Plan evaluation and monitoring in ten U.S. cities, and an assay of land use and transportation integration indicators
Bibliographic record
Abstract
Plan evaluation and monitoring is often determined to be an under-developed step in the planning process. There is an extensive literature on the theory behind effective plan evaluation and monitoring, including the use of indicators. However, there is a need to research more closely how city planners are currently conducting plan evaluation and monitoring ‗on the ground'. This paper's background consists of a literature review of plan evaluation and monitoring, and the general use of indicators. Methods of examination of current practices include three parts: 1) a review of indicators that examine the integration of land use and transportation, and an examination of the current practices in ten United States cities consisting of 2) a review of procedures and documents related to plan evaluation and monitoring that are evident from city government websites, and 3) a telephone interview with a senior level planner. Results for part 1 include a list of practical indicators for land use and transportation. Part 2 and 3 show a wide variety of practices in plan evaluation and monitoring, and interviews revealed many recommendations to planners as they improve or develop plan monitoring and evaluation programs. Practices range from annual reports, to plan consistency reviews during ordinance updates, to indicator monitoring from a department outside the planning department, and many more. Recommendations include designing plans with the aim of monitoring in mind, and the importance of choosing a few critical things to measure rather than an intensive and comprehensive data analysis approach, and many more. This paper points to the need for further research on the relative effectiveness of various approaches under different circumstances, and further surveys of plan evaluation and monitoring at different scales and different types of cities. Keywords: plan evaluation, plan monitoring, indicators
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".