VAST · Sonnet · C4 — citations

Vastopolis outbreak investigation. Citation list (design cites library workflows in prose form).

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C4 Citation list (design cites library workflows in prose form)

Here is the list of papers/workflows from the Workflow Library that I drew on for the Vastopolis outbreak-analysis design, with a note on what specific approach or logic was taken from each:

Papers Cited in the Recommended Workflow

  1. Jigsaw: Supporting Investigative Analysis through Interactive Visualization J. Stasko, C. Görg, and Z. Liu. Information Visualization, 7(2), 118–132, 2008. doi: 10.1057/palgrave.ivs.9500180 Used for: entity/keyword extraction from microblog text (Stage A), human correction of extraction errors, and the raw-text drill-down/document-verification logic (Stage G); also informed the co-occurrence/relation-extraction approach for testing person-to-person transmission (Stage F).

  2. From Movement Tracks through Events to Places: Extracting and Characterizing Significant Places from Mobility Data G. Andrienko, N. Andrienko, C. Hurter, S. Rinzivillo, and S. Wrobel. IEEE VAST, 2011. doi: 10.1109/VAST.2011.6102454 Used for: the core event-to-place extraction logic in Stage C — converting geolocated illness-report "events" into density-clustered "significant places" using combined spatio-temporal distance functions, then aggregating events into per-place time series.

  3. Cluster and Calendar Based Visualization of Time Series Data J. J. van Wijk and E. R. van Selow. IEEE InfoVis '99, 1999. doi: 10.1109/INFVIS.1999.801851 Used for: the daily-profile clustering and baseline-deviation detection approach in Stage B, applied to detect the onset day(s) that break from the "normal" pre-outbreak pattern.

  4. A Visual Analytics Framework for Spatio-Temporal Analysis and Modelling N. Andrienko and G. Andrienko. Data Mining and Knowledge Discovery, 27(1), 55–83, 2013. doi: 10.1007/s10618-012-0285-7 Used for: the zone-grouping, representative-curve modelling, and residual-inspection logic in Stage D — clustering the 13 zones by temporal incidence similarity and checking spatial coherence against the map.

  5. Detecting Stable Cross-Impact Patterns in Bivariate Time Series G. Andrienko, N. Andrienko, M. Akila, B. Kathirgamanathan, and M. Ponce-de-Leon. IEEE TVCG, 32(7), 5996–6013, 2026. doi: 10.1109/TVCG.2026.3676810 Used for: the lagged cross-impact/Ikat-plot methodology in Stage E, adapted to test whether wind direction/timing at the candidate origin is followed by consistent downwind incidence increases (airborne hypothesis testing).

  6. Human-in-the-Loop: Visual Analytics for Building Models Recognizing Behavioral Patterns in Time Series (Feature Engineering for Behaviour Pattern Recognition) N. Andrienko, G. Andrienko, A. Artikis, P. Mantenoglou, and S. Rinzivillo. IEEE Computer Graphics and Applications, 44(3), 14–29, 2024. doi: 10.1109/MCG.2024.3379851 Used for: the human-in-the-loop validation principle applied in Stage A — visually checking extracted symptom/severity indicators against representative examples before trusting them for further analysis.