Visual Analytics Plan Presentation

Postcard

GROUP 30

• Md Istain Ahmed

• Emon Basher

• Samiul Islam Sami

M1: List & Filters MANDATORY

Effective Data Exploration

  • Dynamic List: Sidebar with scrollable postcard items showing key metadata.
  • Global Filters: Multi-select for countries, years, and semantic topics.
  • Range Filtering: Slider for travel distance and temporal selection.
M1 Sketch

M2: Spatial Clustering MANDATORY

Unsupervised Feature Grouping

  • Feature Extraction: Using CLIP model for high-dimensional image embeddings.
  • Similarity Metric: Calculating Cosine Similarity between image vectors.
  • Algorithm: Applying K-Means to group postcards into clusters.
M2 Sketch

M3: Linked Views MANDATORY

Dual-View Interaction

  • Interactive Linking: Selecting a cluster in the spatial view highlights relevant paths on the map.
  • Geo-Spatial Path: World map with Bezier curves representing postcard journeys.
  • Visual Consistency: Color-coding clusters to match map paths.
M3 Sketch

E2 & E5: Outliers & Animation ELECTIVE

Anomalies & Temporal Flow

  • E2 (Outliers): Visually highlighting "Late Arrivals" (cards with outlier travel durations) on the map.
  • E5 (Animation): Smooth animation of dots moving along the paths to show postcard flow over time.
E2: Late Arrivals
E5: Map Animation

E3: Semantic Zooming ELECTIVE

Detail on Demand

  • Dynamic Zooming: Seamlessly transitioning from cluster dots to high-res postcard thumbnails.
  • Performance Fix: Smart rendering logic to maintain 60fps while handling large image datasets.
Open Question: What are the best rendering strategies (e.g., Canvas vs. WebGL, lazy loading) to prevent lag and maintain 60fps when semantically zooming into thousands of high-res images?
E3 Sketch

Programming Stack ⚙️

Tools & Frameworks

  • Backend/Processing: Python (Pandas, Scikit-Learn, CLIP).
  • Frontend: HTML5, CSS3, JavaScript (ES6+).
  • Libraries: Leaflet.js (Map), D3.js (Spatial Clustering).
🐍 ➜ 🌐

Python Processing ➜ Web Visualization

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