Spatial Accessibility Disparities and Users’ Opinion Assessment of Khartoum State Public Green Spaces
Bibliographic record
Abstract
Khartoum State, the capital of Sudan is situated in the Sahelian and Sudanese arid zone and it is expected to face excessive dry climate conditions due to climate change. In such an eventuality, accessible Urban Green Spaces (UGSs) represent a traditional adaptation measure for its growing population. This study aims to describe and explain the spatial disparities and potential accessibilities to public green space of Khartoum State and to determine users’ opinions about them using Geographic Information Systems (GIS) and questionnaires. It assesses the potential accessibility to 44 urban parks, community gardens, and city forests in Khartoum State for both drivers and pedestrians in 5, 10, and 15 min. The results showed inequitable access opportunities to UGSs, mostly in favour of first-class residential areas. Within a 5-min walking interval, the UGSs service zone covered <1% of the population. Furthermore, users’ opinion assessment revealed that 80% of the parks require entrance fees; however, 75% of the respondents show the willingness to bear extra travel costs and time to access well-equipped parks. The study concludes that potential accessibility to UGSs does not correspond to the international quality of life indices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".