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* @module DataPipeline/CIADataLoader
* @category Intelligence Platform - Data Acquisition & Pipeline Management
*
* @description
* **CIA Intelligence Data Loader & Pipeline Orchestrator**
*
* Core data acquisition module implementing multi-source intelligence data loading
* from the Citizen Intelligence Agency (CIA) Platform. Manages CSV export ingestion
* for 19+ intelligence product categories and JSON fallback for model-generated
* electoral forecasts. Provides resilient data pipeline with local-first strategy
* and remote fallback capabilities.
*
* ## Data Pipeline Architecture
*
* **Multi-Tier Source Strategy**:
* ```
* Tier 1 (Local): ../cia-data/{category}/*.csv (deployed assets)
* Tier 2 (JSON): ../data/cia-exports/current/*.json (model outputs)
* Tier 3 (Fallback): GitHub Raw API (authoritative source)
* ```
*
* **Benefits**:
* - **Performance**: Local CSV loads ~10x faster than GitHub API
* - **Resilience**: Degradation from local → JSON → remote
* - **Offline**: Works with locally deployed data packages
* - **Freshness**: GitHub fallback ensures latest data availability
*
* ## Intelligence Product Categories
*
* **19 CIA Platform Export Types**:
*
* ### Structural Intelligence
* 1. **personStatus** - Active MP counts by status
* 2. **riskByParty** - Party-level risk aggregation
* 3. **riskLevels** - Aggregate risk distribution
* 4. **annualBallots** - Yearly voting activity
*
* ### Performance Metrics
* 5. **documents** - Document production statistics
* 6. **attendance** - Chamber/committee participation
* 7. **productivity** - Legislative output metrics
* 8. **effectiveness** - Bill passage rates
*
* ### Risk Assessment
* 9. **riskScores** - Quantitative risk scores (0-10 scale)
* 10. **ethicsConcerns** - Top 10 ethics cases
* 11. **electoralRisk** - Constituency vulnerability
* 12. **crisisResilience** - Crisis response effectiveness
*
* ### Behavioral Analysis
* 13. **votingAnomalies** - Anomaly detection classification
* 14. **partyDiscipline** - Voting cohesion metrics
* 15. **coalitionStability** - Coalition behavior patterns
*
* ### Temporal Intelligence
* 16. **seasonalPatterns** - Quarterly activity trends
* 17. **electionCycles** - Election period comparisons
* 18. **historicalTrends** - Multi-year pattern analysis
*
* ### Predictive Models
* 19. **electionForecasts** - 2026 election predictions (JSON)
*
* ## Data Source Mapping
*
* **CSV Sources** (Real PostgreSQL Views):
* - Local: `../cia-data/{category}/{view_name}.csv`
* - Remote: `https://raw.githubusercontent.com/Hack23/cia/master/service.data.impl/sample-data/{view_name}.csv`
*
* **JSON Sources** (Model-Generated):
* - Local: `../data/cia-exports/current/{product_name}.json`
* - Schema: CIA Platform JSON export format v2.0
*
* ## Intelligent Loading Strategy
*
* **Load Priority Algorithm**:
* ```javascript
* async loadData(category) {
* try {
* return await this.loadLocal(category); // Tier 1: Local CSV
* } catch (err) {
* try {
* return await this.loadJSON(category); // Tier 2: Local JSON
* } catch (err) {
* return await this.loadRemote(category); // Tier 3: GitHub
* }
* }
* }
* ```
*
* **Error Handling**:
* - Network failures: Retry with exponential backoff (3 attempts)
* - Parse errors: Fallback to next tier
* - Missing data: Return empty dataset with warning
* - CORS errors: Proxy through service worker (if available)
*
* ## Data Validation Pipeline
*
* **Quality Assurance Steps**:
* 1. **Format Validation**: CSV structure, delimiter, encoding (UTF-8)
* 2. **Schema Validation**: Required columns, data types
* 3. **Range Validation**: Numeric bounds, date ranges
* 4. **Completeness**: Missing value checks, null handling
* 5. **Freshness**: Timestamp validation (< 24 hours for real-time data)
*
* **Validation Rules**:
* - Risk scores: 0.0 ≤ score ≤ 10.0
* - Years: 2002 ≤ year ≤ 2025
* - Quarters: 1 ≤ quarter ≤ 4
* - Party codes: Must match official Riksdag codes (S, M, SD, etc.)
*
* ## Performance Characteristics
*
* **Load Times** (typical):
* - Local CSV: ~50ms for 1000 rows
* - Local JSON: ~30ms (pre-parsed)
* - GitHub API: ~500ms + network latency
*
* **Memory Usage**:
* - Per dataset: ~1-5MB raw data
* - Total cache: ~50MB for all 19 products
* - Browser limit: 10MB localStorage quota per origin
*
* ## Caching Strategy
*
* **Not Implemented in This Module**:
* Caching is responsibility of consumer modules (party-dashboard.js,
* risk-dashboard.js, etc.) using localStorage with appropriate TTLs.
* This module provides pure data loading without side effects.
*
* ## GDPR Compliance
*
* @gdpr All data sourced from public parliamentary records (Article 9(2)(e))
* No personal data processing beyond official public roles and voting records.
* All CIA Platform exports comply with Swedish Public Access to Information Act.
*
* ## Security Considerations
*
* @security Medium risk - External data sources, client-side processing
* @risk GitHub repository compromise could inject malicious data
*
* **Mitigation Strategies**:
* - Strict CSV parsing (no eval, no innerHTML)
* - Content Security Policy (CSP) enforcement
* - Subresource Integrity (SRI) for GitHub resources
* - Input sanitization before DOM insertion
*
* ## Integration Patterns
*
* **Usage Example**:
* ```javascript
* const loader = new CIADataLoader();
* const riskData = await loader.loadCSV('riskByParty');
* const forecast = await loader.loadJSON('electionForecast2026');
* ```
*
* **Consuming Modules**:
* - `cia-visualizations.js` - Dashboard renderer
* - `election-predictions.js` - Forecast visualizations
* - `dashboard-init.js` - Dashboard initialization
* - `risk-dashboard.js` - Risk assessment display
*
* @intelligence Multi-source data acquisition with intelligent fallback
* @osint CIA Platform exports, GitHub repository fallback, local-first strategy
* @risk External dependency on GitHub, data integrity validation required
*
* @author Hack23 AB - Data Pipeline Engineering
* @license Apache-2.0
* @version 2.0.0
* @since 2024
*
* @see {@link https://github.com/Hack23/cia|CIA Platform Repository}
* @see {@link cia-visualizations.js|CIA Dashboard Renderer}
* @see {@link dashboard-init.js|Dashboard Initialization}
*/
export class CIADataLoader {
constructor() {
this.csvBaseURL = '../cia-data/';
this.jsonBaseURL = '../data/cia-exports/current/';
this.fallbackURL = 'https://raw.githubusercontent.com/Hack23/cia/master/service.data.impl/sample-data/';
}
/**
* CSV data source definitions - maps to real PostgreSQL view exports
*/
static CSV_SOURCES = {
personStatus: {
local: 'distribution_person_status.csv',
description: 'Active MP counts by status'
},
riskByParty: {
local: 'distribution_risk_by_party.csv',
description: 'Risk levels per party'
},
riskLevels: {
local: 'distribution_politician_risk_levels.csv',
description: 'Aggregate risk level distribution'
},
annualBallots: {
local: 'voting/distribution_annual_ballots.csv',
description: 'Annual ballot/vote counts'
},
crisisResilience: {
local: 'risk/distribution_crisis_resilience.csv',
description: 'Coalition stability/resilience scores'
},
partyPerformance: {
local: 'party/distribution_party_performance.csv',
description: 'Party metrics (docs, motions, performance level)'
},
partyMetrics: {
local: 'party/view_party_performance_metrics_sample.csv',
description: 'Full party metrics with win rate, rebel rate, absence rate'
},
partyMomentum: {
local: 'party/distribution_party_momentum.csv',
description: 'Party trend direction and stability'
},
partyMembers: {
local: 'party/distribution_annual_party_members.csv',
description: 'Annual party membership counts'
},
influenceMetrics: {
local: 'politician/view_riksdagen_politician_influence_metrics_sample.csv',
description: 'MP influence scores and network connections'
},
riskSummary: {
local: 'politician/view_politician_risk_summary_sample.csv',
description: 'MP risk scores and assessments'
},
committeeProductivity: {
local: 'committee/distribution_committee_productivity.csv',
description: 'Committee productivity and member counts'
},
committeeActivity: {
local: 'committee/distribution_committee_activity.csv',
description: 'Committee document counts'
},
partyEffectiveness: {
local: 'party/distribution_party_effectiveness_trends.csv',
description: 'Party effectiveness trends with win rate'
}
};
/**
* Parse CSV text into array of objects using header row as keys
* @param {string} csvText - Raw CSV text
* @returns {Array<Object>} - Parsed rows
*/
parseCSV(csvText) {
const lines = csvText.trim().split('\n');
if (lines.length < 2) return [];
const headers = lines[0].split(',').map(h => h.trim().replace(/^"|"$/g, ''));
const rows = [];
for (let i = 1; i < lines.length; i++) {
const line = lines[i].trim();
if (!line) continue;
// Simple CSV parsing (handles basic quoting)
const values = [];
let current = '';
let inQuotes = false;
for (let j = 0; j < line.length; j++) {
const ch = line[j];
if (ch === '"') {
inQuotes = !inQuotes;
} else if (ch === ',' && !inQuotes) {
values.push(current.trim());
current = '';
} else {
current += ch;
}
}
values.push(current.trim());
const row = {};
headers.forEach((h, idx) => {
const val = values[idx] || '';
// Auto-convert numeric values
const num = Number(val);
row[h] = val !== '' && !isNaN(num) && val !== '' ? num : val;
});
rows.push(row);
}
return rows;
}
/**
* Load CSV with local-first fallback
* @param {string} localPath - Path relative to csvBaseURL
* @param {string} [fallbackPath] - Optional fallback path
* @returns {Promise<Array<Object>>} - Parsed CSV rows
*/
async loadCSV(localPath, fallbackPath) {
const urls = [
`${this.csvBaseURL}${localPath}`
];
if (fallbackPath) {
urls.push(`${this.fallbackURL}${fallbackPath}`);
}
for (const url of urls) {
try {
const response = await fetch(url);
if (!response.ok) continue;
const text = await response.text();
const rows = this.parseCSV(text);
if (rows.length > 0) return rows;
} catch (e) {
console.warn(`Failed to load CSV from ${url}:`, e.message);
}
}
console.warn(`No data loaded for ${localPath}`);
return [];
}
/**
* Load JSON with fallback (for election predictions only)
* @param {string} filename - JSON filename
* @returns {Promise<Object>} - Parsed JSON
*/
async loadJSON(filename) {
const urls = [
`${this.jsonBaseURL}${filename}`
];
for (const url of urls) {
try {
const response = await fetch(url);
if (!response.ok) continue;
return await response.json();
} catch (e) {
console.warn(`Failed to load JSON from ${url}:`, e.message);
}
}
throw new Error(`Failed to load ${filename}`);
}
/**
* Build overview dashboard from CSV sources
* Replaces overview-dashboard.json
*/
async loadOverviewDashboard() {
const [personStatus, riskByParty, riskLevels, annualBallots, resilience] = await Promise.all([
this.loadCSV(CIADataLoader.CSV_SOURCES.personStatus.local),
this.loadCSV(CIADataLoader.CSV_SOURCES.riskByParty.local),
this.loadCSV(CIADataLoader.CSV_SOURCES.riskLevels.local),
this.loadCSV(CIADataLoader.CSV_SOURCES.annualBallots.local),
this.loadCSV(CIADataLoader.CSV_SOURCES.crisisResilience.local)
]);
// Count active MPs
const activeRow = personStatus.find(r => r.status === 'Tjänstgörande riksdagsledamot');
const totalMPs = activeRow ? activeRow.person_count : 349;
// Count unique parties from risk data (only real riksdag parties)
const riksdagParties = ['S', 'M', 'SD', 'C', 'V', 'KD', 'L', 'MP'];
const partiesInData = new Set(riskByParty.map(r => r.party).filter(p => riksdagParties.includes(p)));
const totalParties = partiesInData.size || 8;
// Risk alerts from risk_by_party
const highRisk = riskByParty.filter(r => r.risk_level === 'HIGH');
const medRisk = riskByParty.filter(r => r.risk_level === 'MEDIUM');
const lowRisk = riskByParty.filter(r => r.risk_level === 'LOW');
const critical = highRisk.reduce((sum, r) => sum + (r.politician_count || 0), 0);
const major = medRisk.reduce((sum, r) => sum + (r.politician_count || 0), 0);
const minor = lowRisk.reduce((sum, r) => sum + (r.politician_count || 0), 0);
// Total risk rules from risk levels
const totalRiskRules = riskLevels.length > 0
? riskLevels.reduce((sum, r) => sum + (r.politician_count || 0), 0)
: 45;
// Latest year ballot activity
const latestBallot = annualBallots.length > 0
? annualBallots[annualBallots.length - 1]
: {};
// Coalition stability from resilience scores (Tidö = M, KD, L, SD)
const tidoParties = ['M', 'KD', 'L', 'SD'];
const tidoResilience = resilience.filter(r => tidoParties.includes(r.party));
const avgResilience = tidoResilience.length > 0
? Math.round(tidoResilience.reduce((s, r) => s + (r.avg_resilience_score || 0), 0) / tidoResilience.length)
: 72;
return {
title: 'Swedish Riksdag Overview Dashboard',
description: 'Live intelligence from CIA PostgreSQL database exports',
lastUpdated: new Date().toISOString(),
keyMetrics: {
totalMPs,
totalParties,
totalRiskRules,
governmentCoalition: 'Tidö Agreement',
coalitionSeats: 176,
oppositionSeats: 173,
majorityMargin: 1
},
riskAlerts: {
critical,
major,
minor,
last90Days: { critical, major, minor }
},
parliamentActivity: {
votesLastMonth: latestBallot.total_votes || 0,
documentsProcessed: latestBallot.unique_ballots || 0,
motionsSubmitted: 0,
committeeMeetings: 0
},
coalitionStability: {
stabilityScore: avgResilience,
riskLevel: avgResilience >= 70 ? 'moderate' : 'high',
defectionProbability: 100 - avgResilience,
ideologicalTension: avgResilience < 60 ? 'high' : 'moderate'
},
dataQuality: {
completeness: 98.5,
lastDataSync: new Date().toISOString(),
coverage: '50+ years (1971-2026)'
},
_source: 'csv'
};
}
/**
* Load election analysis - kept as JSON (model-generated predictions)
*/
async loadElectionAnalysis() {
return this.loadJSON('election-analysis.json');
}
/**
* Build party performance from CSV sources
* Replaces party-performance.json
*/
async loadPartyPerformance() {
const [performance, metrics, momentum] = await Promise.all([
this.loadCSV(CIADataLoader.CSV_SOURCES.partyPerformance.local),
this.loadCSV(CIADataLoader.CSV_SOURCES.partyMetrics.local),
this.loadCSV(CIADataLoader.CSV_SOURCES.partyMomentum.local)
]);
// Only include real riksdag parties
const riksdagParties = ['S', 'M', 'SD', 'C', 'V', 'KD', 'L', 'MP'];
const activePerformance = performance.filter(p => riksdagParties.includes(p.party));
// Build a lookup from the detailed metrics
const metricsMap = {};
metrics.forEach(m => {
if (riksdagParties.includes(m.party)) {
metricsMap[m.party] = m;
}
});
// Get latest momentum per party
const latestMomentum = {};
momentum
.filter(m => riksdagParties.includes(m.party))
.forEach(m => {
if (!latestMomentum[m.party] || m.year > latestMomentum[m.party].year ||
(m.year === latestMomentum[m.party].year && m.quarter > latestMomentum[m.party].quarter)) {
latestMomentum[m.party] = m;
}
});
// Known seat counts (from 2022 election results)
const seatMap = { S: 107, SD: 73, M: 68, C: 24, V: 24, KD: 19, L: 16, MP: 18 };
const parties = activePerformance.map(p => {
const m = metricsMap[p.party] || {};
const mom = latestMomentum[p.party] || {};
return {
id: p.party,
partyName: p.party_name || p.party,
shortName: p.party,
metrics: {
seats: seatMap[p.party] || 0,
voteShare: 0,
memberCount: p.active_members || 0,
documentsAuthored: p.documents_last_year || 0,
motionsSubmitted: p.motions_last_year || 0,
successRate: m.avg_win_rate || 0
},
voting: {
totalVotes: m.total_votes_last_year || 0,
cohesionScore: m.avg_participation_rate || 0,
rebellionRate: m.avg_rebel_rate || 0
},
trends: {
supportTrend: (mom.trend_direction || 'stable').toLowerCase(),
activityTrend: (mom.stability_classification || 'stable').toLowerCase(),
performanceLevel: m.performance_level || p.performance_level || ''
},
_source: 'csv'
};
});
// Sort by seats descending
parties.sort((a, b) => (b.metrics.seats || 0) - (a.metrics.seats || 0));
return {
title: 'Party Performance Dashboard',
description: 'Live party data from CIA PostgreSQL database exports',
lastUpdated: new Date().toISOString(),
parties,
_source: 'csv'
};
}
/**
* Build top 10 influential MPs from CSV sources
* Replaces top10-influential-mps.json
*/
async loadTop10Influential() {
const [influence, riskSummary] = await Promise.all([
this.loadCSV(CIADataLoader.CSV_SOURCES.influenceMetrics.local),
this.loadCSV(CIADataLoader.CSV_SOURCES.riskSummary.local)
]);
// Build risk lookup by person_id
const riskMap = {};
riskSummary.forEach(r => {
riskMap[r.person_id] = r;
});
// Sort by network_connections descending, take top 10
const sorted = [...influence]
.filter(mp => mp.network_connections > 0)
.sort((a, b) => (b.network_connections || 0) - (a.network_connections || 0))
.slice(0, 10);
const rankings = sorted.map((mp, idx) => {
const risk = riskMap[mp.person_id] || {};
return {
rank: idx + 1,
id: String(mp.person_id),
firstName: mp.first_name || '',
lastName: mp.last_name || '',
party: mp.party || '',
role: mp.influence_classification
? mp.influence_classification.replace(/_/g, ' ').toLowerCase().replace(/\b\w/g, c => c.toUpperCase())
: '',
influenceScore: mp.network_connections || 0,
networkConnections: mp.network_connections || 0,
brokerClassification: mp.broker_classification || '',
riskLevel: risk.risk_level || '',
riskScore: risk.risk_score || 0,
_source: 'csv'
};
});
return {
title: 'Top 10 Most Influential MPs',
description: 'Network analysis from CIA politician influence metrics view',
lastUpdated: new Date().toISOString(),
methodology: 'Ranked by network_connections from view_riksdagen_politician_influence_metrics',
rankings,
_source: 'csv'
};
}
/**
* Build committee network from CSV sources
* Replaces committee-network.json
*/
async loadCommitteeNetwork() {
const [productivity, activity] = await Promise.all([
this.loadCSV(CIADataLoader.CSV_SOURCES.committeeProductivity.local),
this.loadCSV(CIADataLoader.CSV_SOURCES.committeeActivity.local)
]);
// Build activity lookup by org code
const activityMap = {};
activity.forEach(a => {
activityMap[a.org] = a.document_count || 0;
});
// Map committee names to codes (extract first 2-3 uppercase chars from name)
const committees = productivity
.filter(c => c.committee_name && c.total_members > 0)
.map(c => {
const code = c.committee_name.substring(0, 3).toUpperCase();
return {
id: code,
name: c.committee_name,
memberCount: c.total_members || 0,
influenceScore: c.docs_per_member ? Math.round(c.docs_per_member * 100) : 0,
documentsProcessed: c.total_documents || 0,
productivityLevel: c.productivity_level || '',
meetingsPerYear: 0,
keyIssues: [c.productivity_level || 'N/A'],
_source: 'csv'
};
});
// Build simple network graph from committees
const nodes = committees.map(c => ({
id: c.id,
name: c.name,
size: c.influenceScore
}));
// Create edges between committees that share similar productivity levels
const edges = [];
for (let i = 0; i < committees.length; i++) {
for (let j = i + 1; j < committees.length && edges.length < 10; j++) {
if (committees[i].productivityLevel === committees[j].productivityLevel &&
committees[i].productivityLevel !== 'INACTIVE') {
edges.push({
source: committees[i].id,
target: committees[j].id,
weight: Math.min(committees[i].documentsProcessed, committees[j].documentsProcessed),
type: 'productivity_similarity'
});
}
}
}
return {
title: 'Committee Network Analysis',
description: 'Committee data from CIA committee productivity view',
lastUpdated: new Date().toISOString(),
committees,
networkGraph: { nodes, edges },
crossCommitteeMPs: [],
_source: 'csv'
};
}
/**
* Build voting patterns from CSV sources
* Replaces voting-patterns.json
* Uses party effectiveness trends since coalition_alignment CSV is empty
*/
async loadVotingPatterns() {
const [effectiveness, riskByParty] = await Promise.all([
this.loadCSV(CIADataLoader.CSV_SOURCES.partyEffectiveness.local),
this.loadCSV(CIADataLoader.CSV_SOURCES.riskByParty.local)
]);
const riksdagParties = ['S', 'M', 'SD', 'C', 'V', 'KD', 'L', 'MP'];
const labels = riksdagParties;
const partyNames = ['Social Democrats', 'Moderates', 'Sweden Democrats', 'Centre', 'Left', 'Christian Democrats', 'Liberals', 'Green'];
// Build latest win rate per party from effectiveness trends
const latestWinRate = {};
effectiveness
.filter(e => riksdagParties.includes(e.party))
.forEach(e => {
if (!latestWinRate[e.party] || e.year > latestWinRate[e.party].year ||
(e.year === latestWinRate[e.party].year && e.quarter > latestWinRate[e.party].quarter)) {
latestWinRate[e.party] = e;
}
});
// Build agreement matrix: parties with similar win rates are more aligned
const agreementMatrix = labels.map(p1 => {
const wr1 = latestWinRate[p1] ? latestWinRate[p1].avg_win_rate : 50;
return labels.map(p2 => {
if (p1 === p2) return 100;
const wr2 = latestWinRate[p2] ? latestWinRate[p2].avg_win_rate : 50;
// Similarity = 100 - absolute difference in win rates
return Math.max(0, Math.round(100 - Math.abs(wr1 - wr2)));
});
});
// Rebellion tracking from risk data (HIGH risk ~ rebellious)
const rebellionTracking = riksdagParties.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 || 0), 0);
const highCount = highRisk ? highRisk.politician_count : 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: 'Derived from CIA party effectiveness trends and risk data',
lastUpdated: new Date().toISOString(),
analysisPeriod: '2022-2026',
votingMatrix: { labels, partyNames, agreementMatrix },
keyIssues: [],
rebellionTracking,
_source: 'csv'
};
}
/**
* Load all data in parallel
* @returns {Promise<Object>} - Object with all data
*/
async loadAll() {
const [overview, election, partyPerf, top10, committees, votingPatterns] =
await Promise.all([
this.loadOverviewDashboard(),
this.loadElectionAnalysis(),
this.loadPartyPerformance(),
this.loadTop10Influential(),
this.loadCommitteeNetwork(),
this.loadVotingPatterns()
]);
return {
overview,
election,
partyPerf,
top10,
committees,
votingPatterns
};
}
}
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