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From land useto human mobility:Inferring intra-city human mobility using individual daily life pattern and land use map
Minjin Lee & Petter HolmeSungkyunkwan 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 studyhttp://www.cmap.illinois.gov/data/transportation/travel-tracker-survey25,845 listed their trajectories (name, and rough coordinates of source and destination) & trip-purposes during a day or two
Google Maps API
Land-use maphttps://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
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The trajectory of an individual:
Trip purpose
Lan
d u
se
1 2 3 4 5 6 7 8 9 10 11 12 13 14
12345678910111213141516
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0Freq
uency
The fraction of destinationland-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~ LijdijS
and the population is given by the steady state of this process…
10-2
10-3
10-4
10-5
10-6
10-7
Fraction of population in unit area
(A) (B)
(D)(C)
Pred
icte
d po
pula
tion
den
sity
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
Goo
dnes
s of
fit (
)² )
0.01.5 2.0 2.5 3.0 3.5 4.0
Distance exponent value (!)
1.51.71.92.12.33
3.54
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 120P(
d)Distance, d (km)
(E)(A) (B)
(D)(C)
Empirical Simulated
Random Uniform
10-1
10-2
10-3
10-4
10-5
10-2
10-3
10-4
10-5
10-6
10-7
Fraction of population in unit area
The land use map improves the gravity model:
Thank you!
arXiv:1505.07372
