Do Local Landscape Elements Enhance Individuals’ Place Attachment to New Environments? A Cross-Regional Comparative Study in China
Bibliographic record
Abstract
Globalization and urbanization have made many Chinese cities lose their distinct characteristics and have led to emotional sense of loss for individuals. Place attachment, as encompassing place dependence and place identity, is the positive emotion that describes the psychological connections between people and a certain place. Many studies have indicated that people develop place attachment toward a certain place by long-term interaction with that place. However, few studies have demonstrated that place attachment might also be evoked by a landscape that looks familiar, but with which a person has not had long-term interactions. It is important to understand the role of place attachment in urban design, as neglecting place attachment can have a negative impact on the outcomes of urban planning and urban design. In this study we explored the contributions of local landscape elements to people’s place attachment to a new physical environment by means of a cross-regional comparative study. Three groups of respondents living in three different areas of China were chosen, and a photo-based approach was used to examine the association between local landscape elements and place attachment. The results indicate, first, that local landscapes positively contribute to residents’ place attachment. Next, an individual’s place attachment to new environments can be enhanced by adding familiar local landscape elements. Findings suggest that planners and designers can build stronger place attachment by integrating landscape elements that are familiar to people. This can have implications, for example, when creating links between newcomers and the new environments to which they have moved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".