Urban Harmony: Integrating Spatial Suitability and Socio-Economic Factors to Enhance Quality of Life in Kotalama Riverbank Settlements, Malang City, Indonesia
Bibliographic record
Abstract
This study aims to understand the influence of spatial suitability and standard of living on the quality of life of residents in the riverside settlement of Kotalama, Malang City, in the context of sustainable urban planning.The methodology applied involves the use of Geographic Information System (GIS)-based overlay analysis to evaluate land use suitability, as well as the use of multiple linear regression analysis to examine the relationship between the standard of living variables and community-reported perceptions of quality of life.The results show that increasing distance from the river, improving housing quality, and increasing asset ownership are significantly associated with improved quality of life.These results emphasize the importance of integrating spatial suitability and socio-economic factors into urban planning policies to support sustainable development.The implications of this study are highly relevant to the achievement of SDG 11 which targets the creation of inclusive, safe, resilient, and sustainable cities and communities.This study proposes that urban planning policies should prioritize improving housing quality and providing better access to infrastructure to improve quality of life, while reducing the risk of natural disasters such as floods and landslides.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".