Fine-scale population estimation: how Landsat ETM+ imagery can improve population distribution mapping
Bibliographic record
Abstract
The spectral response based and land use based methods were compared for population estimation with Landsat Enhanced Thematic Mapper Plus (ETM+) imagery for Marion County, Indiana, USA. With the spectral response based method, impervious surface and vegetation fractions and land surface temperature derived from the Landsat ETM+ thermal band along with spectral bands were tested for estimating population density at the census block group level. With the land use based method, land use and land cover images were first extracted from Landsat ETM+ reflected bands. The population count for the block groups was estimated based on the area of residential land use. Population was then dasymetrically redistributed onto a 30 m grid based on a land cover and land use map of the study area. A comparative analysis shows that the spectral response method produced a larger mean relative error of 237% for population density at the block group, whereas the land use based method yielded a much better estimation, with a mean relative error of 21.4% for population counts for the block group. It is suggested that the dasymetric map provides a more accurate portrayal of population distribution than a choropleth map at small geographical scales such as that of a US county.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".