Strategic Planning for Sustainable Development Using Spatio-Temporal Analysis
Bibliographic record
Abstract
This study presents a strategic planning framework for sustainable development through comprehensive spatio-temporal analysis, using Nusa Lembongan's mangrove ecosystem as a case study.The research implements an innovative methodological approach combining temporal satellite imagery analysis from 2014 to 2024 with systematic stakeholder feedback assessment to develop evidence-based sustainable development strategies.Longitudinal analysis using Landsat 8 OLI satellite imagery reveals significant development patterns, with NDVI calculations demonstrating sustained ecosystem resilience in 89% of the study area while identifying critical transition zones where development pressures have increased moderate NDVI values from 32 to 60 pixels.Strategic analysis of development impacts shows that 65% of environmental pressure concentrates in specific development nodes, particularly in areas of intensive infrastructure utilization.The research identifies three primary strategic focus areas through stakeholder feedback analysis: environmental resource management (156 documented concerns), infrastructure development impacts (122 cases), and sustainable resource utilization (134 instances).Integration of spatial data with stakeholder input enables the formulation of targeted development strategies, with particular emphasis on areas showing increased pressure, as evidenced by the emergence of low NDVI values in 5 pixels by 2024.The findings demonstrate the effectiveness of spatio-temporal analysis in strategic planning, providing quantifiable metrics for sustainable development decision-making while highlighting specific areas requiring immediate intervention.This research establishes a replicable framework for evidence-based strategic planning that balances development imperatives with environmental sustainability, offering practical insights for policymakers and development practitioners in similar contexts.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".