Spatial Typology of Regional Development in Metropolitan PEKANSIKAWAN, Riau Province
Bibliographic record
Abstract
The spatial disparity in the development of the PEKANSIKAWAN metropolitan area (comprising Pekanbaru City, Kampar Regency, Siak Regency, and Pelalawan Regency) highlights Pekanbaru City as the economic and administrative center, while the surrounding areas serve as supporting regions with potential in agriculture, plantations, and industry.This study aims to analyze the spatial distribution of regional development and examine the typology of the PEKANSIKAWAN metropolitan area.The spatial distribution analysis was conducted using the scalogram method with Microsoft Excel and ArcGIS as analytical tools.The second objective of this study is to analyze the metropolitan PEKANSIKAWAN regional typology using the Rustiadi Quantitative Zoning (RQZ) method, also utilizing ArcGIS for analysis.The findings from the first objective indicate that villages with the highest level of development (Hierarchy I) are concentrated in the western corridor of the PEKANSIKAWAN metropolitan area, covering most of Pekanbaru City and parts of Kampar and Siak Regencies.Meanwhile, villages classified under Hierarchies II and III are more widely dispersed, characterized by limited basic infrastructure, low accessibility, and weak economic potential.The results of the second objective, based on three different spatial weight model simulations, reveal that the PEKANSIKAWAN metropolitan area consists of three regional typologies: (1) Cluster 1, forming a rural settlement area (kampung tua) characterized by dryland forests and non-industrial plantation forests (Kampar Regency); (2) Cluster 2, representing an urbanized area (Pekanbaru City); and (3) Cluster 3, forming a rural area dominated by industrial plantation forest concessions and large-scale palm oil plantations (Siak and Pelalawan Regencies).This study recommends implementing affirmative policies to encourage Cluster 3 to become a more inclusive area that actively contributes to generating a multiplier effect for the development of its surrounding regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".