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urban_streets — Urban street networks (2017)

Description

Street networks of 1-square-mile samples of 20 cities around the world, constructed by P. Crucitti, V. Latora and S. Porta from maps, and distributed as part of the material of the book "Complex Networks: Principles, Methods and Applications" by V. Latora, V. Nicosia and G. Russo. Nodes are street intersections (and dead ends), and an undirected edge is a street segment between two intersections (the "primal" representation of the street network).

The cities are Ahmedabad, Barcelona, Bologna, Brasilia, Cairo, Irvine (two samples, irvine1 and irvine2), London, Los Angeles, New Delhi, New York, Paris, Richmond, San Francisco, Savannah, Seoul, Venice, Vienna, Walnut Creek and Washington.

The networks have the following properties:

  • (Node) pos: the coordinates $(x, y)$ of the intersection in the map.
  • (Edge) length: the length of the street segment, in meters.
Tags
Transportation Roads Unweighted Metadata
Added
2021-12-25
Citation
Upstream URL OK
https://www.complex-networks.net/datasets.html#chap8
Networks
Tip: click on a column header to sort the list, or hover over it for a description.
Column legend
$\left<k\right>$
Mean degree
$\sigma_k$
Standard deviation of the degrees
$\lambda_h$
Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$
Random walk mixing time
$r$
Degree assortativity
$c$
Global clustering coefficient
$\oslash$
(Pseudo-) diameter
$S$
Relative size of the largest component
NPs
Node properties
EPs
Edge properties
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv
ahmedabad 2,870 4,387 3.06 0.61 2.28 1823.51 0.01 0.04 86 1.00 Undirected Unipartite pos length 95 KiB 122 KiB 113 KiB 96 KiB
barcelona 210 323 3.08 1.22 2.67 104.54 -0.04 0.03 24 1.00 Undirected Unipartite pos length 7 KiB 9 KiB 8 KiB 7 KiB
bologna 541 773 2.86 0.95 2.32 272.44 0.01 0.03 38 1.00 Undirected Unipartite pos length 19 KiB 23 KiB 21 KiB 19 KiB
brasilia 179 230 2.57 1.06 2.25 174.37 -0.06 0.07 22 1.00 Undirected Unipartite pos length 6 KiB 8 KiB 7 KiB 6 KiB
cairo 1,496 2,255 3.01 0.65 2.30 887.34 -0.01 0.04 51 1.00 Undirected Unipartite pos length 51 KiB 64 KiB 58 KiB 50 KiB
irvine1 32 36 2.25 1.32 1.87 17.25 -0.14 0.08 8 1.00 Undirected Unipartite pos length 1 KiB 2 KiB 1 KiB 1 KiB
irvine2 217 227 2.09 1.08 1.58 413.16 -0.24 0.02 23 0.82 Undirected Unipartite pos length 7 KiB 9 KiB 8 KiB 7 KiB
london 488 730 2.99 0.93 2.41 286.56 -0.03 0.02 33 1.00 Undirected Unipartite pos length 17 KiB 22 KiB 20 KiB 17 KiB
los-angeles 240 340 2.83 1.27 2.47 167.58 -0.05 0.00 22 1.00 Undirected Unipartite pos length 8 KiB 11 KiB 10 KiB 8 KiB
new-delhi 252 334 2.65 1.01 2.14 168.90 -0.09 0.03 25 1.00 Undirected Unipartite pos length 9 KiB 11 KiB 10 KiB 8 KiB
new-york 248 419 3.38 0.81 2.69 115.08 0.16 0.03 24 1.00 Undirected Unipartite pos length 9 KiB 12 KiB 10 KiB 9 KiB
paris 335 494 2.95 1.07 2.52 185.78 -0.01 0.05 28 1.00 Undirected Unipartite pos length 12 KiB 15 KiB 13 KiB 12 KiB
richmond 697 1,086 3.12 0.91 2.56 490.36 0.06 0.06 49 1.00 Undirected Unipartite pos length 24 KiB 30 KiB 28 KiB 24 KiB
san-francisco 169 271 3.21 1.18 2.77 111.81 0.06 0.00 21 1.00 Undirected Unipartite pos length 6 KiB 8 KiB 7 KiB 6 KiB
savannah 584 958 3.28 0.78 2.55 443.49 0.16 0.00 39 1.00 Undirected Unipartite pos length 19 KiB 24 KiB 22 KiB 19 KiB
seoul 869 1,307 3.01 0.83 2.38 518.79 0.05 0.04 50 1.00 Undirected Unipartite pos length 29 KiB 37 KiB 33 KiB 29 KiB
venice 1,840 2,407 2.62 1.02 2.43 4647.90 -0.10 0.04 102 1.00 Undirected Unipartite pos length 60 KiB 77 KiB 71 KiB 60 KiB
vienna 467 692 2.96 0.96 2.52 231.25 0.02 0.01 32 1.00 Undirected Unipartite pos length 16 KiB 21 KiB 18 KiB 16 KiB
walnut-creek 169 197 2.33 1.11 1.81 150.67 -0.13 0.00 22 1.00 Undirected Unipartite pos length 6 KiB 8 KiB 7 KiB 6 KiB
washington 192 303 3.16 1.29 2.83 73.15 -0.04 0.04 21 1.00 Undirected Unipartite pos length 7 KiB 9 KiB 7 KiB 7 KiB
Ridiculograms*
* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.