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Election Cycle Intelligence Dashboard - Multi-Cycle Comparative Analysis (1994-2034) #62

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Election Cycle Intelligence Dashboard - Multi-Cycle Comparative Analysis (1994-2034)#62
#69
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@pethers

Description

@pethers
pethers
opened on Feb 9, 2026

📋 Issue Type

Feature - New Dashboard Component

🎯 Objective

Create an interactive Election Cycle Intelligence Dashboard that visualizes 40 years of Swedish Parliament election cycles (1994-2034), showing party performance evolution, decision-making effectiveness, predictive risk forecasting, and temporal voting patterns across 9+ election cycles.

📊 Current State

  • Data Available: 9 election cycles (1994-2034) with comprehensive metrics
  • Parties: 8 major parties + 30+ historical/minor parties
  • Metrics: Performance scores, win rates, decision effectiveness, risk forecasting
  • Granularity: Cycle-level (4-year periods), annual, and semester breakdowns
  • Dashboard: Not yet implemented

🚀 Desired State

A comprehensive election cycle dashboard with:

  1. Multi-cycle timeline visualization (1994-2034)
  2. Party performance trends with comparative analysis
  3. Decision intelligence metrics (approval rates, legislative effectiveness)
  4. Predictive risk forecasting (STABLE, RAPID_ESCALATION, confidence levels)
  5. Temporal trend analysis (attendance, voting patterns, volatility)
  6. Interactive filters (party, cycle, semester, metrics)

📊 CIA Data Integration Context

CIA Products

  • Election Cycle Analysis - Historical patterns and trend forecasting
  • Party Longitudinal Analysis - 50+ years of party evolution
  • Predictive Intelligence - Risk forecasting and early warning

Data Sources

Primary CSV Files (from Hack23/cia repository):

  1. view_election_cycle_comparative_analysis_sample.csv (2,000+ records)

    • Fields: election_cycle_id, cycle_year, calendar_year, semester, party
    • Metrics: performance_score, party_win_rate, party_participation_rate, discipline_score
    • Rankings: rank_by_performance, ntile_party_tier, competitiveness_index
    • Trends: change_performance_pct, performance_trend (stable, improving, declining)

  2. view_election_cycle_decision_intelligence_sample.csv (1,500+ records)

    • Fields: total_proposals, approved_proposals, rejected_proposals
    • Metrics: avg_approval_rate, decision_effectiveness (LOWLY_EFFECTIVE, MODERATELY_EFFECTIVE, HIGHLY_EFFECTIVE)
    • Ministry: ministry_impact_score, ministries_with_decisions
    • Rankings: rank_by_success_rate, ntile_effectiveness
    • Trends: change_success_pct, decision_trend, legislative_momentum

  3. view_election_cycle_predictive_intelligence_sample.csv (40 records)

    • Categories: risk_forecast_category (STABLE, RAPID_ESCALATION)
    • Metrics: politicians_at_risk, avg_risk_score_change, ministries_at_risk
    • Trends: avg_party_win_rate_trend, parties_with_increasing_absence
    • Forecast: risk_trajectory, forecast_confidence (low, moderate, high), predictive_alert_level

  4. view_election_cycle_temporal_trends_sample.csv (70 records)

    • Pre-election: is_pre_election_semester, months_until_election
    • Activity: active_politicians, avg_attendance_rate, total_ballots, total_votes
    • Performance: avg_win_rate, avg_rebel_rate, violation_count
    • Decisions: total_decisions, avg_approval_rate, avg_committee_productivity
    • Volatility: stddev_attendance, stddev_win_rate, volatility_assessment
    • Trends: change_attendance_pct, change_decisions_pct, forecast_trend

Sample Data Insights

Party Performance (1994-2034):

Decision Intelligence Highlights:

  • Highly Effective Decisions: 100% approval rate (e.g., KD 2025 spring, MP 2015 spring, S 2016 spring)
  • Legislative Momentum: Ranges from 0.02 (LOWLY_EFFECTIVE) to 60.44 (HIGHLY_EFFECTIVE)
  • Ministry Impact Scores: 88.01 to 100.00 across different cycles
  • Approval Rates: 0% (LOWLY_EFFECTIVE) to 100% (HIGHLY_EFFECTIVE)

Predictive Intelligence (2022-2030):

  • 2026-2030 Cycle: RAPID_ESCALATION forecast, 331 politicians at risk, avg risk score change +11.96
  • 2022-2026 Cycle: STABLE category, 0 politicians at risk (spring/autumn 2023-2025)
  • Forecast Confidence: Ranges from low_confidence (baseline) to high_confidence (2022-2026)
  • Predictive Alert Level: All current forecasts show "low" alert level

Temporal Trends (Pre-Election):

  • 2026-2030 Cycle (55 months pre-election): 349 active politicians, 84.75% attendance, 212,192 ballots
  • 2022-2026 Cycle (19 months pre-election): 363 politicians, 84.91% attendance, avg approval rate 41.74%
  • Volatility Assessment: Consistently "stable" across 2022-2026 cycle
  • Performance Scores: Range from 70.59 to 74.25 across different semesters

Implementation Notes

Data Fetching:

const CIA_DATA_URLS = {
  comparative: 'https://raw.githubusercontent.com/Hack23/cia/master/service.data.impl/sample-data/view_election_cycle_comparative_analysis_sample.csv',
  decision: 'https://raw.githubusercontent.com/Hack23/cia/master/service.data.impl/sample-data/view_election_cycle_decision_intelligence_sample.csv',
  predictive: 'https://raw.githubusercontent.com/Hack23/cia/master/service.data.impl/sample-data/view_election_cycle_predictive_intelligence_sample.csv',
  temporal: 'https://raw.githubusercontent.com/Hack23/cia/master/service.data.impl/sample-data/view_election_cycle_temporal_trends_sample.csv'
};

Caching Strategy: 24-hour LocalStorage cache with key prefix riksdag_election_cycle_

🔧 Implementation Approach

1. HTML Structure (index.html + 14 language variants)

<section id="election-cycle-dashboard" class="dashboard-section">
  <h2>🗳️ Election Cycle Intelligence (1994-2034)</h2>
  
  <!-- Filters -->
  <div class="dashboard-filters">
    <select id="cycle-filter"><!-- 1994-1998, 1998-2002, ..., 2030-2034 --></select>
    <select id="party-filter"><!-- All Parties, M, S, SD, C, V, MP, KD, L --></select>
    <select id="metric-filter"><!-- Performance, Decisions, Risk, Attendance --></select>
  </div>
  
  <!-- Visualizations -->
  <div class="dashboard-grid">
    <!-- Chart 1: Election Cycle Timeline -->
    <div class="chart-card wide">
      <h3>Election Cycle Performance Timeline</h3>
      <canvas id="cycle-timeline-chart"></canvas>
      <p class="chart-description">Party performance evolution across 9 election cycles (1994-2034)</p>
    </div>
    
    <!-- Chart 2: Decision Effectiveness Matrix -->
    <div class="chart-card">
      <h3>Decision Effectiveness Heatmap</h3>
      <div id="decision-heatmap"></div>
      <p class="chart-description">Legislative approval rates by party and cycle</p>
    </div>
    
    <!-- Chart 3: Risk Forecasting -->
    <div class="chart-card">
      <h3>Predictive Risk Forecasting</h3>
      <canvas id="risk-forecast-chart"></canvas>
      <p class="chart-description">Risk trajectory and confidence levels (2022-2034)</p>
    </div>
    
    <!-- Chart 4: Temporal Trends -->
    <div class="chart-card wide">
      <h3>Temporal Voting Patterns</h3>
      <canvas id="temporal-trends-chart"></canvas>
      <p class="chart-description">Attendance, ballots, and volatility trends</p>
    </div>
    
    <!-- Chart 5: Party Comparative Analysis -->
    <div class="chart-card">
      <h3>Party Tier Distribution</h3>
      <canvas id="party-tier-chart"></canvas>
      <p class="chart-description">Performance tiers (ntile_party_tier: 1-4)</p>
    </div>
  </div>
</section>

2. JavaScript Implementation (scripts/election-cycle-dashboard.js)

// Data Manager with CSV fetching and caching
class ElectionCycleDataManager {
  async fetchData() {
    // Fetch 4 CSV files with 24h caching
    // Parse with PapaParse.js
    // Aggregate by cycle, party, semester
  }
}

// Chart Renderers using Chart.js and D3.js
class ElectionCycleCharts {
  renderCycleTimeline(data) {
    // Chart.js line chart (x: cycle_year, y: performance_score)
    // 8 lines for major parties (M, S, SD, C, V, MP, KD, L)
    // Tooltips: win_rate, discipline_score, rank
  }
  
  renderDecisionHeatmap(data) {
    // D3.js heat map (x: cycle, y: party, color: approval_rate)
    // Color scale: red (0%) → yellow (50%) → green (100%)
    // Labels: decision_effectiveness (LOWLY, MODERATELY, HIGHLY)
  }
  
  renderRiskForecast(data) {
    // Chart.js scatter chart with error bars
    // X: cycle_year, Y: avg_risk_score_change
    // Size: politicians_at_risk
    // Color: risk_forecast_category (STABLE=green, RAPID_ESCALATION=red)
    // Annotations: forecast_confidence levels
  }
  
  renderTemporalTrends(data) {
    // Chart.js multi-axis line chart
    // Left Y-axis: avg_attendance_rate (%), total_ballots
    // Right Y-axis: avg_approval_rate (%)
    // X-axis: calendar_year + semester
    // Highlight: is_pre_election_semester (shaded area)
  }
  
  renderPartyTierChart(data) {
    // Chart.js stacked bar chart
    // X: cycle_year
    // Y: count of parties
    // Stacks: ntile_party_tier (1=Tier 1, 2=Tier 2, 3=Tier 3, 4=Tier 4)
    // Colors: Tier 1=#2e7d32, Tier 2=#66bb6a, Tier 3=#fbc02d, Tier 4=#f57c00
  }
}

// Translations for 14 languages
const TRANSLATIONS = {
  en: { title: "Election Cycle Intelligence", ... },
  sv: { title: "Valcykel Intelligens", ... },
  // ... 12 more languages
};

3. CSS Styling (existing styles.css)

  • Use existing .dashboard-section, .dashboard-grid, .chart-card styles
  • Cyberpunk theme colors: --primary-cyan, --primary-magenta, --primary-yellow
  • Responsive breakpoints: 320px (mobile), 768px (tablet), 1024px (desktop)

4. Multi-Language Support

Update all 14 language files with localized text:

  • index.html (English)
  • index_sv.html (Swedish)
  • index_da.html (Danish), index_no.html (Norwegian), index_fi.html (Finnish)
  • index_de.html (German), index_fr.html (French), index_es.html (Spanish), index_nl.html (Dutch)
  • index_ar.html (Arabic, RTL), index_he.html (Hebrew, RTL)
  • index_ja.html (Japanese), index_ko.html (Korean), index_zh.html (Chinese)

🤖 Recommended Agent

data-visualization-specialist - Expert in Chart.js/D3.js, interactive dashboards, political data visualization

Rationale:

  • Complex multi-chart dashboard with 5 visualizations
  • D3.js heat map for decision effectiveness
  • Chart.js time series with annotations for pre-election periods
  • Performance optimization for large datasets (2,000+ records)
  • Responsive, accessible chart design

✅ Acceptance Criteria

Functionality

  • Fetches 4 CIA CSV files from GitHub raw URLs
  • Implements 24-hour LocalStorage caching
  • Renders 5 interactive visualizations (timeline, heatmap, scatter, multi-axis, stacked bar)
  • Filters work correctly (cycle, party, metric)
  • Tooltips show detailed metrics on hover
  • Handles missing/null data gracefully

Data Accuracy

  • Correctly parses CSV with 2,000+ records
  • Aggregates data by cycle, party, semester
  • Calculates derived metrics (trends, rankings, percentiles)
  • Displays accurate values matching CIA sample data

Performance

  • Dashboard loads in < 3 seconds
  • Chart rendering completes in < 1 second
  • Smooth interactions (no lag on filter changes)
  • Efficient data processing (no browser freezing)

Accessibility (WCAG 2.1 AA)

  • Keyboard navigation works (tab through filters/charts)
  • Screen reader announces chart data
  • ARIA labels on all interactive elements
  • Color contrast ≥ 4.5:1 (text), ≥ 3:1 (charts)
  • Focus indicators visible

Responsive Design

  • Mobile (320px-767px): Single column, stacked charts
  • Tablet (768px-1023px): 2-column grid
  • Desktop (1024px+): 3-column grid, wide charts span 2 columns
  • Touch-friendly controls (44x44px minimum)

Multi-Language

  • All 14 language files updated with dashboard section
  • Labels, tooltips, descriptions localized
  • RTL support for Arabic and Hebrew
  • Swedish political terms preserved (party abbreviations)

Quality Gates

  • HTML validation passes (HTMLHint)
  • No broken links (linkinator)
  • Security headers validated
  • D3.js/Chart.js CDN with SRI hashes

📚 References

Repository

CIA Data

ISMS Policy

Architecture

  • C4 Model: ARCHITECTURE.md (Static site architecture)
  • Design System: styles.css (lines 6800-7151 dashboard patterns)

🏷️ Labels

type:feature, priority:high, component:visualization, component:data-integration, agent:data-visualization-specialist

Data Scale: 9 election cycles × 8+ parties × 4 years × 2 semesters = 2,000+ data points
Visualization Complexity: 5 charts (line, heatmap, scatter, multi-axis, stacked bar)
Impact: ⭐⭐⭐⭐⭐ Reveals 40 years of Swedish political evolution and predictive insights

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