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Progressive SDG Network Visualization Specification

Core Challenge

Create progressively sophisticated SDG network visualizations that integrate multiple open data APIs to display environmental, scientific, and sustainable development data as interactive force-directed graphs. Each iteration should discover new data sources and enhance visual/interactive capabilities.

Output Requirements

File Naming: sdg_viz_[iteration_number].html

Content Structure: Self-contained HTML file with embedded D3.js visualization

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>SDG Network Visualization [Iteration Number] - [Data Source Theme]</title>
    <style>
        /* Complete styling for force-directed graph */
        /* Node styling: colors, borders, sizes, labels */
        /* Edge styling: colors, widths, strengths, patterns */
        /* Interactive controls and UI elements */
        /* Responsive design for all screen sizes */
    </style>
</head>
<body>
    <div id="visualization-container">
        <!-- Main force-directed graph canvas -->
        <svg id="network-graph"></svg>

        <!-- Interactive controls -->
        <div id="controls">
            <!-- Filters, search, zoom controls -->
        </div>

        <!-- Information panel -->
        <div id="info-panel">
            <!-- Node/edge details, data source attribution -->
        </div>

        <!-- Legend -->
        <div id="legend">
            <!-- Color coding, node types, edge meanings -->
        </div>
    </div>

    <!-- D3.js library -->
    <script src="https://d3js.org/d3.v7.min.js"></script>

    <script>
        // API data fetching and integration
        // Force-directed graph implementation
        // Interactive behaviors and animations
        // Data transformation and network construction
    </script>

    <!-- Web Source Attribution -->
    <footer>
        <p><strong>Data Sources:</strong> [List of APIs used]</p>
        <p><strong>Web Learning Source:</strong> [URL researched for this iteration]</p>
        <p><strong>Techniques Applied:</strong> [Specific learnings from web source]</p>
    </footer>
</body>
</html>

Progressive Enhancement Dimensions

Phase 1: Foundation (Iterations 1-5)

Focus: Basic network visualization with single API integration

Node Enhancements:

  • Size based on data properties (e.g., population, magnitude)
  • Color coding by category/type (using color scales)
  • Clear labels with smart positioning
  • Hover tooltips with basic information

Edge Enhancements:

  • Width based on relationship strength
  • Color coding for relationship types
  • Straight lines with arrows for directionality
  • Dashed/solid patterns for different relationship classes

Interactivity:

  • Drag nodes to reposition
  • Zoom and pan controls
  • Click to highlight connected nodes
  • Basic filtering by node type

Data Integration:

  • Single API source per iteration
  • Simple JSON data transformation
  • Basic node-edge relationship mapping
  • Manual API endpoint definition

Phase 2: Intermediate (Iterations 6-12)

Focus: Multi-source integration and enhanced interactivity

Node Enhancements:

  • Dynamic sizing with min/max constraints
  • Multi-property color encoding (fill + border)
  • Icon/symbol overlays for node types
  • Rich tooltips with formatted data tables
  • Node clustering and grouping
  • Custom shapes for different entity types

Edge Enhancements:

  • Curved edges for better visibility
  • Dynamic edge strength from data properties
  • Animated flow along edges
  • Edge bundling for cleaner layouts
  • Multiple edge types with different styles

Interactivity:

  • Search functionality to find nodes
  • Multi-select for comparison
  • Time-based filtering/animation
  • Expandable/collapsible node groups
  • Side panel with detailed statistics
  • Export data/visualization options

Data Integration:

  • Combine 2-3 API sources
  • Automatic data merging by key fields
  • API discovery via Swagger/OpenAPI specs
  • Error handling for API failures
  • Caching for performance

Phase 3: Advanced (Iterations 13-20)

Focus: Sophisticated algorithms and real-time capabilities

Node Enhancements:

  • Force-based clustering algorithms
  • Community detection visualization
  • Centrality measures (betweenness, closeness)
  • Node importance ranking
  • Multi-level node hierarchies
  • Animated node state transitions

Edge Enhancements:

  • Weighted force simulation
  • Edge confidence/uncertainty visualization
  • Temporal edge evolution
  • Hierarchical edge bundling
  • Edge crossing minimization

Interactivity:

  • Real-time data updates
  • Graph layout algorithm selection
  • Advanced filtering with boolean logic
  • Shortest path highlighting
  • Neighborhood exploration
  • Graph metric dashboards
  • Collaborative filtering

Data Integration:

  • 4+ API sources integrated
  • Automated API endpoint discovery
  • Data quality indicators
  • Cross-API entity resolution
  • Streaming data integration
  • Webhook support for updates

Phase 4: Expert (Iterations 21+)

Focus: Novel techniques and AI-enhanced features

Node Enhancements:

  • ML-based node positioning
  • Anomaly detection highlighting
  • Predictive node attributes
  • Semantic node embedding
  • Dynamic node importance
  • Multi-dimensional projection

Edge Enhancements:

  • Probabilistic edge rendering
  • Predicted relationship suggestions
  • Edge importance scoring
  • Dynamic edge weight learning
  • Causal relationship inference

Interactivity:

  • Natural language queries
  • AI-powered graph insights
  • Automatic pattern detection
  • Interactive graph queries (Cypher-like)
  • Graph comparison tools
  • VR/AR integration capabilities

Data Integration:

  • Federated query across APIs
  • Automatic schema alignment
  • Knowledge graph construction
  • Semantic web integration (RDF, SPARQL)
  • Real-time distributed data
  • Blockchain data sources

API Discovery Strategy

Target Data Domains

Each iteration should discover and integrate APIs from these domains:

Environmental Data:

  • Air quality (OpenAQ, EPA APIs)
  • Water quality (USGS, EPA Water)
  • Climate data (NOAA, NASA)
  • Weather (OpenWeatherMap, Weather.gov)
  • Ocean data (NOAA Ocean, Marine APIs)

Scientific Data:

  • Biodiversity (GBIF, iNaturalist)
  • Forest data (Global Forest Watch)
  • Soil data (SoilGrids, ISRIC)
  • Satellite imagery (NASA Earth, Sentinel)
  • Seismic data (USGS Earthquake)

Sustainable Development:

  • UN SDG API (existing baseline)
  • World Bank Open Data
  • OECD Statistics
  • WHO Global Health
  • FAO Agricultural Data

Discovery Process:

  1. Web Search for APIs: Use WebSearch to find: "[domain] API swagger site:github.com"
  2. Swagger/OpenAPI Analysis: Parse API specs to find endpoints
  3. Data Quality Check: Verify API has relevant network-ready data
  4. Integration Planning: Map API data to nodes and edges
  5. Documentation: Record API details in visualization

API Selection Criteria

  • Open Access: Free or freemium tier available
  • REST/JSON: Easy integration with JavaScript
  • Documentation: Well-documented endpoints
  • Reliability: Active maintenance and uptime
  • Data Richness: Complex enough for network visualization
  • SDG Relevance: Connects to sustainability goals

Network Data Model

Node Schema

{
    id: "unique_identifier",
    name: "Display Name",
    type: "category", // e.g., "country", "indicator", "species"
    group: "group_name", // for color coding
    size: numeric_value, // for visual sizing
    properties: {
        // Domain-specific attributes
        value: number,
        unit: "string",
        timestamp: "ISO_date",
        source_api: "api_name",
        // ... additional properties
    },
    x: number, // force layout position
    y: number
}

Edge Schema

{
    source: "node_id",
    target: "node_id",
    type: "relationship_type",
    strength: numeric_value, // for force simulation
    weight: numeric_value, // for visual width
    properties: {
        correlation: number,
        confidence: number,
        source_api: "api_name",
        // ... additional properties
    }
}

Web Learning Integration

Each Iteration Must:

  1. Fetch Assigned URL: Use WebFetch to retrieve documentation/tutorial
  2. Extract Techniques: Identify 1-3 specific techniques from the source
  3. Apply Learning: Implement the technique in the visualization
  4. Document Source: Clearly attribute the web source and what was learned
  5. Demonstrate Improvement: Show measurable enhancement from previous iteration

Web Source Categories

D3.js Techniques:

  • Observable HQ notebooks (d3.js examples)
  • D3.js official documentation
  • Mike Bostock's blocks
  • D3 Graph Gallery
  • Interactive graph tutorials

Force-Directed Graphs:

  • Force simulation parameters
  • Collision detection
  • Link distance functions
  • Clustering algorithms
  • Layout optimization

Data Integration:

  • REST API patterns
  • CORS handling
  • Async data loading
  • Data transformation pipelines
  • Error handling strategies

Visual Design:

  • Color theory for networks
  • Graph readability principles
  • Interactive UX patterns
  • Animation best practices
  • Accessibility in visualizations

Quality Standards

Functional Requirements

  • ✅ Fetches data from at least one open API
  • ✅ Renders as force-directed graph with D3.js
  • ✅ Nodes and edges have meaningful properties from data
  • ✅ Interactive (hover, click, drag minimum)
  • ✅ Responsive design works on desktop and mobile
  • ✅ Clear data source attribution
  • ✅ Error handling for API failures

Visual Excellence

  • ✅ Professional color scheme with good contrast
  • ✅ Readable labels and legends
  • ✅ Smooth animations (60fps)
  • ✅ Clear visual hierarchy
  • ✅ Consistent styling throughout
  • ✅ Loading states for async operations

Technical Quality

  • ✅ Self-contained HTML file (or minimal external deps)
  • ✅ Clean, commented JavaScript code
  • ✅ Efficient force simulation (no lag)
  • ✅ Proper error handling and fallbacks
  • ✅ Browser compatibility (modern browsers)
  • ✅ No console errors

Progressive Learning

  • ✅ Demonstrates new technique from web source
  • ✅ Builds upon previous iterations where appropriate
  • ✅ Documents what was learned and how applied
  • ✅ Shows measurable improvement
  • ✅ Explores new API sources

Iteration Evolution Strategy

Starting Point (Iteration 1)

  • Use UN SDG API (baseline from existing project)
  • Basic force-directed graph with D3.js v7
  • Simple node/edge styling
  • Basic hover tooltips
  • Learn from: D3.js force simulation documentation

Early Iterations (2-5)

  • Add one new API per iteration
  • Focus on core graph enhancements
  • Build interactive controls
  • Learn: Color scales, tooltips, filtering

Middle Iterations (6-12)

  • Combine multiple APIs
  • Advanced interactivity
  • Custom layouts and algorithms
  • Learn: Graph algorithms, data merging, UX patterns

Advanced Iterations (13-20)

  • Real-time data integration
  • Complex algorithms (community detection, centrality)
  • Rich dashboards
  • Learn: Advanced D3, graph theory, streaming data

Expert Iterations (21+)

  • AI/ML integration
  • Novel visualization techniques
  • Cross-platform capabilities
  • Learn: Cutting-edge graph visualization research

Success Metrics

Each iteration should improve on at least 3 of these metrics:

Data Richness:

  • Number of API sources integrated
  • Total nodes in network
  • Total edges in network
  • Data update frequency

Visual Quality:

  • Color coding sophistication
  • Label readability score
  • Animation smoothness
  • Layout clarity

Interactivity:

  • Number of interactive features
  • Response time to user actions
  • Filter/search capabilities
  • Information density

Technical Innovation:

  • New techniques applied
  • Performance improvements
  • Code quality metrics
  • Browser compatibility

Ultra-Thinking Directive

Before each iteration, deeply consider:

API Discovery:

  • What domain provides the most value for SDG insights?
  • Which APIs have network-ready data structures?
  • How can multiple APIs be meaningfully combined?
  • What data relationships would be most revealing?

Visualization Strategy:

  • What graph layout best reveals the data patterns?
  • How can node/edge properties enhance understanding?
  • What interactivity would provide the most insight?
  • How does this build on previous iterations?

Web Learning Application:

  • What specific technique from the web source applies here?
  • How can this technique improve the visualization?
  • What adaptations are needed for our use case?
  • How does this advance the state of the art?

User Value:

  • What insights does this visualization reveal?
  • How intuitive is the interaction model?
  • What questions can users answer with this tool?
  • How does this serve SDG analysis and understanding?

Generate visualizations that:

  • Discover New Data: Find and integrate novel API sources
  • Apply Web Knowledge: Demonstrate clear learning from web research
  • Enhance Progressively: Each iteration measurably better than the last
  • Serve SDGs: Provide genuine value for sustainable development insights
  • Inspire Exploration: Create compelling, informative, interactive experiences