Achieving interoperability of smart city data: An analysis of 311 data
Bibliographic record
Abstract
A major challenge in making cities smarter is performing comparative analyses across two or more cities, or within a city across two or more departments. The problem is that data models and the underlying semantics of their content differ, making analysis difficult at best and erroneous at worst. This paper explores the hypothesis that a single, interoperable (i.e., shareable) data model/ontology can be designed for one category of city data: openly published 311 call centre data. 311 is a service provided by many North American cities that responds to non-emergency questions and reports made by the public. It has rapidly become the single point of contact for city services, inquiries, etc. We perform a semantic analysis of the content of 311 open datasets from four cities. The result of the analysis is that existing 311 datasets combine multiple semantic dimensions in their data making it impossible to perform comparative analysis. We then construct a 311 Reference Ontology that separates the semantic dimensions, and show how 311 data from multiple cities can be mapped onto the 311 Reference Ontology. We also demonstrate how the ontology can be used to support analysis
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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 | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.023 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".