Spatial Patterns Analysis of Economic Central Place (Case Study: The Prospective New Autonomous Region of North Sukabumi Regency)
Bibliographic record
Abstract
This research aims to assess which areas within the Prospective New Autonomous Region of North Sukabumi Regency have the potential to serve as economic growth centers that can foster inclusive and sustainable development.The Scalogram Analysis identified that in the Prospective New Autonomous Region of North Sukabumi Regency, accessibility and facilities are critical variables influencing the hierarchy of economic growth centers.The analysis revealed that 22 villages qualify for the 1st hierarchy, 50 villages for the 2nd hierarchy, and 91 villages for the 3rd hierarchy.The spatial autocorrelation calculations with Moran's method indicated a positive spatial autocorrelation.Furthermore, the LISA analysis identified significant groupings of villages.As a result, Karang Tengah Village in Cibadak Sub District is recommended as a potential economic growth center, fulfilling the criteria for a 1st hierarchy area located in Quadrant I (High-High).The discovery of an economic growth center in Karang Tengah Village, Cibadak Subdistrict, has important implications as an alternative capital for the Prospective New Autonomous Region of North Sukabumi Regency.The designation of this village can facilitate policy making in development planning, improve accessibility and public services, and develop infrastructure that supports regional economic growth.Thus, it is hoped that Karang Tengah Village can function as a center of government and a sustainable economic driver for the Prospective New Autonomous Region of North Sukabumi Regency.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".