Integrating Remote Sensing, Consumer Preferences, and Sustainable Marketing: A GWR Study of Urban Growth and Heat Island in BSD City, Indonesia
Bibliographic record
Abstract
Urban expansion in BSD City, Indonesia, has generated major environmental and behavioral shifts.This study employed Landsat 8 surface reflectance imagery (2020-2023) and spatial statistics to assess vegetation cover, built-up growth, land surface temperature (LST), and consumer demand for eco-friendly housing.Preprocessing included atmospheric correction, emissivity adjustment, and cloud masking, while indices were validated with high-resolution imagery to ensure accuracy.NDVI values ranged from -0.125 to 0.375 (peak 0.188), NDBI from -0.219 to 0.094 (peak -0.063), and the Urban Index from 0.188 to 0.500 (peak 0.375), indicating compact urban development with stressed vegetation.LST ranged between 32-68℃, with a dominant mode at 40℃, revealing thermal stress concentrated in urban cores.Spatial heterogeneity was confirmed through coefficient of variation (NDVI max 0.625), Moran's I (-0.004 to 0.016), and Gi* hotspots (-2.0 to 1.75).Geographically Weighted Regression (GWR) showed localized associations between vegetation decline, built-up intensity, and LST anomalies.Annual reports suggested eco-branding strategies were most effective in greener and cooler districts, linking environmental attributes with housing demand.Findings should be interpreted cautiously since consumer data were aggregated and lacked neighborhood-scale resolution.The study highlights the role of integrating remote sensing, spatial modelling, and behavioral insights in guiding sustainable urban planning and ecooriented marketing.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".