Position Validation in Crowdsourced Accessibility Mapping
Bibliographic record
Abstract
We live in a society in which instant gratification is expected: we demand constantly up-to-date information, which is reflected in our reliance on maps for navigation. Volunteered geographical information (VGI) and geocrowdsourcing make this demand attainable, with popular examples being Waze and OpenStreetMap, where maps are updated quickly by citizen contributors with current base data and features. At George Mason University (in Fairfax, Virginia), the Office of Disability Services releases a traditional paper accessibility map once annually. Owing to its production methods and format, this accessibility map does not capture the transient obstacles that occur frequently throughout campus, rendering it less useful to disabled pedestrians. To fix this dilemma and establish a more useful accessibility system, we have created an application in which contributors report transient obstacles that may impede pedestrian navigation, including sidewalk obstructions, construction detours, and other obstacles that may affect pathway walkability. One of the concerns associated with VGI and geocrowdsourced information is quality assurance, which is imperative when the usage scenarios (including blind, visually impaired, and mobility-impaired navigation) depend on positional accuracy. This study attempts to address the concerns related to the quality assurance of VGI, specifically quality assessment of the positional accuracy of the geocrowdsourced spatial data. We present our quality assessment techniques and novel methods for assessing the consistency of positional characteristics of geocrowdsourced spatial data related to accessibility. These methods rely on moderated positional assessments, geotags extracted from contributed images, and gazetteer-based geoparsing of location descriptions. Finally, we base our methods and approaches on research contributions and best practices from past and current efforts in accessibility mapping.
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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 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".