Fork of Hello VizHub
An interactive D3.js force-directed graph illustrates a spatio-temporal malaria incidence analysis pipeline, showing data flow from input through parallel statistical and machine learning model branches to a unified evaluation framework. The SVG visualization uses `d3.forceSimulation` with link, charge, center, and y forces to maintain a top-to-bottom layout while allowing users to drag nodes. Nodes are color-coded rectangles with multi-line labels, and edges include arrow markers to indicate directional flow between analysis components.
AI-generated descriptionSpatio-Temporal Malaria Incidence Analysis Pipeline
This visualization displays the workflow for analyzing spatio-temporal malaria incidence data using both statistical and machine learning approaches.
Features
- Interactive Graph: Drag nodes to explore relationships between different analysis components
- Statistical Models: Bayesian, CAR, and Gaussian approaches for parameter estimation
- Machine Learning Models: Random Forest, XGBoost, and CNN/LSTM for pattern learning
- Unified Evaluation: Comprehensive assessment framework considering accuracy, uncertainty, and interpretability
Components
- Input: Spatio-Temporal malaria incidence data
- Processing: Two parallel pipelines (statistical and ML-based)
- Output: Unified evaluation framework combining insights from both approaches
How to Use
The graph is interactive - you can drag nodes around to better understand the flow of data and analysis through different stages of the pipeline.