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

- 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:

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)

Residential area
Residential area
Religious facility
Governmental service
Retail center
The trajectory of an individual:

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

Land use transition probability matrix:
lots of structure →
could increase predictability of mobility

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…

10
-2
10
-3
10-4
10-5
10
-6
10-7
Fractionofpopulationinunitarea
(A) (B)
(D)(C)
Predictedpopulationdensity

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:

(’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:

Thank you!
arXiv:1505.07372