Evaluating a custom address locator built from public data to improve geocoding results
Bibliographic record
Abstract
In addressing the limitations of online geocoding services, particularly in rural, remote, and newly developed areas, this study focused on developing and evaluating a custom address locator using public data for British Columbia, Canada. The study was proposed to achieve three objectives: Identify and evaluate suitable data sources from various levels of government and Statistics Canada to create an address locator. The evaluation criteria focused on public availability, essential attributes, currency, and supported formats. Build a custom address locator to improve geocoding results. The custom locator was built using multiple datasets with different geometry types, allowing for a single locator that can search for address point locations, interpolated street locations, and points of interest. Evaluate the quality of the geocoding results from the custom locator and Google Maps, focusing on match rate and spatial accuracy in various geographical settings, including urban and rural areas. The custom locator performed well compared to Google Geocoding API in match rate and spatial accuracy, particularly in rural areas. In urban settings, the custom locator maintained a competitive accuracy level, suggesting its broad applicability across different geographical contexts. These results highlight the potential of locally tailored geocoding solutions, leveraging public datasets, to surpass conventional online services in accuracy and reliability. The study contributes to a deeper understanding of geocoding processes and underscores the value of custom geocoding tools in improving geocoding services within British Columbia and potentially other similar regions.
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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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".