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Street Centrality and the Location of Economic Activities in Barcelona

Author

Listed:
  • Sergio Porta
  • Vito Latora
  • Fahui Wang
  • Salvador Rueda
  • Emanuele Strano
  • Salvatore Scellato
  • Alessio Cardillo
  • Eugenio Belli
  • Francisco CÃ rdenas
  • Berta Cormenzana
  • Laura Latora

Abstract

The paper examines the geography of three street centrality indices and their correlations with various types of economic activities in Barcelona, Spain. The focus is on what type of street centrality (closeness, betweenness and straightness) is more closely associated with which type of economic activity (primary and secondary). Centralities are calculated purely on the street network by using a multiple centrality assessment model, and a kernel density estimation method is applied to both street centralities and economic activities to permit correlation analysis between them. Results indicate that street centralities are correlated with the location of economic activities and that the correlations are higher with secondary than primary activities. The research suggests that, in urban planning, central urban arterials should be conceived as the cores, not the borders, of neighbourhoods.

Suggested Citation

  • Sergio Porta & Vito Latora & Fahui Wang & Salvador Rueda & Emanuele Strano & Salvatore Scellato & Alessio Cardillo & Eugenio Belli & Francisco CÃ rdenas & Berta Cormenzana & Laura Latora, 2012. "Street Centrality and the Location of Economic Activities in Barcelona," Urban Studies, Urban Studies Journal Limited, vol. 49(7), pages 1471-1488, May.
    • Handle: RePEc:sae:urbstu:v:49:y:2012:i:7:p:1471-1488
    DOI: 10.1177/0042098011422570
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    References listed on IDEAS

    as
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    Cited by:

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    2. Boeing, Geoff, 2019. "Street Network Models and Measures for Every U.S. City, County, Urbanized Area, Census Tract, and Zillow-Defined Neighborhood," SocArXiv 7fxjz, Center for Open Science.
    3. Ding, Rui & Ujang, Norsidah & Hamid, Hussain bin & Manan, Mohd Shahrudin Abd & Li, Rong & Wu, Jianjun, 2017. "Heuristic urban transportation network design method, a multilayer coevolution approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 71-83.
    4. Ding, Rui & Ujang, Norsidah & Hamid, Hussain bin & Manan, Mohd Shahrudin Abd & He, Yuou & Li, Rong & Wu, Jianjun, 2018. "Detecting the urban traffic network structure dynamics through the growth and analysis of multi-layer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 800-817.
    5. Zhigang Han & Caihui Cui & Changhong Miao & Haiying Wang & Xiang Chen, 2019. "Identifying Spatial Patterns of Retail Stores in Road Network Structure," Sustainability, MDPI, vol. 11(17), pages 1-20, August.
    6. Agryzkov, Taras & Tortosa, Leandro & Vicent, Jose F., 2019. "A variant of the current flow betweenness centrality and its application in urban networks," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 600-615.
    7. Nadia M. Viljoen & Johan W. Joubert, 2018. "The Road most Travelled: The Impact of Urban Road Infrastructure on Supply Chain Network Vulnerability," Networks and Spatial Economics, Springer, vol. 18(1), pages 85-113, March.
    8. César Ducruet & Laurent Beauguitte, 2014. "Spatial Science and Network Science: Review and Outcomes of a Complex Relationship," Networks and Spatial Economics, Springer, vol. 14(3), pages 297-316, December.

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