Using Geographical Weighting and Knowledge Graph Centrality to Identify Key Management Areas for Shared Bikes
Bibliographic record
Abstract
Owing to the increasing demand for improving the utilization rate of shared bikes, identifying their key management areas is necessary for shared‐bike companies to effectively allocate resources and formulate efficient management strategies. However, traditional methods often assist management decisions by analyzing the spatial distribution characteristics of each influencing factor of shared‐bike usage and fail to quantitatively consider the comprehensive impact of influencing factors on the management area. Therefore, a new method for identifying key management areas for shared bikes using geographical weighting and knowledge graph centrality is proposed. In this study, a multiscale geographically weighted Poisson regression (MGWPR) model was initially used to explore the influencing factors of shared‐bike usage and their degrees of influence. The regression model results were linked to geographical statistical units to construct a knowledge graph of factors affecting shared‐bike usage in each district, and the weighted degree centrality classification results of nodes in each district were used to assist in identifying their key management areas. The results showed that the proposed method could quantitatively measure the comprehensive impact of different factors in each district on the utilization rate of shared bikes and could effectively identify their key management areas, thereby assisting enterprises in formulating appropriate shared‐bike management strategies and improving the efficiency of shared‐bike management decision‐making.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".