Exploring the urban form of Qom (Iran) at the local scale : movement-activity pattern versus network centrality

Zamani, Vahid and Ghalehnoee, Mahmoud and Mohammadi, Mahmoud; (2022) Exploring the urban form of Qom (Iran) at the local scale : movement-activity pattern versus network centrality. In: Annual Conference Proceedings of the XXVIII International Seminar on Urban Form. University of Strathclyde Publishing, Glasgow, pp. 794-801. ISBN 9781914241161

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Structural features of the street network - as the intersection of two systems of movement and activity - strongly influence the pedestrian/motorized traffic flow and the distribution of commercial/service (CS) activities throughout large cities. Qom, a large old city in central Iran with a diverse typology of urban form in the historic, middle and peripheral areas, encounters a serious conflict between the functions of the two systems following recently road constructions and increasing automobile dependence. To take a step towards addressing the problem, this paper aims at in-depth explaining the relationship between the street network centrality and the patterns of street layout and CS activity locations in Qom at the local scale of the street network micro-structure, through adopting a quantitative approach by utilising an integration of spatial and statistical tools/techniques. Firstly, ten morphologically-homogenous high-centralised superblocks - called local morphological zones (LMZs) – which are identified through modelling street network centrality index of local closeness (LCNC) using Multiple Centrality Assessment (MCA), are classified employing hierarchical cluster analysis, regarding the street layout and CS activity location patterns by analysing topological/geometrical indicators. Secondly, the relationship between LCNC and the patterns are investigated using Pearson’s correlation coefficient and Lorenz curve. The findings reveal that the LMZs’ patterns of CS activity locations can be obviously classified into two clusters (the older and newer LMZs mostly stand in two different clusters) while regarding the street layout, the LMZ within the city’s historic core stands alone in a cluster; moreover, average LCNC of the LMZs have no significant correlation with indicators of activity location in comparison with the significant correlation with three indicators of street layout pattern, while at the micro-level of the LMZs, CS activity distribution alongside street network segments has more relationship with LCNC compared with the street nodes.

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