Abstract
AbstractSpace Syntax and the theory of natural movement demonstrated that spatial morphology is a primary factor influencing movement. This paper investigates to what extent spatial morphology at different scales (node, community and global network) influences the use of public space by micromobility. An axial map and corresponding network for Lisbon’s walkable and open public space, and data from e-scooters parking locations, is used as case study. Relevant metrics and their correlations (intelligibility, accessibility, permeability and local dimension) for the quantitative characterization of spatial morphology properties are described and computed for Lisbon’s axial map. Communities are identified based on the network topological structure in order to investigate how these properties are affected at different scales in the case study. The resulting axial line clustering is compared via the variation of information metric with the clustering obtained from e-scooters’ proximity. The results obtained enable to conclude that the space syntax properties are scale dependent in Lisbon’s pedestrian network. On the other hand both the correlation between these properties, the number of scooters and the variation of information between clusters indicate that the spatial morphology is not the only factor influencing micromobility. Through the comparative analysis between the main properties of the public space network of Lisbon and data collected from e-scooters locations in a timeframe, centrality becomes a dynamic concept, relying not only on the static topological properties of the urban network, but also on other quantitative and qualitative factors, since the flows’ operating on the network will operate several transformations on the spatial network properties through time, uncovering spatiotemporal dynamics.
Funder
Fundação para a Ciência e a Tecnologia
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,Computer Networks and Communications,Multidisciplinary
Reference36 articles.
1. Arthur D, Vassilvitskii S (2007) k-means++: the advantages of careful seeding. In: SODA ’07: proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, pp 1027–1035
2. Barabási A-L, Pósfai M (2016) Network science. Cambridge University Press, Cambridge
3. Barthelemy M (2019) The statistical physics of cities. Nat Rev Phys 1:406–415
4. Batty M (2013) The new science of cities. The MIT Press, London
5. Batty M (2018) Digital twins. Environ Plan B Urban Anal City Sci 45(5):817–820
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献