Population Surface Generation: Separating Urban and Rural
Bibliographic record
Abstract
This article addresses parameter selection issues in population density mapping. We combined residential building and census block population data to generate population surface maps in a low-density (rural) county. We demonstrate how objective factors can be incorporated to guide the mapping process. Thiessen polygon sizes of the residential buildings and the overall size of census places were used to guide the separation of urban (small polygon) and rural (large polygon) areas. Building–building distances were used to identify the appropriate data resolution for analysis. Six neighbourhood sizes and three distance decay functions were compared for urban and rural parameter choices, respectively. The “real” population count surrounding a (any) point within a distance (i.e., a radius, or the neighbourhood size) was used as an objective criterion to evaluate the accuracy of distance decay functions. The surface wavelength, measured as the average distance between neighbouring surface peaks, was used to evaluate the effect of neighbourhood size on the spatial scale (i.e., coarseness) of the output population surface. A procedure is proposed to preserve the population volume for urban and rural areas, and potentially for more detailed subdivisions. The neighbourhood size caused the most dramatic variations in the resultant population density patterns. Of the functions tested, the Gaussian distance decay function produced the most accurate density readings after volume preservation. Population volume preservation effectively avoided urban population “pollution” of rural density values.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".