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

doi: 10.4121/13564292.v2
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/13564292
Datacite citation style:
Bai, Nan; Nourian, Pirouz; Huang, Weixin; Wang, Lu; Raoul, Bunschoten et. al. (2021): Data underlying the manuscript: Investigating Rural Public Spaces with Cultural Significance using Morphological, Cognitive, and Behavioural Data. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/13564292.v2
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
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version 2 - 2021-09-27 (latest)
version 1 - 2021-02-17
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usage stats
1995
views
1
citations
4541
downloads
geolocation
Anyi, Jiangxi, China
time coverage
2018-2020
licence
cc-by-sa.png logo CC BY-SA 4.0
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.
history
  • 2021-02-17 first online
  • 2021-09-27 published, posted
publisher
4TU.ResearchData
language
zh-hans
funding
  • National Key Research and Development Program of China (NO. 2018YFD1100303)
organizations
TU Delft, Faculty of Architecture and the Built Environment
Tsinghua University
Technische Universität Berlin

DATA

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