Influence of Accessibility on Land Use and Landscape Pattern Based on Mapping Knowledge Domains: Review and Implications
Bibliographic record
Abstract
Research on the influence of accessibility on land use and landscape patterns is one of the most important subfields in landscape ecology and transportation geography. In this review article, we use CiteSpace and VOSviewer to analyze relevant information, including the number of published papers, highly cited literature, high-frequency keywords, periodicals, and the leading countries conducting research on this particular field. Based on the mapping knowledge domain theory and summarizing method, this research, using an extensive review of the existing literature to analyze the influence of accessibility on land use and landscape patterns, the following conclusions have been reached: first, most of the relevant studies are conducted by applying theories on landscape ecology rather than on transportation geography, and the measure index of accessibility is relatively simple. Second, while accessibility has played a key role in analyzing the interactions between transportation, land use, and landscape patterns, studies on the long-term effect of transportation on land use and land patterns are extremely important. Also, different road types have been found to impose different effects. Third, research on the functional landscape in inner cities has become a significant research focus, particularly with the progress in big data. And fourth, improvements in data acquisition and processing have greatly benefited the field, specifically with recent advancements in GIS and RS technology. However, studies on landscape patterns with regional perspectives have largely been insufficient, especially those conducted over long time scales.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".