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From land use to human mobility

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Talk for the NetSci satellite UrbanNet, Zaragoza, Spain, June 1, 2015

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From land use to human mobility

  1. 1. From land use to human mobility: Inferring intra-city human mobility using individual daily life pattern and land use map Minjin Lee & Petter Holme Sungkyunkwan University arXiv:1505.07372
  2. 2. - Standing in for the main author … and this is really her project. - Heard about this talk today: ‣ Naoki Masuda: “Sorry I can’t see you talk today.” ‣ Me: “What are you talk about? I’ll only talk tomorrow.” - New to the subject. - No slides prepared. - I think this talk will be too short, but: “nobody has been killed for giving a too short talk”. Apologies and excuses:
  3. 3. Questions: Predicting human intra-city mobility (statistics of human travel) What can land-use maps tell us? Data: Chicago origin-destination study http://www.cmap.illinois.gov/data/transportation/travel-tracker-survey 25,845 listed their trajectories (name, and rough coordinates of source and destination) & trip-purposes during a day or two Google Maps API Land-use map https://datahub.cmap.illinois.gov/dataset/land-use-inventory-for-northeast-illinois-2005 49 categories, both relating to the activity (e.g. entertainment) or physical composition (e.g. river)
  4. 4. Residential area Residential area Religious facility Governmental service Retail center The trajectory of an individual:
  5. 5. Trip purpose Landuse 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Frequency The fraction of destination land-use type per trip purpose: Strong correlation between land use and trip purpose
  6. 6. Land use transition probability matrix: lots of structure → could increase predictability of mobility
  7. 7. Model relating land use and mobility: The flux from location i to j is proportional to: - The population at i, pi. - The transition matrix entry ij (the mean flux between land-use types i to j). - The the distance dependence from the gravity model. Tij pi~ Lij dij S and the population is given by the steady state of this process…
  8. 8. 10 -2 10 -3 10-4 10-5 10 -6 10-7 Fractionofpopulationinunitarea (A) (B) (D)(C) Predictedpopulationdensity
  9. 9. P(d) 100 10-1 10-2 10-3 10-4 10-5 10-6 101 102 100 0.5 1.0 1.5 2.0 2.5 3.0 Goodnessoffit(²) 0.0 1.5 2.0 2.5 3.0 3.5 4.0 Distance exponent value (𝜎) 1.5 1.7 1.9 2.1 2.3 3 3.5 4 Empirical (A) (B) Distance, d(km) Calibrating the gravity exponent:
  10. 10. (’Chicago_empirical_density_ratio_1015.txt’)matrix (’lu_list1_100_eigen_1006_2_5.txt’)matrix 100 0 20 40 60 80 100 120 P(d) Distance, d (km) (E) (A) (B) (D)(C) Empirical Simulated Random Uniform10-1 10-2 10-3 10-4 10-5 10-2 10-3 10 -4 10-5 10-6 10-7 Fractionofpopulationinunitarea The land use map improves the gravity model:
  11. 11. Thank you! arXiv:1505.07372

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