Mapping Exurban Development: Can Road and Census Data Act as Surrogates?
Bibliographic record
Abstract
Exurban development, characterized by low-density residential development, is one of the leading anthropogenic causes of land transformation. A major obstacle to studying this phenomenon is a lack of spatially explicit data. In this article, two commonly employed indirect approaches that use readily available road and census data as surrogates of exurban development are examined for their ability to delineate exurban development across large spatial extents. The study area is the heterogeneous exurban region of Peterborough County, Ontario, Canada. Comparing correlations between road density–based maps, dasymetric dwelling-count maps, and the reference data at multiple scales produced mixed results. Of the two methods, road density generally performed better, except when the census units were of relatively small size. Overall, the results highlight the way in which heterogeneity within a large study area can greatly obscure surrogate relationships that may be evident at smaller spatial extents, where conditions are relatively more homogeneous, making the use of these indirect methods challenging for large spatial extents. In particular, the geographic and historic context of the study area significantly influences the effectiveness of these methods, which should therefore be used with caution in mapping exurban development.
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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 | 0.048 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".