Identifying and Prioritising Public Space Demands in Historic Districts: Perspectives from Tourists and Local Residents in Yangzhou
Bibliographic record
Abstract
With the ongoing advancement of urban renewal and cultural tourism, public spaces within historic cultural districts face dual challenges of structural complexity and diverse user demands. There is an urgent need to establish a scientific, user-oriented evaluation system to enhance spatial quality and user satisfaction. This study takes the Nanhesha Historic and Cultural Quarter in Yangzhou as a case study, focusing on two primary user groups: tourists and local residents. Employing semi-structured interviews and grounded theory, it distils a demand evaluation framework comprising four dimensions—spatial structure, environmental perception, socio-cultural aspects, and facility systems—with a total of 21 indicators. Subsequently, employing the Delphi method, experts were invited to refine the indicators through two rounds of deliberation. The Kano model was then applied to classify the demand attributes of different groups, identifying five common demands and sixteen differentiated demands. These were categorised into three sensitivity levels. Further integrating the Satisfaction Increment Index (SII), Dissatisfaction Decrement Index (DDI), and sensitivity values, a two-dimensional prioritisation model was constructed. This yielded a unified three-tier priority system alongside independent ranking frameworks for each user group. Findings reveal that visitors prioritise immediate experiential attributes such as spatial accessibility, appropriate scale, and environmental cleanliness, whereas residents favour long-term usage-oriented aspects including cultural expression, convenient facilities, and climate adaptability. This research not only enriches the theoretical framework for studying public space perception in historic cultural districts but also provides actionable evaluation criteria and practical pathways for multi-stakeholder spatial optimisation design. It offers guidance for the high-quality, refined development of public spaces within historic quarters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".