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An Urban Morphogenesis Model Capturing Interactions Between Networks and Territories

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The Mathematics of Urban Morphology

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.

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References

  • Anas, A., Arnott, R., and Small, K. A. (1998). Urban spatial structure. Journal of Economic Literature, 36(3): pp. 1426–1464.

    Google Scholar 

  • 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 

  • 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 

  • 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 

  • Batty, M. and Longley, P. A. (1994). Fractal cities: a geometry of form and function. Academic Press.

    Google Scholar 

  • Boeing, G. (2017a). A multi-scale analysis of 27,000 urban street networks. arXiv preprint arXiv:1705.02198.

  • Boeing, G. (2017b). Osmnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65:126–139.

    Article  Google Scholar 

  • 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 

  • Chen, Y. (2016). Normalizing and Classifying Shape Indexes of Cities by Ideas from Fractals. arXiv preprint arXiv:1608.08839.

  • Chodrow, P. S. (2017). Structure and information in spatial segregation. Proceedings of the National Academy of Sciences, 114(44):11591–11596.

    Article  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Crucitti, P., Latora, V., and Porta, S. (2006). Centrality measures in spatial networks of urban streets. Physical Review E, 73(3):036125.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • EUROSTAT (2014). Eurostat geographical data. http://ec.europa.eu/eurostat/web/gisco.

  • Frey, R., McNeil, A. J., and Nyfeler, M. (2001). Copulas and credit models. Risk, 10(111114.10).

    Google Scholar 

  • Girres, J.-F. and Touya, G. (2010). Quality assessment of the french openstreetmap dataset. Transactions in GIS, 14(4):435–459.

    Article  Google Scholar 

  • 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.

  • 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 

  • Haggett, P. and Chorley, R. J. (1970). Network analysis in geography. St. Martin’s Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hansen, W. G. (1959). How accessibility shapes land use. Journal of the American Institute of planners, 25(2):73–76.

    Article  MathSciNet  Google Scholar 

  • Harris, P., Brunsdon, C., and Charlton, M. (2011). Geographically weighted principal components analysis. International Journal of Geographical Information Science, 25(10):1717–1736.

    Article  Google Scholar 

  • Hillier, B. and Hanson, J. (1989). The social logic of space. Cambridge university press.

    Google Scholar 

  • 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 

  • Josselin, D., Labatut, V., and Mitsche, D. (2016). Straightness of rectilinear vs. radio-concentric networks: modeling simulation and comparison. arXiv preprint arXiv:1609.05719.

  • Lagesse, C. (2015). Read Cities through their Lines. Methodology to characterize spatial graphs. arXiv preprint arXiv:1512.01268.

  • 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 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Louf, R. and Barthelemy, M. (2014). A typology of street patterns. Journal of The Royal Society Interface, 11(101):20140924.

    Article  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • 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.

  • Moudon, A. V. (1997). Urban morphology as an emerging interdisciplinary field. Urban morphology, 1(1):3–10.

    Google Scholar 

  • OpenStreetMap (2012). Openstreetmap. http://www.openstreetmap.org.

  • Osmosis (2016). Osmosis. http://wiki.openstreetmap.org/wiki/Osmosis.

  • Páez, A. and Scott, D. M. (2005). Spatial statistics for urban analysis: a review of techniques with examples. GeoJournal, 61(1):53–67.

    Article  Google Scholar 

  • Paquot, T. (2010). L’abc de l’urbanisme. IAU - UPEC.

    Google Scholar 

  • Pumain, D. (2011). Systems of cities and levels of organisation. In Morphogenesis, pages 225–249. Springer.

    Google Scholar 

  • 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 

  • Raimbault, J. (2017a). Identification de causalités dans des données spatio-temporelles. In Spatial Analysis and GEOmatics 2017.

    Google Scholar 

  • Raimbault, J. (2017b). Modeling the co-evolution of urban form and transportation networks. In Conference on Complex Systems 2017.

    Google Scholar 

  • Raimbault, J. (2018a). Calibration of a Density-based Model of Urban Morphogenesis. forthcoming in PlOS ONE. arXiv:1708.06743.

  • Raimbault, J. (2018b). Modeling the co-evolution of cities and networks. arXiv preprint arXiv:1804.09430.

  • Raimbault, J. (2018c). Multi-modeling the morphogenesis of transportation networks. In Artificial Life Conference Proceedings, pages 382–383. MIT Press.

    Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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

  • Schmitt, C. (2014). Modélisation de la dynamique des systèmes de peuplement: de SimpopLocal à SimpopNet. PhD thesis, Paris 1.

    Google Scholar 

  • Schwarz, N. (2010). Urban form revisited—selecting indicators for characterising european cities. Landscape and Urban Planning, 96(1):29 – 47.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Trépanier, M., Morency, C., and Agard, B. (2009). Calculation of transit performance measures using smartcard data. Journal of Public Transportation, 12(1):5.

    Article  Google Scholar 

  • Tsai, Y.-H. (2005). Quantifying urban form: compactness versus’ sprawl’. Urban studies, 42(1):141–161.

    Article  Google Scholar 

  • Watson, M. W. (1993). Measures of fit for calibrated models. Journal of Political Economy, 101(6):1011–1041.

    Article  Google Scholar 

  • Wegener, M. and Fürst, F. (2004). Land-use transport interaction: state of the art. Available at SSRN 1434678.

    Google Scholar 

  • West, G. (2017). Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Lifein Organisms, Cities, Economies, and Companies. Penguin.

    Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

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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.

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Correspondence to Juste Raimbault .

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Raimbault, J. (2019). An Urban Morphogenesis Model Capturing Interactions Between Networks and Territories. In: D'Acci, L. (eds) The Mathematics of Urban Morphology. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser, Cham. https://doi.org/10.1007/978-3-030-12381-9_17

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