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Structural robustness and service reachability in urban settings

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Abstract

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.

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Notes

  1. See http://www.cityresilienceindex.org/.

  2. https://foursquare.com/.

  3. http://www.openstreetmap.org.

  4. http://mapzen.com.

  5. https://developer.foursquare.com/categorytree.

References

  • Albert R, Jeong H, Barabási A-L (2000) Error and attack tolerance of complex networks. Nature 406(6794):378–382

    Article  Google Scholar 

  • Arcaute E, Molinero C, Hatna E, Murcio R, Vargas-Ruiz C, Masucci AP, Batty M (2016) Cities and regions in britain through hierarchical percolation. R Soc Open Sci 3(4):150691

    Article  MathSciNet  Google Scholar 

  • Barrington-Leigh C, Millard-Ball A (2017) The world’s user-generated road map is more than 80% complete. PLoS ONE 12(8):e0180698. https://doi.org/10.1371/journal.pone.0180698

  • Berry BJL, Pred A (1965) Central place studies: a bibliography of theory and applications, vol 1. Regional Science Research Institute, Philadelphia

    Google Scholar 

  • Bollobás B (1985) Random graphs. Academic, London

    MATH  Google Scholar 

  • Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26(9):1124–1137

    Article  MATH  Google Scholar 

  • Buldyrev SV, Parshani R, Paul G, Stanley HE, Havlin S (2010) Catastrophic cascade of failures in interdependent networks. Nature 464(7291):1025–1028

    Article  Google Scholar 

  • Callaway DS, Newman ME, Strogatz SH, Watts DJ (2000) Network robustness and fragility: percolation on random graphs. Phys Rev Lett 85(25):5468

    Article  Google Scholar 

  • Cardillo A, Scellato S, Latora V, Porta S (2006) Structural properties of planar graphs of urban street patterns. Phys Rev E 73:066107

    Article  Google Scholar 

  • Chen C, Lu C, Huang Q, Yang Q, Gunopulos D, Guibas L (2016) City-scale map creation and updating using gps collections. In: Proceedings of the 22Nd ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’16, pp 1465–1474, New York, NY, USA, ACM

  • Christaller W (1966) Central places in southern Germany. Prentice-Hall, Upper Saddle River

    Google Scholar 

  • Cohen R, Erez K, Ben-Avraham D, Havlin S (2000) Resilience of the internet to random breakdowns. Phys Rev Lett 85(21):4626

    Article  Google Scholar 

  • Gao J, Buldyrev SV, Stanley HE, Havlin S (2012) Networks formed from interdependent networks. Nat Phys 8(1):40–48

    Article  Google Scholar 

  • Hao J, Orlin JB (1994) A faster algorithm for finding the minimum cut in a directed graph. J Algorithms 17(3):424–446

    Article  MathSciNet  MATH  Google Scholar 

  • Jiang B, Claramunt C (2004) Topological analysis of urban street networks. Environ Plan 31(1):151–162

    Article  Google Scholar 

  • Li D, Fu B, Wang Y, Lu G, Berezin Y, Stanley HE, Havlin S (2015) Percolation transition in dynamical traffic network with evolving critical bottlenecks. Proc Nat Acad Sci 112(3):669–672

    Article  Google Scholar 

  • Louf R, Barthelemy M (2014) A typology of street patterns. J R Soc Interface 11(101):20140924

    Article  Google Scholar 

  • Meerow S, Newell JP, Stults M (2016) Defining urban resilience: a review. Landsc Urban Plan 147:38–49

    Article  Google Scholar 

  • Porta S, Crucitti P, Latora V (2006) The network analysis of urban streets: a dual approach. Physica A 369(2):853–866

    Article  MATH  Google Scholar 

  • Stanojevic R, Abbar S, Thirumuruganathan S, Morales GDF, Chawla S, Filali F, Aleimat A (2018) Road network fusion for incremental map updates. In: To appear in the special volume of Springer’s (Lecture notes in cartography and geoinformation (LBS 2018.)). Zurich, Switzerland

  • Stoer M, Wagner F (1997) A simple min-cut algorithm. J ACM 44(4):585–591

    Article  MathSciNet  MATH  Google Scholar 

  • Wang J (2015) Resilience of self-organised and top-down planned cities. A case study on london and beijing street networks. PLoS ONE 10(12):1–20

    Google Scholar 

  • Yang D, Zhang D, Chen L, Qu B (2015) Nationtelescope: monitoring and visualizing large-scale collective behavior in LBSNs. J Netw Comput Appl 55:170–180

    Article  Google Scholar 

  • Yang D, Zhang D, Qu B (2016) Participatory cultural mapping based on collective behavior data in location-based social networks. ACM Trans Intell Syst Technol 7(3):30

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Qatar Computing Research Institute - HBKU, PoBox. 5825, Doha, Qatar

    Sofiane Abbar & Tahar Zanouda

  2. Internet Interdisciplinary Institute (IN3-UOC), Universitat Oberta de Catalunya, 08018, Barcelona, Catalonia, Spain

    Javier Borge-Holthoefer

Authors
  1. Sofiane Abbar
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  2. Tahar Zanouda
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  3. Javier Borge-Holthoefer
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Corresponding author

Correspondence to Sofiane Abbar.

Additional information

Responsible editor: Katharina Morik, Fosca Giannotti, Marta Gonzalez, Ioannis Katakis.

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Abbar, S., Zanouda, T. & Borge-Holthoefer, J. Structural robustness and service reachability in urban settings. Data Min Knowl Disc 32, 830–847 (2018). https://doi.org/10.1007/s10618-018-0551-4

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  • DOI: https://doi.org/10.1007/s10618-018-0551-4

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