Spatial Structure of Road Infrastructure In Ekiti State, Nigeria: Options for Transformation
Bibliographic record
Abstract
The extent to which a nation’s landmass is covered by road network is an index of the degree of mobility of people, goods and services within the country, and the quality of the network measures the ease and cost of that mobility. Roads dominates the transport sector in most developing countries carrying eighty to ninety percent of passenger and freight. It plays a critical role in the entire transportation chain in that it connects other modes of transportation and permeates all aspects of modern economic activities in the economy. Hence the objectives of this study which examines spatial structure of road infrastructure in Ekiti State Nigeria; options for transformation are to determine the level of connectivity of road networks in the study area and also to determine the most accessible node from other significant nodes in the study area. To get a topological characteristics of the transportation network in the study area, a graph of the road network of Ekiti State was abstracted from the map sourced from the federal Road Maintenance Agency FERMA (Ado Ekiti Office). The most commonly used graph theoretic measurement of Karsky (1963) namely; the Beta (β) index, the Gamma (γ) index and the Alpha (α) index were used to determine the connectivity of road networks in the State. Shimbel index and associated number methods were also used to determine the most accessible node from other notable nodes in the study area. The results showed that the connectivity level is low with Beta index 1.39, Gamma index 0.49 and Alpha index 0.24. Result from the accessibility analyses showed that Ado-Ekiti is the most accessible node from other notable nodes on the study area. The study recommends options for the transformation of the road transport sub-sector in the study area. Key words: Spatial; Structure; Road; Infrastructure; Transportation
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".