Assessing Disparities in Road Transport Services in Kenya: Geographic Information System and Machine Learning Approach
Bibliographic record
Abstract
Regional disparities in road infrastructure access in Kenya hinder socioeconomic development and challenge progress toward the Vision 2030 goals. This study addresses spatial inequality in transport provision by developing a transport network need index (TNNI) based on socioeconomic indicators and comparing it with existing road network density, termed transport network provision (TNP). The difference between TNNI and TNP yields the index of disparity between needs and provision (IDNP), identifying counties that are over- or under-served relative to their development needs. Machine learning models are employed to predict road density based on socioeconomic features, with the best-performing model selected through accuracy metrics. Shapley additive explanations (SHAP) are used to interpret the model and determine the empirical importance of each input feature. These SHAP-based weights are compared with literature-based weights used in TNNI to assess disparities in factor prioritization. Results highlight critical spatial disparities, with counties such as Turkana, Wajir, and Mandera falling into the most under-served category (IDNP >0.8). SHAP analysis reveals that population density is the dominant driver of current road provision (56.8% weight), while poverty and unemployment, despite their policy relevance, are underrepresented in actual infrastructure allocation. This twofold analysis—identifying both regional service gaps through IDNP and mismatches in factor prioritization—provides critical insights for policymakers. By pinpointing under-served regions and highlighting which socioeconomic drivers are being overlooked, the study offers a robust, data-driven framework for guiding equitable infrastructure investments, advancing transport justice, and supporting diversity and inclusion in Kenya’s road transport planning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".