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The Signature of Organic Urban Growth

Degree Distribution Patterns of the City’s Street Network Structure
  • Leonard NilssonEmail author
  • Jorge Gil
Chapter
Part of the Modeling and Simulation in Science, Engineering and Technology book series (MSSET)

Abstract

Cities are complex, perhaps one of the most complex kinds of structure created by humans. Some cities have been planned to a large extent while others have grown organically. The outcome of planning and growth processes is the diverse morphological built form and street patterns observed in cities today. So far, in urban planning and history research, cities are classified as planned or organic, largely based on the visual assessment of their urban morphology. To understand cities and their characteristics, it is necessary to develop methods that quantitatively describe these characteristics and enable objective comparisons between cities. Using graph representations of the street network of cities, it is possible to calculate measures that seem to exhibit patterns with scale-free properties (Jiang in Phys A Stat Mech Appl 384(2):647–655, 2007). And thanks to progress in the field of complexity studies, it is also possible to test if and to what extent the distribution of a measure in a collection of elements fits a power law distribution (Clauset et al. in SIAM Rev 51:43, 2009). In this chapter, we show that the degree distribution of a city’s street network seems to fit a power law distribution for cities that are considered to have mostly grown organically. On the other hand, cities that are planned to a large extent do not exhibit such a good fit. This result is relevant since a power law distribution is a signature of multiplicative growth processes. Furthermore, the result of the quantitative classification method correlates well with the results of earlier qualitative morphological classifications of cities.

Keywords

Street networks Spatial morphology Growth patterns Power law Degree distribution 

References

  1. Barabasi, A. L. & Albert, R. (1999), Emergence of scaling in random networks. Science, 286(5439), 509–512.Google Scholar
  2. Barthélemy, M. (2011), Spatial networks. Physics Reports, 499(1–3), 1–101.Google Scholar
  3. Batty, M. (2013), A new science of cities.Google Scholar
  4. Batty, M. & Longley, P. (1994), Fractal Cities: A Geometry of Form and Function (Vol. 1). San Francisco and London: Academic Press.Google Scholar
  5. Beaverstock JV, Smith RG, Taylor PJ (1999) A roster of world cities. Cities 16:445–458.  https://doi.org/10.1016/s0264-2751(99)00042-6.
  6. Bettencourt LMA, Lobo J, Strumsky D, West GB (2010) Urban Scaling and Its Deviations: Revealing the Structure of Wealth, Innovation and Crime across Cities. PLoS ONE 5:e13541.  https://doi.org/10.1371/journal.pone.0013541.
  7. Clark C (1951) Urban Population Densities. Journal of the Royal Statistical Society Series A (General) 114:490–496.  https://doi.org/10.2307/2981088.
  8. Clauset, A. (2007), Fitting a power-law distribution. http://tuvalu.santafe.edu/~aaronc/powerlaws/
  9. Clauset, A., Rohilla Shalizi, C. & Newman, M. E. J. (2009), Power-law distributions in empirical data. 51, 43.Google Scholar
  10. Crucitti, P., Latora, V. & Porta, S. (2006a), Centrality in networks of urban streets. Chaos, 16(1), 015113.Google Scholar
  11. Crucitti, P., Latora, V. & Porta, S. (2006b), Centrality Measures in Spatial Networks of Urban Streets.Google Scholar
  12. Dalton N (2001) Fractional configurational analysis and a solution to the Manhattan problem. In: Peponis J, Wineman J, Bafna S (eds) Proceedings of the 3rd International Space Syntax Symposium. Georgia Institute of Technology, Atlanta, Georgia, USA, pp 26.1–26.13.Google Scholar
  13. Dalton N, Peponis J, Conroy Dalton R (2003) To tame a TIGER one has to know its nature: extending weighted angular integration analysis to the description of GIS road-centerline data for large scale urban analysis. In: Hanson J (ed) Proceedings of the 4th International Space Syntax Symposium. London, UK, pp 65.1–65.10.Google Scholar
  14. Dhanani, A., et al. (2012), FROM THE AXIAL LINE TO THE WALKED LINE: evaluating the utility of commercial and user‐generated street network datasets in space syntax analysis. Paper presented at the Eighth International Space Syntax Symposium, Santiago de Chile.Google Scholar
  15. Figueiredo, L. & Amorim, L. (2005), Continuity lines in the axial system.Google Scholar
  16. Gil J (2017) Street network analysis “edge effects”: Examining the sensitivity of centrality measures to boundary conditions. Environment and Planning B: Urban Analytics and City Science 44:819–836.  https://doi.org/10.1177/0265813516650678.
  17. Gil J, Beirão JN, Montenegro N, Duarte JP (2012) On the discovery of urban typologies: data mining the many dimensions of urban form. Urban Morphology 16:27–40.Google Scholar
  18. Gillespie, C. S. (2015), Fitting Heavy Tailed Distributions: The poweRlaw Package. Journal of Statistical Software, 64(2).Google Scholar
  19. Harris CD (1943) A Functional Classification of Cities in the United States. Geographical Review 33:86–99.  https://doi.org/10.2307/210620.
  20. Haklay, M. (2010), How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets. Environment and Planning B-Planning & Design, 37(4), 682–703.Google Scholar
  21. Hillier, B. (1996), Space is the Machine: a configurational theory of architecture. Cambridge: Cambridge University Press.Google Scholar
  22. Hillier, B. (1999), The hidden geometry of deformed grids: or, why space syntax works, when it looks as though it shouldn’t. Environment and Planning B-Planning & Design, 26(2), 169–191.Google Scholar
  23. Hillier, B. & Hanson, J. (1984), The social logic of space. Cambridge: Cambridge University Press.Google Scholar
  24. Jiang, B. (2007), A topological pattern of urban street networks: Universality and peculiarity. Physica A: Statistical Mechanics and its Applications, 384(2), 647–655.Google Scholar
  25. Jiang, B. (2009), Ranking spaces for predicting human movement in an urban environment. International Journal of Geographical Information Science, 23(7), 823–837.Google Scholar
  26. Jiang, B. (2015). Axwoman 6.3: An ArcGIS extension for urban morphological analysis. University of Gävle, Sweden. Retrieved from http://fromto.hig.se/~bjg/Axwoman/.
  27. Jiang, B. & Claramunt, C. (2004), A structural approach to the model generalization of an urban street network. Geoinformatica, 8(2), 157–171.Google Scholar
  28. Jiang, B., Claramunt, C. & Batty, M. (1999), Geometric accessibility and geographic information: extending desktop GIS to space syntax. Computers, Environment and Urban Systems, 23, 127–146.Google Scholar
  29. Jiang, B. & Liu, C. (2009), Street-based topological representations and analyses for predicting traffic flow in GIS. International Journal of Geographical Information Science, 23(9), 1119–1137.Google Scholar
  30. Jiang, B., Zhao, S. & Yin, J. (2008), Self-organized natural roads for predicting traffic flow: a sensitivity study. Journal of Statistical Mechanics: Theory and Experiment, 2008(07), P07008.Google Scholar
  31. Kolovou I, Gil J, Karimi K, Law S, Versluis L (2017) Road Centre Line Simplification Principles for Angular Segment Analysis. In: Proceedings of the 11th Space Syntax Symposium. Universidade de Lisboa, IST, Lisbon, Portugal, pp 163.1–163.16.Google Scholar
  32. Kostof, S. K. & Tobias, R. (1991), The city shaped: urban patterns and meanings through history. London: Thames & Hudson.Google Scholar
  33. Krenz K (2017) Employing Volunteered Geographic Information in Space Syntax Analysis. In: Proceedings of the 11th Space Syntax Symposium. Universidade de Lisboa, IST, Lisbon, Portugal, pp 150.1–150.26.Google Scholar
  34. Kuffner, J. J. L., S.M. (2009). Space-filling trees, Carnegie Mellon University.Google Scholar
  35. Louf R, Barthelemy M (2014) A typology of street patterns. Journal of The Royal Society Interface 11:20140924.  https://doi.org/10.1098/rsif.2014.0924.
  36. Nelson HJ (1955) A Service Classification of American Cities. Economic Geography 31:189–210.  https://doi.org/10.2307/142045.
  37. Marshall, S. (2015), Line structure representation for road network analysis. Journal of Transport and Land Use, 8(2).Google Scholar
  38. Porta, S., Crucitti, P. & Latora, V. (2006a), The network analysis of urban streets: A dual approach. Physica a-Statistical Mechanics and Its Applications, 369(2), 853–866.Google Scholar
  39. Porta, S., Crucitti, P. & Latora, V. (2006b), The Network Analysis of Urban Streets: A Primal Approach. Environment and Planning B: Planning and Design, 33(5), 705–725.Google Scholar
  40. R Core Team (2016), R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
  41. Shpuza, E. (2014), Allometry in the Syntax of Street Networks: Evolution of Adriatic and Ionian Coastal Cities 1800–2010. Environment and Planning B: Planning and Design, 41(3), 450–471.Google Scholar
  42. Shpuza, E. (2017), Relative size measures of urban form based on allometric subtraction. Environment and Planning B: Urban Analytics and City Science, 44(1), 141–159.Google Scholar
  43. Thomson, R. C. (2003), Bending the axial line: Smoothly continuous road centre-line segments as a basis for road network analysis.Google Scholar
  44. Turner, A. (2007), From axial to road-centre lines: a new representation for space syntax and a new model of route choice for transport network analysis. Environment and Planning B-Planning & Design, 34(3), 539–555.Google Scholar
  45. Volchenkov, D. & Blanchard, P. (2008), Scaling and universality in city space syntax: Between Zipf and Matthew. Physica a-Statistical Mechanics and Its Applications, 387(10), 2353–2364.Google Scholar
  46. 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), e0141736.Google Scholar
  47. Watts, D. J. (2004), The “new” science of networks. Annual Review of Sociology, 30, 243–270.Google Scholar
  48. Watts, D. J. & Strogatz, S. H. (1998), Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442.Google Scholar
  49. Webster, C. J. (1995), Urban Morphological Fingerprints. Environ Plann B Plann Des 22:279–297.  https://doi.org/10.1068/b220279.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Architecture and Civil EngineeringChalmers University of TechnologyGothenburgSweden

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