Galois's algebraic structure and bipartite graph spatio-structural analytics for urban public transportation system assessment

Boulmakoul, Badredine; Besri, Zineb; Karim, Lamia; Boulmakoul, Azedine; Lbath, Ahmed

doi:10.1016/j.procs.2017.05.314

Cited by 1 publication

(1 citation statement)

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“…The computation of the four centralities relies on the concepts of shortest paths and random walks, respectively, which is believed to correspond with the space discovery modes with different levels of familiarity (Nourian, 2016). All four centralities are common in social network analysis and space syntax, capturing related yet distinct characteristics of the nodes as either street-lines or junctions (Agryzkov et al, 2019; Agryzkov et al, 2019; Boulmakoul et al, 2017; Curado et al, 2020; Nourian, 2016; Nourian et al, 2016; Page et al, 1998; Porta et al, 2006; Taheri et al, 2017; Wasserman and Faust, 1994). The centrality computation yields three groups of vectorswhere σv(γ,G) denotes the centrality index γ (one of the four indices) of node v (either a street-centreline or a street junction) computed in graph bold-italicG (one of the three graphs).…”

Section: Methodsmentioning

confidence: 99%

Investigating rural public spaces with cultural significance using morphological, cognitive and behavioural data

Bai

Nourian

Roders

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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During the rural [re]vitalization process in China, national strategies required rural public spaces with cultural significance to be identified before planning decision-making. However, places identified as culturally significant by planners and visitors can differ from the ones mostly used and valued by locals. Even if there is a growing interest in integrating local perspectives and experiences in planning, studies seldom discuss and compare openly the adequacy of spatial configuration, cognition and behaviour to support it. This study took Anyi Historic Village Cluster as a case study to empirically investigate rural public spaces with three distinct, yet related approaches: (1) Morphological: spatial network centralities based on space syntax; (2) Cognitive: Lynchian village images with semi-structured interviews; (3) Behavioural: spatiotemporal occupation patterns using Wi-Fi positioning tracking. Significant places valued and used by locals and non-locals were detected with the multi-source data. Furthermore, multivariant regression models managed to characterize the relationship among different aspects of investigated rural public spaces, which also helped diagnose places of interest to prioritize in planning, demonstrating the advantage of integrating the sources of information in practice instead of studying them apart. Results can also assist rural planning on how to identify what to preserve, what to enhance, and how to develop such spaces, without overlooking the local needs or losing the rural identity.

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