Advertisement

Population Mobility Modeling Based on Call Detail Records of Mobile Phones for Heat Exposure Assessment in Dhaka, Bangladesh

  • Shinya YasumotoEmail author
  • Chiho Watanabe
  • Ayumi Arai
  • Ryosuke Shibasaki
  • Kei Oyoshi
Chapter

Abstract

The daily journeys people make are known to have significant effects on human health. Previously, capturing and modeling population mobility was difficult or costly, especially in developing countries. However, the spread of mobile phones now allows us to generate population mobility data relatively easily. Using call detail records (CDRs) of mobile phones in Dhaka, Bangladesh, we generated a dataset, known as a “dynamic census,” which modeled how people move daily and predicted their population characteristics. In this study, we implemented a heat exposure assessment that integrated population mobility extracted from the dynamic census. The result shows that incorporating population mobility can alter heat exposure assessments, regardless of population characteristics. Specifically, it was found that the heat exposure of people from suburban areas is underestimated if their mobility is not integrated into the model. Generating the dynamic census is still under active development. With future development of the dataset, it will be possible to do further analyses, such as incorporating seasonal changes in mobility, greater sample size, or wider study areas for environmental risk assessments.

Keywords

GIS Remote sensing Population mobility Heat exposure Bangladesh 

Reference

  1. 1.
    Almeida SP, Casimiro E, Calheiros J (2010) Effects of apparent temperature on daily mortality in Lisbon and Oporto, Portugal. Environ Health 9:12.  https://doi.org/10.1186/1476-069X-9-12 CrossRefGoogle Scholar
  2. 2.
    Arai A, Sekimoto Y (2013) Emergence of large-scale data capturing mass population movement and its applications. J Jpn Soc Photogramm Remote Sens 52(6):327–331 in JapaneseCrossRefGoogle Scholar
  3. 3.
    Beckx C, Int Panis L, Arentze TA, Janssens D, Torfs R, Broekx S, Wets G (2009a) A dynamic activity-based population modelling approach to evaluate exposure to air pollution: methods and application to Dutch urban area. Environ Impact Assess Rev 29(3):179–185.  https://doi.org/10.1016/j.eiar.2008.10.001 CrossRefGoogle Scholar
  4. 4.
    Beckx C, Int Panis L, Uljee I, Arentze T, Janssens D, Wets G (2009b) Disaggregation of nation-wide dynamic population exposure estimates in the Netherlands: applications of activity-based transport models. Atmos Environ 43:5454–5462.  https://doi.org/10.1016/j.atmosenv.2009.07.035 CrossRefGoogle Scholar
  5. 5.
    Briggs D (2005) The role of GIS: coping with space (and time) in air pollution exposure assessment. J Toxicol Environ Health 68(13–14):1243–1261.  https://doi.org/10.1080/15287390590936094 CrossRefGoogle Scholar
  6. 6.
    Dewulf B, Neutens T, Lefebvre W, Seynaeve G, Vanpoucke C, Beckx C, Van de Weghe N (2016) Dynamic assessment of exposure to air pollution using mobile phone data. Int J Health Geogr 15:14CrossRefGoogle Scholar
  7. 7.
    Dhondt S, Beckx C, Degraeuwe B, Lefebvre W, Kochan B, Bellemans T, Panis LI, Macharis C, Putman K (2012) Health impact assessment of air pollution using a dynamic exposure profile: implications for exposure and health impact estimates. Environ Impact Assess Rev 36:42–51.  https://doi.org/10.1016/J.EIAR.2012.03.004 CrossRefGoogle Scholar
  8. 8.
    Hansen A, Bi P, Nitschke M, Ryan P, Pisaniello D, Tucker G (2008) The effect of heat waves on mental health in a temperate Australian City. Environ Health Perspect 116(10):1369–1375.  https://doi.org/10.1289/ehp.11339 CrossRefGoogle Scholar
  9. 9.
    Hashizume M, Armstrong B, Hajat S, Wagatsuma Y, Faruque AS, Hayashi T, Sack DA (2007) Association between climate variability and hospital visits for non-cholera diarrhoea in Bangladesh: effects and vulnerable groups. Int J Epidemiol 36:1030–1037.  https://doi.org/10.1093/ije/dym148 CrossRefGoogle Scholar
  10. 10.
    Hashizume M, Wagatsuma Y, Hayashi T, Saha SK, Streatfield K, Yunus M (2009) The effect of temperature on mortality in rural Bangladesh--a population-based time-series study. Int J Epidemiol 38:1697–1699.  https://doi.org/10.1093/ije/dyn376 CrossRefGoogle Scholar
  11. 11.
    Hägerstrand T (1970) What about people in regional science. Pap Reg Sci Assoc 24(1):6–21.  https://doi.org/10.1111/j.1435-5597.1970.tb01464.x CrossRefGoogle Scholar
  12. 12.
    Kanasugi H, Sekimoto Y, Kurokawa M (2013) Spatiotemporal route estimation consistent with human mobility using cellular network data. Inernational workshop on the impact of human mobility in pervasive systems and application, San DiegoGoogle Scholar
  13. 13.
    Laaidi K, Zeghnoun A, Dousset B, Bretin P, Vandentorren S, Giraudet E, Beaudeau P (2012) The impact of Heat Islands on mortality in Paris during the august 2003 heat wave. Environ Health Perspect 120:254–259.  https://doi.org/10.1289/ehp.1103532 CrossRefGoogle Scholar
  14. 14.
    Marshall JD, Granvold PW, Hoats AS, McKone TE, Deakin E, W Nazaroff W (2006) Inhalation intake of ambient air pollution in California’s south coast Air Basin. Atmos Environ 40(23):4381–4392CrossRefGoogle Scholar
  15. 15.
    Muzzini E, Aparicio G (2013) Bangladesh – the path to middle-income status from an urban perspective directions in development; countries and regions. Worldbank Publications, Washington, DCCrossRefGoogle Scholar
  16. 16.
    Nasrin S (2016) Work travel condition by gender-analysis for Dhaka city. MedCrave Online J Civil Eng 1(3):00017Google Scholar
  17. 17.
    Oliveira R, Moura K, Viana J, Tigre R, Sampaio B (2015) Commute duration and health: empirical evidence from Brazil. Transp Res A Policy Pract 80:62–75CrossRefGoogle Scholar
  18. 18.
    University of Tokyo (2017) People Flow Project (PFLOW). http://pflow.csis.u-tokyo.ac.jp/home/
  19. 19.
    Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86(3):370–384CrossRefGoogle Scholar
  20. 20.
    Walsleben JA, Norman RG, Novak RD, O’Malley EB, Rapoport DM, Strohl KP (1999) Sleep habits of Long Island rail road commuters. Sleep 22(6):728–734CrossRefGoogle Scholar
  21. 21.
    Wan Z (2008) New refinements and validation of the MODIS land-surface temperature/emissivity products. Remote Sens Environ 112:59–74CrossRefGoogle Scholar
  22. 22.
    Wesolowski A, Eagle N, Tatem AJ, Smith DL, Noor AM, Snow RW, Buckee CO (2012) Quantifying the impact of human mobility on malaria. Science 338(6104):267–270CrossRefGoogle Scholar
  23. 23.
    World Bank (2011) World development indicators. World Bank, Washington, DC. http://data.worldbank.org/data-catalog/world-development-indicators Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Shinya Yasumoto
    • 1
    • 2
    Email author
  • Chiho Watanabe
    • 3
    • 4
  • Ayumi Arai
    • 5
  • Ryosuke Shibasaki
    • 5
  • Kei Oyoshi
    • 6
  1. 1.Chubu Institute for Advanced StudiesChubu UniversityKasugaiJapan
  2. 2.Department of Human Ecology, School of International Health, Graduate School of MedicineThe University of TokyoTokyoJapan
  3. 3.Department of Human Ecology, School of International Health, Graduate School of MedicineThe University of TokyoTokyoJapan
  4. 4.Current affiliation: National Institute for Environmental StudiesTsukubaJapan
  5. 5.Center for Spatial Information ScienceUniversity of TokyoKashiwaJapan
  6. 6.Earth Observation Research CenterJapan Aerospace Exploration AgencyTsukubaJapan

Personalised recommendations