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An Urban Morphogenesis Model Capturing Interactions Between Networks and Territories

  • Juste RaimbaultEmail author
Chapter
Part of the Modeling and Simulation in Science, Engineering and Technology book series (MSSET)

Abstract

Urban systems are composed of complex couplings of several components, and more particularly between the built environment and transportation networks. Their interaction is involved in the emergence of the urban form. We propose in this chapter to introduce an approach to urban morphology grasping both aspects and their interaction. We first define complementary measures and study their empirical values and their spatial correlations on European territorial systems. The behavior of indicators and correlations suggest underlying non-stationary and multi-scalar processes. We then introduce a generative model of urban growth at a mesoscopic scale. Given a fixed exogenous growth rate, population is distributed following a preferential attachment depending on a potential controlled by the local urban form (density, distance to network) and network measures (centralities and generalized accessibilities), and then diffused in space to capture urban sprawl. Network growth is included through a multi-modeling paradigm: implemented heuristics include biological network generation and gravity potential breakdown. The model is calibrated both at the first (measures) and second (correlations) order, the later capturing indirectly relations between networks and territories.

Keywords

Urban morphology Road network topology Spatial correlations Urban morphogenesis Reaction–diffusion Coevolution 

Notes

Acknowledgements

Results obtained in this paper were computed on the vo.complex-system.eu virtual organization of the European Grid Infrastructure (http://www.egi.eu). We thank the European Grid Infrastructure and its supporting National Grid Initiatives (France-Grilles in particular) for providing the technical support and infrastructure.

References

  1. Anas, A., Arnott, R., and Small, K. A. (1998). Urban spatial structure. Journal of Economic Literature, 36(3): pp. 1426–1464.Google Scholar
  2. Badariotti, D., Banos, A., and Moreno, D. (2007). Conception d’un automate cellulaire non stationnaire à base de graphe pour modéliser la structure spatiale urbaine: le modèle remus. Cybergeo: European Journal of Geography.Google Scholar
  3. Banos, A. and Genre-Grandpierre, C. (2012). Towards new metrics for urban road networks: Some preliminary evidence from agent-based simulations. In Agent-based models of geographical systems, pages 627–641. Springer.Google Scholar
  4. Batista e Silva, F., Gallego, J., and Lavalle, C. (2013). A high-resolution population grid map for europe. Journal of Maps, 9(1):16–28.Google Scholar
  5. Batty, M. and Longley, P. A. (1994). Fractal cities: a geometry of form and function. Academic Press.Google Scholar
  6. Boeing, G. (2017a). A multi-scale analysis of 27,000 urban street networks. arXiv preprint arXiv:1705.02198.
  7. Boeing, G. (2017b). Osmnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65:126–139.CrossRefGoogle Scholar
  8. Brunsdon, C., Fotheringham, A., and Charlton, M. (2002). Geographically weighted summary statistics–a framework for localised exploratory data analysis. Computers, Environment and Urban Systems, 26(6):501–524.Google Scholar
  9. Chen, Y. (2016). Normalizing and Classifying Shape Indexes of Cities by Ideas from Fractals. arXiv preprint arXiv:1608.08839.
  10. Chodrow, P. S. (2017). Structure and information in spatial segregation. Proceedings of the National Academy of Sciences, 114(44):11591–11596.MathSciNetCrossRefGoogle Scholar
  11. Cottineau, C., Hatna, E., Arcaute, E., and Batty, M. (2017). Diverse cities or the systematic paradox of urban scaling laws. Computers, environment and urban systems, 63:80–94.CrossRefGoogle Scholar
  12. Crucitti, P., Latora, V., and Porta, S. (2006). Centrality measures in spatial networks of urban streets. Physical Review E, 73(3):036125.CrossRefGoogle Scholar
  13. D’Acci, L. (2015). Mathematize urbes by humanizing them. cities as isobenefit landscapes: psycho-economical distances and personal isobenefit lines. Landscape and Urban Planning, 139:63–81.CrossRefGoogle Scholar
  14. EUROSTAT (2014). Eurostat geographical data. http://ec.europa.eu/eurostat/web/gisco.
  15. Frey, R., McNeil, A. J., and Nyfeler, M. (2001). Copulas and credit models. Risk, 10(111114.10).Google Scholar
  16. Girres, J.-F. and Touya, G. (2010). Quality assessment of the french openstreetmap dataset. Transactions in GIS, 14(4):435–459.CrossRefGoogle Scholar
  17. Gollini, I., Lu, B., Charlton, M., Brunsdon, C., and Harris, P. (2013). Gwmodel: an r package for exploring spatial heterogeneity using geographically weighted models. arXiv preprint arXiv:1306.0413.
  18. Guérois, M. and Paulus, F. (2002). Commune centre, agglomération, aire urbaine: quelle pertinence pour l’étude des villes? Cybergeo: European Journal of Geography.Google Scholar
  19. Haggett, P. and Chorley, R. J. (1970). Network analysis in geography. St. Martin’s Press.Google Scholar
  20. Haklay, M. (2010). How good is volunteered geographical information? a comparative study of openstreetmap and ordnance survey datasets. Environment and planning B: Planning and design, 37(4):682–703.CrossRefGoogle Scholar
  21. Hansen, W. G. (1959). How accessibility shapes land use. Journal of the American Institute of planners, 25(2):73–76.CrossRefGoogle Scholar
  22. Harris, P., Brunsdon, C., and Charlton, M. (2011). Geographically weighted principal components analysis. International Journal of Geographical Information Science, 25(10):1717–1736.CrossRefGoogle Scholar
  23. Hillier, B. and Hanson, J. (1989). The social logic of space. Cambridge university press.Google Scholar
  24. Josselin, D. and Ciligot-Travain, M. (2013). Revisiting the optimal center location. a spatial thinking based on robustness, sensitivity, and influence analysis. Environment and Planning B: Planning and Design, 40(5):923–941.Google Scholar
  25. Josselin, D., Labatut, V., and Mitsche, D. (2016). Straightness of rectilinear vs. radio-concentric networks: modeling simulation and comparison. arXiv preprint arXiv:1609.05719.
  26. Lagesse, C. (2015). Read Cities through their Lines. Methodology to characterize spatial graphs. arXiv preprint arXiv:1512.01268.
  27. Le Néchet, F. (2009). Quantifier l’éloignement au modèle de bussière: monocentrisme contre “acentrisme”. In Neuvièmes rencontres de Théo Quant, pages 19–p.Google Scholar
  28. Le Néchet, F. (2010). Approche multiscalaire des liens entre mobilité quotidienne, morphologie et soutenabilité des métropoles européennes: cas de Paris et de la région Rhin-Ruhr. PhD thesis, Université Paris-Est.Google Scholar
  29. Le Néchet, F. (2015). De la forme urbaine à la structure métropolitaine: une typologie de la configuration interne des densités pour les principales métropoles européennes de l’audit urbain. Cybergeo: European Journal of Geography.Google Scholar
  30. Lee, M., Barbosa, H., Youn, H., Holme, P., and Ghoshal, G. (2017). Morphology of travel routes and the organization of cities. Nature communications, 8(1):2229.CrossRefGoogle Scholar
  31. Leung, Y., Mei, C.-L., and Zhang, W.-X. (2000). Statistical tests for spatial nonstationarity based on the geographically weighted regression model. Environment and Planning A, 32(1):9–32.CrossRefGoogle Scholar
  32. Louf, R. and Barthelemy, M. (2014). A typology of street patterns. Journal of The Royal Society Interface, 11(101):20140924.CrossRefGoogle Scholar
  33. Louf, R., Jensen, P., and Barthelemy, M. (2013). Emergence of hierarchy in cost-driven growth of spatial networks. Proceedings of the National Academy of Sciences, 110(22):8824–8829.MathSciNetCrossRefGoogle Scholar
  34. Moosavi, V. (2017). Urban morphology meets deep learning: Exploring urban forms in one million cities, town and villages across the planet. arXiv preprint arXiv:1709.02939.
  35. Moudon, A. V. (1997). Urban morphology as an emerging interdisciplinary field. Urban morphology, 1(1):3–10.Google Scholar
  36. OpenStreetMap (2012). Openstreetmap. http://www.openstreetmap.org.
  37. Páez, A. and Scott, D. M. (2005). Spatial statistics for urban analysis: a review of techniques with examples. GeoJournal, 61(1):53–67.CrossRefGoogle Scholar
  38. Paquot, T. (2010). L’abc de l’urbanisme. IAU - UPEC.Google Scholar
  39. Pumain, D. (2011). Systems of cities and levels of organisation. In Morphogenesis, pages 225–249. Springer.Google Scholar
  40. Pumain, D. (2012). Urban systems dynamics, urban growth and scaling laws: The question of ergodicity. In Complexity Theories of Cities Have Come of Age, pages 91–103. Springer.Google Scholar
  41. Raimbault, J. (2017a). Identification de causalités dans des données spatio-temporelles. In Spatial Analysis and GEOmatics 2017.Google Scholar
  42. Raimbault, J. (2017b). Modeling the co-evolution of urban form and transportation networks. In Conference on Complex Systems 2017.Google Scholar
  43. Raimbault, J. (2018a). Calibration of a Density-based Model of Urban Morphogenesis. forthcoming in PlOS ONE. arXiv:1708.06743.
  44. Raimbault, J. (2018b). Modeling the co-evolution of cities and networks. arXiv preprint arXiv:1804.09430.
  45. Raimbault, J. (2018c). Multi-modeling the morphogenesis of transportation networks. In Artificial Life Conference Proceedings, pages 382–383. MIT Press.Google Scholar
  46. Raimbault, J., Banos, A., and Doursat, R. (2014). A hybrid network/grid model of urban morphogenesis and optimization. In 4th International Conference on Complex Systems and Applications, pages 51–60.Google Scholar
  47. Reuillon, R., Leclaire, M., and Rey-Coyrehourcq, S. (2013). Openmole, a workflow engine specifically tailored for the distributed exploration of simulation models. Future Generation Computer Systems, 29(8):1981–1990.CrossRefGoogle Scholar
  48. Rui, Y. and Ban, Y. (2014). Exploring the relationship between street centrality and land use in stockholm. International Journal of Geographical Information Science, 28(7):1425–1438.  https://doi.org/10.1080/13658816.2014.893347
  49. Schmitt, C. (2014). Modélisation de la dynamique des systèmes de peuplement: de SimpopLocal à SimpopNet. PhD thesis, Paris 1.Google Scholar
  50. Schwarz, N. (2010). Urban form revisited—selecting indicators for characterising european cities. Landscape and Urban Planning, 96(1):29 – 47.Google Scholar
  51. Stevens, F. R., Gaughan, A. E., Linard, C., and Tatem, A. J. (2015). Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PLoS ONE, 10(2):1–22.CrossRefGoogle Scholar
  52. Tero, A., Takagi, S., Saigusa, T., Ito, K., Bebber, D. P., Fricker, M. D., Yumiki, K., Kobayashi, R., and Nakagaki, T. (2010). Rules for biologically inspired adaptive network design. Science, 327(5964):439–442.MathSciNetCrossRefGoogle Scholar
  53. Trépanier, M., Morency, C., and Agard, B. (2009). Calculation of transit performance measures using smartcard data. Journal of Public Transportation, 12(1):5.CrossRefGoogle Scholar
  54. Tsai, Y.-H. (2005). Quantifying urban form: compactness versus’ sprawl’. Urban studies, 42(1):141–161.CrossRefGoogle Scholar
  55. Watson, M. W. (1993). Measures of fit for calibrated models. Journal of Political Economy, 101(6):1011–1041.CrossRefGoogle Scholar
  56. Wegener, M. and Fürst, F. (2004). Land-use transport interaction: state of the art. Available at SSRN 1434678.Google Scholar
  57. West, G. (2017). Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Lifein Organisms, Cities, Economies, and Companies. Penguin.Google Scholar
  58. Zhang, T. and Zhou, B. (2014). Test for the first-order stationarity for spatial point processes in arbitrary regions. Journal of agricultural, biological, and environmental statistics, 19(4):387–404.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.UPS CNRS 3611 ISC-PIFParisFrance
  2. 2.UMR CNRS 8504 Géographie-citésParisFrance

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