What is mixed use? Presenting an interaction method for measuring land use mix
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent decades, the mixing of complementary land uses has become an increasingly important goal in transportation and land use planning. Land uses mix has been shown to be an influential factor in travel behavior (mode choice and distance traveled), improved health outcomes, and neighborhood-level quality of life. However, quantifying the extent to which a given area is mixed-use has proven difficult. Much of the existing research on the mixing of land uses has focused on the presence and proportion of different uses as opposed to the extent to which they actually interact with one another. This study proposes a new measure of land use mix, a land use interaction method—which accounts for the extent to which complementary land uses adjoin one another—using only basic land use data. After mapping and analyzing the results, several statistical models are built to show the relationship between this new measure and reported travel behavior. The models presented show the usefulness of the approach by significantly improving the model fit in comparison to a commonly-used land use mix index, while controlling for socio-demographic and built form factors in three large Canadian cities (Vancouver, Toronto, and Montreal). Our results suggest that simple, area-based, measures of land use mix do not adequately capture the subtleties of land use mix. The degree to which an area shows fine-grained patterns of land use is shown to be more highly correlated with behavior outcomes than indices based solely on the proportions of land use categories.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.010 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it