{
  "chat_uuid": "3ba86aa1-3157-425b-bf52-800cfe5da76d",
  "history": [
    {
      "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.",
      "role": "user"
    },
    {
      "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.",
      "role": "assistant"
    },
    {
      "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.",
      "role": "user"
    },
    {
      "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.",
      "role": "assistant"
    },
    {
      "content": "Make a list of papers from which you have taken relevant approaches.",
      "role": "user"
    },
    {
      "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.",
      "role": "assistant"
    },
    {
      "content": "Thank you. I am now uploading the ATWL specification and one example workflow expressed in ATWL. Having designed this workflow in natural language, please now design it again using ATWL — not as a translation of what you wrote above, but as a fresh design decision at each point: where ATWL's constructs (loops, specification artifacts, actor designations, assess/generate-knowledge transforms) suggest a better-structured way to organise a step than your original natural-language description did, please use them, even where that means departing substantially from your original phase structure.\n\nPlease do not treat fidelity to your original description as a goal in itself. The aim is the best workflow design you can produce in ATWL for this task, informed by having already thought about the problem, not a record of your earlier reasoning.",
      "role": "user"
    },
    {
      "content": "<think>I'm approaching the Vastopolis task by mapping out the core data structures: microblog messages with their embedded location, timestamp, and text; the reference layers like highways and hospitals that provide spatial context; and the population data distributed across thirteen zones. These elements form the foundation for how I'll organize the workflow.\n\nThe analysis unfolds across five phases: first extracting and characterizing illness-related events from the raw messages, then validating those extractions with human feedback. Next I'm partitioning the data temporally to detect when the outbreak began, followed by spatial clustering to pinpoint where it originated. Finally, I'm normalizing the findings against population density and grouping zones by their temporal patterns to understand the broader geographic spread.\n\nThen I need to layer in environmental factors—examining how wind patterns correlate with case incidence across different zones and lags to test whether airborne transmission played a role. I'll also check for person-to-person transmission signals and waterborne pathways by extracting social connections and co-location data from the messages, overlaying them with geographic features like water bodies and roads. The final synthesis brings all these hypotheses together into a comprehensive report.\n\nNow I'm thinking through how to structure this as a proper workflow with artifacts and data transformations, starting with defining what data I'm working with—the raw messages with their embedded entities, timestamps, GPS coordinates, and the map layers as contextual reference.\n\nFor the map data, I could break it down into separate artifacts for different feature types—roads, water bodies, hospitals, landmarks—but that gets unwieldy. Instead, I'll treat all map features as a single entity set where each feature (road segment, hospital, landmark, water body) is an entity with its type and spatial geometry as attributes. For zones, I'm defining them as bounded spatial regions with their own internal structure.\n\nNow I'm moving into the transformation pipeline. The first step is to extract illness-relevant messages using a symptom lexicon, which filters the raw messages and creates a new entity set of illness reports with symptom terms, severity flags, and references to the original text. I should characterize the messages first to extract symptom features, then use that to define which ones qualify as illness reports.\n\nFor the extraction transform, I'm using a characterize operation to pull out symptom and severity indicators from the message text through NLP extraction, guided by the symptom lexicon. This produces feature vectors for each message capturing what symptoms are mentioned and how severe they seem. Then I need to loop in human validation to catch false positives and negatives, since the analyst will need to review the extraction quality.\n\nIn the validation loop, I'm filtering messages by those extracted symptom features to identify illness candidates as elementary entities with symptom type, severity, and raw text. Then I visualize a sample of these candidates so the analyst can manually check whether the extraction is working correctly.\n\nAfter assessing the extraction quality through manual review, if it needs refinement I update the symptom lexicon based on what I learned, then loop back to re-extract with the improved specification. Otherwise I exit the loop.\n\nI'm restructuring so the extraction step runs inside the loop each iteration, since it depends on the current lexicon. Before the loop starts, I initialize the lexicon specification, then in each iteration I extract features, assess quality, and conditionally refine the lexicon before the next pass.\n\nNow I'm defining the transforms that happen within each loop cycle: extracting symptom indicators from the messages using the current lexicon, filtering to identify illness reports based on those features, and setting up a visualization step to sample and review the results.\n\nThen there's a quality assessment phase where a human manually checks the sampled reports to evaluate extraction accuracy. If the quality check reveals issues, the lexicon gets refined based on that feedback and the loop continues with the updated rules. Otherwise, the extraction process completes.\n\nOnce the loop finishes, I have both the illness reports and the final symptom features ready. Now I'm moving into temporal onset detection, which requires partitioning time into discrete bins—hours or days—to aggregate the reports into counts. I also need to map these reports spatially by zone, so I'm thinking about how to join the illness data with zone information and arrange the reports by their geographic location. So I'm defining \"zone-day\" as the core analysis unit by grouping illness reports into spatial-temporal bins—each report gets assigned to its zone and day. This creates a new entity set where each zone-day combination becomes an observation, with aggregated counts across all reports falling into that bin. I could simplify this by using ATWL's contextualise operation separately for spatial and temporal dimensions—first arranging reports by zone via point-in-polygon spatial join, then handling the calendar dimension independently. Rather than trying to declare a derived zone-time context upfront, I can use define-unit to create the combined grouping through aggregation across both spatial and temporal positions. So arrangement works as input to aggregation transforms—that's the key insight. For the spatial case, I'd use a map-based contextualise to assign reports to zones via point-in-polygon logic, producing an arrangement of reports positioned within zone contexts. For temporal binning, though, it's cleaner to apply define-unit directly on the reports themselves to create time-partitioned episodes, which aligns better with how the framework treats internal structure.\n\nNow I'm thinking about combining both dimensions—I need zone-day entities that capture the cross-product of spatial and temporal grouping. These entities would have an internal episode structure containing the illness reports that fall within that specific zone and day, with embedment in both space (the zone identifier) and time (the day). I'm defining a transform that applies time-partitioning and spatial grouping together to create these zone-day bins.\n\nThen I need to characterize these units by computing aggregate statistics like report counts and severity-weighted measures, creating a feature vector for each zone-day. After that, I'll need to normalize these counts by the population of each zone to get rates rather than raw counts.\n\nFinally, I'm arranging these zone-day units into a timeline view organized by zone for visualization, potentially combining this with a spatial map-based arrangement that uses zones as the geographic context and time as a secondary dimension. Defining the transform to partition illness reports by day, where each day entity captures both an hourly profile vector and zone incidence vector as features. Defining the feature vector that captures each day's spatial and temporal illness patterns. Then I'm clustering days by their profile similarity relative to baseline, grouping together days with comparable shapes—like distinguishing pre-outbreak baseline days from outbreak days—and assigning cluster labels to each day.\n\nNow I'm arranging these clustered days on a calendar view using the date and weekday information, then visualizing the cluster assignments across the calendar to see the temporal pattern of anomalies.\n\nFrom this visualization, I'm identifying the onset day and the earliest zones showing elevated incidence through human interpretation, capturing this as a textual description of when the outbreak began and which areas were affected first. Then I'm moving toward generating knowledge from these observations.\n\nI'm thinking about the scope here — with three interconnected questions around origin, spread, and mechanism, the full specification will be substantial, but I want to maintain the same level of detail as the example while covering all the ground needed. I'll organize this into distinct phases using a consistent template to keep it readable despite the complexity. I'm continuing to flesh out the workflow phases, recognizing this is complex but necessary given the three interconnected investigation strands. I'm restructuring the order to minimize redundancy: extraction and validation first, then zone-day aggregation as a foundation, followed by onset detection, origin place identification, spread patterns across zones, wind cross-impact analysis, person-to-person and waterborne pathway checks, and finally synthesis with comparative hypothesis assessment. For each phase I'm defining the transforms, artifacts, and features crisply—specifying entities, embedment, and representation forms—and incorporating assessment loops where analysts need to inspect and iteratively refine parameters like clustering distance or windowing tolerances. Phase 7 involves extracting social co-location relations from text and overlaying candidate origins against water bodies and infrastructure to evaluate waterborne and contact-chain hypotheses, all feeding into a final synthesis phase that compares the competing mechanisms and generates recommendations.</think>\n\nBelow is the ATWL redesign. Rather than mirroring my earlier natural-language phase structure, I rebuilt the workflow around ATWL's artifact/transform vocabulary, and used explicit `loop` constructs only where a tunable specification genuinely needs calibration against visual/human judgment (extraction lexicon, spatio-temporal clustering distance, cross-impact window/lag/tolerance). Other interpretive steps are modeled as single-pass `abstract`→`generate-knowledge` transforms with `actor: human`, since the human-in-the-loop character is already carried by the actor designation rather than requiring a formal refinement loop at every stage.\n\nStructural choices are informed by:\n- **Jigsaw** (Stasko, Görg & Liu 2008) — entity/relation extraction with human correction (Phase 1, Phase 7 social relations)\n- **From Movement Tracks through Events to Places** (Andrienko et al. 2011) — event→cluster→place derivation logic (Phase 4)\n- **Cluster and Calendar Based Visualization** (van Wijk & van Selow 1999) — calendar arrangement for onset detection (Phase 3)\n- **A Visual Analytics Framework for Spatio-Temporal Analysis and Modelling** (Andrienko & Andrienko 2013) — zone-profile clustering against a map/road context (Phase 5)\n- **Detecting Stable Cross-Impact Patterns in Bivariate Time Series** (Andrienko et al. 2026) — lagged cross-impact matrix with calibration loop (Phase 6)\n- The feature/model-refinement loop pattern from the example HITL workflow — reused structurally for Phases 1, 4, and 6\n\n```atwl\nworkflow vastopolis-outbreak-origin-and-mechanism\n  template: define-unit (extract illness reports) ->\n            loop(characterise -> define-unit -> visualise -> assess) ->\n            characterise (zone membership) -> define-unit (zone-day units) -> characterise (aggregate, normalise) ->\n            define-unit (group into days) -> characterise (day profile) -> define-unit (cluster-by-similarity) ->\n            contextualise (calendar) -> visualise -> abstract -> generate-knowledge (onset) ->\n            loop(define-unit (spatio-temporal place clustering) -> define-unit (derive-places) ->\n                 characterise -> visualise -> abstract -> assess) -> generate-knowledge (origin) ->\n            define-unit (cluster zones by incidence profile) -> visualise -> abstract -> generate-knowledge (spread pattern) ->\n            loop(characterise (wind cross-impact) -> visualise -> abstract -> assess) ->\n            generate-knowledge (airborne hypothesis) ->\n            characterise (social relations) -> visualise -> abstract ->\n            generate-knowledge (person-to-person & waterborne hypotheses) ->\n            assess (compare hypotheses) -> generate-knowledge (synthesis)\n  description: \"Investigative visual analytics workflow to identify the spatio-temporal\n    origin of the Vastopolis flu outbreak and to determine its most plausible transmission\n    mechanism (person-to-person, airborne, waterborne, or other) from geolocated microblog\n    text, map context, zone population, and wind data.\"\n\n# ============================================================================\n# GIVEN (EXOGENOUS) ARTIFACTS\n# ============================================================================\n\nartifact D_messages : entities\n  origin: given\n  internal structure: elementary\n  embedment: {set, time, space}\n  features:\n    - id: text\n      value structure: atomic\n      value type: text\n      description: \"Free-form message content\"\n  description: \"Geolocated microblog messages posted from mobile devices over\n    several days, each with timestamp, GPS coordinates, and text\"\n\nartifact D_zones : entities\n  origin: given\n  internal structure: region\n  embedment: space\n  features:\n    - id: population\n      value structure: atomic\n      value type: numeric\n      description: \"Resident population of the zone\"\n  description: \"13 city zones with known boundaries and population statistics\"\n\nartifact D_weather : entities\n  origin: given\n  internal structure: elementary\n  embedment: time\n  features:\n    - id: wind_speed\n      value structure: atomic\n      value type: numeric\n      description: \"Daily wind speed\"\n    - id: wind_direction\n      value structure: atomic\n      value type: numeric\n      description: \"Daily wind direction (degrees)\"\n  description: \"Daily citywide weather observations\"\n\nartifact D_road_network : entities\n  origin: given\n  internal structure: formation\n  description: \"Single network entity representing highways and transit routes\n    across the metropolitan area\"\n\nartifact D_water_bodies : entities\n  origin: given\n  internal structure: region\n  embedment: space\n  features:\n    - id: water_type\n      value structure: atomic\n      value type: categorical\n      description: \"Reservoir, river, lake, treatment facility, etc.\"\n  description: \"Water bodies and related infrastructure shown on the city map\"\n\nartifact D_landmarks : entities\n  origin: given\n  internal structure: elementary\n  embedment: space\n  features:\n    - id: landmark_type\n      value structure: atomic\n      value type: categorical\n      description: \"Hospital, stadium, park, venue, etc.\"\n  description: \"Named landmarks and hospitals on the city map\"\n\nartifact D_calendar : entities\n  origin: given\n  internal structure: elementary\n  embedment: time\n  features:\n    - id: weekday\n      value structure: atomic\n      value type: categorical\n      description: \"Day of week\"\n  description: \"Calendar days spanning the observation period\"\n\nartifact K_mechanism_criteria : knowledge\n  origin: given\n  representation form: \"diagnostic checklist\"\n  description: \"Domain public-health knowledge describing the expected\n    spatial/temporal/contextual signatures of person-to-person, airborne,\n    waterborne, and other transmission mechanisms\"\n\nartifact S_symptom_lexicon : specification\n  origin: given\n  representation form: \"lexicon / rules\"\n  description: \"Initial keyword and pattern lexicon for flu-like symptoms,\n    severity indicators (hospitalisation, death), and illness-relevance\"\n\n# ============================================================================\n# PHASE 1: EXTRACT AND VALIDATE ILLNESS-REPORT EVENTS FROM TEXT\n# cf. Jigsaw (Stasko, Görg & Liu 2008) — entity extraction with human correction\n# ============================================================================\n\nassign:\n  S_lexicon_current := S_symptom_lexicon\n\nloop L_extraction:\n  purpose: \"Extract illness-report events from raw microblog text and validate\n    extraction quality against manual sample review\"\n  until: \"Sampled extracted reports are judged accurate (low false positive/\n    negative rate) and severity coding is consistent\"\n  body:\n\n    transform T_extract_symptom_features :\n      intent: characterise\n      manner: \"extract (symptom and severity indicators from text via lexicon/NLP)\"\n      input: D_messages, S_lexicon_current\n      output: F_symptom_features\n      actor: hybrid\n      description: \"Detect symptom mentions, severity level, and illness-relevance\n        flag for each message\"\n\n    artifact F_symptom_features : feature(D_messages)\n      value structure: vector\n      value type: {categorical, ordinal}\n      description: \"Symptom-term matches, inferred severity level, and\n        illness-relevance flag per message\"\n\n    transform T_filter_illness :\n      intent: define-unit\n      manner: \"filter\"\n      input: D_messages, F_symptom_features\n      output: D_illness_reports\n      actor: machine\n      description: \"Retain only messages flagged as illness-relevant, carrying\n        forward symptom type, severity, and original text\"\n\n    artifact D_illness_reports : entities\n      internal structure: elementary\n      embedment: {set, time, space}\n      features:\n        - id: symptom_type\n          value structure: atomic\n          value type: categorical\n          description: \"Type of symptom mentioned\"\n        - id: severity\n          value structure: atomic\n          value type: ordinal\n          description: \"Severity level (mild / severe / death-associated)\"\n        - id: text\n          value structure: atomic\n          value type: text\n          description: \"Original message text, retained for drill-down\"\n      description: \"Geolocated, time-stamped illness-report events extracted\n        from microblog text\"\n\n    transform T_vis_sample :\n      intent: visualise\n      manner: \"sampled-record review\"\n      input: D_illness_reports\n      output: V_sample_reports\n      actor: machine\n      layout: \"record list linked to a small map inset\"\n      form: \"text list with location/time annotations\"\n      encoding: \"list rows from D_illness_reports; map marker from spatial\n        component; colour from severity\"\n      description: \"Sample of extracted reports for manual inspection\"\n\n    transform T_assess_extraction :\n      intent: assess\n      manner: \"judge extraction precision/recall via manual sample review\"\n      input: V_sample_reports\n      output: K_extraction_quality\n      actor: human\n\n    artifact K_extraction_quality : knowledge(D_illness_reports)\n      representation form: \"quality judgment\"\n      description: \"Judgment of whether extracted reports correctly capture\n        illness-relevance and severity, and whether the lexicon needs revision\"\n\n    if K_extraction_quality indicates refinement needed:\n      then:\n        transform T_refine_lexicon :\n          intent: generate-knowledge\n          manner: \"revise extraction lexicon/rules\"\n          input: K_extraction_quality, S_lexicon_current\n          output: S_lexicon_r\n          actor: human\n\n        artifact S_lexicon_r : specification\n          representation form: \"lexicon / rules\"\n          description: \"Revised symptom/severity lexicon addressing observed\n            false positives/negatives\"\n\n        assign:\n          S_lexicon_current := S_lexicon_r\n      else:\n        exit loop L_extraction\n\nend loop L_extraction\n\n# ============================================================================\n# PHASE 2: SPATIAL JOIN AND ZONE-DAY AGGREGATION\n# ============================================================================\n\ntransform T_zone_membership :\n  intent: characterise\n  manner: \"define-relations (spatial containment / point-in-polygon join)\"\n  input: D_illness_reports, D_zones\n  output: F_zone_membership\n  actor: machine\n  description: \"Determine which of the 13 zones contains each illness report\"\n\nartifact F_zone_membership : feature(D_illness_reports)\n  value structure: atomic\n  value type: reference\n  description: \"Zone identifier containing each report\"\n\ntransform T_define_zone_days :\n  intent: define-unit\n  manner: \"group-by-entity and time-partitioning\"\n  input: D_illness_reports, F_zone_membership\n  output: D_zone_days\n  actor: machine\n  description: \"Group illness reports into zone x day units\"\n\nartifact D_zone_days : entities\n  internal structure: episode\n  embedment: {space, time}\n  features:\n    - id: zone_id\n      value structure: atomic\n      value type: reference\n      description: \"Zone identifier\"\n    - id: day\n      value structure: atomic\n      value type: temporal\n      description: \"Calendar day\"\n  description: \"Zone-day units aggregating illness reports\"\n\ntransform T_aggregate_counts :\n  intent: characterise\n  manner: \"aggregate (count and severity-weighted count)\"\n  input: D_zone_days, D_illness_reports\n  output: F_zone_day_counts\n  actor: machine\n\nartifact F_zone_day_counts : feature(D_zone_days)\n  value structure: vector\n  value type: numeric\n  description: \"Raw report count and severity-weighted count per zone-day\"\n\ntransform T_normalise_incidence :\n  intent: characterise\n  manner: \"normalise by population (per-capita incidence)\"\n  input: F_zone_day_counts, D_zones\n  output: F_zone_day_incidence\n  actor: machine\n\nartifact F_zone_day_incidence : feature(D_zone_days)\n  value structure: atomic\n  value type: numeric\n  description: \"Population-normalised incidence rate for the zone-day,\n    comparable across zones of different size\"\n\n# ============================================================================\n# PHASE 3: TEMPORAL ONSET DETECTION\n# cf. Cluster and Calendar Based Visualization (van Wijk & van Selow 1999)\n# ============================================================================\n\ntransform T_define_days :\n  intent: define-unit\n  manner: \"group-by-entity (all zones per calendar day)\"\n  input: D_zone_days\n  output: D_days\n  actor: machine\n\nartifact D_days : entities\n  internal structure: group/cluster\n  embedment: time\n  features:\n    - id: date\n      value structure: atomic\n      value type: temporal\n      description: \"Calendar date\"\n  description: \"Daily units grouping all 13 zone-day entities for that day\"\n\ntransform T_day_profile :\n  intent: characterise\n  manner: \"aggregate (assemble per-zone incidence vector)\"\n  input: D_days, F_zone_day_incidence\n  output: F_day_profile\n  actor: machine\n\nartifact F_day_profile : feature(D_days)\n  value structure: vector\n  value type: numeric\n  representation form: \"13-element vector of per-zone incidence\"\n  description: \"Spatial incidence profile for each day\"\n\ntransform T_cluster_days :\n  intent: define-unit\n  manner: \"cluster-by-similarity (day-profile shape vs. pre-outbreak baseline)\"\n  input: D_days, F_day_profile\n  output: D_day_clusters, F_day_cluster_label\n  actor: machine\n\nartifact D_day_clusters : entities\n  internal structure: group/cluster\n  embedment: set\n  description: \"Groups of days with similar profile shape (baseline vs. anomalous)\"\n\nartifact F_day_cluster_label : feature(D_days)\n  value structure: atomic\n  value type: categorical\n  description: \"Cluster membership per day\"\n\ntransform T_contextualise_calendar :\n  intent: contextualise\n  manner: \"calendar-based\"\n  input: D_days, D_calendar\n  output: A_days_calendar\n  actor: machine\n\nartifact A_days_calendar : arrangement(D_days)\n  context: D_calendar\n  principle: \"calendar(year, month, weekday)\"\n  description: \"Calendar-cell placement of each day\"\n\ntransform T_vis_calendar :\n  intent: visualise\n  manner: \"coloured-marks\"\n  input: A_days_calendar, F_day_cluster_label, F_day_profile\n  output: V_calendar\n  actor: machine\n  layout: \"calendar grid\"\n  form: \"coloured marks with linked per-zone profile glyphs\"\n  encoding: \"position from A_days_calendar; colour from F_day_cluster_label;\n    glyph shape from F_day_profile\"\n\ntransform T_abstract_onset :\n  intent: abstract\n  manner: \"perception-and-interpretation\"\n  input: V_calendar\n  output: P_onset\n  actor: human\n  description: \"Identify the day(s) that break from baseline and the zone(s)\n    first showing elevated incidence\"\n\nartifact P_onset : pattern(D_days, D_zone_days)\n  representation form: \"textual description\"\n  description: \"Onset date(s) and earliest-affected zone(s)\"\n\ntransform T_knowledge_onset :\n  intent: generate-knowledge\n  manner: \"formulate-statements\"\n  input: P_onset\n  output: K_onset\n  actor: human\n\nartifact K_onset : knowledge(D_days, D_zone_days)\n  representation form: \"statements\"\n  description: \"Outbreak onset date and earliest-affected zone(s)\"\n\n# ============================================================================\n# PHASE 4: SPATIO-TEMPORAL PLACE EXTRACTION (CANDIDATE ORIGIN)\n# cf. From Movement Tracks through Events to Places (Andrienko et al. 2011)\n# ============================================================================\n\nartifact S_place_cluster_params : specification\n  origin: given\n  representation form: \"parameter settings\"\n  description: \"Initial spatial/temporal distance thresholds for density-based\n    clustering of illness reports\"\n\nassign:\n  S_place_params_current := S_place_cluster_params\n\nloop L_origin_clustering:\n  purpose: \"Identify candidate outbreak-origin place(s) via spatio-temporal\n    density clustering of illness reports\"\n  until: \"Resulting place clusters are spatially coherent, temporally tight,\n    and interpretable against map landmarks and water bodies\"\n  body:\n\n    transform T_cluster_reports :\n      intent: define-unit\n      manner: \"cluster-by-similarity (spatio-temporal density clustering)\"\n      input: D_illness_reports, S_place_params_current\n      output: D_report_clusters, F_report_cluster_label\n      actor: machine\n\n    artifact D_report_clusters : entities\n      internal structure: group/cluster\n      embedment: set\n      description: \"Density-based clusters of illness reports close in both\n        space and time; unclustered reports treated as noise\"\n\n    artifact F_report_cluster_label : feature(D_illness_reports)\n      value structure: atomic\n      value type: categorical\n      description: \"Cluster (or noise) membership per report\"\n\n    transform T_derive_places :\n      intent: define-unit\n      manner: \"derive-places (spatial hull aggregation of clusters)\"\n      input: D_report_clusters\n      output: D_places\n      actor: machine\n\n    artifact D_places : entities\n      internal structure: region\n      embedment: space\n      features:\n        - id: onset_time\n          value structure: atomic\n          value type: temporal\n          description: \"Time of earliest report in the place cluster\"\n      description: \"Candidate significant places delineated from report clusters\"\n\n    transform T_place_timeseries :\n      intent: characterise\n      manner: \"aggregate (per-place daily incidence time series)\"\n      input: D_places, D_report_clusters\n      output: F_place_timeseries\n      actor: machine\n\n    artifact F_place_timeseries : feature(D_places)\n      value structure: vector\n      value type: numeric\n      representation form: \"daily incidence counts time series\"\n      description: \"Time series of report counts for each candidate place\"\n\n    transform T_vis_places :\n      intent: visualise\n      manner: \"map overlay with linked timelines\"\n      input: D_places, F_place_timeseries, D_landmarks, D_water_bodies, D_road_network\n      output: V_places_map\n      actor: machine\n      layout: \"geographic map with road network, landmarks, and water bodies overlaid\"\n      form: \"cluster hulls coloured by onset time; point/line map layers; linked\n        small-multiple incidence curves per place\"\n      encoding: \"hull position/extent from D_places; colour from onset_time;\n        curve shape from F_place_timeseries\"\n\n    transform T_abstract_origin :\n      intent: abstract\n      manner: \"perception-and-interpretation\"\n      input: V_places_map\n      output: P_origin_candidate\n      actor: human\n      description: \"Identify the earliest/most severe place and its associated\n        landmark type (venue, water source, transit hub, etc.)\"\n\n    artifact P_origin_candidate : pattern(D_places)\n      representation form: \"textual description\"\n      description: \"Candidate origin place and its physical character\"\n\n    transform T_assess_places :\n      intent: assess\n      manner: \"judge cluster coherence and interpretability\"\n      input: V_places_map, P_origin_candidate\n      output: K_place_quality\n      actor: human\n\n    artifact K_place_quality : knowledge(D_places)\n      representation form: \"quality judgment\"\n      description: \"Judgment of whether clustering parameters yield coherent,\n        interpretable place clusters\"\n\n    if K_place_quality indicates refinement needed:\n      then:\n        transform T_refine_place_params :\n          intent: generate-knowledge\n          manner: \"revise clustering parameters\"\n          input: K_place_quality, S_place_params_current\n          output: S_place_params_r\n          actor: human\n\n        artifact S_place_params_r : specification\n          representation form: \"parameter settings\"\n          description: \"Revised spatial/temporal distance thresholds\"\n\n        assign:\n          S_place_params_current := S_place_params_r\n      else:\n        exit loop L_origin_clustering\n\nend loop L_origin_clustering\n\ntransform T_knowledge_origin :\n  intent: generate-knowledge\n  manner: \"formulate-statements\"\n  input: P_origin_candidate\n  output: K_origin\n  actor: human\n\nartifact K_origin : knowledge(D_places, P_origin_candidate)\n  representation form: \"statements\"\n  description: \"Identified candidate outbreak origin place and its character\n    (e.g., water source, public venue, transit hub)\"\n\n# ============================================================================\n# PHASE 5: ZONE-LEVEL SPREAD PATTERN\n# cf. A Visual Analytics Framework for Spatio-Temporal Analysis and Modelling\n#     (Andrienko & Andrienko, 2013)\n# ============================================================================\n\ntransform T_cluster_zones :\n  intent: define-unit\n  manner: \"cluster-by-similarity (zone incidence-profile similarity)\"\n  input: D_zones, F_zone_day_incidence\n  output: D_zone_groups, F_zone_group_label\n  actor: machine\n\nartifact D_zone_groups : entities\n  internal structure: group/cluster\n  embedment: set\n  description: \"Groups of zones with similar temporal incidence profiles\"\n\nartifact F_zone_group_label : feature(D_zones)\n  value structure: atomic\n  value type: categorical\n  description: \"Zone-group membership\"\n\ntransform T_vis_zone_spread :\n  intent: visualise\n  manner: \"map overlay with linked time series\"\n  input: D_zones, F_zone_group_label, F_zone_day_incidence, D_road_network\n  output: V_zone_spread\n  actor: machine\n  layout: \"geographic map of zones with road network overlaid; linked aligned\n    time-series small multiples per zone\"\n  form: \"choropleth coloured by group membership; line network; small-multiple\n    line charts\"\n  encoding: \"zone fill colour from F_zone_group_label; line-chart shape from\n    F_zone_day_incidence\"\n\ntransform T_abstract_spread :\n  intent: abstract\n  manner: \"perception-and-interpretation\"\n  input: V_zone_spread, K_origin\n  output: P_spread_pattern\n  actor: human\n  description: \"Relate zone grouping and timing lag to the road/transit network\n    and to the candidate origin place: contiguous road-following diffusion vs.\n    disconnected shared-cause pattern\"\n\nartifact P_spread_pattern : pattern(D_zone_groups, D_zones)\n  representation form: \"textual description\"\n  description: \"Characterisation of how incidence spreads across zones relative\n    to road connectivity and the candidate origin\"\n\ntransform T_knowledge_spread :\n  intent: generate-knowledge\n  manner: \"formulate-statements\"\n  input: P_spread_pattern\n  output: K_spread_pattern\n  actor: human\n\nartifact K_spread_pattern : knowledge(D_zone_groups)\n  representation form: \"statements\"\n  description: \"Conclusions about the geographic diffusion pattern of the outbreak\"\n\n# ============================================================================\n# PHASE 6: WIND CROSS-IMPACT ANALYSIS (AIRBORNE HYPOTHESIS)\n# cf. Detecting Stable Cross-Impact Patterns in Bivariate Time Series\n#     (Andrienko et al., 2026)\n# ============================================================================\n\nartifact S_crossimpact_params : specification\n  origin: given\n  representation form: \"parameter settings\"\n  description: \"Initial window length, lag range, and tolerance for cross-impact\n    detection between wind and zone incidence series\"\n\nassign:\n  S_crossimpact_current := S_crossimpact_params\n\nloop L_crossimpact:\n  purpose: \"Test whether zone incidence changes follow wind direction/timing\n    consistent with airborne dispersal from the candidate origin\"\n  until: \"Cross-impact pattern is stable and clearly interpretable across a\n    reasonable parameter range\"\n  body:\n\n    transform T_characterise_crossimpact :\n      intent: characterise\n      manner: \"define-relations (time-lagged, direction-aware cross-impact)\"\n      input: D_weather, F_zone_day_incidence, D_zones, K_origin, S_crossimpact_current\n      output: F_crossimpact_matrix\n      actor: machine\n      description: \"Compute lagged association between daily wind vector (relative\n        to candidate origin bearing) and each zone's incidence series\"\n\n    artifact F_crossimpact_matrix : feature(D_zones)\n      value structure: matrix\n      value type: numeric\n      representation form: \"time x lag cross-impact score matrix (Ikat-style)\"\n      description: \"Per-zone matrix of lagged association strength between wind\n        and incidence\"\n\n    transform T_vis_crossimpact :\n      intent: visualise\n      manner: \"matrix-plot\"\n      input: F_crossimpact_matrix, D_weather, F_zone_day_incidence\n      output: V_crossimpact\n      actor: machine\n      layout: \"time-by-lag Ikat matrix per zone, linked to raw wind and incidence series\"\n      form: \"coloured matrix cells with drill-down line charts\"\n      encoding: \"cell colour from F_crossimpact_matrix; linked charts from raw series\"\n\n    transform T_abstract_crossimpact :\n      intent: abstract\n      manner: \"find-salient-groups\"\n      input: V_crossimpact\n      output: P_wind_pattern\n      actor: human\n      description: \"Identify zones/lags with stable, plausible-incubation-consistent\n        positive impact aligned with downwind direction\"\n\n    artifact P_wind_pattern : pattern(F_crossimpact_matrix)\n      representation form: \"textual description\"\n      description: \"Zones and lags showing a stable wind-direction-consistent\n        impact pattern, if any\"\n\n    transform T_assess_crossimpact :\n      intent: assess\n      manner: \"judge stability and plausibility of detected impact pattern\"\n      input: V_crossimpact, P_wind_pattern\n      output: K_crossimpact_quality\n      actor: human\n\n    artifact K_crossimpact_quality : knowledge(F_crossimpact_matrix)\n      representation form: \"quality judgment\"\n      description: \"Judgment of whether the detected pattern is robust or an\n        artefact of window/lag/tolerance settings\"\n\n    if K_crossimpact_quality indicates refinement needed:\n      then:\n        transform T_refine_crossimpact_params :\n          intent: generate-knowledge\n          manner: \"revise window/lag/tolerance settings\"\n          input: K_crossimpact_quality, S_crossimpact_current\n          output: S_crossimpact_r\n          actor: human\n\n        artifact S_crossimpact_r : specification\n          representation form: \"parameter settings\"\n          description: \"Revised cross-impact window length, lag range, tolerance\"\n\n        assign:\n          S_crossimpact_current := S_crossimpact_r\n      else:\n        exit loop L_crossimpact\n\nend loop L_crossimpact\n\ntransform T_knowledge_airborne :\n  intent: generate-knowledge\n  manner: \"formulate-statements\"\n  input: P_wind_pattern\n  output: K_airborne_hypothesis\n  actor: human\n\nartifact K_airborne_hypothesis : knowledge(F_crossimpact_matrix)\n  representation form: \"statements\"\n  description: \"Assessment of support for airborne/wind-driven transmission\n    from the candidate origin\"\n\n# ============================================================================\n# PHASE 7: PERSON-TO-PERSON AND WATERBORNE MECHANISM CHECKS\n# cf. Jigsaw (Stasko, Görg & Liu 2008) — relation extraction from text\n# ============================================================================\n\ntransform T_social_relations :\n  intent: characterise\n  manner: \"extract (social/co-location mentions and shared-venue references)\"\n  input: D_illness_reports\n  output: F_social_relations\n  actor: hybrid\n  description: \"Detect mentions of shared households, workplaces, schools,\n    transit lines, or events across reports\"\n\nartifact F_social_relations : feature(D_illness_reports)\n  value structure: relational configuration\n  value type: {text, reference}\n  representation form: \"co-occurrence network of reports linked via shared\n    persons, venues, or transit references\"\n  description: \"Contact/co-location network derived from message text\"\n\ntransform T_vis_mechanism :\n  intent: visualise\n  manner: \"map overlay linked to relation network\"\n  input: D_places, D_zones, F_zone_group_label, D_water_bodies, D_road_network,\n         F_social_relations\n  output: V_mechanism_overlay\n  actor: machine\n  layout: \"geographic map overlay of origin place, zone groups, water bodies,\n    and road network, linked to a social co-occurrence network diagram\"\n  form: \"map layers plus node-link diagram\"\n  encoding: \"map layers from D_places/D_zones/D_water_bodies/D_road_network;\n    node-link diagram from F_social_relations\"\n\ntransform T_abstract_mechanism_signatures :\n  intent: abstract\n  manner: \"perception-and-interpretation\"\n  input: V_mechanism_overlay, K_mechanism_criteria\n  output: P_mechanism_signatures\n  actor: human\n  description: \"Check spread pattern against road/transit-following contact\n    chains versus alignment with water-body/service-area boundaries, using the\n    domain diagnostic checklist\"\n\nartifact P_mechanism_signatures : pattern(V_mechanism_overlay)\n  representation form: \"textual description\"\n  description: \"Degree to which observed spread matches person-to-person\n    (contact-chain/road-following) versus waterborne (water-source/service-area)\n    signatures\"\n\ntransform T_knowledge_person_to_person :\n  intent: generate-knowledge\n  manner: \"formulate-statements\"\n  input: P_mechanism_signatures, K_mechanism_criteria\n  output: K_person_to_person_hypothesis\n  actor: human\n\nartifact K_person_to_person_hypothesis : knowledge(F_social_relations)\n  representation form: \"statements\"\n  description: \"Assessment of support for person-to-person transmission\"\n\ntransform T_knowledge_waterborne :\n  intent: generate-knowledge\n  manner: \"formulate-statements\"\n  input: P_mechanism_signatures, K_mechanism_criteria\n  output: K_waterborne_hypothesis\n  actor: human\n\nartifact K_waterborne_hypothesis : knowledge(D_water_bodies, D_places)\n  representation form: \"statements\"\n  description: \"Assessment of support for waterborne transmission\"\n\n# ============================================================================\n# PHASE 8: SYNTHESIS\n# ============================================================================\n\ntransform T_assess_hypotheses :\n  intent: assess\n  manner: \"compare hypotheses against diagnostic criteria and evidence strength\"\n  input: K_origin, K_spread_pattern, K_airborne_hypothesis,\n         K_person_to_person_hypothesis, K_waterborne_hypothesis,\n         K_mechanism_criteria\n  output: K_hypothesis_assessment\n  actor: human\n\nartifact K_hypothesis_assessment : knowledge(K_origin, K_spread_pattern)\n  representation form: \"comparative judgment\"\n  description: \"Ranking of transmission-mechanism hypotheses by evidentiary\n    support; flags whether conclusion is well-supported or inconclusive\"\n\nif K_hypothesis_assessment indicates inconclusive:\n  then:\n    transform T_recommend_further_data :\n      intent: generate-knowledge\n      manner: \"derive-rules\"\n      input: K_hypothesis_assessment\n      output: K_further_investigation\n      actor: human\n\n    artifact K_further_investigation : knowledge(K_hypothesis_assessment)\n      representation form: \"recommendations\"\n      description: \"Recommended additional data collection or targeted\n        investigation to resolve ambiguity (e.g., water-testing, contact tracing)\"\n  else:\n    transform T_final_synthesis :\n      intent: generate-knowledge\n      manner: \"write-summary\"\n      input: K_hypothesis_assessment, K_onset\n      output: K_final_report\n      actor: human\n\n    artifact K_final_report : knowledge(K_hypothesis_assessment, K_onset)\n      representation form: \"statements and recommendations\"\n      description: \"Final conclusions on outbreak origin, spread pattern, and\n        most-supported transmission mechanism, with recommended emergency\n        management actions\"\n```",
      "role": "assistant"
    },
    {
      "content": "Please summarise how the ATWL design departs from the natural language version.",
      "role": "user"
    },
    {
      "content": "<think>I'm identifying the main structural differences between the two approaches. The natural language version describes iteration informally, while the ATWL design formalizes explicit calibration loops for the extraction lexicon, clustering parameters, and cross-impact calculations. The phase boundaries also shift—the natural language outlines seven stages, but ATWL reorganizes this into eight phases with different breakpoints. Additionally, population normalization becomes an explicit transformation step rather than being embedded within other processes.\n\nThe ATWL design also separates assessment as a distinct transform type from abstraction, whereas the natural language version blends judgment and interpretation together. Actor designations are now explicit (human, machine, or hybrid), making the division of labor clearer. The calendar-based clustering analysis transforms from a narrative description into a formal pipeline with define-unit, contextualize, visualize, and abstract steps. Wind analysis gains an explicit calibration loop with assess-and-refine cycles, and the final synthesis stage introduces conditional branching—if results are inconclusive, it recommends further data collection; otherwise, it proceeds to the final report. The design also distinguishes between zone-day units, day units, and place units as separate entity types.\n\nThe person-to-person and waterborne transmission checks are now separated into two distinct knowledge-generation transforms that share visualization and abstraction steps but produce independent outputs, whereas the natural language version combined them into a single stage. Knowledge artifacts are explicitly typed with dependencies tracked between them. Notably, the mechanism-checklist stage and zone-spread phase operate as single-pass transforms without iteration loops, since there's no tunable parameter requiring calibration—a distinction the natural language version didn't formalize. The assess transform is applied specifically to extraction quality, place-cluster quality, cross-impact quality, and final hypothesis comparison.\n\nI'm organizing the major design departures thematically rather than phase-by-phase: explicit loop calibration only where specs are genuinely tunable, separation of assess versus abstract versus generate-knowledge as distinct transform intents, granular unit definition at multiple levels, explicit actor designation throughout, conditional branching in final synthesis, different phase merging and splitting patterns, and explicit dependency chains for features and knowledge artifacts.</think>\n\nHere is a summary of how the ATWL version departs from my earlier natural-language design, organized by the main structural decisions that changed.\n\n## 1. Loops were localized to genuinely tunable specifications, not sprinkled everywhere\n\nThe natural-language version described \"iterative refinement\" loosely across nearly every stage (calibrate clustering distance, calibrate lag window, refine hypotheses). In ATWL I had to be explicit about *what specification artifact is actually being revised* in each loop, which forced a more disciplined allocation:\n\n- **Phase 1** (symptom/severity extraction) — loops on `S_symptom_lexicon`, exiting when sampled extraction quality is judged acceptable.\n- **Phase 4** (origin place clustering) — loops on `S_place_cluster_params`, exiting when cluster coherence/interpretability is judged acceptable.\n- **Phase 6** (wind cross-impact) — loops on `S_crossimpact_params` (window/lag/tolerance), exiting when the impact pattern is judged stable.\n\nPhases 3, 5, and 7 (calendar onset detection, zone spread-pattern reading, mechanism checklist) were **not** given loops, because there is no tunable specification artifact driving them — they are single-pass interpretive judgments over a fixed visualization. The NL version did not make this distinction; ATWL's requirement to name a `specification` artifact for every loop forced me to separate \"steps with a calibratable parameter\" from \"steps that are pure human interpretation.\"\n\n## 2. `abstract`, `assess`, and `generate-knowledge` were pulled apart as distinct operations\n\nIn the natural-language description, \"the analyst inspects the pattern, judges it, and concludes something\" was usually one narrative sentence. ATWL's type system forced me to separate:\n\n- **`abstract`** — perceiving a pattern in a visualization (e.g., `P_origin_candidate`, `P_wind_pattern`, `P_mechanism_signatures`) — this is about *what is seen*.\n- **`assess`** — judging quality/adequacy of a result against a criterion (e.g., `K_extraction_quality`, `K_place_quality`, `K_crossimpact_quality`, and the final `K_hypothesis_assessment`) — this is about *whether to trust or accept it*.\n- **`generate-knowledge`** — turning an accepted pattern into a stated conclusion (e.g., `K_onset`, `K_origin`, `K_airborne_hypothesis`) — this is about *committing to a claim*.\n\nThis is a real structural gain over the NL version: it exposes that \"judging whether the clustering worked\" (assess, feeds back into the loop) and \"concluding this is the outbreak origin\" (generate-knowledge, feeds forward into synthesis) are different acts that the NL prose had merged into a single \"judgment point.\"\n\n## 3. Unit definition was made explicit and granular at each aggregation level\n\nThe NL version moved fairly informally from \"messages\" to \"zones\" to \"days\" to \"places,\" describing aggregation in passing. ATWL required each grain to be a named `define-unit` transform producing a typed entity artifact:\n\n- `D_illness_reports` (filtered messages)\n- `D_zone_days` (zone × day units, via spatial join + temporal partition)\n- `D_days` (day units grouping all zones, for calendar analysis)\n- `D_places` (spatio-temporal clusters aggregated into regions)\n- `D_zone_groups` (zones clustered by profile similarity)\n\nThis is a genuine departure: the NL version conflated \"cluster days by shape\" and \"cluster zones by profile\" and \"cluster events into places\" as similar-sounding operations happening within one narrative stage (Stage B/D), whereas ATWL's insistence on distinct `entities` artifacts per grain made clear these are four separate unit-definition operations feeding different downstream analyses, not one generic \"clustering step.\"\n\n## 4. Population normalization became its own explicit transform, not an aside\n\nIn the NL version, per-capita normalization was mentioned parenthetically inside Stage C (\"normalize event counts... by population\"). In ATWL it is promoted to a first-class transform (`T_normalise_incidence`) producing its own feature artifact (`F_zone_day_incidence`), because every downstream phase (calendar clustering, place time series, zone grouping, cross-impact) explicitly consumes this normalized feature rather than raw counts. Making it explicit surfaced that *all* subsequent spatial/temporal comparisons depend on this one normalization choice — a dependency that was easy to lose track of in prose but is now structurally visible.\n\n## 5. The person-to-person/waterborne stage was restructured around one shared visualization feeding two independent knowledge outputs\n\nThe NL version (Stage F) treated person-to-person and waterborne checks as two separate sub-narratives with separate evidence gathering. In ATWL, I redesigned this as: one `characterise` (social relations extraction), one combined `visualise` (map + network overlay), one `abstract` (mechanism signatures against the domain checklist), and then **two parallel `generate-knowledge` transforms** off the same abstracted pattern — `K_person_to_person_hypothesis` and `K_waterborne_hypothesis`. This is a better design than the NL version because it makes explicit that both hypotheses are being read off the *same* visual evidence rather than requiring separate investigative passes, which is actually closer to how an analyst would work.\n\n## 6. Synthesis was restructured with an explicit branch instead of a single \"write report\" step\n\nThe NL version ended with a single Stage G describing dashboard synthesis and report writing as one continuous activity. ATWL's `assess` on `K_hypothesis_assessment` naturally suggested a conditional branch that wasn't explicit before:\n\n- **if inconclusive** → `generate-knowledge` produces `K_further_investigation` (recommend water testing, contact tracing, etc.) instead of a final conclusion\n- **else** → `generate-knowledge` produces `K_final_report`\n\nThis branch is a genuine addition: the NL version implicitly assumed the analysis would converge to a conclusion, whereas ATWL's assess/branch structure makes \"the evidence may not resolve the mechanism, and that itself is an actionable output\" a first-class outcome of the workflow.\n\n## 7. Actor designation made the human/machine division explicit throughout\n\nThe NL version described the whole workflow as human-supervised without specifying, transform by transform, what is automatable. ATWL's `actor` field forced this out: extraction and clustering steps are `machine` or `hybrid`, while every `abstract`, `assess`, and `generate-knowledge` transform is `actor: human`. This makes explicit — in a way the NL version left implicit — that the workflow's analytical judgment load sits entirely at the interpretation/validation points, not at the data-processing points, which is a useful design fact for an analyst planning tool support.",
      "role": "assistant"
    }
  ]
}