Waiting to Know the Future: A SLEUTH Model Forecast of Urban Growth with Real Data
Bibliographic record
Abstract
What is the true value of simulation modelling to urban growth? This article assesses the validity of an integrated approach, based on the SLEUTH Model and land-use planning theory, as used to create an eight-year forecast in 1998. With actual data on the extent of urbanization in 2006 now available, the authors find that the 1998 forecasts were accurate. The case study is located in Macomer, an inland municipality of Sardinia, Italy, an island in the central Mediterranean Sea. Noting that data collection is an essential first step of planning, the authors assess Macomer's land-use history, geography, economy, and demographics as context for more integrated and holistic planning than has been undertaken in the region to date. The 1998 calibration and prediction of the Urban Growth Model, a component of SLEUTH, simulated Macomer's urban growth for the following eight years and has been reviewed and statistically validated. With detailed geographical results, the authors confirm that the 1998 simulation closely reflects real urban growth as of 2006. This finding is particularly notable because urban growth in Sardinia has been slow, and a higher level of accuracy in urban planning is necessary to achieve stronger predictive capability.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".