Deconstructing laws of accessibility and facility distribution in cities

Interplay between facilities and population in cities is studied and a universal function is found to model optimal accessibility. The era of the automobile has seriously degraded the quality of urban life through costly travel and visible environmental effects. A new urban planning paradigm must be at the heart of our road map for the years to come, the one where, within minutes, inhabitants can access their basic living needs by bike or by foot. In this work, we present novel insights of the interplay between the distributions of facilities and population that maximize accessibility over the existing road networks. Results in six cities reveal that travel costs could be reduced in half through redistributing facilities. In the optimal scenario, the average travel distance can be modeled as a functional form of the number of facilities and the population density. As an application of this finding, it is possible to estimate the number of facilities needed for reaching a desired average travel distance given the population distribution in a city.

[1]  Hernán D. Rozenfeld,et al.  The Area and Population of Cities: New Insights from a Different Perspective on Cities , 2009, 1001.5289.

[2]  M. Barthelemy,et al.  From mobile phone data to the spatial structure of cities , 2014, Scientific Reports.

[3]  Marta C. González,et al.  A data science framework for planning the growth of bicycle infrastructures , 2020 .

[4]  Dong Wei,et al.  Encapsulating Urban Traffic Rhythms into Road Networks , 2014, Scientific Reports.

[5]  David Dohan,et al.  K-median Algorithms: Theory in Practice , 2015 .

[6]  Marta C. González,et al.  Measuring the Impacts of Economic Well Being in Commuting Networks - A Case Study of Bogota, Colombia , 2017 .

[7]  M. Barthelemy,et al.  A typology of street patterns , 2014, Journal of The Royal Society Interface.

[8]  Daniele Quercia,et al.  Mining Urban Deprivation from Foursquare: Implicit Crowdsourcing of City Land Use , 2014, IEEE Pervasive Computing.

[9]  Jordan Frith,et al.  Communicating Through Location: The Understood Meaning of the Foursquare Check-In , 2014, J. Comput. Mediat. Commun..

[10]  FrithJordan.  Communicating Through Location , 2014 .

[11]  Sofiane Abbar,et al.  Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach , 2019, Comput. Environ. Urban Syst..

[12]  E. Mills,et al.  Urbanization and Urban Problems , 1979 .

[13]  António Pais Antunes,et al.  An Accessibility–Maximization Approach to Road Network Planning , 2003 .

[14]  M. Barthelemy,et al.  Modeling the polycentric transition of cities. , 2013, Physical review letters.

[15]  Jiang Zhang,et al.  Simple spatial scaling rules behind complex cities , 2017, Nature Communications.

[16]  E. Glaeser.  Cities, Productivity, and Quality of Life , 2011, Science.

[17]  D. Levinson.  Network Structure and City Size , 2012, PloS one.

[18]  H. Dadashpoor,et al.  Is inequality in the distribution of urban facilities inequitable? Exploring a method for identifying spatial inequity in an Iranian city , 2016 .

[19]  P. Apparicio,et al.  Measuring the Accessibility of Services and Facilities for Residents of Public Housing in Montreal , 2006 .

[20]  M. E. J. Newman,et al.  Power laws, Pareto distributions and Zipf's law , 2005 .

[21]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[22]  Michael Batty,et al.  Fractal Cities: A Geometry of Form and Function , 1996 .

[23]  Zhuolin Tao,et al.  Spatial optimization of residential care facility locations in Beijing, China: maximum equity in accessibility , 2014, International Journal of Health Geographics.

[24]  Beom Jun Kim,et al.  Scaling laws between population and facility densities , 2009, Proceedings of the National Academy of Sciences.

[25]  C. Tsinaraki,et al.  Using Foursquare place data for estimating building block use , 2017 .

[26]  Marc Barthelemy,et al.  Spatial Networks , 2010, Encyclopedia of Social Network Analysis and Mining.

[27]  Marta C. González,et al.  Understanding congested travel in urban areas , 2016, Nature Communications.

[28]  Alex Pentland,et al.  Urban characteristics attributable to density-driven tie formation , 2012, Nature Communications.

[29]  A. Ellaway,et al.  Do poorer people have poorer access to local resources and facilities? The distribution of local resources by area deprivation in Glasgow, Scotland☆ , 2008, Social science & medicine.

[30]  Mauricio G. C. Resende,et al.  A fast swap-based local search procedure for location problems , 2007, Ann. Oper. Res..

[31]  Gorjan Alagic,et al.  #p , 2019, Quantum information & computation.

[32]  M. Barthelemy,et al.  How congestion shapes cities: from mobility patterns to scaling , 2014, Scientific Reports.

[33]  Yanyan Xu,et al.  Collective benefits in traffic during mega events via the use of information technologies , 2016, Journal of The Royal Society Interface.

[34]  Nikolaos Geroliminis,et al.  City size, network structure and traffic congestion , 2013 .

[35]  W. Alonso.  Location And Land Use , 1964 .

[36]  Marta C. González,et al.  The path most traveled: Travel demand estimation using big data resources , 2015, Transportation Research Part C: Emerging Technologies.

[37]  Christa Brelsford,et al.  Toward cities without slums: Topology and the spatial evolution of neighborhoods , 2018, Science Advances.

[38]  Marc Barthelemy,et al.  From global scaling to the dynamics of individual cities , 2017, Proceedings of the National Academy of Sciences.

[39]  M. Moses,et al.  Cities as Organisms: Allometric Scaling of Urban Road Networks , 2008 .

[40]  K. Battle,et al.  A global map of travel time to cities to assess inequalities in accessibility in 2015 , 2018, Nature.

[41]  Marc Barthelemy,et al.  Emergence of hierarchy in cost-driven growth of spatial networks , 2013, Proceedings of the National Academy of Sciences.

[42]  Thomas Ertl,et al.  Semantic Enrichment of Movement Behavior with Foursquare–A Visual Analytics Approach , 2015, IEEE Transactions on Visualization and Computer Graphics.

[43]  Vito Latora,et al.  Elementary processes governing the evolution of road networks , 2012, Scientific Reports.

[44]  Luís M. A. Bettencourt,et al.  Professional diversity and the productivity of cities , 2012, Scientific Reports.

[45]  Michael T. Gastner,et al.  Optimal design of spatial distribution networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[46]  A. N. White.  Accessibility and Public Facility Location , 1979 .

[47]  C. Clark.  Urban Population Densities , 1951 .

[48]  K. Tsou,et al.  An accessibility-based integrated measure of relative spatial equity in urban public facilities , 2005 .

[49]  L. Bettencourt,et al.  A unified theory of urban living , 2010, Nature.

[50]  Zbigniew Smoreda,et al.  The scaling of human interactions with city size , 2012, Journal of The Royal Society Interface.

[51]  D. Helbing,et al.  Growth, innovation, scaling, and the pace of life in cities , 2007, Proceedings of the National Academy of Sciences.

[52]  L. Bettencourt,et al.  Heterogeneity and scale of sustainable development in cities , 2017, Proceedings of the National Academy of Sciences.

[53]  M. Barthelemy.  The statistical physics of cities , 2019, Nature Reviews Physics.

[54]  Cecilia Mascolo,et al.  Discovering Latent Patterns of Urban Cultural Interactions in WeChat for Modern City Planning , 2018, KDD.

[55]  H. Stanley,et al.  Modelling urban growth patterns , 1995, Nature.

[56]  P. Albuquerque,et al.  Urban Centrality: A Simple Index , 2013 .

[57]  L. Bettencourt,et al.  Supplementary Materials for The Origins of Scaling in Cities , 2013 .