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Party Performance Across Election Cycles - Longitudinal Analysis (2002-2026) #8209

@pethers

Description

@pethers

🎯 Objective

Create longitudinal views tracking party performance metrics across all election cycles (2002-2026), enabling cross-cycle comparative analysis of party effectiveness, coalition dynamics, and electoral trends.

📋 Background

Political parties evolve significantly across election cycles - coalition alignments shift, leadership changes, policy positions adapt. The platform needs systematic views to track these longitudinal patterns, supporting predictive models for party trajectories and coalition formations.

📊 Current State (Measured Metrics)

  • Existing Party Views: 13 party-related views (performance, momentum, coalitions)
  • Temporal Coverage: Mostly annual or recent data
  • Cross-Cycle Analysis: ❌ MISSING - No systematic party evolution tracking across 24 years
  • Coalition History: Limited to 5-year alignment matrix (view_riksdagen_coalition_alignment_matrix)
  • Gap: Cannot analyze party transformations like "S" performance 2002 vs 2022

✅ Acceptance Criteria

  • Create view_riksdagen_party_longitudinal_performance tracking 7 metrics per cycle
  • Create view_riksdagen_party_coalition_evolution showing alliance shifts across cycles
  • Create view_riksdagen_party_electoral_trends with seat count, vote share evolution
  • Support queries comparing party performance across specific election cycles
  • Calculate trend indicators: ascending, stable, descending party trajectories
  • Integration with Comparative Analysis Framework (Framework 2)

🛠️ Implementation Guidance

Files to Create:

  • service.data.impl/src/main/resources/db/migration/party-longitudinal-views.xml
  • service.data.impl/sample-data/framework-validation/comparative/test_2_4_party_longitudinal.csv

View Structure Example:

CREATE OR REPLACE VIEW view_riksdagen_party_longitudinal_performance AS
WITH election_cycles AS (
    SELECT 
        party,
        CASE 
            WHEN EXTRACT(YEAR FROM ballot_date) BETWEEN 2002 AND 2005 THEN 2002
            WHEN EXTRACT(YEAR FROM ballot_date) BETWEEN 2006 AND 2009 THEN 2006
            WHEN EXTRACT(YEAR FROM ballot_date) BETWEEN 2010 AND 2013 THEN 2010
            WHEN EXTRACT(YEAR FROM ballot_date) BETWEEN 2014 AND 2017 THEN 2014
            WHEN EXTRACT(YEAR FROM ballot_date) BETWEEN 2018 AND 2021 THEN 2018
            WHEN EXTRACT(YEAR FROM ballot_date) BETWEEN 2022 AND 2025 THEN 2022
            ELSE 2026
        END AS election_year,
        COUNT(DISTINCT ballot_id) AS total_ballots,
        COUNT(DISTINCT person_id) AS active_members,
        AVG(CASE WHEN vote = 'Ja' AND won THEN 1 ELSE 0 END) * 100 AS win_rate,
        AVG(CASE WHEN vote = party_vote THEN 1 ELSE 0 END) * 100 AS party_discipline,
        COUNT(DISTINCT document_id) AS documents_authored,
        AVG(rebel_rate) AS avg_rebel_rate,
        SUM(leadership_positions) AS leadership_count
    FROM vote_data vd
    JOIN person_data pd ON vd.person_id = pd.person_id
    LEFT JOIN document_data dd ON pd.person_id = dd.person_id
    GROUP BY party, election_year
),
trend_analysis AS (
    SELECT 
        party, election_year,
        total_ballots, active_members, win_rate, party_discipline,
        LAG(win_rate) OVER (PARTITION BY party ORDER BY election_year) AS prev_win_rate,
        LAG(active_members) OVER (PARTITION BY party ORDER BY election_year) AS prev_members,
        LEAD(win_rate) OVER (PARTITION BY party ORDER BY election_year) AS next_win_rate
    FROM election_cycles
)
SELECT 
    party, election_year,
    total_ballots, active_members, win_rate, party_discipline,
    win_rate - prev_win_rate AS win_rate_change,
    active_members - prev_members AS membership_change,
    CASE 
        WHEN win_rate > prev_win_rate AND next_win_rate > win_rate THEN 'ASCENDING'
        WHEN win_rate < prev_win_rate AND next_win_rate < win_rate THEN 'DESCENDING'
        ELSE 'STABLE'
    END AS trajectory
FROM trend_analysis
ORDER BY party, election_year;

Coalition Evolution Tracking:

CREATE OR REPLACE VIEW view_riksdagen_party_coalition_evolution AS
SELECT 
    p1.party AS party_1,
    p2.party AS party_2,
    election_year,
    AVG(alignment_rate) AS avg_alignment,
    COUNT(*) AS joint_votes,
    CASE 
        WHEN AVG(alignment_rate) > 70 THEN 'STRONG_COALITION'
        WHEN AVG(alignment_rate) > 50 THEN 'MODERATE_COALITION'
        ELSE 'OPPOSITION'
    END AS coalition_strength
FROM view_riksdagen_vote_data_ballot_party_summary_annual p1
JOIN view_riksdagen_vote_data_ballot_party_summary_annual p2 
  ON p1.ballot_id = p2.ballot_id AND p1.party < p2.party
GROUP BY p1.party, p2.party, election_year
ORDER BY party_1, party_2, election_year;

📚 Related Documentation

🤖 Recommended Agent

Agent: @hack23-test-specialist
Rationale: Requires comprehensive longitudinal data validation across 7 election cycles with complex trend calculations and coalition mapping.

For implementation, the Test Specialist will:

  • Design party evolution tracking with proper cycle boundaries
  • Create validation test cases for known party transformations (e.g., S declining 2002-2014, then recovering)
  • Implement coalition evolution tracking with historical accuracy validation
  • Ensure query performance for 8 major parties × 7 cycles analysis
  • Document party trajectory classification methodology

📊 Intelligence Value

⭐⭐⭐⭐⭐ VERY HIGH - Essential for understanding Swedish party system evolution, predicting future coalitions, and analyzing democratic stability.

🔗 Dependencies

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