Study Urban Street Network with Centrality Analysis Using GIS (Case Study: Central Restricted Zone in Tehran)

Document Type : Original Article

Author

Logistic, Systems Management & Disaster Department, Road, Housing & Urban Development Research Center

Abstract

The analysis of urban street centrality indices can be used for decision-making in transportation and traffic management in metropolitan cities. Centrality measurement is often used in urban network analysis related to urban development. In this paper, in the first step, centrality indices with a wide range of applications in the performance of the street network were examined, and two important known indices were used. Then, using the centrality toolbar in the geographic information system (GIS) environment, the necessary analysis was performed in the urban network of the studied area. The population of adjacent buildings was determined as the weight affecting the network elements (nodes and arcs). The results of the study were compared for the street network, medical centers, and at the scale of the district. The final result made it possible to identify a dense street network, main roads with high demand, and medical centers with more centrality. Also, the degree of correlation of these indices with each other and in relation to each district was determined. On the other hand, the possibility of clustering the entire districts was provided.

Keywords


Abbassi, M. R., & Farbod, Y. (2009). Faulting and folding in quaternary deposits of Tehran’s Piedmont (Iran). Journal of Asian Earth Sciences, 34(4), 522–531. https://doi.org/10.1016/j.jseaes.2008.08.001
Agryzkov, T., Oliver, J. L., Tortosa, L., & Vicent, J. (2014). A new betweenness centrality measure based on an algorithm for ranking the nodes of a network. Applied Mathematics and Computation, 244, 467–478. https://doi.org/10.1016/j.amc.2014.07.026
Ahmadi, M. (2017). The special committee to investigate the incident at the Plasco Building: First national report Plasco (in Persian). Tehran, Iran: Special Committee on Plasco.
Baghestani, A., & Borhani, R. (2025). An economic analysis of cordon pricing (Case study: New York City CBD). International Journal of Transportation Engineering, 13(1), 2103–2117. https://doi.org/10.22119/ijte.2025.508635.1688
Baghestani, A., Heshami, S. & Mahpour, A. 2025a. A decision tree approach for modal shift from online taxi to private car during the COVID-19 pandemic. Iranian Journal Of Science And Technology, Transactions Of Civil Engineering.
Baghestani, A., Heshami, S. & Mahpour, A. 2025b. The impact of pandemic experiences on future similar situations: analysis of latent variables and trip purposes in ride-hailing choice. Transportation Planning And Technology, 1-20.
Baghestani, A., Heshami, S., Mahpour, A., Sadeghitabar, S. & Borhani, R. 2025c. Behavioral intentions towards ride-hailing services during the pandemic: using the health belief model and the moderating role of health norms and age. Journal Of Transport & Health, 44, 102153.
Bavelas, A. (1950). Communication patterns in task-oriented groups. The Journal of the Acoustical Society of America, 22(6), 725–730. https://doi.org/10.1121/1.1906679
Crucitti, P., Latora, V., & Porta, S. (2006). Centrality in networks of urban streets. Chaos: An Interdisciplinary Journal of Nonlinear Science, 16(1). https://doi.org/10.1063/1.2150162
Derrible, S. (2012, March 1). Network centrality of metro systems. Massachusetts Institute of Technology. http://hdl.handle.net/1721.1/77611
Derrible, S. (2017). An evaluation of transportation network robustness against extreme flooding: A GIS-based approach. In TRB 96th Annual Meeting Compendium of Papers. Washington, DC: Transportation Research Board.
Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239. https://doi.org/10.1016/0378-8733(78)90021-7
Kermanshah, A., & Derrible, S. (2016). A geographical and multi-criteria vulnerability assessment of transportation networks against extreme earthquakes. Reliability Engineering & System Safety, 153, 39–49. https://doi.org/10.1016/j.ress.2016.04.007
Luo, W., & Wang, F. (2003). Measures of spatial accessibility to health care in a GIS environment: Synthesis and a case study in the Chicago region. Environment and Planning B: Planning and Design, 30(6), 865–884. https://doi.org/10.1068/b29120
Mahpour, A., Baghestani, A., & Heshami, S. (2025). Developing a Practical Pricing Framework for Airport Parking Infrastructure. Interdisciplinary Journal of Civil Engineering, 1(1), 1–8.
 
Moinfar, A. A., Mahdavian, A., & Maleky, E. (1994). Historical and instrumental data collection of Iran. Tehran, Iran: Iranian Cultural Fairs Institute.
Neutens, T. (2015). Accessibility, equity and health care: Review and research directions for transport geographers. Journal of Transport Geography, 43, 14–27. https://doi.org/10.1016/j.jtrangeo.2014.12.006
Novak, D. C., & Sullivan, J. L. (2014). A link-focused methodology for evaluating accessibility to emergency services. Decision Support Systems, 57, 309–319. https://doi.org/10.1016/j.dss.2013.09.015
Owen, S. H., & Daskin, M. S. (1998). Strategic facility location: A review. European Journal of Operational Research, 111(3), 423–447. https://doi.org/10.1016/s0377-2217(98)00186-6
Ozbil, A., Peponis, J., & Stone, B. (2011). Understanding the link between street connectivity, land use and pedestrian flows. Urban Design International, 16(2), 125–141. https://doi.org/10.1057/udi.2011.2
Peponis, J., Bafna, S., & Zhang, Z. (2008). The connectivity of streets: Reach and directional distance. Environment and Planning B: Planning and Design, 35(5), 881–901. https://doi.org/10.1068/b33088
Porta, S., Strano, E., Iacoviello, V., Messora, R., Latora, V., Cardillo, A., Wang, F., & Scellato, S. (2009). Street centrality and densities of retail and services in Bologna, Italy. Environment and Planning B: Planning and Design, 36(3), 450–465. https://doi.org/10.1068/b34098
Pouryari, M., Mahboobi Ardakani, A. R., & Hassani, N. (2021). A multi-criteria vulnerability of urban transportation systems analysis against earthquake considering topological and geographical method: A case study. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 46(3), 2147–2160. https://doi.org/10.1007/s40996-021-00699-4
Schuurman, N., Fiedler, R. S., Grzybowski, S. C., & Grund, D. (2006). Defining rational hospital catchments for non-urban areas based on travel-time. International Journal of Health Geographics, 5(1), 43. https://doi.org/10.1186/1476-072x-5-43
Sevtsuk, A., & Mekonnen, M. (2012). Urban network analysis: A new toolbox for ArcGIS. Revue Internationale de Géomatique, 22(2), 287–305. https://doi.org/10.3166/rig.22.287-305
Siewwuttanagul, S., Inohae, T., & Mishima, N. (2016). An investigation of urban gravity to develop a better understanding of the urbanization phenomenon using centrality analysis on GIS platform. Procedia Environmental Sciences, 36, 191–198. https://doi.org/10.1016/j.proenv.2016.09.032
Statistical Center of Iran (SCoI). (1996–2014). Tehran statistical yearbook. Tehran, Iran: Statistical Center of Iran.
Tehran Comprehensive Transportation & Traffic Studies Company (TCTTS). (2016). Selected data of Tehran transportation. Tehran, Iran: TCTTS.
Tehran Disaster Mitigation and Management Organization (TDMMO). (2010). Determining site magnification coefficients, extracting fragility function, and evaluating fatalities due to earthquake for Tehran buildings: Final report (Ch. 3, 4). Tehran, Iran: TDMMO.
Wang, F., Antipova, A., & Porta, S. (2011). Street centrality and land use intensity in Baton Rouge, Louisiana. Journal of Transport Geography, 19(2), 285–293. https://doi.org/10.1016/j.jtrangeo.2010.01.004
Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236. https://doi.org/10.2307/2282967