Morphology of travel routes and the organization of cities

1. Morphology of travel routes and the
organization of cities
Minjin Lee, Hugo Barbosa, Hyejin Youn, Gourab Ghoshal
Petter Holme, Tokyo Institute of Technology

5. Fractal structure Street network analysis
Polycentric or monocentric
R. Loufand M. Barthelemy (2014) Scientific Reports
R. Murcio el al. (2015) PRE
Street networks
Footprints of socioeconomic interactions
Statistical properties
Masucci (2009) Eur. Phys. J. B
Crucitti et al. (2006) PRE

9. Travel routes to probe urban
organization

10. O
D
Seoul
Ilsan
SKKU
Shortest
Travel routes
Show how people utilize streets
O
D
Seoul
Ilsan
SKKU
Fastest

11. Macro-scale (Street network) Micro-scale (Street, Block, Degrees)
Meso-scale (Travel routes)
Travel routes
Show how people utilize streets

12. Hidden forces shaping routes

13. Decentralized
Congestion near the center
Expansion of urban area
Centralized
Agglomeration of activity near the center
High connectivity in the center
Travel routes
Radial forces

14. Travel routes
Radial forces—toy example

15. Change the geometric layouts,
while keeping the topological
structure
Shortest routes
Radial forces—toy example

16. Change the geometric layouts,
while keeping the topological
structure
Shortest routes
Radial forces—toy example

17. Change the geometric layouts,
while keeping the topological
structure
Shortest routes
Radial forces—toy example

18. Change the geometric layouts,
while keeping the topological
structure
Shortest routes
Radial forces—toy example

19. Change the geometric layouts,
while keeping the topological
structure
Change the topology by increasing
the connectivity to the center
Shortest routes
Radial forces—toy example

20. 46
14
1
8
13
10
Open streetmap
92 most populous cities

21. Travel route sampling
10km
O
Fastest route
DShortest route
Radius : 2, 5, 10, 15, 20, 30 km
Angular interval : 10, 20, , 180 degree
3150 OD pairs per radius
Travel route sampling

22. outer
inner
1. Inness (I) = Ain (inner polygon) - Aout (outer polygon)
Inness
Summarizing radial forces

23. Inness avg. of all cities

24. Inness
Avg. & s.d.

25. Inness
Angular and radial dependence

26. Inness individual variations

27. Urbanareasize(km2)
A
B
C
Average inness
Standarddeviationofinness
Inness patterns of individual cities
Avg. & s.d. of inness vs city size
A
B
C

28. Inness pattern of individual cities
Clusters of cities

29. Shortest
Fastest
Inness pattern of individual cities

30. Inness pattern of individual cities
Corr. coeff. betw. shortest / fastest routes

31. Inness pattern of individual cities
Other examples

32. Connections to economic
geography

33. Infrastructural advances
Christallerian Central Place Löschian Dematerialization
Proxy for socioeconomic development

34. • We conducted a systematic mesoscale study of street morphology through the introduction of a novel metric
that we term inness.
• Routes across cities display a measurable drift (or inward bias) towards the city center, with the degree of
inward drift balanced by a competing force due to congestion that varies across the cities.
• !e average statistical patterns of routes, is consistent with a core-periphery structure, and hierarchical
levels of routing in the majority of cities.
• Inness patterns of a city can be understood as a product of urban development and geographical or artificial
constraints of cities.
• Taken together the results are indicative of a presence of intrinsic hierarchical relationships inherent in the
process of urban socioeconomic evolution.
Takeaways
Minjin Lee, Hugo Barbosa, Hyejin Youn, Gourab Ghoshal, Petter Holme, arXiv:1701.02973