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* @module CIA/Loaders/Voting
* @category Intelligence Platform - Data Acquisition & Pipeline Management
*
* @description
* Builds the voting patterns analysis from CIA CSV exports.
* Prefers the real coalition alignment matrix when available; falls back to
* win-rate similarity when not. Derives rebellion tracking from per-party
* risk distribution.
*
* @author Hack23 AB - Data Pipeline Engineering
* @license Apache-2.0
* @since 2026
*/
import type { CSVRow, RebellionEntry, VotingPatterns } from '../types.js';
import type { LoadCSV } from '../csv-utils.js';
import { CSV_SOURCES, RIKSDAG_PARTIES } from '../sources.js';
/**
* Build `VotingPatterns` from CSV sources.
*
* Uses real coalition alignment data for the agreement matrix when present;
* otherwise derives a similarity matrix from party effectiveness win rates.
*
* @param loadCSV - CSV loader closure
* @returns Voting patterns analysis with party-pair agreement matrix
*/
export async function loadVotingPatterns(loadCSV: LoadCSV): Promise<VotingPatterns> {
const [coalitionAlignment, effectiveness, riskByParty] = await Promise.all([
loadCSV(CSV_SOURCES.coalitionAlignment.local),
loadCSV(CSV_SOURCES.partyEffectiveness.local),
loadCSV(CSV_SOURCES.riskByParty.local)
]);
const labels = [...RIKSDAG_PARTIES];
const partyNames = [
'Social Democrats', 'Moderates', 'Sweden Democrats', 'Centre',
'Left', 'Christian Democrats', 'Liberals', 'Green'
];
const alignmentLookup: Record<string, number> = {};
coalitionAlignment
.filter(r => RIKSDAG_PARTIES.includes(r.party1 as string) && RIKSDAG_PARTIES.includes(r.party2 as string))
.forEach(r => {
const key1 = `${r.party1}:${r.party2}`;
const key2 = `${r.party2}:${r.party1}`;
const rate = Math.round(((r.alignment_rate as number) || 0) * 100);
alignmentLookup[key1] = rate;
alignmentLookup[key2] = rate;
});
const hasAlignmentData = Object.keys(alignmentLookup).length > 0;
let agreementMatrix: number[][];
if (hasAlignmentData) {
agreementMatrix = labels.map(p1 =>
labels.map(p2 => {
if (p1 === p2) return 100;
return alignmentLookup[`${p1}:${p2}`] ?? 50;
})
);
} else {
const latestWinRate: Record<string, CSVRow> = {};
effectiveness
.filter(e => RIKSDAG_PARTIES.includes(e.party as string))
.forEach(e => {
const party = e.party as string;
Eif (
!latestWinRate[party] ||
(e.year as number) > (latestWinRate[party].year as number) ||
((e.year as number) === (latestWinRate[party].year as number) &&
(e.quarter as number) > (latestWinRate[party].quarter as number))
) {
latestWinRate[party] = e;
}
});
agreementMatrix = labels.map(p1 => {
const wr1 = latestWinRate[p1] ? (latestWinRate[p1].avg_win_rate as number) : 50;
return labels.map(p2 => {
if (p1 === p2) return 100;
const wr2 = latestWinRate[p2] ? (latestWinRate[p2].avg_win_rate as number) : 50;
return Math.max(0, Math.round(100 - Math.abs(wr1 - wr2)));
});
});
}
const rebellionTracking: RebellionEntry[] = RIKSDAG_PARTIES
.map(party => {
const partyRisks = riskByParty.filter(r => r.party === party);
const highRisk = partyRisks.find(r => r.risk_level === 'HIGH');
const total = partyRisks.reduce((s, r) => s + ((r.politician_count as number) || 0), 0);
const highCount = highRisk ? (highRisk.politician_count as number) : 0;
const rebellionRate = total > 0 ? Math.round((highCount / total) * 100 * 10) / 10 : 0;
return {
party,
rebellionRate,
trend: rebellionRate > 25 ? 'increasing' : rebellionRate > 15 ? 'stable' : 'decreasing'
};
})
.filter(r => r.rebellionRate > 0);
return {
title: 'Voting Patterns Analysis',
description: hasAlignmentData
? 'Real coalition alignment data from CIA voting analysis'
: 'Derived from CIA party effectiveness trends and risk data',
lastUpdated: new Date().toISOString(),
analysisPeriod: '2022-2026',
votingMatrix: { labels, partyNames, agreementMatrix },
keyIssues: [],
rebellionTracking,
_source: 'csv'
};
}
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