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Data underlying the manuscript: Investigating Rural Public Spaces with Cultural Significance using Morphological, Cognitive, and Behavioural Data

posted on 2021-09-27, 08:12 authored by Nan BaiNan Bai, Pirouz NourianPirouz Nourian, Weixin Huang, Lu Wang, Bunschoten Raoul, Ana Pereira Roders
This is the research data for the manuscript "Investigating Rural Public Spaces with Cultural Significance using Morphological, Cognitive, and Behavioural Data".
The following is the original abstract:
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 perceived as 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 respectively 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 benefit from development without overlooking the local needs or losing the rural identity.


National Key Research and Development Program of China (NO. 2018YFD1100303)






Time coverage



Anyi, Jiangxi, China


TU Delft, Faculty of Architecture and the Built Environment Tsinghua University Technische Universität Berlin