The Characteristics of Visitor Behavior and Driving Factors in Urban Mountain Parks: A Case Study of Fuzhou, China
Bibliographic record
Abstract
Previous studies have focused on the linear relationship between recreation behavior and environmental variables. However, to inform the planning and design of recreational spaces, it is essential to understand the factors that contribute to differences in the spatial distribution of recreation behavior. This study investigates the characteristics of visitor behavior in urban mountain parks in Fuzhou City, Fujian Province, China. It describes the distribution of tourist numbers and the diversity of behaviors in these parks and explores the landscape driving factors of visitor behavior, as well as the interaction effects between the factors from the perspective of spatial driving forces. The results indicate that (1) The observed behaviors in the three parks are primarily access behaviors. The number of visitors and the diversity of behaviors show a high level in the morning and evening and a low level in the midday. (2) There was minimal variation in behavioral composition and behavioral diversity among the study plots of different elevation gradients in the three parks. However, the contrasts between different landscape types were more pronounced, with impermeable plazas exhibiting the highest behavioral diversity and park roads demonstrating the most homogeneous behavioral diversity. (3) The impact of environmental factors was more pronounced than that of landscape pattern factors. The environmental factors that most strongly influenced passing, dynamic, and static behaviors were spatial connectivity value, hard space proportion, and number of recreational facilities, respectively. In contrast, the hard space proportion was the strongest driver of behavioral diversity. Moreover, the interaction between the hard space proportion and spatial connectivity value was more pronounced in driving behavioral diversity, as well as the three behaviors.
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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".