Geospatial Approaches to Measuring Personal Heat Exposure and Related Health Effects in Urban Settings

  • Margaret M. SuggEmail author
  • Christopher M. Fuhrmann
  • Jennifer D. Runkle
Part of the Global Perspectives on Health Geography book series (GPHG)


Recent and projected changes in temperature extremes, including the intensification of heat waves, present a persistent health threat for urban residents. Due to limitations in data availability and the spatial representativeness of fixed-site temperature observations, there exists a notable gap in the geospatial sciences on the multi-scale characterization of geographic patterns of extreme heat and the associated correlation with individual vulnerability in urban settings. Studies employing individual-level exposure assessment methodologies are sparse. Yet rapid advancements in low-cost wearable sensors and other mobile technologies can be leveraged to capture geo-referenced environmental exposure (e.g., temperature) and health data (e.g., physiologic strain) to better understand and quantify the impacts of variations in individual microclimates. The emergence of new technologies and rich spatial datasets requires multi-disciplinary collaboration to advance the science on place-based exposure to thermal extremes and the associated health impacts for at-risk populations in urban environments.


Urban health Personal heat exposure Wearable sensors Temperature-health events Climate change 


  1. An, L., Tsou, M. H., Crook, S. E., Chun, Y., Spitzberg, B., Gawron, J. M., & Gupta, D. K. (2015). Space–time analysis: Concepts, quantitative methods, and future directions. Annals of the Association of American Geographers, 105(5), 891–914.CrossRefGoogle Scholar
  2. Basu, R., & Samet, J. M. (2002). An exposure assessment study of ambient heat exposure in an elderly population in Baltimore, Maryland. Environmental Health Perspectives, 110(12), 1219.CrossRefGoogle Scholar
  3. Berko, J., Ingram, D. D., Saha, S., & Parker, J. D. (2014). Deaths attributed to heat, cold, and other weather events in the United States, 2006–2010. National Health Statistics Reports, 30, 1–15.Google Scholar
  4. Bernhard, M. C., Kent, S. T., Sloan, M. E., Evans, M. B., McClure, L. A., & Gohlke, J. M. (2015). Measuring personal heat exposure in an urban and rural environment. Environmental Research, 137, 410–418.CrossRefGoogle Scholar
  5. Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., et al. (2017). Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? Environment International, 99, 293–302.CrossRefGoogle Scholar
  6. Chan, Y. F. Y., Bot, B. M., Zweig, M., Tignor, N., Ma, W., Suver, C., et al. (2018). The asthma mobile health study, smartphone data collected using ResearchKit. Scientific Data, 5, 180096.CrossRefGoogle Scholar
  7. Chapman, L., Muller, C. L., Young, D. T., Warren, E. L., Grimmond, C. S. B., Cai, X. M., & Ferranti, E. J. (2015). The Birmingham urban climate laboratory: An open meteorological test bed and challenges of the smart city. Bulletin of the American Meteorological Society, 96(9), 1545–1560.CrossRefGoogle Scholar
  8. Chapman, L., Bell, C., & Bell, S. (2017). Can the crowdsourcing data paradigm take atmospheric science to a new level? A case study of the urban heat island of London quantified using Netatmo weather stations. International Journal of Climatology, 37(9), 3597–3605.CrossRefGoogle Scholar
  9. Dėdelė, A., Miškinytė, A., Česnakaitė, I., & Gražulevičienė, R. (2018). Effects of individual and environmental factors on GPS-based time allocation in Urban microenvironments using GIS. Applied Sciences, 8(10), 2007.CrossRefGoogle Scholar
  10. Demšar, U., & Virrantaus, K. (2010). Space-time density of trajectories: Exploring spatiotemporal patterns in movement data. International Journal of Geographical Information Science, 24, 1527–1542.CrossRefGoogle Scholar
  11. De Nazelle, A., Seto, E., Donaire-Gonzalez, D., Mendez, M., Matamala, J., Nieuwenhuijsen, M. J., & Jerrett, M. (2013). Improving estimates of air pollution exposure through ubiquitous sensing technologies. Environmental Pollution, 176, 92–99.CrossRefGoogle Scholar
  12. Desjardins, M. R., Hohl, A., Griffith, A., & Delmelle, E. (2018). A space–time parallel framework for fine-scale visualization of pollen levels across the Eastern United States. Cartography and Geographic Information Science, 1–13.
  13. Dewulf, B., Neutens, T., Van Dyck, D., De Bourdeaudhuij, I., Panis, L. I., Beckx, C., & Van de Weghe, N. (2016). Dynamic assessment of inhaled air pollution using GPS and accelerometer data. Journal of Transport & Health, 3(1), 114–123.CrossRefGoogle Scholar
  14. Dias, D., & Tchepel, O. (2014). Modelling of human exposure to air pollution in the urban environment: A GPS-based approach. Environmental Science and Pollution Research, 21(5), 3558–3571.CrossRefGoogle Scholar
  15. Diez-Roux, A. V. (2000). Multilevel analysis in public health research. Annual Review of Public Health, 21(1), 171–192.CrossRefGoogle Scholar
  16. Dons, E., Laeremans, M., Orjuela, J. P., Avila-Palencia, I., Carrasco-Turigas, G., Cole-Hunter, T., et al. (2017). Wearable sensors for personal monitoring and estimation of inhaled traffic-related air pollution: Evaluation of methods. Environmental Science & Technology, 51(3), 1859–1867.CrossRefGoogle Scholar
  17. Ebi, K. L., Teisberg, T. J., Kalkstein, L. S., Robinson, L., & Weiher, R. F. (2004). Heat watch/warning systems save lives: Estimated costs and benefits for Philadelphia 1995–98. Bulletin of the American Meteorological Society, 85(8), 1067–1074.CrossRefGoogle Scholar
  18. ESRI. (2018). ArcPro: Release 2.2.4. Redlands: Environmental Systems Research Institute.Google Scholar
  19. Fang, T. B., & Lu, Y. (2011). Constructing a near real-time space-time cube to depict urban ambient air pollution scenario. Transactions in GIS, 15(5), 635–649.CrossRefGoogle Scholar
  20. Fang, T. B., & Lu, Y. (2012). Personal real-time air pollution exposure assessment methods promoted by information technological advances. Annals of GIS, 18(4), 279–288.CrossRefGoogle Scholar
  21. Fischer, E. M., Oleson, K. W., & Lawrence, D. M. (2012). Contrasting urban and rural heat stress responses to climate change. Geophysical Research Letters, 39(3), L03705.
  22. Friel, S., Hancock, T., Kjellstrom, T., McGranahan, G., Monge, P., & Roy, J. (2011). Urban health inequities and the added pressure of climate change: An action-oriented research agenda. Journal of Urban Health, 88(5), 886.CrossRefGoogle Scholar
  23. Gao, M., Cao, J., & Seto, E. (2015). A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2. 5 in Xi'an, China. Environmental Pollution, 199, 56–65.CrossRefGoogle Scholar
  24. Hägerstrand, T. (1967). Innovation diffusion as a spatial process. Chicago: The University of Chicago Press.Google Scholar
  25. Hägerstrand, T. (1970). What about people in regional science? Papers of the Regional Science Association, 24, 7–21.CrossRefGoogle Scholar
  26. Hancke, G. P., Silva Bde, C., & Hancke, G. P., Jr. (2012). The role of advanced sensing in smart cities. Sensors, 13(1), 393–425.CrossRefGoogle Scholar
  27. Heaviside, C., Macintyre, H., & Vardoulakis, S. (2017). The urban heat island: Implications for health in a changing environment. Current Environmental Health Reports, 4(3), 296–305.CrossRefGoogle Scholar
  28. Heimann, I., Bright, V. B., McLeod, M. W., Mead, M. I., Popoola, O. A. M., Stewart, G. B., & Jones, R. L. (2015). Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors. Atmospheric Environment, 113, 10–19.CrossRefGoogle Scholar
  29. Helbich, M. (2018). Toward dynamic urban environmental exposure assessments in mental health research. Environmental Research, 161, 129–135.CrossRefGoogle Scholar
  30. Hondula, D. M., Balling, R. C., Andrade, R., Krayenhoff, E. S., Middel, A., Urban, A., Georgescu, M., & Sailor, D. J. (2017). Biometeorology for cities. International Journal of Biometeorology, 61, S59–S69.CrossRefGoogle Scholar
  31. Hondula, D. M., Balling, R. C., Vanos, J. K., & Georgescu, M. (2015a). Rising temperatures, human health, and the role of adaptation. Curr Clim Change Rep (Vol. 1, p. 144).Google Scholar
  32. Hondula, D. M., Davis, R. E., Saha, M. V., Wegner, C. R., & Veazey, L. M. (2015b). Geographic dimensions of heat-related mortality in seven U.S. cities. Environmental Research, 138, 439–452.CrossRefGoogle Scholar
  33. Jenerette, G. D., Harlan, S., Buyanteuv, A., Stefanov, W. L., Declet-Barreto, J., Ruddel, B. L., Wyint, S. W., Kaplan, S., & Li, X. (2016). Micro-scale urban surface temperatures are related to land-cover features and residential heat related health impacts in Phoenix, AZ USA. Landscape Ecology, 31(4), 745–760.CrossRefGoogle Scholar
  34. Karimi, M., Nazari, R., Vant-Hull, B., & Khanbilvardi, R. (2015). Urban heat island assessment with temperature maps using high resolution datasets measured at street level. International Journal of the Constructed Environment, 6, 17–26.CrossRefGoogle Scholar
  35. Karimi, M., Vant-Hull, B., Nazari, R., Mittenzwei, M., & Khanbilvardi, R. (2017). Predicting surface temperature variation in urban settings using real-time weather forecasts. Urban Climate, 20, 192–201.CrossRefGoogle Scholar
  36. Kestens, Y., Wasfi, R., Naud, A., & Chaix, B. (2017). “Contextualizing context”: Reconciling environmental exposures, social networks, and location preferences in health research. Current Environmental Health Reports, 4(1), 51–60.CrossRefGoogle Scholar
  37. Klepeis, N. E., Nelson, W. C., Ott, W. R., Robinson, J. P., Tsang, A. M., Switzer, P., Behar, J. V., Hern, S. C., & Engelmann, W. H. (2001). The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants. Journal of Exposure Analysis and Environmental Epidemiology, 11, 231–252.CrossRefGoogle Scholar
  38. Klinenberg, E. (2002). Heat wave: A social autopsy of disaster in Chicago. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  39. Kuras, E. R., Hondula, D. M., & Brown-Saracino, J. (2015). Heterogeneity in individually experienced temperatures (IETs) within an urban neighborhood: Insights from a new approach to measuring heat exposure. International Journal of Biometeorology, 59(10), 1363–1372.CrossRefGoogle Scholar
  40. Kuras, E., Bernhard, M., Calkins, M., Ebi, K., Hess, J., Kintziger, K., Jagger, M., Middel, A., Scott, A., Spector, J., Uejio, C., Vanos, J., Zaitchik, B., Gohlke, J., & Hondula, D. (2017). Opportunities and challenges for personal heat exposure research. Environmental Health Perspectives, 125, 085001.CrossRefGoogle Scholar
  41. Kwan, M. P. (2009). From place-based to people-based exposure measures. Social Science & Medicine, 69(9), 1311–1313.CrossRefGoogle Scholar
  42. Kwan, M. P. (2012). How GIS can help address the uncertain geographic context problem in social science research. Annals of GIS, 18(4), 245–255.CrossRefGoogle Scholar
  43. Kwan, M. P. (2013). Beyond space (as we knew it): Toward temporally integrated geographies of segregation, health, and accessibility: Space–time integration in geography and GIScience. Annals of the Association of American Geographers, 103(5), 1078–1086.CrossRefGoogle Scholar
  44. Kwan, M.-P. (2000). Interactive geovisualization of activity travel patterns using three-dimensional geographical information systems: A methodological exploration with a large data set. Transportation Research Part C, 8, 185–203.CrossRefGoogle Scholar
  45. Longo, J., Kuras, E., Smith, H., Hondula, D. M., & Johnston, E. (2017). Technology use, exposure to natural hazards, and being digitally invisible: Implications for policy analytics. Policy & Internet, 9(1), 76–108.CrossRefGoogle Scholar
  46. Macintyre, H. L., Heaviside, C., Taylor, J., Picetti, R., Symonds, P., Cai, X. M., & Vardoulakis, S. (2018). Assessing urban population vulnerability and environmental risks across an urban area during heatwaves–Implications for health protection. Science of the Total Environment, 610, 678–690.CrossRefGoogle Scholar
  47. Macintyre, S., Ellaway, A., & Cummins, S. (2002). Place effects on health: How can we conceptualise, operationalise and measure them? Social Science & Medicine, 55(1), 125–139.CrossRefGoogle Scholar
  48. Mehdipoor, H., Vanos, J. K., Zurita-Milla, R., & Cao, G. (2017). Emerging technologies for biometeorology. International Journal of Biometeorology, 61, S81–S88.CrossRefGoogle Scholar
  49. Meier, F., Fenner, D., Grassmann, T., Otto, M., & Scherer, D. (2017). Crowdsourcing air temperature from citizen weather stations for urban climate research. Urban Climate, 19, 170–191.CrossRefGoogle Scholar
  50. Muller, C. L., Chapman, L., Johnston, S., Kidd, C., Illingworth, S., Foody, G., et al. (2015). Crowdsourcing for climate and atmospheric sciences: Current status and future potential. International Journal of Climatology, 35(11), 3185–3203.CrossRefGoogle Scholar
  51. National Oceanic and Atmospheric Administration. (2019). Natural hazard statistics. National Weather Service, Office of Climate, Water, and Weather Services.
  52. NCA4 Health Ch, Ebi, K. L., Balbus, J. M., Luber, G., Bole, A., Crimmins, A., Glass, G., Saha, S., Shimamoto, M. M., Trtanj, J., & White-Newsome, J. L. (2018). Human Health. In D. R. Reidmiller, C. W. Avery, D. R. Easterling, K. E. Kunkel, K. L. M. Lewis, T. K. Maycock, & B. C. Stewart (Eds.), Impacts, risks, and adaptation in the United States: Fourth National Climate Assessment, Volume II. Washington, DC: U.S. Global Change Research Program. Scholar
  53. Nethery, E., Mallach, G., Rainham, D., Goldberg, M. S., & Wheeler, A. J. (2014). Using Global Positioning Systems (GPS) and temperature data to generate time-activity classifications for estimating personal exposure in air monitoring studies: An automated method. Environmental Health, 13(1), 33.CrossRefGoogle Scholar
  54. Nguyen, J. L., Schwartz, J., & Dockery, D. W. (2014). The relationship between indoor and outdoor temperature, apparent temperature, relative humidity, and absolute humidity. Indoor Air, 24(1), 103–112.CrossRefGoogle Scholar
  55. Oliver, N., Matic, A., & Frias-Martinez, E. (2015). Mobile network data for public health: Opportunities and challenges. Frontiers in Public Health, 3, 189.CrossRefGoogle Scholar
  56. Openshaw, S. (1984). The modifiable areal unit problem. Norwich: Geo Books.Google Scholar
  57. Ostherr, K., Borodina, S., Bracken, R. C., Lotterman, C., Storer, E., & Williams, B. (2017). Trust and privacy in the context of user-generated health data. Big Data & Society, 4(1), 2053951717704673.CrossRefGoogle Scholar
  58. Quinn, A., Tamerius, J. D., Perzanowski, M., Jacobson, J. S., Goldstein, I., Acosta, L., & Shaman, J. (2014). Predicting indoor heat exposure risk during extreme heat events. Science of the Total Environment, 490, 686–693.CrossRefGoogle Scholar
  59. Reid, C. E., O’neill, M. S., Gronlund, C. J., Brines, S. J., Brown, D. G., Diez-Roux, A. V., & Schwartz, J. (2009). Mapping community determinants of heat vulnerability. Environmental Health Perspectives, 117(11), 1730.CrossRefGoogle Scholar
  60. Reis, S., Liška, T., Vieno, M., Carnell, E. J., Beck, R., Clemens, T., et al. (2018). The influence of residential and workday population mobility on exposure to air pollution in the UK. Environment International, 121, 803–813.CrossRefGoogle Scholar
  61. Rainham, D. (2016). A wireless sensor network for urban environmental health monitoring: UrbanSense. IOP Conference Series: Earth and Environmental Science, 34(1), 012028. IOP Publishing.CrossRefGoogle Scholar
  62. Ryan, P. H., Son, S. Y., Wolfe, C., Lockey, J., Brokamp, C., & LeMasters, G. (2015). A field application of a personal sensor for ultrafine particle exposure in children. Science of the Total Environment, 508, 366–373.CrossRefGoogle Scholar
  63. Sarofim, M. C., Saha, S., Hawkins, M. D., Mills, D. M., Hess, J., Horton, R., Kinney, P., Schwartz, J., & Juliana, A. S. (2016). Ch. 2: Temperature-related death and illness. In The impacts of climate change on human health in the United States: A scientific assessment (pp. 43–68). Washington, DC: U.S. Global Change Research Program. Scholar
  64. Schneider, P., Castell, N., Vogt, M., Dauge, F. R., Lahoz, W. A., & Bartonova, A. (2017). Mapping urban air quality in near real-time using observations from low-cost sensors and model information. Environment International, 106, 234–247.CrossRefGoogle Scholar
  65. Sheridan, S. C., & Allen, M. J. (2018). Temporal trends in human vulnerability to excessive heat. Environmental Research Letters, 13, 043001.CrossRefGoogle Scholar
  66. Sherwood, S. C., & Huber, M. (2010a). An adaptability limit to climate change due to heat stress. Proceedings of the National Academy of Sciences, 107(21), 9552–9555.CrossRefGoogle Scholar
  67. Steinle, S., Reis, S., Sabel, C. E., Semple, S., Twigg, M. M., Braban, C. F., et al. (2015). Personal exposure monitoring of PM2. 5 in indoor and outdoor microenvironments. Science of the Total Environment, 508, 383–394.CrossRefGoogle Scholar
  68. Sherwood, S. C., & Huber, M. (2010b). An adaptability limit to climate change due to heat stress. Proceedings of the National Academy of Sciences, 107(21), 9552–9555. Scholar
  69. Sugg, M. M., Fuhrmann, C. M., & Runkle, J. D. (2018). Temporal and spatial variation in personal ambient temperatures for outdoor working populations in the southeastern USA. International Journal of Biometeorology, 62, 1521.CrossRefGoogle Scholar
  70. Tsin, P. K., Knudby, A., Krayenhoff, E. S., Ho, H. C., Brauer, M., & Henderson, S. B. (2016). Microscale mobile monitoring of urban air temperature. Urban Climate, 18, 58–72.CrossRefGoogle Scholar
  71. Tunstall, H. V., Shaw, M., & Dorling, D. (2004). Places and health. Journal of Epidemiology & Community Health, 58(1), 6–10.CrossRefGoogle Scholar
  72. Uejio, C. K., Morano, L. H., Jung, J., Kintziger, K., Jagger, M., Chalmers, J., & Holmes, T. (2018). Occupational heat exposure among municipal workers. International Archives of Occupational and Environmental Health, 91, 705–715.CrossRefGoogle Scholar
  73. Vant-Hull, B., Karimi, M., Sossa, A., Wisanto, J., Nazari, R., & Khanbilvardi, R. (2014). Fine structure in Manhattan’s daytime urban heat island: A new dataset. Journal of Urban and Environmental Engineering, 8, 59–74.CrossRefGoogle Scholar
  74. Vlahov, D., & Galea, S. (2002). Urbanization, urbanicity, and health. Journal of Urban Health, 79(1), S1–S12.CrossRefGoogle Scholar
  75. Wong, E., Akbari, H., Bell, R., & Cole, D. (2011). Reducing urban heat islands: Compendium of strategies. Environmental Protection Agency. Retrieved 12 May 2011.Google Scholar
  76. Yoo, E., Rudra, C., Glasgow, M., & Mu, L. (2015). Geospatial estimation of individual exposure to air pollutants: Moving from static monitoring to activity-based dynamic exposure assessment. Annals of the Association of American Geographers, 105(5), 915–926.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Margaret M. Sugg
    • 1
    Email author
  • Christopher M. Fuhrmann
    • 2
  • Jennifer D. Runkle
    • 3
  1. 1.Department of Geography and PlanningAppalachian State UniversityBooneUSA
  2. 2.Department of GeosciencesMississippi State UniversityStarkvilleUSA
  3. 3.North Carolina Institute for Climate Studies, North Carolina State UniversityAshevilleUSA

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