Redrawing the Planners’ Circle
Bibliographic record
Abstract
Problem, research strategy, and findings For decades, planners have been drawing circles of a quarter-mile radius to determine easily walkable distances for neighborhood and activity center planning. However, the radius of such “planners’ circles,” or walksheds, is often informed more by convention than by data. Here we examine walk-trip distances based on two national household travel surveys for the United States and Germany. We describe how walk distances vary by personal and trip characteristics, with a particular focus on trip purpose and pedestrian age. We conducted both univariate and multivariate analyses to compare patterns between the United States and Germany. The multivariate analysis examines quantile regressions for 50th, 75th, and 90th percentiles to understand both typical and longer walk distances. The observed distances that people walk vary significantly across age groups, trip purposes, and national contexts. Leisure trips tend to be longest, whereas shopping and errand trips tend to be shortest. There are substantial differences between the United States and Germany in the average lengths of walks (mean/median walk distance: Germany, 1,490/980 m, 0.93/0.61 miles; United States, 970/530 m, 0.60/0.33 miles) and in the effects of independent variables. A significant portion of the variation in walk-trip distances between the United States and Germany is likely due to Germany’s higher quality walk environments.Takeaway for practice Rather than always resort to a quarter-mile or 400-m radius, planners can use the data here to customize the size of the planners’ circle, or walkshed, they draw to take into account the primary trip purposes and demographic segments under consideration. Moreover, planners can draw circles with a shorter radius corresponding to the 50th percentile to plan for the most common walk-trip lengths while also considering larger circles corresponding to the 75th and 90th percentiles to provide more supportive and safer pedestrian environments for longer trips.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".