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Netsci 2017

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Morphology of travel routes and the organization of cities

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Netsci 2017

  1. 1. Morphology of travel routes and the organization of cities Minjin Lee, Hugo Barbosa, Hyejin Youn, Gourab Ghoshal Petter Holme, Tokyo Institute of Technology
  2. 2. 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
  3. 3. Travel routes to probe urban organization
  4. 4. O D Seoul Ilsan SKKU Shortest Travel routes
 Show how people utilize streets O D Seoul Ilsan SKKU Fastest
  5. 5. Macro-scale (Street network) Micro-scale (Street, Block, Degrees) Meso-scale (Travel routes) Travel routes
 Show how people utilize streets
  6. 6. Hidden forces shaping routes
  7. 7. 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
  8. 8. Travel routes
 Radial forces—toy example
  9. 9. Change the geometric layouts, while keeping the topological structure Shortest routes
 Radial forces—toy example
  10. 10. Change the geometric layouts, while keeping the topological structure Shortest routes
 Radial forces—toy example
  11. 11. Change the geometric layouts, while keeping the topological structure Shortest routes
 Radial forces—toy example
  12. 12. Change the geometric layouts, while keeping the topological structure Shortest routes
 Radial forces—toy example
  13. 13. 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
  14. 14. 46 14 1 8 13 10 Open streetmap
 92 most populous cities
  15. 15. 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

  16. 16. outer inner 1. Inness (I) = Ain (inner polygon) - Aout (outer polygon) Inness Summarizing radial forces
  17. 17. Inness avg. of all cities
  18. 18. Inness Avg. & s.d.
  19. 19. Inness Angular and radial dependence
  20. 20. Inness individual variations
  21. 21. Urbanareasize(km2) A B C Average inness Standarddeviationofinness Inness patterns of individual cities Avg. & s.d. of inness vs city size A B C
  22. 22. Inness pattern of individual cities Clusters of cities
  23. 23. Shortest Fastest Inness pattern of individual cities
  24. 24. Inness pattern of individual cities Corr. coeff. betw. shortest / fastest routes
  25. 25. Inness pattern of individual cities Other examples
  26. 26. Connections to economic geography
  27. 27. Infrastructural advances Christallerian Central Place Löschian Dematerialization Proxy for socioeconomic development
  28. 28. • 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

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