Market Area Delineation for Airports to Predict the Spread of Infectious Disease

  • Carmen Huber
  • Claus RinnerEmail author
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Air travel facilitates the international spread of infectious disease. While global air travel data represent the volume of travel between airports, identifying which airport an infected individual might use, or where a disease might spread after an infected passenger deplanes, remains a largely unexplored area of research and public health practice. This gap can be addressed by estimating airport catchment areas. This research aims to determine how existing market area delineation techniques estimate airport catchments differently, and which techniques are best suited to anticipate where infectious diseases may spread. Multiple techniques were tested for airports in the Province of Ontario, Canada: circular buffers, drive-time buffers, Thiessen polygons, and the Huff model, with multiple variations tested for some techniques. The results were compared qualitatively and quantitatively based on spatial patterns as well as area and population of each catchment area. There were notable differences, specifically between deterministic and probabilistic approaches. Deterministic techniques may only be suitable if all airports in a study area are similar in terms of attractiveness. The probabilistic Huff model appeared to produce more realistic results because it accounted for variation in airport attractiveness. Additionally, the Huff model requires few inputs and therefore would be efficient to execute in situations where time, resources, and data are limited.


Airport catchments Huff model Infectious disease Public health Retail geography 



Partial funding of this research from the Natural Sciences and Engineering Research Council of Canada is gratefully acknowledged.


  1. Augustyniak W, Olipra Ł (2014) The potential catchment area of Polish regional airports. J Int Stud 7(3):144–154. Scholar
  2. Başar G, Bhat C (2004) A parameterized consideration set model for airport choice: an application to the San Francisco Bay Area. Trans Res Part B: Methodological 38(10):889–904. Scholar
  3. Bell DM (2004) Public health interventions and SARS spread, 2003. Emerg Infect Dis 10(11):1900Google Scholar
  4. Bilotkach V, Clougherty JA, Mueller J, Zhang A (2012) Regulation, privatization, and airport charges: panel data evidence from European airports. J Reg Eco 42(1):73–94. Scholar
  5. Bird BH, McElroy AK (2016) Rift Valley fever virus: unanswered questions. Antivir Res 132:274–280CrossRefGoogle Scholar
  6. Bogoch II, Brady OJ, Kraemer MUG, German M, Creatore MI, Brent S, et al (2016a) Potential for Zika virus introduction and transmission in resource-limited countries in Africa and the Asia-Pacific region: a modelling study. Lancet Infect Dis 16(11):1237–1245CrossRefGoogle Scholar
  7. Bogoch II, Brady OJ, Kraemer MUG, German M, Creatore MI, Kulkarni MA, et al (2016b) Anticipating the international spread of Zika virus from Brazil. Lancet 387(10016):335–336CrossRefGoogle Scholar
  8. Boots B (1980) Weighting Thiessen polygons. Econ Geogr 56(3):248–259. Scholar
  9. Boots B, South R (1997) Modeling retail trade areas using higher-order, multiplicatively weighted Voronoi diagrams. New York 73(4):519–536CrossRefGoogle Scholar
  10. Brent SE, Watts A, Cetron M, German M, Kraemer UG, Bogoch II, et al (2018) International travel between global urban centres vulnerable to yellow fever transmission. Bull World Health Organ 96(5):343–354BCrossRefGoogle Scholar
  11. Centers for Disease Control and Prevention (2017) South Florida maps. Retrieved 5 Feb 2018, from
  12. Cervero R, Round A, Goldman T, Wu K-L (1995) BART @ 20 series rail access modes and catchment areas for the BART system Robert Cervero Kang-Li Wu UCTC No. 307 The University of California Transportation Center University of California. BART @ 20 SeriesGoogle Scholar
  13. Debrezion G, Pels E, Rietveld P (2009) Modelling the joint access mode and railway station choice. Transp Res Part E: Logist Transp Rev 45(1):270–283CrossRefGoogle Scholar
  14. Dolega L, Pavlis M, Singleton A (2016) Estimating attractiveness, hierarchy and catchment area extents for a national set of retail centre agglomerations. J Retail Consum Serv 28:78–90CrossRefGoogle Scholar
  15. Fauci AS, Morens DM (2016) Zika virus in the Americas—yet another arbovirus threat. N Engl J Med 363(1):601–604CrossRefGoogle Scholar
  16. Golnar AJ, Kading RC, Hamer GL (2016) Quantifying the potential pathways and locations of Rift Valley fever virus entry into the United States. Transbound Emerg Dis 65(1):85–95CrossRefGoogle Scholar
  17. Government of Ontario (2018) About Ontario. Retrieved 2 Feb 2018, from
  18. Hatcher MJ, Dick JTA, Dunn AM (2012) Disease emergence and invasions. Funct Ecol 26(6):1275–1287. Scholar
  19. Hernandez T, Lea T, Bermingham P (2004) What’s in a trade area? TorontoGoogle Scholar
  20. Hess S, Polak JW (2005) Mixed logit modelling of airport choice in multi-airport regions. J Air Trans Manage 11(2):59–68. Scholar
  21. Huff DL (1963) A probabilistic analysis of shopping center trade areas. Land Econ 39(1):81–90. Scholar
  22. Huff DL (2003) Parameter estimation in the Huff model. ArcUser 34–36. Retrieved from
  23. Huff DL, Black WC (1997) The Huff model in retrospect. Appl Geogr Stud 1(2):83–93CrossRefGoogle Scholar
  24. Kilpatrick AM, Daszak P, Goodman SJ, Rogg H, Kramer LD, Cedeño V, Cunningham AA (2006) Predicting pathogen introduction: West Nile virus spread to Galápagos. Conserv Biol 20(4):1224–1231CrossRefGoogle Scholar
  25. Leon S (2011) Airport choice modeling: empirical evidence from a non-hub airport. J Trans Res Forum 50(2):5–16.
  26. Levitt P, Jaworsky BN (2007) Transnational studies: past developments and future trends. Ann Rev Sociol 33:129–156Google Scholar
  27. Lieshout R (2012) Measuring the size of an airport’s catchment area. J Trans Geo 25:27–34. Scholar
  28. Lin T, Xia J, Robinson TP, Olaru D, Smith B, Taplin J, Cao B (2016) Enhanced Huff model for estimating Park and Ride (PnR) catchment areas in Perth, WA. J Transp Geogr 54:336–348CrossRefGoogle Scholar
  29. Lounibos LP (2002) Invasions by insect vectors of human disease. Annu Rev Entomol 47:233–266CrossRefGoogle Scholar
  30. McLay P, Reynolds-Feighan A (2006) Competition between airport terminals: the issues facing Dublin Airport. Trans Res Part A: Policy and Practice 40(2):181–203. Scholar
  31. Muller MP, Richardson SE, McGeer A, Dresser L, Raboud J, Mazzulli T, Canadian SARS Research Network, et al (2006) Early diagnosis of SARS: Lessons from the Toronto SARS outbreak. Eur J Clin Microbiol Infect Dis 25(4):230–237. Scholar
  32. Ontario Ministry of Finance (2017) 2016 census highlights: factsheet 8. Retrieved from
  33. Ontario Ministry of Natural Resources (2010) Ontario road network: segment with address, captured February 2010. Retrieved from
  34. PortsToronto (2018) Billy Bishop Toronto City Airport. Retrieved from
  35. Powers AM (2015) Risks to the Americas associated with the continued expansion of chikungunya virus. J Gen Virol 96(1):1–5. Scholar
  36. Reilly WJ (1931) The law of retail gravitation. University of California, New YorkGoogle Scholar
  37. Sanko N, Shoji K (2009) Analysis on the structural characteristics of the station catchment area in Japan. In: 11th conference on competition and ownership in land passenger transportGoogle Scholar
  38. Statistics Canada (2016a) Air passenger traffic and flights—Table 401-0044. Retrieved from
  39. Statistics Canada (2016b). Boundary files. Retrieved from
  40. Summers A (2013) Pandemic flu: lessons from the Toronto SARS outbreak. Emerg Nurse 17(5):16–19CrossRefGoogle Scholar
  41. Suzuki Y (2007) Modeling and testing the “two-step” decision process of travelers in airport and airline choices. Trans Res Part E: LogTrans Rev 43(1):1–20. Scholar
  42. Tatem AJ, Hay SI, Rogers DJ (2006) Global traffic and disease vector dispersal. Proc Natl Acad Sci 103(16):6242–6247CrossRefGoogle Scholar
  43. The SARS Commission (2006) Spring of fear. SARS commission final report, vol. 2. TorontoGoogle Scholar
  44. Upchurch C, Kuby M, Zoldak M, Barranda A (2004) Using GIS to generate mutually exclusive service areas linking travel on and off a network. J Trans Geo 12(1):23–33. Scholar
  45. Wang F (2000) Modeling commuting patterns in Chicago in a GIS environment: a job accessibility perspective. Prof Geo 52(1):120–133. Scholar
  46. WHO (2018) Ebola virus disease—democratic Republic of the Congo. Retrieved from
  47. Wittman MD (2014) An assessment of air service accessibility in U.S. metropolitan regions, 2007–2012. Cambridge.Google Scholar
  48. Yamada I (2016) Thiessen polygons. Int Encycl Geogr People Earth Environ Technol 1–6Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Geography and Environmental StudiesRyerson UniversityTorontoCanada

Personalised recommendations