Bibliographic record
Abstract
BACKGROUND: Physical activity is an important determinant of health. Walking is the most common physical activity performed by adults and the presence of sidewalks along roads is a determinant of walking. Geographic information systems (GIS) can be used to measure sidewalks; however, GIS sidewalk data are difficult to access. The purpose of this study was to present a new GIS method for measuring the distance and coverage of sidewalks along roadways. METHODS: The new method contains three stages. Stage 1 involves calculating the distance of all road segments within the region of interest (e.g., neighborhood), extracting geospatial information on these road segments, and saving this information as a Google Earth file. This stage was performed in ArcGIS software. Stage 2 involves opening the extracted road segment geospatial data in Google Earth, visually examining road segments to see if they contain sidewalks, and deleting road segments without sidewalks. Stage 3 involves importing the modified road geospatial data into ArcGIS and calculating the length of road segments with sidewalks. The new method was tested in 315 sites across Canada. Each site consisted of a one km radius circular buffer surrounding a school. RESULTS: A detailed, step-by-step protocol is provided in the paper. The length of road segments with sidewalks in the testing sites ranged from 0.00 to 55.05 km (median 16.20 km). When expressed relative to the length of all road segments, the length of road segments with sidewalks ranged from 0% to 100% (median 53%). By comparison to urban testing sites, rural sites had shorter sidewalk lengths and a smaller proportion of the roads had sidewalk coverage. CONCLUSION: This study provides a new GIS protocol that researchers can use to measure the distance and coverage of sidewalks along roadways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".