• Yongmei LuEmail author
  • Eric Delmelle
Part of the Global Perspectives on Health Geography book series (GPHG)


This chapter provides an overview of the background and content of this book. Starting with a discussion on the recent edited volumes on or closely related to urban health, this chapter highlights the need for a book on geospatial technologies for the study of urban health. The uniqueness of geospatial approaches to investigate urban health issues can be attributed to the spatial perspective and the lens of place. This chapter further argues that the continuous development in geospatial technologies, coupled with recent advances in communication and information technologies, portable sensor technologies, and the various social media and open data, has played an essential role for the modelling of environment exposure and health risk. However, there still exist challenges for urban health studies. These challenges maybe rooted in, among the multiple causes, a lack of understanding of the micro-level health decisions and the methodological limitation to address the Uncertain Geospatial Contextual Problem. This chapter finishes with a section-by-section and chapter-by-chapter overview of the empirical studies included in this book volume. This overview is provided to illustrate the organization of this book and to serve as a guide for a reader to navigate through the book chapters.


Urban health Geospatial technologies Health risk Health service access Health lifestyle Health management 


  1. Althoff, T., White, R. W., & Horvitz, E. (2016). Influence of Pokémon Go on physical activity: study and implications. Journal of Medical Internet Research., 18(12), e315.CrossRefGoogle Scholar
  2. Boulos, M. N. K., Lu, Z., Guerrero, P., Jennett, C., & Steed, A. (2017). From urban planning and emergency training to Pokémon Go: Applications of virtual reality GIS (VRGIS) and augmented reality GIS (ARGIS) in personal, public and environmental health. International Journal of Health Geographics, 16(7), 1–11.Google Scholar
  3. Corburn, J. (2009). Towards the healthy city: People, places, and the politics of urban planning. Cambridge, MA: The MIT Press.CrossRefGoogle Scholar
  4. Dummer, T. J. (2008). Health geography: Supporting public health policy and planning. CMAJ: Canadian Medical Association journal = journal de l'Association medicale canadienne, 178(9), 1177–1180.CrossRefGoogle Scholar
  5. Fang, B. T., & Lu, Y. (2011). Constructing near real-time space-time cube to depict urban ambient air pollution scenario. Transactions in GIS, 15(5), 635–649.CrossRefGoogle Scholar
  6. 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
  7. Freudenberg, N., Klitzman, S., & Saegert, S. (2009). Urban health and society: Interdisciplinary approaches to research and practice. San Francisco: Jpssey-Bass.Google Scholar
  8. Galea, S., & Vlahov, D. (2006). Handbook of urban health: Populations, methods, and practice. New York: Springer-Verlag.Google Scholar
  9. Hynes, H. P., & Lopez, R. (2009). Urban health: Readings in the social, built, and physical environments of U.S. Cities. Sudbury, MA: Jones and Bartlett Publishers.Google Scholar
  10. Kirby, R. S., Delmelle, E., & Eberth, J. M. (2017). Advances in spatial epidemiology and geographic information systems. Annals of Epidemiology, 27(1), 1–9.CrossRefGoogle Scholar
  11. Kwan, M.-P. (2012). The uncertain geographic context problem. Annals of the Association of American Geographers, 102(5), 958–968.CrossRefGoogle Scholar
  12. Lu, Y., & Fang, T. B. (2015). Examining personal air pollution exposure, intake, and health danger zone using time geography and 3d geovisualization. ISPRS International Journal of Geo-Information., 4(1), 32–46.CrossRefGoogle Scholar
  13. Lu, Y., & Lu, F. (2018). Physical activities, BMI, and accessibility to and utilization of facilities. Paper presented at the Annual Meeting of American Association of Geographers. New Orleans, LA. April 10–14, 2018.Google Scholar
  14. McLafferty, S. L. (2003). GIS and health care. Annual Review of Public Health, 24, 25–42.CrossRefGoogle Scholar
  15. Miller, H. J., & Tolle, K. (2016). Big data for healthy cities: Using location-aware technologies, open data and 3D urban models to design healthier built environments. Built Environment, 42(3), 441–456.CrossRefGoogle Scholar
  16. Nykiforuk, C. I., & Flaman, L. M. (2011). Geographic information systems (GIS) for health promotion and public health: A review. Health Promotion Practice, 12, 63–73.CrossRefGoogle Scholar
  17. Nguyen, Q. C., Kath, S., Meng, H. W., Li, D., Smith, K. R., VanDerslice, J. A., Wen, M., & Li, F. (2016). Leveraging geotagged Twitter data to examine neighborhood happiness, diet, and physical activity. Applied geography (Sevenoaks, England), 73, 77–88.CrossRefGoogle Scholar
  18. Park, Y. M., & Kwan, M.-P. (2017). Multi-contextual segregation and environmental justice research: Toward fine-scale spatiotemporal approaches. International Journal of Environmental Research and Public Health, 14, 1205.CrossRefGoogle Scholar
  19. Robertson, C., & Feick, R. (2018). Inference and analysis across spatial supports in the big data era: Uncertain point observations and geographic context. Transactions in GIS, 22, 455–476. Scholar
  20. Sarkar, C., Webster, C., & Gallacher, J. (2014). Healthy cities: Public health through urban planning. Cheltenham: Edward Elgar.Google Scholar
  21. United Nations, Department of Economic and Social Affairs (UN DESA). (2018). World Urbanization Prospects. Last accessed on 23 Feb 2019.
  22. Vlahov, D. J., Boufford, I., Pearson, C., & Norris, L. (2010). Urban health: Global perspective. San Francisco: John Wilson & Sons.Google Scholar
  23. Wang, S., & Moriarty, P. (2018). Big data for urban health and Well-being. In S. J. Wang & P. Moriarty (Eds.), Big Data for Urban Sustainability (pp. 119–140). Cham: Springer International Publishing AG.CrossRefGoogle Scholar
  24. Whitman, S., Shah, A., & Benjamins, M. (2011). Urban health: Combating disparities with local data. New York: Oxford University Press.Google Scholar
  25. Yang, W., & Mu, L. (2015). GIS analysis of depression among Twitter users. Applied Geography, 60, 217–223.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of GeographyTexas State UniversitySan MarcosUSA
  2. 2.Department of Geography & Earth SciencesThe University of North Carolina at CharlotteCharlotteUSA

Personalised recommendations