Introduction
Background
Urban resilience
the ability of an urban system, its social units (such as individuals, communities, institutions, governments, etc.), and its technical units (urban infrastructure) to recover from hazards while maintaining functional continuity of their substituents and as a whole, and mitigating negative impacts of future hazards through practice of resilience planning.
Mobility and social resilience
Urban mobility
Urban mobility resilience vs. transportation system resilience
Disruption in mobility networks vs. disruption in transportation networks
Motifs in mobility networks
Motif | |||
---|---|---|---|
Examples of abundance | Many nodes of the network are not connected to each other. The structure means there is a lack of economic and social mobility, and thus the nodes are more vulnerable to economic or social disturbances. | The destination node in this motif is socially and economically accommodating other nodes. It allows for knowledge sharing between all the nodes in the motif. The origin nodes are highly dependent on the destination node. | There is a richness of mobility happening between different parts of the network. The connectivity allows social and economic upward mobility. The structure can also be resilient to economic or social disturbances. |
Methodology and data
Data
Network measures
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Each node is a block group. Section 3.1 contains more explanation on nodes of the network.
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Weighted edges are the number of people traveling between two nodes. Wij is the weight of the edge going from node i to node j.
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Density (q) is a redundancy measure for the whole network that shows the fraction between the total number of links (m) and the maximum possible number of links (\( \frac{n\times \left(n-1\right)}{2} \)).$$ q=\frac{2m}{n\times \left(n-1\right)} $$
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Node connectivity is the minimum number of nodes that needs to be removed for the network to be disconnected.
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Network motifs are recurring patterns of connections in a network.
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Network communities are locally dense subgraphs of the network that are formed in real-world networks (Yang and Leskovec 2015).
Network communities
Motif detection
Results
Mobility communities
Other characteristics of the network communities
G0 | G1 | G2 | G3 | G4 | G5 | G6 | |
---|---|---|---|---|---|---|---|
Network Measures | |||||||
Density | 0.014 | 0.022 | 0.028 | 0.078 | 0.056 | 0.064 | 0.067 |
Node connectivity | 1 | 1 | 3 | 6 | 4 | 2 | 2 |
Average node degree | 2.193 | 2.879 | 4.8 | 10.642 | 7.098 | 9.282 | 4.967 |
Highest node degree | 7 | 13 | 55 | 43 | 28 | 47 | 37 |
Socio-economic characteristics | |||||||
Median household income (USD) | 20,126 | 22,286 | 52,645 | 20,629 | 29,274 | 71,017 | 25,642 |
Percentage college educated | 18.11 | 24.99 | 50.06 | 44.81 | 59.80 | 63.00 | 24.67 |
White to non-white ratio | 0.219 | 0.351 | 1.857 | 0.923 | 2.125 | 3 | 0.562 |
Multinomial regression
Network motifs
Code | Motif | G0 | G1 | G2 | G3 | G4 | G5 | G6 |
---|---|---|---|---|---|---|---|---|
003 | 38,439 | 30,686 | 25,273 | 17,434 | 15,980 | 14,223 | 2728 | |
012 | 3047 | 2883 | 3373 | 4494 | 3150 | 3663 | 1082 | |
102 | 118 | 539 | 329 | 1874 | 1153 | 1009 | 55 | |
021D | 16 | 25 | 31 | 99 | 47 | 77 | 42 | |
021 U | 17 | 23 | 143 | 127 | 113 | 166 | 72 | |
021C | 20 | 20 | 46 | 145 | 78 | 109 | 42 | |
111D | 3 | 16 | 32 | 212 | 132 | 125 | 19 | |
111 U | 3 | 19 | 15 | 157 | 45 | 94 | 6 | |
030 T | 0 | 4 | 10 | 48 | 14 | 27 | 10 | |
030C | 1 | 0 | 0 | 1 | 1 | 1 | 1 | |
201 | 0 | 1 | 0 | 54 | 26 | 24 | 1 | |
120D | 0 | 1 | 4 | 42 | 19 | 25 | 2 | |
120 U | 0 | 1 | 2 | 33 | 21 | 16 | 0 | |
120C | 0 | 2 | 1 | 18 | 8 | 13 | 0 | |
210 | 0 | 0 | 1 | 42 | 25 | 22 | 0 | |
300 | 0 | 0 | 0 | 24 | 13 | 6 | 0 |
Bigger motifs
Entire Network | G0 | G1 | G2 | G3 | G4 | G5 | G6 | |
---|---|---|---|---|---|---|---|---|
Frequency | 1354 | 205 | 41 | 352 | 355 | 215 | 327 | 202 |
Frequency | 1894 | 549 | 27 | 692 | 668 | 409 | 669 | 329 |
Frequency | 3012 | 1216 | 29 | 1684 | 1128 | 484 | 1114 | 381 |