A <scp>GIS</scp>‐based land‐use diversity index model to measure the degree of suburban sprawl
Bibliographic record
Abstract
This paper describes a GIS‐based land‐use diversity measure for residential neighbourhoods – the land‐use diversity index (or LDI) model – as a possible urban sustainability criterion. The term ‘land‐use diversity’ is proposed as representative of many physical attributes of neighbourhood form opposite to typical sprawl patterns. A diverse neighbourhood is one with a mixture of compatible land uses and housing types, containing an array of amenities in reasonable proximity to where people live. The prototype version of the LDI model incorporates 34 input variables, structured around four sub‐indices. Its range of expected values are explored through four case study applications. Theoretically, index values can vary between 0 and 1, where 1 represents a condition of greater ‘land‐use diversity’. The two traditional urban neighbourhoods fared well (index values ranging between 0.627 and 0.726) because they have a greater range of land uses and neighbourhood amenities, a better integration of housing types and are more concentrated. These two neighbourhoods meet many of the ‘exuberant diversity’ criteria described by Jacobs. The two suburban neighbourhoods scored lower index values (between 0.250 and 0.363), indicating variables different to those for traditional urban forms. The LDI model differs from existing sprawl measures fundamentally, as it attempts to measure sprawl at a finer resolution (i.e. at the neighbourhood scale). It is anticipated the LDI model will assist with planning new, and reconfiguring old, neighbourhoods as they strive to meet smart growth criteria now being considered by many cities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".