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

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

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

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.
(’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.
Thank you!
arXiv:1505.07372