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      "content": "You have received a document titled Workflow Library: Titles and Summaries. This document contains natural-language summaries of 53 visual analytics workflows drawn from the published research literature. Each summary describes the analytical goal, the workflow structure, and the methodological approach of a specific published workflow. The summaries were extracted from a public workflow library curated by our research group.\n\nPlease read the summaries carefully before we proceed. In the next message I will describe a specific visual analytics design task. Your job will be to design an analytical workflow that addresses the task, drawing on the workflows in the library where relevant. The library is a resource — you may consult it to identify workflow structures, analytical strategies, or methodological approaches that could inform your recommendation for the target task. You are not obliged to use the library workflows, and you may combine elements from multiple workflows, adapt them, or design new steps as the task requires.\n\nPlease describe your recommended workflow in natural language, in sufficient detail that a research analyst could use it as a design template. Where you draw on specific library workflows, please cite them by name so the analyst can trace the sources of your recommendation.\n\nBefore we proceed, could you briefly confirm that you have absorbed the library, and let me know if you have any questions before the task is described? Please reply in English only.",
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      "content": "I have carefully read through the Workflow Library, which contains 53 workflow summaries spanning a wide range of visual analytics domains, including:\n\n- **Temporal/sequential data**: calendar-based clustering, event sequence simplification, event thread analysis, cross-impact detection in time series, IVESA event sequences\n- **Spatial/movement data**: mobility graphs, movement track analysis, progressive trajectory clustering\n- **Network/graph analysis**: dynamic network snapshots, NodeTrix, state transition graphs, RelEx overlay networks\n- **Model understanding & ML interpretability**: EventAction recommendation, exploratory model analysis, binary classifier diagnostics, RfX ensemble exploration, TensorFlow dataflow graphs, What-If Tool, explAIner, RuleSense, compound potency models, VBridge healthcare explanations\n- **Cohort/subgroup analysis**: COQUITO, Coral, Kokiri, single-cell omics cohort comparison\n- **Clustering & dimensionality reduction workflows**: SOMFlow, progressive high-dimensional analysis, knowledge-incorporated embeddings, EnsembleLens\n- **Domain-specific design studies**: DPVis (disease progression), IRVINE (acoustic diagnostics), StratomeX (cancer genomics), Tuner (segmentation parameters), ParaGlide (simulation parameter spaces), Hybrid Visual Steering (engineering simulations), QVis (spatiotemporal ensembles)\n- **Eye-tracking & multimodal analysis**: ISeeCube, Space-Time gaze analytics, Active Gaze Labeling, Image-Based Projection Labeling, Multimodal Collaborative Design\n- **Text/investigative/sensemaking**: Jigsaw, SensePath, topic modeling (UTOPIAN), Visual Movie Analytics, LFPeers temporal peer comparison\n- **Regression/statistical modeling**: Partition-based regression, spatio-temporal modelling, feature engineering for behavior recognition\n\nI have absorbed the analytical goals, workflow structures, and methodological logic of each entry, including the key visual encodings, iterative loops, and citation details needed to trace design decisions back to specific sources.\n\nI'm ready for the design task. I have no questions at this stage — please go ahead and describe the target task, and I will draw on relevant workflows from this library to construct a detailed recommended workflow.",
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      "content": "A public health team in Vastopolis, a metropolitan area of about two million residents, has observed a sharp increase in flu-like illnesses over the past few days, with several deaths believed to be associated with the current outbreak. The team wants to understand where the outbreak started and how the infection is being transmitted (person-to-person, airborne, waterborne, or something else) so that emergency management resources can be deployed appropriately.\n\nThe available data consists of: geolocated microblog messages posted from mobile devices over a period of several days, each with a timestamp, GPS coordinates, and text content; a map of the metropolitan area showing highways, hospitals, landmarks, and water bodies; population statistics for 13 city zones; and daily weather observations including wind speed and direction.\n\nThe analyst will need to inspect intermediate results visually and use domain knowledge to interpret patterns and make judgements at each stage.\n\nDesign an analytical workflow for this problem.",
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      "content": "<think>I'm setting up a framework to analyze the Vastopolis outbreak using the microblog data, city geography, population zones, and weather patterns to trace where the outbreak started and how it spread through the population.\n\nI'm drawing on several visualization and analysis techniques from the literature: Jigsaw for extracting entities and symptoms from the microblog text, movement clustering methods to identify significant locations where people congregated, temporal pattern visualization to track when cases appeared, and spatio-temporal modeling to understand how the infection propagated across zones. Some approaches like disease progression visualization and event sequence analysis could help me stage how the infection evolved over time.\n\nThe core workflow is filtering geolocated microblogs for flu-related content, then geocoding and timestamping them to build a spatio-temporal dataset. From there I'd apply density clustering to pinpoint outbreak origins, analyze temporal case counts to determine when and where it started, overlay geographic features like water bodies and highways to test transmission routes, and incorporate wind data to evaluate whether airborne spread explains the observed patterns. Then I'd validate the extracted entities through manual sampling to ensure the classifier is catching the right signals. For the temporal analysis, I'm aggregating message counts across the city and by zone to spot when the outbreak begins, using a calendar view to identify the sharp uptick in activity and clustering similar days to pinpoint the anomaly onset.\n\nFor the spatial component, I'm treating each geolocated flu message as an event and applying density-based clustering to find spatial hotspots and their timing, which helps identify the likely origin versus later spread zones. I'll also normalize incidence rates by population across the 13 zones to account for differences in baseline activity.\n\nIn Stage 4, I'm overlaying the hotspot locations and spread direction against contextual map features—highways for mobility-driven transmission, water bodies for potential contamination sources, landmarks like hospitals that might indicate reporting bias or point-source events—and then comparing the spatial spread pattern of new cases against wind direction and speed data to test whether airborne transmission aligns with the observed spread. cases concentrated in zones sharing that water source rather than spreading diffusively. For case linking, I'd extract social mentions from microblogs—coworkers, shared locations, family connections—to map person-to-person transmission chains and validate whether clusters align with social networks or workplaces. Then I'd iterate through spatial clusters, temporal curves, wind patterns, and text evidence together to test which transmission hypothesis best fits the data.\n\nThe system should coordinate multiple linked views: a map showing heatmaps, hotspot clusters, wind vectors, water bodies, and highways; a timeline tracking case counts colored by cluster; a text panel for reading actual microblog content; and a correlation view checking wind lag against spread timing. I'd also examine severity mentions and death locations—early severe cases near the true source would narrow down the origin point given the incubation period.\n\nNow I'm structuring this as a design template that references established visualization workflows: entity extraction and symptom parsing from raw data with human feedback, temporal aggregation using hourly bins across the outbreak window with burst detection metrics, and spatial hotspot identification to pinpoint significant locations.\n\nFor the spatio-temporal component, I'm drawing on frameworks that group zones by similar temporal patterns and model their growth curves, then examining lagged correlations between wind conditions and disease spread using cross-impact detection methods.\n\nThen I'd extract person-to-person relationships from text data, overlay everything onto a map with contextual features like highways and hospitals, and iterate through the analysis with human feedback to refine hypotheses and build the final narrative.\n\nNow I'm structuring the full answer as a linear pipeline with distinct stages, each tied to specific analytical workflows so someone can actually follow and reproduce the approach. I'll frame it around three core questions—when and where it started, how it spread spatially over time, and what the transmission mechanism was—then walk through stages A through G with the appropriate visual encodings, interactions, and decision points where an analyst needs to make judgments.</think>\n\n# Recommended Analytical Workflow: Vastopolis Outbreak — Origin and Transmission Mechanism\n\nThe task decomposes into three linked analytical questions that should be addressed in sequence but revisited iteratively:\n\n1. **When and where did the outbreak begin?** (spatio-temporal origin detection)\n2. **How did it spread?** (diffusion pattern in space and time)\n3. **What is the likely transmission mechanism** — person-to-person, airborne, waterborne, or other — inferred from how the spread pattern relates to human mobility infrastructure, water bodies, and wind?\n\nBecause microblog text is unstructured, population data is tabular, and weather is a bivariate time series, the workflow combines text-mining/entity-extraction logic (from **Jigsaw**), event-to-place extraction (from **\"From Movement Tracks through Events to Places\"**, Andrienko et al. 2011), spatio-temporal grouping and modelling (from **\"A Visual Analytics Framework for Spatio-Temporal Analysis and Modelling,\"** Andrienko & Andrienko 2013), calendar/temporal-cluster reasoning (from **van Wijk & van Selow's Cluster and Calendar-Based Visualization**), and lagged cross-series analysis (from **\"Detecting Stable Cross-Impact Patterns in Bivariate Time Series,\"** Andrienko et al. 2026) to test the wind/airborne hypothesis specifically.\n\n---\n\n## Stage A — Extract candidate illness events from microblog text\n\n**Goal:** turn raw geolocated text into a structured set of \"illness-report events,\" each with a location, timestamp, and semantic content.\n\n- Build a lexicon of flu-related symptom terms (fever, cough, vomiting, headache, chills, \"sick,\" \"dying,\" \"hospital,\" \"ER,\" etc.) and apply keyword/entity extraction to every message, analogous to Jigsaw's entity extraction step where persons, locations, organizations, and dates are pulled from text. Here the extracted \"entities\" are symptom mentions, severity indicators (e.g., mentions of death or hospitalization), and place/landmark references embedded in the text (which may be more precise than the GPS tag, e.g., \"at the lake festival\").\n- As in Jigsaw, treat extraction as imperfect: the analyst samples and corrects false positives/negatives (e.g., \"flu-like\" sarcasm, unrelated \"sick of my job\" posts) before trusting the derived event set. This mirrors Jigsaw's entity-correction loop and the human-in-the-loop feature-validation step from **\"Feature Engineering for Behaviour Pattern Recognition\"** (Andrienko et al. 2024), where engineered indicators are checked visually against representative examples before being trusted for further analysis.\n- Tag each retained message with a severity level (mild symptom mention vs. severe/death mention), since severity will later help disambiguate early point-source cases from general community spread.\n- Output: a table of geolocated, time-stamped \"illness events,\" each with symptom type(s), severity, and raw text (kept for later drill-down reading).\n\n---\n\n## Stage B — Temporal overview: detect onset and growth pattern\n\n**Goal:** establish *when* the sharp increase began and whether it has a single abrupt onset (point-source signature) or a gradual ramp (person-to-person diffusion signature).\n\n- Aggregate illness-event counts into hourly (or 6-hour) bins, city-wide and per zone, producing a short multivariate time series (13 zones × time).\n- Use a timeline/heatmap overview in the spirit of **IVESA**: sort zones by onset time, event density, or burst intensity; visually inspect for gaps, sudden bursts, and regularity versus irregularity of report accumulation.\n- Where the data spans enough days to show daily rhythm (e.g., normal daytime microblogging vs. night silence), apply the **van Wijk Cluster-and-Calendar** logic: split the series into daily profiles, cluster days by shape (using a normalized or offset-corrected distance so that the outbreak's abnormal shape stands out from the \"normal\" pre-outbreak baseline), and display cluster membership per day. This immediately flags the specific day(s) that break from the baseline pattern — the operational start of the outbreak — without requiring the analyst to predefine a threshold.\n- Judgment point: identify the earliest zone(s) whose case curve deviates from baseline. These are candidates for the spatial origin, to be confirmed in Stage C.\n\n---\n\n## Stage C — Spatial hotspot / significant-place extraction\n\n**Goal:** convert scattered illness events into a small number of interpretable \"significant places,\" and identify which one is earliest (candidate origin).\n\nThis stage directly follows the workflow in **\"From Movement Tracks through Events to Places\"** (Andrienko et al. 2011), substituting illness-report events for the paper's m-events:\n\n- Apply density-based clustering (DBSCAN-like) to the illness events in space and time jointly, using a distance function that combines spatial proximity and temporal proximity (and optionally symptom similarity), so that events close in space but far apart in time are *not* merged, and events in different unrelated locations are not merged. Isolated events are treated as noise.\n- Build spatial buffers/hulls around each event cluster to delineate candidate \"outbreak places\" (e.g., a park, a water treatment plant vicinity, a stadium, a hospital catchment).\n- For each place, compute a small time series of event counts, enabling a \"when did this place become active\" comparison across places — mirroring the paper's aggregation of events by place into time series.\n- Cross-reference the earliest, most severe cluster(s) with the city map's landmark layer (hospitals, water bodies, event venues) to see whether the earliest cluster coincides with a specific fixed feature (e.g., a reservoir, a festival ground) — a signature consistent with a point-source (waterborne or common-exposure) event, as opposed to diffuse residential spread consistent with person-to-person transmission.\n- Normalize event counts per place/zone by the 13-zone **population statistics**, producing incidence-per-capita rather than raw counts, since a raw count of reports may simply reflect population density or Twitter usage density rather than true attack rate.\n\n---\n\n## Stage D — Spatio-temporal grouping and growth modelling across zones\n\n**Goal:** determine whether the 13 zones behave as one expanding front (diffusion) or as several independently-seeded clusters (multiple point sources / common exposure).\n\nFollowing the **Spatio-temporal Analysis and Modelling** framework (Andrienko & Andrienko, 2013):\n\n- Treat each zone's per-capita incidence time series as one spatial time series. Cluster zones by similarity of temporal profile (e.g., k-means or hierarchical clustering on normalized incidence curves), using a linked time-graph + map display so the analyst can check whether temporally similar zones are also spatially adjacent (supporting contiguous diffusion, e.g., along highways) or spatially disconnected (supporting a shared non-contact cause, e.g., a common water source or wind-borne plume).\n- Fit a simple growth model (e.g., exponential/logistic onset curve) to each group's representative curve and inspect residuals over time and space, as in the source workflow, to detect zones that deviate from the dominant growth pattern — these deviating zones are candidates for a distinct secondary source or a differently-timed exposure (e.g., a downwind zone affected only after a wind-driven delay).\n- Overlay zone clusters on the map together with the highway network: if the incidence-similar/temporally-lagged zones are strung along major highways or transit corridors, this favors person-to-person/mobility-driven spread; if they are unrelated to the road network but spatially coincide with a shared water body or its service area, this favors waterborne transmission; if they align with a downwind sector regardless of road connectivity or population contact, this favors airborne spread.\n\n---\n\n## Stage E — Wind–spread cross-impact analysis (testing the airborne hypothesis specifically)\n\n**Goal:** rigorously test whether the direction and timing of spread is statistically consistent with wind advection from the candidate origin, using a lagged-correlation approach rather than eyeballing alone.\n\nThis stage directly adapts **\"Detecting Stable Cross-Impact Patterns in Bivariate Time Series\"** (Andrienko et al., 2026):\n\n- Define the **base series** as the daily wind vector (speed and direction) at/near the candidate origin place identified in Stage C.\n- Define the **response series** as a directional exposure measure per zone: e.g., the daily per-capita incidence in each of the other 12 zones, or better, a projected \"downwind alignment score\" per zone (how well the zone's bearing from the origin matches the day's wind direction).\n- Slide a time window across the base series and compare it with time-shifted windows of each zone's response series over a range of plausible incubation lags (using a tolerance-based association measure such as the paper's tolerance-Kendall's-tau, which is robust to noisy daily fluctuations).\n- Visualize the resulting time×lag impact matrix with an Ikat-style plot for each zone (or for an aggregated \"downwind vs upwind\" comparison), and calibrate window length, lag range, and tolerance interactively while cross-checking against the actual wind and incidence series behind any cell.\n- **Interpretation:** if downwind zones show a consistent positive impact at a lag plausible for airborne incubation/dispersal, and upwind/crosswind zones do not, this is strong evidence for airborne transmission from the identified origin. If no stable directional lag pattern emerges (spread is equally likely upwind, downwind, and cross-wind, or tracks population/road proximity instead), airborne transmission from a single point source is less supported, and the analyst should weight person-to-person or waterborne explanations more heavily.\n\n---\n\n## Stage F — Person-to-person and waterborne hypothesis checks\n\n**Goal:** directly test the two remaining mechanisms using complementary evidence, since Stage E only tests the wind/airborne hypothesis.\n\n- **Person-to-person check:** using the corrected illness-event set from Stage A, extract social/co-location mentions from the text (e.g., \"my coworker,\" \"my whole family,\" shared venues, shared bus/train lines) — the same entity/relation-extraction logic as **Jigsaw**, here applied to build a light co-occurrence network of people/places mentioned together. If cases chain along social/mobility links (school, workplace, transit routes) and the spatial growth in Stage D tracks the highway/transit network rather than a single fixed landmark, this supports person-to-person spread following normal human contact and commuting patterns.\n- **Waterborne check:** overlay the earliest and largest clusters from Stage C directly on the map's water-body layer and any known water-distribution/service-area boundaries for the 13 zones (if available via the population/zone metadata). A waterborne signature would show: (a) an origin cluster physically coincident with a water source (reservoir, treatment plant, river) rather than a crowded public venue; (b) case distribution matching the water utility's service zones rather than geographic proximity or road adjacency; (c) no dependence on wind direction (ruling out Stage E's airborne signature) and no dependence on social/mobility chains (ruling out Stage F's person-to-person signature).\n- Because these are complementary, mutually-exclusive-leaning hypotheses, structure this stage as a comparison table/checklist: for each candidate mechanism, list the diagnostic spatial/temporal/contextual signature expected, and whether the data satisfies it — supporting transparent, auditable reasoning rather than a single black-box conclusion.\n\n---\n\n## Stage G — Synthesis, drill-down, and reporting\n\n**Goal:** consolidate findings into a defensible narrative the public-health team can act on.\n\n- Maintain a coordinated multi-view dashboard throughout: (1) a map view showing hotspot clusters, water bodies, highways, hospitals, and wind vectors per day; (2) a timeline/calendar view showing zone-level incidence and cluster membership over time; (3) a text drill-down panel to re-read raw microblog content behind any cluster or spike (essential domain-knowledge check, as emphasized throughout Jigsaw's Document View and the raw-record drill-downs in DPVis and Kokiri); (4) the Ikat-style wind-impact panel from Stage E; (5) a zone table with population-normalized rates.\n- Use an iterative \"propose → visualize → validate against raw text/map → refine\" loop, consistent with the general pattern seen across the library (e.g., **explAIner's** understand–diagnose–refine cycle and **SOMFlow's** provenance-tracked iterative refinement): each hypothesis about origin or mechanism should be checked against raw messages and map context before being accepted, and parameter choices (clustering distance, lag window, tolerance) should be logged so the reasoning is reproducible.\n- Deliverable: a short evidence-linked report identifying (a) the most probable origin place and time window, (b) the dominant spread pattern (contiguous/road-following vs. wind-directional vs. water-service-area-bound), and (c) the best-supported transmission mechanism with the specific visual/statistical evidence (Stage C cluster map, Stage D growth/residual plots, Stage E Ikat wind-lag plot, Stage F checklist) cited for each claim, so emergency-management resources can be targeted (e.g., quarantine/contact-tracing near roads, water-supply intervention, or air-quality/evacuation measures) accordingly.",
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      "content": "Make a list of papers from which you have taken relevant approaches.",
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      "content": "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:\n\n## Papers Cited in the Recommended Workflow\n\n1. **Jigsaw: Supporting Investigative Analysis through Interactive Visualization**\n   J. Stasko, C. Görg, and Z. Liu. *Information Visualization*, 7(2), 118–132, 2008. doi: 10.1057/palgrave.ivs.9500180\n   *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).\n\n2. **From Movement Tracks through Events to Places: Extracting and Characterizing Significant Places from Mobility Data**\n   G. Andrienko, N. Andrienko, C. Hurter, S. Rinzivillo, and S. Wrobel. IEEE VAST, 2011. doi: 10.1109/VAST.2011.6102454\n   *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.\n\n3. **Cluster and Calendar Based Visualization of Time Series Data**\n   J. J. van Wijk and E. R. van Selow. IEEE InfoVis '99, 1999. doi: 10.1109/INFVIS.1999.801851\n   *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.\n\n4. **A Visual Analytics Framework for Spatio-Temporal Analysis and Modelling**\n   N. Andrienko and G. Andrienko. *Data Mining and Knowledge Discovery*, 27(1), 55–83, 2013. doi: 10.1007/s10618-012-0285-7\n   *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.\n\n5. **Detecting Stable Cross-Impact Patterns in Bivariate Time Series**\n   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\n   *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).\n\n6. **Human-in-the-Loop: Visual Analytics for Building Models Recognizing Behavioral Patterns in Time Series** (Feature Engineering for Behaviour Pattern Recognition)\n   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\n   *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.",
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