Revisiting Crisis Maps with Geo-temporal Tag Visualization
Bibliographic record
Abstract
Crowd sourced crisis mapping is a relatively new phenomenon and platform that enables the collection and visualization of real-time crisis data submitted by users through social media tools and cellular technologies. Crisis maps are generally used by both state and non-state actors for sense-making and as a reference point for action. The current crisis map visualizations only show the location from which documents such as reports or short messages have been generated. Such a limited representation fails to immediately show important content, such as themes from a document and their changes over time. As a result, sense-making becomes time-consuming and cognitively demanding. We present a new visualization approach, Geo-Temporal Tag Visualization (GTViz), which treats the tags on the crowd sourced reports as spatio-temporal textual datasets. GTViz embeds a map to represent the spatial properties of the dataset and compactly shows the changes of key terms from the data source over time. We demonstrate the value of GTViz through two case studies and a controlled user study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".