Publications. Scholar
  43. Ord, J. K. and A. Getis (1995). “Local spatial autocorrelation statistics – distributional issues and an application.” Geographical Analysis 27[s](4): 286–306.Google Scholar
  44. Buechley, R. W., J. Van Bruggen, & L. E. Trippi (1972). “Heat island=death island?” Environmental Research 5: 85–92.CrossRefGoogle Scholar
  45. Remais, J., S. Liang, et al. (2008). “Coupling hydrologic and infectious disease models to explain regional differences in schistosomiasis transmission in southwestern China.” Environmental Science & Technology 42[s](7): 2643–2649.CrossRefGoogle Scholar
  46. Robinson, P. J. (2001). “On the definition of a heat wave.” Journal of Applied Meteorology 40[s](4): 762–775.CrossRefGoogle Scholar
  47. Rosenzweig, C., Solecki, W.D., Parshall, L., Chopping, M., Pope, G., & Goldberg, R. (2005). “Characterizing the urban heat island in current and future climates in New Jersey.” Environmental Hazards 6: 51–62.CrossRefGoogle Scholar
  48. Saaroni, H., Ben-Dor, E., Bitan, A., & Potchter, O. (2000). “Spatial distribution and microscale characteristics of the urban heat island in Tel-Aviv, Israel.” Landscape and Urban Planning 48: 1–18.CrossRefGoogle Scholar
  49. Schaeffer, B., B. Mondet, et al. (2008). “Using a climate-dependent model to predict mosquito abundance: Application to Aedes (Stegomyia) africanus and Aedes (Diceromyia) furcifer (Diptera: Culicidae).” Infection Genetics and Evolution 8[s](4): 422–432.CrossRefGoogle Scholar
  50. Semenza, J. C., McCullough, J.E., Flanders, W.D., McGeehin, M., & Lumpkin, J.R. (1999). “Excess hospital admissions during the July 1995 heat wave in Chicago.” American Journal of Preventative Medicine 16[s](4): 269–277.CrossRefGoogle Scholar
  51. Semenza, J. C., D. J. Wilson, et al. (2008). “Public perception and behavior change in relationship to hot weather and air pollution.” Environmental Research 107[s](3): 401–411.CrossRefGoogle Scholar
  52. Sheridan, S. C. (2007). “A survey of public perception and response to heat warnings across four North American cities: an evaluation of municipal effectiveness.” International Journal of Biometeorology 52[s](1): 3–15.CrossRefGoogle Scholar
  53. Sheridan, S. C., and Kalkstein, L.S. (2004). “Progress in heat watch-warning system technology.” American Meteorological Society BAMS 85: 1931–1941.CrossRefGoogle Scholar
  54. Smoyer, K. E. (1998a). “A comparative analysis of heat waves and associated mortality in St. Louis, Missouri – 1980 and 1995.” International Journal of Biometeorology 42(1): 44–50.Google Scholar
  55. Smoyer, K. E. (1998b). “Putting risk in its place: Methodological considerations for investigating extreme event health risk.” Social Science & Medicine 47(11): 1809–1824.Google Scholar
  56. Smoyer, K. E., Rainham, D.G., & Hewko, J.N. (2000). “Heat-stress-related mortality in five cities in Southern Ontario.” International Journal of Meteorology 44: 190–197.Google Scholar
  57. Tiangco, M., A. M. F. Lagmay, et al. (2008). “ASTER-based study of the night-time urban heat island effect in Metro Manila.” International Journal of Remote Sensing 29[s](10): 2799–2818.CrossRefGoogle Scholar
  58. Unwin, A. (1996). “Geary’s contiguity ratio.” Economic and Social Review 27[s](2): 145–159.Google Scholar
  59. Vezzani, D. and A. E. Carbajo (2008). “Aedes aegypti, Aedes albopictus, and dengue in Argentina: current knowledge and future directions.” Memorias do Instituto Oswaldo Cruz 103[s](1): 66–74.Google Scholar
  60. von Storch, H. and N. Stehr (2006). “Anthropogenic climate change: A reason for concern since the 18th century and earlier.” Geografiska Annaler: Series A, Physical Geography 88A[s](2): 107–113.CrossRefGoogle Scholar
  61. Voogt, J. A., & Oke, T.R. (2003). “Thermal remote sensing of urban climates.” Remote Sensing of Environment 86: 370–384.CrossRefGoogle Scholar
  62. Wang, K. C., J. K. Wang, et al. (2007). “Influences of urbanization on surface characteristics as derived from the Moderate-Resolution Imaging Spectroradiometer: A case study for the Beijing metropolitan area.” Journal of Geophysical Research-Atmospheres 112: D22S06, doi: 10.1029/2006JD007997.Google Scholar
  63. Wang, W. W., L. Z. Zhu, et al. (2004). “An analysis on spatial variation of urban human thermal comfort in Hangzhou, China.” Journal of Environmental Sciences-China 16[s](2): 332–338.Google Scholar
  64. Warwick, K. (1995). “A critique of neural networks for discrete-time linear-control.” International Journal of Control 61[s](6): 1253–1264.CrossRefGoogle Scholar
  65. Wong, D. W. S. (1999). “Geostatistics as measures of spatial segregation.” Urban Geography 20[s](7): 635–647.CrossRefGoogle Scholar
  66. Wong, D. W. S. (2003). “Implementing spatial segregation measures in GIS.” Computers, Environment, and Urban Systems 27: 53–70.CrossRefGoogle Scholar
  67. Wong, D. W. S. (2005). ‘‘Formulating a general spatial segregation measure.” The Professional Geographer 57[s](2): 285–294.CrossRefGoogle Scholar
  68. Zhang, T. L. and G. Lin (2008). “Identification of local clusters for count data: a model-based Moran’s I test.” Journal of Applied Statistics 35[s](3): 293–306.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Daniel P. Johnson
    • 1
  1. 1.Indiana University Purdue University at IndianapolisIndianapolisUSA

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