Robustness and Resilience of Cities Around the World

Robustness and Resilience of Cities Around the World

Tahar ZanoudaSofiane Abbar Javier Borge-holthoefer

@UrbComp. San Francisco, California. Aug'2016

Heather Leson*

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Urban Resilience

25+ definitions from different domains (Meerow et al.)– Engineering sci, Environment sci, Social sci, etc.

• Capacity of a urban area to confront uncertainty and/or risk

• Capacity of a system to recover its initial state after a shock

S. Meerow, J. P. Newell, and M. Stults. Defining urban resilience: A review. Landscape and Urban Planning, 147:38-49, 2016

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Helping cities around the world become more

resilient to the physical, social, and economic challenges

that are a growing part of the 21st century.

Rockefeller 100RC 1/2

City Resilience Index

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Rockefeller 100RC 2/2

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Random failure

Reachability of services

Targeted attacks

Road Networks!

Rockefeller 100RC 2/2

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Large Scale Study of Road Networks Resilience

50+ cities from 6 continents

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Graph GenerationFrom Open Street Map to Road Networks

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Generated Road NetworksDoha

Riyadh

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Robustness

• Network Robustness has long tradition in complex systems/applied physics

• Approached by percolation processes

• Two types of percolation• Site percolation: nodes removal• Bond percolation: edges removal

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Road Network Robustness Through Bond Percolation

• Failures– Random removal of edges / probabilistic

• Attacks– Targeted removal of edges (e.g., based on their

centrality scores) / deterministic

Cities may react differently to these two types of percolation

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Approach 1/2

• While size(GCC) != size(SLCC) do: – Remove an edge from the graph– Report the size of the new GCC (Giant Connected

Component)– Report the size of the new SLCC (Second Largest

Connected Component)

• Percolation threshold (pc) is observed at the fraction of removed edges in which SLCC maximizes

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Approach 2/2

Size of components (fraction of

#nodes)

Percentage of removed edges

GCC

SLCC

Pc = 27% percolation threshold

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Robustness Results

RandomFailure

Targeted Attack

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Robustness vs. Resilience

Robustness– How much can you

take before you fall down

Resilience– How long does it take

you to stand up again #R

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Resilience

• Group Foursquare locations by categories– Medical centers, transportation & travel, food, etc.

• Assign each foursquare location (f_l) to the nearest node n (intersection) in the graph iff dist(f_l, n) <= d

• Compute service availability in the giant connected component before and after failures

Can the city keep servicing its population?

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Resilience results 1/2

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Resilience results 2/2

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Sofiane AbbarUrbComp. San Francisco, CA. Aug'2016

Future work

• Dynamical robustness– Assess the impact of percolation on the

traffic status (congestion levels)

• Multi-model robustness– Consider multiple transportation layers– Run percolation on the multiplex

Thank you!Sofiane Abbar · sabbar@qf.org.qa

We are hiring!- Post-Docs (urban computing)- Research Associate (urban computing)