The concept of city or urban resilience has emerged as one of the key challenges for the next decades. As a consequence, institutions like the United Nations or Rockefeller Foundation have embraced initiatives that increase or improve it. These efforts translate into funded programs both for action “on the ground” and to develop quantification of resilience, under the for of an index. Ironically, on the academic side there is no clear consensus regarding how resilience should be quantified, or what it exactly refers to in the urban context. Here we attempt to link both extremes providing an example of how to exploit large, publicly available, worldwide urban datasets, to produce objective insight into one of the possible dimensions of urban resilience. We do so via well-established methods in complexity science, such as percolation theory –which has a long tradition at providing valuable information on the vulnerability in complex systems. Our findings uncover large differences among studied cities, both regarding their infrastructural fragility and the imbalances in the distribution of critical services. Paper available at: https://arxiv.org/abs/1608.01709

1. Robustness and Resilience of
Cities Around the World
Tahar ZanoudaSofiane Abbar Javier
Borge-holthoefer
@UrbComp. San Francisco, California. Aug'2016
Heather Leson*

2. 2
Sofiane Abbar
UrbComp. 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

3. 3
Sofiane Abbar
UrbComp. 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

4. 4
Sofiane Abbar
UrbComp. San Francisco, CA. Aug'2016
Rockefeller 100RC 2/2

5. 5
Sofiane Abbar
UrbComp. San Francisco, CA. Aug'2016
Random failure
Reachability of services
Targeted attacks
Road Networks!
Rockefeller 100RC 2/2

6. 6
Sofiane Abbar
UrbComp. San Francisco, CA. Aug'2016
Large Scale Study of Road
Networks Resilience
50+ cities from 6 continents

7. 7
Sofiane Abbar
UrbComp. San Francisco, CA. Aug'2016
Graph Generation
From Open Street Map to Road Networks

8. 8
Sofiane Abbar
UrbComp. San Francisco, CA. Aug'2016
Generated Road Networks
Doha
Riyadh

9. 9
Sofiane Abbar
UrbComp. 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

10. 10
Sofiane Abbar
UrbComp. 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

11. 11
Sofiane Abbar
UrbComp. 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

12. 12
Sofiane Abbar
UrbComp. San Francisco, CA. Aug'2016
Approach 2/2
Size of
components
(fraction of
#nodes)
Percentage of removed edges
GCC
SLCC
Pc = 27%
percolation
threshold

13. 13
Sofiane Abbar
UrbComp. San Francisco, CA. Aug'2016
Robustness Results
Random
Failure
Targeted
Attack

14. 14
Sofiane Abbar
UrbComp. 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
#RIP_Muhammad_Ali
(1941–2016)

15. 15
Sofiane Abbar
UrbComp. 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?

16. 16
Sofiane Abbar
UrbComp. San Francisco, CA. Aug'2016
Resilience results 1/2

17. 17
Sofiane Abbar
UrbComp. San Francisco, CA. Aug'2016
Resilience results 2/2

18. 18
Sofiane Abbar
UrbComp. 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

19. Thank you!
Sofiane Abbar · sabbar@qf.org.qa
We are hiring!
- Post-Docs (urban computing)
- Research Associate (urban computing)