IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v36y2009i3p450-465.html
   My bibliography  Save this article

Street Centrality and Densities of Retail and Services in Bologna, Italy

Author

Listed:
  • Sergio Porta
  • Emanuele Strano
  • Valentino Iacoviello
  • Roberto Messora
  • Vito Latora
  • Alessio Cardillo
  • Fahui Wang

    (Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA)

  • Salvatore Scellato

    (Scuola Superiore di Catania, Via San Nullo, 5/i, 95123 Catania, Italy)

Abstract

This paper examines the relationship between street centrality and densities of commercial and service activities in the city of Bologna, northern Italy. Street centrality is calibrated in a multiple centrality assessment model composed of multiple measures such as closeness, betweenness, and straightness. Kernel density estimation is used to transform datasets of centrality and activities to one scale unit for analysis of correlation between them. Results indicate that retail and service activities in Bologna tend to concentrate in areas with better centralities. The distribution of these activities correlates highly with the global betweenness of the street network, and also, to a slightly lesser extent, with the global closeness. This confirms the hypothesis that street centrality plays a crucial role in shaping the formation of urban structure and land uses.

Suggested Citation

  • Sergio Porta & Emanuele Strano & Valentino Iacoviello & Roberto Messora & Vito Latora & Alessio Cardillo & Fahui Wang & Salvatore Scellato, 2009. "Street Centrality and Densities of Retail and Services in Bologna, Italy," Environment and Planning B, , vol. 36(3), pages 450-465, June.
    • Handle: RePEc:sae:envirb:v:36:y:2009:i:3:p:450-465
    DOI: 10.1068/b34098
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1068/b34098
    Download Restriction: no
    File URL: https://libkey.io/10.1068/b34098?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Cao, Ricardo & Cuevas, Antonio & Gonzalez Manteiga, Wensceslao, 1994. "A comparative study of several smoothing methods in density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 17(2), pages 153-176, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Delso, Javier & Martín, Belén & Ortega, Emilio, 2018. "A new procedure using network analysis and kernel density estimations to evaluate the effect of urban configurations on pedestrian mobility. The case study of Vitoria –Gasteiz," Journal of Transport Geography, Elsevier, vol. 67(C), pages 61-72.
    2. Huang, Jie & Levinson, David M., 2015. "Circuity in urban transit networks," Journal of Transport Geography, Elsevier, vol. 48(C), pages 145-153.
    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. Scoppa, Martin & Bawazir, Khawla & Alawadi, Khaled, 2019. "Straddling boundaries in superblock cities. Assessing local and global network connectivity using cases from Abu Dhabi, UAE," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 770-782.
    6. Reigadinha, Tânia & Godinho, Pedro & Dias, Joana, 2017. "Portuguese food retailers – Exploring three classic theories of retail location," Journal of Retailing and Consumer Services, Elsevier, vol. 34(C), pages 102-116.
    7. Wang, Xuesong & You, Shikai & Wang, Ling, 2017. "Classifying road network patterns using multinomial logit model," Journal of Transport Geography, Elsevier, vol. 58(C), pages 104-112.
    8. 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.
    9. Arranz-López, Aldo & Soria-Lara, Julio A & López-Escolano, Carlos & Pueyo Campos, Ángel, 2017. "Retail Mobility Environments: A methodological framework for integrating retail activity and non-motorised accessibility in Zaragoza, Spain," Journal of Transport Geography, Elsevier, vol. 58(C), pages 92-103.
    10. Zhang, Tong & Zeng, Zhe & Jia, Tao & Li, Jing, 2016. "Examining the amenability of urban street networks for locating facilities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 469-479.
    11. Han Yue & Xinyan Zhu, 2019. "Exploring the Relationship between Urban Vitality and Street Centrality Based on Social Network Review Data in Wuhan, China," Sustainability, MDPI, vol. 11(16), pages 1-19, August.
    12. Aldo Arranz-López & Julio A. Soria-Lara & Carlos López-Escolano & Ángel Pueyo Campos, 2017. "Making ‘Retail Mobility Environments’ visible for collaborative transport planning," Journal of Maps, Taylor & Francis Journals, vol. 13(1), pages 90-100, January.
    13. Wang, Fahui & Antipova, Anzhelika & Porta, Sergio, 2011. "Street centrality and land use intensity in Baton Rouge, Louisiana," Journal of Transport Geography, Elsevier, vol. 19(2), pages 285-293.
    14. Hu, Xinlei & Huang, Jie & Shi, Feng, 2019. "Circuity in China's high-speed-rail network," Journal of Transport Geography, Elsevier, vol. 80(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    2. Chacón, José E. & Fernández Serrano, Javier, 2024. "Bayesian taut splines for estimating the number of modes," Computational Statistics & Data Analysis, Elsevier, vol. 196(C).
    3. Barbeito, Inés & Cao, Ricardo, 2016. "Smoothed stationary bootstrap bandwidth selection for density estimation with dependent data," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 130-147.
    4. Adriano Z. Zambom & Ronaldo Dias, 2013. "A Review of Kernel Density Estimation with Applications to Econometrics," International Econometric Review (IER), Econometric Research Association, vol. 5(1), pages 20-42, April.
    5. Matthew A. Masten & Alexandre Poirier, 2020. "Inference on breakdown frontiers," Quantitative Economics, Econometric Society, vol. 11(1), pages 41-111, January.
    6. del Rio, Alejandro Quintela, 1996. "Comparison of bandwidth selectors in nonparametric regression under dependence," Computational Statistics & Data Analysis, Elsevier, vol. 21(5), pages 563-580, May.
    7. Eibelshäuser, Steffen & Wilhelm, Sascha, 2017. "Markets Take Breaks: Dynamic Price Competition with Opening Hours," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168247, Verein für Socialpolitik / German Economic Association.
    8. García-Portugués, Eduardo & Crujeiras, Rosa M. & González-Manteiga, Wenceslao, 2013. "Kernel density estimation for directional–linear data," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 152-175.
    9. Heiler, Siegfried & Feng, Yuanhua, 1995. "A simple root n bandwidth selector for nonparametric regression," Discussion Papers, Series II 286, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".
    10. T. Sclocco & M. Marzio, 2001. "A note on kernel density estimation for non-negative random variables," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 10(1), pages 67-79, January.
    11. Miśkiewicz, Janusz, 2016. "Improving quality of sample entropy estimation for continuous distribution probability functions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 473-485.
    12. Jos'e E. Figueroa-L'opez & Cheng Li, 2016. "Optimal Kernel Estimation of Spot Volatility of Stochastic Differential Equations," Papers 1612.04507, arXiv.org.
    13. Wen-Ching Wang, 2018. "Setting up evaluate indicators for slope control engineering based on spatial clustering analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(2), pages 921-939, September.
    14. Moreira, C. & Van Keilegom, I., 2013. "Bandwidth selection for kernel density estimation with doubly truncated data," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 107-123.
    15. J. M. Vilar & R. Cao & M. C. Ausin & C. Gonzalez-Fragueiro, 2009. "Nonparametric analysis of aggregate loss models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(2), pages 149-166.
    16. Emili Tortosa-Ausina, 2000. "Inefficient banks or inefficient assets," Working Papers 0005, Departament Empresa, Universitat Autònoma de Barcelona, revised Dec 2000.
    17. Gonzalez-Manteiga, W. & Sanchez-Sellero, C. & Wand, M. P., 1996. "Accuracy of binned kernel functional approximations," Computational Statistics & Data Analysis, Elsevier, vol. 22(1), pages 1-16, June.
    18. J. S. Marron & S. S. Chung, 2001. "Presentation of smoothers: the family approach," Computational Statistics, Springer, vol. 16(1), pages 195-207, March.
    19. Filippone, Maurizio & Sanguinetti, Guido, 2011. "Approximate inference of the bandwidth in multivariate kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3104-3122, December.
    20. Sohn, Keemin & Kim, Daehyun, 2010. "Zonal centrality measures and the neighborhood effect," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(9), pages 733-743, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:envirb:v:36:y:2009:i:3:p:450-465. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.