Influence of spatial scale on the study of access fairness of urban park green space
Bibliographic record
Abstract
Park green space is an important ecological factor of the urban built-up environment, and it plays an increasingly important role in improving human welfare and the quality of urban life. Accessibility analysis of urban park green space is an issue of social equity and environmental justice that has received widespread attention. The accessibility of a city’s park green space must be evaluated under the correct scale and resolution before it can be applied to urban green space planning. To measure the impact of different research scales on accessibility, Weidu District of Xuchang City, Henan Province, China, was taken as the experimental area. The Gaussian-based two-step floating catchment area method was used to compare and analyze the accessibility differences under three scales: subdistrict, community, and residential quarter. The influence of the source and destination point modes on accessibility was analyzed at the residential quarter scale. Results show that the accessibility of park green space at the subdistrict scale is different from that at the community and residential quarter scales in terms of spatial distribution characteristics and quantitative relationship. The accessibility of the geometric center mode and the entrance and exit mode at the residential quarter scale is similar in overall distribution and different in local quantity. Overall, the accessibility of the park green space at the residential quarter scale under the entrance and exit mode and the spatial fairness of visiting the park green space are better than that under the geometric mode. Therefore, accessibility analysis of park green space should be performed cautiously when taking the aggregation unit as the basic research unit. The accessibility of the aggregation unit is not the statistical summary of its subunits, especially when the area of the aggregation unit is far larger than the walking range of residents, and is thus likely to lead to wrong conclusions. Accessibility analysis should be conducted on the finest scale possible rather than the aggregation scale and use true distance rather than the centroid-to-centroid surrogate to obtain reliable results for further guiding urban green space planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".