OpenEventMap: A Volunteered Location-Based Service
Bibliographic record
Abstract
Our lives are affected by myriads of events happening daily all over the world. For efficient planning and management of complex systems composed of various components, understanding relationships between an event and the reactive behaviour of involved components is vital. Analysing these complex relations demands a spatiotemporal event-based model, in which the event plays a central role. In this article we develop a framework which provides the possibility of mapping and storing event-related information on the OpenStreetMap (OSM) platform by volunteers. The study is divided into two different phases: first, mapping the event elements by adding new attributes adequately designed to encode spatiotemporal and semantic event information; and, second, representing the event-related information on a map by developing a Web application, offering a volunteered location-based service. To facilitate the event-mapping procedure, a Java OpenStreetMap (JOSM) plug-in was developed for volunteers. The plug-in was developed based on the notion of an event to adequately store and manipulate the semantic information of events in the OSM structure. The tool was used by more than 100 volunteers in Munich for the years 2012 to 2014. In addition to manual collection of event-related information by volunteers, a crawling framework was also developed to automatically collect freely available event information from various Web pages on the Internet. The framework extracts the same event elements as the plug-in. But the framework crawls each Web page according to some pre-defined rules and follows a post-processing step, if necessary. The manually collected events along with the crawled event information are visualized in a Web application. The study revealed that adding the possibility of event-oriented mapping to OSM empowers volunteers to collect a higher level of information (event information) for city maps. This information can furthermore be used for strategy development and service planning by decision-makers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".