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* @module Dashboards/AnomalyDetection
* @category Intelligence Analysis - Statistical Outlier Detection & Early Warning
*
* Anomaly Detection & Early Warning Intelligence Dashboard.
*
* Advanced statistical intelligence module implementing Z-score analysis for
* behavioral anomaly detection across Swedish Parliament activity (2002-2025).
* Provides real-time early warning capability for unusual patterns in voting,
* document production, and attendance metrics.
*
* ## Intelligence Methodology
*
* - Detection Threshold: |Z| >= 2.0 (2 standard deviations)
* - Severity Classification: CRITICAL (>3σ), HIGH (2-3σ), MODERATE (1-2σ), LOW (<1σ)
* - Direction Detection: UNUSUALLY_HIGH, UNUSUALLY_LOW, WITHIN_NORMAL_RANGE
* - Temporal Coverage: 23 years × 4 quarters = 92 time periods
*
* ## Data Source
*
* - `view_riksdagen_seasonal_anomaly_detection_sample.csv`
*
* @author Hack23 AB - Political Intelligence Team
* @license Apache-2.0
* @version 2.0.0
* @since 2024
* @see {@link https://github.com/Hack23/cia|CIA Platform Data Pipeline}
*
* @intelligence Statistical Outlier Detection & Early Warning System — advanced Z-score analysis (threshold |Z| >= 2.0) for behavioral anomaly detection across 23 years × 4 quarters = 92 time periods of Swedish Parliament activity. Severity classification: CRITICAL (>3σ), HIGH (2-3σ), MODERATE (1-2σ). Direction detection: UNUSUALLY_HIGH, UNUSUALLY_LOW, WITHIN_NORMAL_RANGE.
*
* @business Algorithmic intelligence differentiator — statistical anomaly detection represents the most technically sophisticated product capability. Demonstrates platform as an AI/ML-adjacent intelligence tool. Highest perceived value for enterprise and institutional clients. Foundation for automated anomaly alerting subscription.
*
* @marketing Alert-driven content generation — each detected anomaly is an automatically generated content opportunity: push notifications, email alerts, social media posts, and newsletter items. Anomaly alerts create FOMO (fear of missing out) driving subscription growth and daily active usage.
* */
import {
logger,
detectLanguage,
showDataSourceDisclaimer,
} from '../shared/index.js';
import type { CSVRow, DataSourceType } from '../shared/index.js';
const d3 = (globalThis as any).d3;
const Chart = (globalThis as any).Chart;
// ============================================================================
// INTERFACES
// ============================================================================
interface AnomalyConfig {
readonly dataUrls: readonly string[];
readonly cacheKey: string;
readonly cacheDuration: number;
readonly alertDismissKey: string;
readonly alertDismissDuration: number;
}
interface AlertConfigEntry {
readonly color: string;
readonly icon: string;
readonly notify: boolean;
}
interface AnomalyStats {
total: number;
anomalyCount: number;
anomalyRate: string;
criticalCount: number;
highCount: number;
moderateCount: number;
ballotAnomalies: number;
documentAnomalies: number;
attendanceAnomalies: number;
avgZScore: string;
}
interface QuarterCounts {
critical: number;
high: number;
moderate: number;
total: number;
}
/** Chart translation strings (full locale). */
interface AnomalyTranslationsFull {
title: string;
severityLabel: string;
typeLabel: string;
directionLabel: string;
yearLabel: string;
allSeverities: string;
allTypes: string;
allDirections: string;
allYears: string;
severity: Record<string, string>;
type: Record<string, string>;
direction: Record<string, string>;
alertPrefix: string;
dismissAlert: string;
loading: string;
error: string;
noData: string;
chartTitles: Record<string, string>;
chartDescriptions: Record<string, string>;
quarters: Record<string, string>;
}
/** Minimal translation for non-EN/SV locales. */
interface AnomalyTranslationsMinimal {
title: string;
severity: Record<string, string>;
}
type AnomalyTranslations = AnomalyTranslationsFull | AnomalyTranslationsMinimal;
// ============================================================================
// CONFIGURATION
// ============================================================================
const CONFIG: AnomalyConfig = {
dataUrls: [
'cia-data/seasonal/view_riksdagen_seasonal_anomaly_detection_sample.csv',
'https://raw.githubusercontent.com/Hack23/cia/master/service.data.impl/sample-data/view_riksdagen_seasonal_anomaly_detection_sample.csv',
],
cacheKey: 'riksdag_anomaly_detection',
cacheDuration: 60 * 60 * 1000,
alertDismissKey: 'anomaly_alert_dismissed',
alertDismissDuration: 24 * 60 * 60 * 1000,
};
const ALERT_CONFIG: Record<string, AlertConfigEntry> = {
CRITICAL: { color: '#d32f2f', icon: '🔴', notify: true },
HIGH: { color: '#f57c00', icon: '🟠', notify: true },
MODERATE: { color: '#fbc02d', icon: '🟡', notify: false },
LOW: { color: '#388e3c', icon: '🟢', notify: false },
};
// ============================================================================
// TRANSLATIONS
// ============================================================================
const TRANSLATIONS: Record<string, AnomalyTranslations> = {
en: {
title: 'Anomaly Detection & Early Warning System',
severityLabel: 'Severity',
typeLabel: 'Type',
directionLabel: 'Direction',
yearLabel: 'Year',
allSeverities: 'All Severities',
allTypes: 'All Types',
allDirections: 'All Directions',
allYears: 'All Years',
severity: { CRITICAL: 'Critical', HIGH: 'High', MODERATE: 'Moderate', LOW: 'Low' },
type: {
BALLOT_ANOMALY: 'Ballot Anomaly',
DOCUMENT_ANOMALY: 'Document Anomaly',
ATTENDANCE_ANOMALY: 'Attendance Anomaly',
NO_ANOMALY: 'No Anomaly',
},
direction: {
UNUSUALLY_HIGH: 'Unusually High',
UNUSUALLY_LOW: 'Unusually Low',
WITHIN_NORMAL_RANGE: 'Within Normal Range',
},
alertPrefix: 'CRITICAL ANOMALY DETECTED',
dismissAlert: 'Dismiss',
loading: 'Loading anomaly data...',
error: 'Error loading data',
noData: 'No anomaly data available',
chartTitles: {
timeline: 'Anomaly Timeline (2002-2025)',
distribution: 'Z-Score Distribution',
typeBreakdown: 'Anomaly Type Distribution',
heatmap: 'Severity Heat Map (Year × Quarter)',
quarterly: 'Anomaly Frequency by Quarter',
recent: 'Recent Anomalies (Last 5)',
},
chartDescriptions: {
timeline: 'Chronological view of detected anomalies with severity coding',
distribution: 'Normal curve with outlier markers (|Z| ≥ 2.0)',
typeBreakdown: 'Ballot vs. Document anomaly distribution',
heatmap: 'Grid showing anomaly severity by year and quarter',
quarterly: 'Q1-Q4 anomaly counts across all years',
recent: 'Most recent anomalies with details',
},
quarters: {
Q1: 'Q1 (Jan-Mar)',
Q2: 'Q2 (Apr-Jun)',
Q3: 'Q3 (Jul-Sep)',
Q4: 'Q4 (Oct-Dec)',
},
},
sv: {
title: 'Anomalidetektering och Tidig Varning',
severityLabel: 'Allvarlighetsgrad',
typeLabel: 'Typ',
directionLabel: 'Riktning',
yearLabel: 'År',
allSeverities: 'Alla allvarlighetsgrader',
allTypes: 'Alla typer',
allDirections: 'Alla riktningar',
allYears: 'Alla år',
severity: { CRITICAL: 'Kritisk', HIGH: 'Hög', MODERATE: 'Måttlig', LOW: 'Låg' },
type: {
BALLOT_ANOMALY: 'Omröstningsanomali',
DOCUMENT_ANOMALY: 'Dokumentanomali',
ATTENDANCE_ANOMALY: 'Närvaroanomali',
NO_ANOMALY: 'Ingen anomali',
},
direction: {
UNUSUALLY_HIGH: 'Ovanligt hög',
UNUSUALLY_LOW: 'Ovanligt låg',
WITHIN_NORMAL_RANGE: 'Inom normalintervall',
},
alertPrefix: 'KRITISK ANOMALI UPPTÄCKT',
dismissAlert: 'Avvisa',
loading: 'Laddar anomalidata...',
error: 'Fel vid laddning av data',
noData: 'Ingen anomalidata tillgänglig',
chartTitles: {
timeline: 'Anomalitidslinje (2002-2025)',
distribution: 'Z-poängfördelning',
typeBreakdown: 'Anomalitypfördelning',
heatmap: 'Allvarlighetsvärmekartan (År × Kvartal)',
quarterly: 'Anomalifrekvens per kvartal',
recent: 'Senaste anomalierna (Senaste 5)',
},
chartDescriptions: {
timeline: 'Kronologisk vy av upptäckta anomalier med allvarlighetskodning',
distribution: 'Normalkurva med utliggare (|Z| ≥ 2.0)',
typeBreakdown: 'Omröstnings- vs. dokumentanomalier',
heatmap: 'Rutnät som visar anomalins allvarlighetsgrad per år och kvartal',
quarterly: 'Q1-Q4 anomaliräkning över alla år',
recent: 'Senaste anomalier med detaljer',
},
quarters: {
Q1: 'Q1 (Jan-Mar)',
Q2: 'Q2 (Apr-Jun)',
Q3: 'Q3 (Jul-Sep)',
Q4: 'Q4 (Okt-Dec)',
},
},
da: { title: 'Anomalidetektering og Tidlig Advarsel', severity: { CRITICAL: 'Kritisk', HIGH: 'Høj', MODERATE: 'Moderat', LOW: 'Lav' } },
no: { title: 'Anomalideteksjon og Tidlig Varsel', severity: { CRITICAL: 'Kritisk', HIGH: 'Høy', MODERATE: 'Moderat', LOW: 'Lav' } },
fi: { title: 'Poikkeavuuksien havaitseminen ja varhainen varoitus', severity: { CRITICAL: 'Kriittinen', HIGH: 'Korkea', MODERATE: 'Kohtalainen', LOW: 'Matala' } },
de: { title: 'Anomalieerkennung und Frühwarnsystem', severity: { CRITICAL: 'Kritisch', HIGH: 'Hoch', MODERATE: 'Mäßig', LOW: 'Niedrig' } },
fr: { title: "Détection d'anomalies et alerte précoce", severity: { CRITICAL: 'Critique', HIGH: 'Élevé', MODERATE: 'Modéré', LOW: 'Faible' } },
es: { title: 'Detección de anomalías y alerta temprana', severity: { CRITICAL: 'Crítico', HIGH: 'Alto', MODERATE: 'Moderado', LOW: 'Bajo' } },
nl: { title: 'Anomaliedetectie en vroegtijdige waarschuwing', severity: { CRITICAL: 'Kritiek', HIGH: 'Hoog', MODERATE: 'Gematigd', LOW: 'Laag' } },
ar: { title: 'اكتشاف الشذوذ والإنذار المبكر', severity: { CRITICAL: 'حرج', HIGH: 'عالي', MODERATE: 'معتدل', LOW: 'منخفض' } },
he: { title: 'זיהוי חריגות והתרעה מוקדמת', severity: { CRITICAL: 'קריטי', HIGH: 'גבוה', MODERATE: 'בינוני', LOW: 'נמוך' } },
ja: { title: '異常検知と早期警告', severity: { CRITICAL: '重大', HIGH: '高', MODERATE: '中', LOW: '低' } },
ko: { title: '이상 탐지 및 조기 경보', severity: { CRITICAL: '치명적', HIGH: '높음', MODERATE: '보통', LOW: '낮음' } },
zh: { title: '异常检测与预警', severity: { CRITICAL: '严重', HIGH: '高', MODERATE: '中等', LOW: '低' } },
};
// ============================================================================
// HELPERS
// ============================================================================
function getTranslations(): AnomalyTranslationsFull {
const lang = detectLanguage();
const t = TRANSLATIONS[lang] || TRANSLATIONS.en;
// Merge minimal translations over the English defaults
if (!('loading' in t)) {
return { ...(TRANSLATIONS.en as AnomalyTranslationsFull), ...t };
}
return t as AnomalyTranslationsFull;
}
/**
* Generate synthetic fallback data when all CSV sources are unavailable.
* Ensures charts render with representative sample data rather than showing
* an error state, consistent with how other dashboards handle data failures.
*/
function generateFallbackData(): CSVRow[] {
const data: CSVRow[] = [];
const quarterLabels = ['Q1_JAN_MAR', 'Q2_APR_JUN', 'Q3_JUL_SEP', 'Q4_OCT_DEC'];
const periods = ['Winter Session', 'Spring Session', 'Summer Recess/Election', 'Autumn Session'];
for (let year = 2018; year <= 2025; year++) {
for (let quarter = 1; quarter <= 4; quarter++) {
// Deterministic, bounded z-scores to avoid random HIGH/CRITICAL anomalies in fallback data
const ballotZRaw = (((year * 31 + quarter * 17) % 300) / 100) - 1.5;
const docZRaw = (((year * 19 + quarter * 23) % 300) / 100) - 1.5;
const ballotZ = ballotZRaw.toFixed(4);
const docZ = docZRaw.toFixed(4);
const absBallotZ = Math.abs(ballotZRaw);
const absDocZ = Math.abs(docZRaw);
const maxZ = Math.max(absBallotZ, absDocZ);
let severity = 'LOW';
let anomalyType = 'NO_ANOMALY';
let direction = 'WITHIN_NORMAL_RANGE';
const dominantZ = absBallotZ >= absDocZ ? ballotZRaw : docZRaw;
if (maxZ >= 3.0) { severity = 'CRITICAL'; anomalyType = absBallotZ >= absDocZ ? 'BALLOT_ANOMALY' : 'DOCUMENT_ANOMALY'; direction = dominantZ > 0 ? 'UNUSUALLY_HIGH' : 'UNUSUALLY_LOW'; }
else if (maxZ >= 2.0) { severity = 'HIGH'; anomalyType = absBallotZ >= absDocZ ? 'BALLOT_ANOMALY' : 'DOCUMENT_ANOMALY'; direction = dominantZ > 0 ? 'UNUSUALLY_HIGH' : 'UNUSUALLY_LOW'; }
else if (maxZ >= 1.0) { severity = 'MODERATE'; }
data.push({
year: String(year),
quarter: String(quarter),
is_election_year: (year === 2022) ? 't' : 'f',
// Deterministic synthetic counts for reproducible fallback data
total_ballots: String(100 + ((year * 13 + quarter * 7) % 200)),
active_politicians: '349',
attendance_rate: '100.00',
documents_produced: String(200 + ((year * 29 + quarter * 11) % 500)),
q_baseline_ballots: '150.00',
q_stddev_ballots: '40.00',
ballot_z_score: ballotZ,
q_baseline_docs: '300.00',
q_stddev_docs: '100.00',
doc_z_score: docZ,
q_baseline_attendance: '100.00',
q_stddev_attendance: '0',
attendance_z_score: '0',
activity_classification: maxZ >= 2 ? 'ANOMALY_DETECTED' : 'NORMAL_ACTIVITY',
quarter_label: quarterLabels[quarter - 1],
parliamentary_period: periods[quarter - 1],
anomaly_type: anomalyType,
anomaly_direction: direction,
max_z_score: maxZ.toFixed(4),
anomaly_severity: severity,
});
}
}
data.sort((a, b) => {
const yearDiff = parseInt(b.year as string, 10) - parseInt(a.year as string, 10);
if (yearDiff !== 0) return yearDiff;
return parseInt(b.quarter as string, 10) - parseInt(a.quarter as string, 10);
});
return data;
}
// ============================================================================
// DATA MANAGER
// ============================================================================
class AnomalyDetectionDataManager {
data: CSVRow[] | null = null;
dataSourceType: DataSourceType = 'live';
private readonly language: string;
constructor() {
this.language = detectLanguage();
}
getTranslations(): AnomalyTranslationsFull {
return getTranslations();
}
async fetchData(): Promise<CSVRow[]> {
// Check cache first
const cached = this.getCachedData();
if (cached) {
logger.debug('Using cached anomaly data');
this.data = cached;
return cached;
}
logger.debug('Fetching fresh anomaly data from CIA...');
let response: Response | null = null;
let lastError: Error | null = null;
for (const url of CONFIG.dataUrls) {
try {
logger.debug(`Attempting to fetch from: ${url}`);
response = await fetch(url);
if (response.ok) {
logger.debug(`✓ Successfully fetched from: ${url}`);
break;
} else {
logger.warn(`⚠ Failed to fetch from ${url}: HTTP ${response.status}`);
lastError = new Error(`HTTP ${response.status}: ${response.statusText}`);
response = null;
}
} catch (error) {
logger.warn(`⚠ Error fetching from ${url}:`, (error as Error).message);
lastError = error as Error;
response = null;
}
}
if (!response) {
logger.warn('All data sources failed, using synthetic fallback data');
const fallback = generateFallbackData();
this.data = fallback;
this.dataSourceType = 'synthetic';
return fallback;
}
const csvText = await response.text();
const parsedData = this.parseCSV(csvText);
this.setCachedData(parsedData);
this.data = parsedData;
logger.debug(`Loaded ${parsedData.length} anomaly records`);
return parsedData;
}
parseCSV(csvText: string): CSVRow[] {
const lines = csvText.trim().split('\n');
const headers = lines[0].split(',').map((h) => h.trim());
const data: CSVRow[] = [];
for (let i = 1; i < lines.length; i++) {
const values = this.parseCSVLine(lines[i]);
if (values.length === headers.length) {
const record: CSVRow = {};
headers.forEach((header, index) => {
record[header] = values[index];
});
data.push(record);
}
}
data.sort((a, b) => {
const yearDiff = parseInt(b.year as string, 10) - parseInt(a.year as string, 10);
if (yearDiff !== 0) return yearDiff;
return parseInt(b.quarter as string, 10) - parseInt(a.quarter as string, 10);
});
return data;
}
parseCSVLine(line: string): string[] {
const values: string[] = [];
let current = '';
let inQuotes = false;
for (let i = 0; i < line.length; i++) {
const char = line[i];
if (char === '"') {
inQuotes = !inQuotes;
} else if (char === ',' && !inQuotes) {
values.push(current.trim());
current = '';
} else {
current += char;
}
}
values.push(current.trim());
return values;
}
getCachedData(): CSVRow[] | null {
try {
const cached = localStorage.getItem(CONFIG.cacheKey);
if (!cached) return null;
const { data, timestamp } = JSON.parse(cached);
const age = Date.now() - timestamp;
if (age < CONFIG.cacheDuration) return data;
localStorage.removeItem(CONFIG.cacheKey);
return null;
} catch {
return null;
}
}
setCachedData(data: CSVRow[]): void {
try {
localStorage.setItem(
CONFIG.cacheKey,
JSON.stringify({ data, timestamp: Date.now() }),
);
} catch (error) {
logger.error('Error setting cache:', error);
}
}
identifyActiveAnomalies(): CSVRow[] {
if (!this.data) return [];
return this.data
.filter((record) => record.anomaly_type !== 'NO_ANOMALY')
.sort(
(a, b) =>
Math.abs(parseFloat(b.max_z_score as string)) -
Math.abs(parseFloat(a.max_z_score as string)),
);
}
calculateAnomalyStats(): AnomalyStats | null {
if (!this.data) return null;
const anomalies = this.identifyActiveAnomalies();
const total = this.data.length;
const anomalyCount = anomalies.length;
const criticalCount = anomalies.filter((a) => a.anomaly_severity === 'CRITICAL').length;
const highCount = anomalies.filter((a) => a.anomaly_severity === 'HIGH').length;
const moderateCount = anomalies.filter((a) => a.anomaly_severity === 'MODERATE').length;
const ballotAnomalies = anomalies.filter((a) => a.anomaly_type === 'BALLOT_ANOMALY').length;
const documentAnomalies = anomalies.filter((a) => a.anomaly_type === 'DOCUMENT_ANOMALY').length;
const attendanceAnomalies = anomalies.filter((a) => a.anomaly_type === 'ATTENDANCE_ANOMALY').length;
const avgZScore =
anomalies.length > 0
? anomalies.reduce((sum, a) => sum + Math.abs(parseFloat(a.max_z_score as string)), 0) /
anomalies.length
: 0;
return {
total,
anomalyCount,
anomalyRate: ((anomalyCount / total) * 100).toFixed(1),
criticalCount,
highCount,
moderateCount,
ballotAnomalies,
documentAnomalies,
attendanceAnomalies,
avgZScore: avgZScore.toFixed(2),
};
}
checkForCriticalAnomalies(): CSVRow | null {
if (!this.data || this.data.length === 0) return null;
const recentQuarters = this.data.slice(0, 2);
const criticalAnomalies = recentQuarters.filter(
(record) =>
record.anomaly_severity === 'CRITICAL' || record.anomaly_severity === 'HIGH',
);
return criticalAnomalies.length > 0 ? criticalAnomalies[0] : null;
}
}
// ============================================================================
// ALERT SYSTEM
// ============================================================================
class AnomalyAlertSystem {
private readonly dataManager: AnomalyDetectionDataManager;
private readonly translations: AnomalyTranslationsFull;
constructor(dataManager: AnomalyDetectionDataManager) {
this.dataManager = dataManager;
this.translations = dataManager.getTranslations();
}
checkAndDisplayAlert(anomaly: CSVRow): void {
if (!anomaly) return;
const dismissedTimestamp = localStorage.getItem(CONFIG.alertDismissKey);
if (dismissedTimestamp) {
const age = Date.now() - parseInt(dismissedTimestamp, 10);
if (age < CONFIG.alertDismissDuration) {
logger.debug('Alert was recently dismissed, not showing');
return;
}
}
const banner = document.getElementById('anomaly-alert-banner');
const message = document.getElementById('alert-message');
if (banner && message) {
const alertText = this.generateAlertMessage(anomaly);
message.textContent = alertText;
const severity = (anomaly.anomaly_severity as string).toLowerCase();
banner.className = `alert-banner ${severity}`;
banner.classList.remove('hidden');
const dismissBtn = banner.querySelector('.dismiss-alert');
if (dismissBtn) {
(dismissBtn as HTMLElement).onclick = () => this.dismissAlert();
}
}
}
dismissAlert(): void {
const banner = document.getElementById('anomaly-alert-banner');
if (banner) {
banner.classList.add('hidden');
localStorage.setItem(CONFIG.alertDismissKey, Date.now().toString());
}
}
generateAlertMessage(anomaly: CSVRow): string {
const year = anomaly.year as string;
const quarter = `Q${anomaly.quarter}`;
const type = anomaly.anomaly_type as string;
const zScore = parseFloat(anomaly.max_z_score as string).toFixed(2);
const direction = anomaly.anomaly_direction as string;
let actualValue = '';
let baseline = '';
if (type === 'BALLOT_ANOMALY') {
actualValue = `${anomaly.total_ballots} ballots`;
baseline = `${Math.round(parseFloat(anomaly.q_baseline_ballots as string))} baseline`;
} else if (type === 'DOCUMENT_ANOMALY') {
actualValue = `${anomaly.documents_produced} documents`;
baseline = `${Math.round(parseFloat(anomaly.q_baseline_docs as string))} baseline`;
}
return `${year} ${quarter} ${type}: ${parseFloat(zScore) > 0 ? '+' : ''}${zScore} Z-score, ${direction} (${actualValue} vs ${baseline})`;
}
}
// ============================================================================
// CHART RENDERERS
// ============================================================================
class AnomalyDetectionCharts {
private readonly dataManager: AnomalyDetectionDataManager;
private readonly translations: AnomalyTranslationsFull;
private chartInstances: Record<string, any> = {};
constructor(dataManager: AnomalyDetectionDataManager) {
this.dataManager = dataManager;
this.translations = dataManager.getTranslations();
}
async renderAll(): Promise<void> {
await this.renderAnomalyTimeline();
await this.renderZScoreDistribution();
await this.renderAnomalyTypeChart();
await this.renderSeverityHeatmap();
await this.renderQuarterlyFrequency();
await this.renderRecentAnomaliesFeed();
}
private async renderAnomalyTimeline(): Promise<void> {
const canvas = document.getElementById('anomaly-timeline-chart') as HTMLCanvasElement | null;
if (!canvas) return;
const data = this.dataManager.data!;
const anomalies = data.filter((r) => r.anomaly_type !== 'NO_ANOMALY');
const dataPoints = anomalies.map((record) => {
const year = parseInt(record.year as string, 10);
const quarter = parseInt(record.quarter as string, 10);
return {
x: year + (quarter - 1) * 0.25,
y: parseFloat(record.max_z_score as string),
record,
};
});
if (this.chartInstances.timeline) this.chartInstances.timeline.destroy();
const ctx = canvas.getContext('2d')!;
this.chartInstances.timeline = new Chart(ctx, {
type: 'scatter',
data: {
datasets: [
{
label: 'Anomalies',
data: dataPoints,
backgroundColor: dataPoints.map((p) =>
this.getSeverityColor(p.record.anomaly_severity as string),
),
borderColor: dataPoints.map((p) =>
this.getSeverityColor(p.record.anomaly_severity as string),
),
pointRadius: dataPoints.map((p) =>
this.getSeverityRadius(p.record.anomaly_severity as string),
),
pointHoverRadius: 8,
},
],
},
options: {
responsive: true,
maintainAspectRatio: false,
plugins: {
legend: { display: false },
tooltip: {
callbacks: {
label: (context: any) => {
const record = context.raw.record;
return [
`${record.year} Q${record.quarter}`,
`Type: ${record.anomaly_type}`,
`Severity: ${record.anomaly_severity}`,
`Z-Score: ${parseFloat(record.max_z_score).toFixed(2)}`,
`Direction: ${record.anomaly_direction}`,
];
},
},
},
},
scales: {
x: {
title: { display: true, text: 'Year' },
ticks: { callback: (value: any) => Math.floor(value) },
},
y: {
title: { display: true, text: 'Z-Score' },
grid: {
color: (context: any) => {
if (context.tick.value === 2.0 || context.tick.value === -2.0) {
return '#f57c00';
}
return 'rgba(255, 255, 255, 0.1)';
},
},
},
},
},
});
}
private async renderZScoreDistribution(): Promise<void> {
const canvas = document.getElementById('zscore-distribution-chart') as HTMLCanvasElement | null;
if (!canvas) return;
const data = this.dataManager.data!;
const zScores: number[] = [];
data.forEach((record) => {
const ballotZ = parseFloat(record.ballot_z_score as string);
const docZ = parseFloat(record.doc_z_score as string);
const attendanceZ = parseFloat(record.attendance_z_score as string);
if (!isNaN(ballotZ)) zScores.push(ballotZ);
if (!isNaN(docZ)) zScores.push(docZ);
if (!isNaN(attendanceZ)) zScores.push(attendanceZ);
});
const bins: { min: number; max: number; count: number; isOutlier: boolean }[] = [];
const binSize = 0.5;
const minZ = -3;
const maxZ = 11;
for (let i = minZ; i < maxZ; i += binSize) {
bins.push({
min: i,
max: i + binSize,
count: 0,
isOutlier: Math.abs(i) >= 2.0 || Math.abs(i + binSize) >= 2.0,
});
}
zScores.forEach((z) => {
const bin = bins.find((b) => z >= b.min && z < b.max);
if (bin) bin.count++;
});
if (this.chartInstances.distribution) this.chartInstances.distribution.destroy();
const ctx = canvas.getContext('2d')!;
this.chartInstances.distribution = new Chart(ctx, {
type: 'bar',
data: {
labels: bins.map((b) => `${b.min.toFixed(1)}`),
datasets: [
{
label: 'Frequency',
data: bins.map((b) => b.count),
backgroundColor: bins.map((b) =>
b.isOutlier ? 'rgba(211, 47, 47, 0.7)' : 'rgba(0, 217, 255, 0.7)',
),
borderColor: bins.map((b) => (b.isOutlier ? '#d32f2f' : '#00d9ff')),
borderWidth: 1,
},
],
},
options: {
responsive: true,
maintainAspectRatio: false,
plugins: {
legend: { display: false },
tooltip: {
callbacks: { label: (context: any) => `Count: ${context.parsed.y}` },
},
},
scales: {
x: { title: { display: true, text: 'Z-Score' } },
y: { title: { display: true, text: 'Frequency' }, beginAtZero: true },
},
},
});
}
private async renderAnomalyTypeChart(): Promise<void> {
const canvas = document.getElementById('anomaly-type-chart') as HTMLCanvasElement | null;
if (!canvas) return;
const anomalies = this.dataManager.identifyActiveAnomalies();
const ballotCount = anomalies.filter((a) => a.anomaly_type === 'BALLOT_ANOMALY').length;
const documentCount = anomalies.filter((a) => a.anomaly_type === 'DOCUMENT_ANOMALY').length;
const attendanceCount = anomalies.filter((a) => a.anomaly_type === 'ATTENDANCE_ANOMALY').length;
if (this.chartInstances.typeChart) this.chartInstances.typeChart.destroy();
const ctx = canvas.getContext('2d')!;
this.chartInstances.typeChart = new Chart(ctx, {
type: 'doughnut',
data: {
labels: ['Ballot Anomaly', 'Document Anomaly', 'Attendance Anomaly'],
datasets: [
{
data: [ballotCount, documentCount, attendanceCount],
backgroundColor: [
'rgba(25, 118, 210, 0.8)',
'rgba(56, 142, 60, 0.8)',
'rgba(245, 124, 0, 0.8)',
],
borderColor: ['#1976d2', '#388e3c', '#f57c00'],
borderWidth: 2,
},
],
},
options: {
responsive: true,
maintainAspectRatio: false,
plugins: {
legend: { position: 'bottom' },
tooltip: {
callbacks: {
label: (context: any) => {
const total = ballotCount + documentCount + attendanceCount;
const pct = total > 0 ? ((context.parsed / total) * 100).toFixed(1) : 0;
return `${context.label}: ${context.parsed} (${pct}%)`;
},
},
},
},
},
});
}
private async renderSeverityHeatmap(): Promise<void> {
const container = document.getElementById('severity-heatmap');
if (!container) return;
const data = this.dataManager.data!;
container.innerHTML = '';
const years = Array.from(new Set(data.map((r) => parseInt(r.year as string, 10)))).sort();
const quarters = [1, 2, 3, 4];
const width = container.clientWidth || 800;
const height = Math.min(600, years.length * 25);
const margin = { top: 40, right: 40, bottom: 40, left: 60 };
const cellWidth = (width - margin.left - margin.right) / quarters.length;
const cellHeight = (height - margin.top - margin.bottom) / years.length;
const svg = d3
.select(container)
.append('svg')
.attr('width', width)
.attr('height', height);
const cells = svg.selectAll('g').data(data).enter().append('g');
cells
.append('rect')
.attr('x', (d: any) => margin.left + (parseInt(d.quarter) - 1) * cellWidth)
.attr('y', (d: any) => {
const yearIndex = years.indexOf(parseInt(d.year));
return margin.top + yearIndex * cellHeight;
})
.attr('width', cellWidth - 2)
.attr('height', cellHeight - 2)
.attr('fill', (d: any) => this.getHeatmapColor(d.anomaly_severity))
.attr('stroke', '#0a0e27')
.attr('stroke-width', 1)
.style('cursor', 'pointer')
.on('mouseover', function (this: SVGRectElement, event: any, d: any) {
d3.select(this).attr('stroke', '#00d9ff').attr('stroke-width', 2);
d3.select('body')
.append('div')
.attr('class', 'heatmap-tooltip')
.style('position', 'absolute')
.style('background', 'rgba(10, 14, 39, 0.95)')
.style('color', '#fff')
.style('padding', '10px')
.style('border-radius', '4px')
.style('border', '1px solid #00d9ff')
.style('pointer-events', 'none')
.style('z-index', '10000')
.html(
`<strong>${d.year} Q${d.quarter}</strong><br>
Severity: ${d.anomaly_severity}<br>
Type: ${d.anomaly_type}<br>
Max Z-Score: ${parseFloat(d.max_z_score).toFixed(2)}`,
)
.style('left', `${event.pageX + 10}px`)
.style('top', `${event.pageY - 10}px`);
})
.on('mouseout', function (this: SVGRectElement) {
d3.select(this).attr('stroke', '#0a0e27').attr('stroke-width', 1);
d3.selectAll('.heatmap-tooltip').remove();
});
svg
.selectAll('.year-label')
.data(years)
.enter()
.append('text')
.attr('class', 'year-label')
.attr('x', margin.left - 10)
.attr('y', (_d: any, i: number) => margin.top + i * cellHeight + cellHeight / 2)
.attr('text-anchor', 'end')
.attr('dominant-baseline', 'middle')
.attr('fill', '#e0e0e0')
.attr('font-size', '12px')
.text((d: any) => d);
svg
.selectAll('.quarter-label')
.data(quarters)
.enter()
.append('text')
.attr('class', 'quarter-label')
.attr('x', (_d: any, i: number) => margin.left + i * cellWidth + cellWidth / 2)
.attr('y', margin.top - 10)
.attr('text-anchor', 'middle')
.attr('fill', '#e0e0e0')
.attr('font-size', '12px')
.text((d: any) => `Q${d}`);
}
private async renderQuarterlyFrequency(): Promise<void> {
const canvas = document.getElementById('quarterly-frequency-chart') as HTMLCanvasElement | null;
if (!canvas) return;
const anomalies = this.dataManager.identifyActiveAnomalies();
const quarterData: Record<number, QuarterCounts> = {
1: { critical: 0, high: 0, moderate: 0, total: 0 },
2: { critical: 0, high: 0, moderate: 0, total: 0 },
3: { critical: 0, high: 0, moderate: 0, total: 0 },
4: { critical: 0, high: 0, moderate: 0, total: 0 },
};
anomalies.forEach((record) => {
const quarter = parseInt(record.quarter as string, 10);
const severity = record.anomaly_severity as string;
quarterData[quarter].total++;
if (severity === 'CRITICAL') quarterData[quarter].critical++;
else if (severity === 'HIGH') quarterData[quarter].high++;
else if (severity === 'MODERATE') quarterData[quarter].moderate++;
});
if (this.chartInstances.quarterly) this.chartInstances.quarterly.destroy();
const ctx = canvas.getContext('2d')!;
this.chartInstances.quarterly = new Chart(ctx, {
type: 'bar',
data: {
labels: ['Q1', 'Q2', 'Q3', 'Q4'],
datasets: [
{
label: 'Critical',
data: [1, 2, 3, 4].map((q) => quarterData[q].critical),
backgroundColor: 'rgba(211, 47, 47, 0.8)',
borderColor: '#d32f2f',
borderWidth: 1,
},
{
label: 'High',
data: [1, 2, 3, 4].map((q) => quarterData[q].high),
backgroundColor: 'rgba(245, 124, 0, 0.8)',
borderColor: '#f57c00',
borderWidth: 1,
},
{
label: 'Moderate',
data: [1, 2, 3, 4].map((q) => quarterData[q].moderate),
backgroundColor: 'rgba(251, 192, 45, 0.8)',
borderColor: '#fbc02d',
borderWidth: 1,
},
],
},
options: {
responsive: true,
maintainAspectRatio: false,
plugins: { legend: { position: 'bottom' } },
scales: {
x: { stacked: true, title: { display: true, text: 'Quarter' } },
y: {
stacked: true,
title: { display: true, text: 'Anomaly Count' },
beginAtZero: true,
ticks: { stepSize: 1 },
},
},
},
});
}
private async renderRecentAnomaliesFeed(): Promise<void> {
const container = document.getElementById('recent-anomalies-feed');
if (!container) return;
const anomalies = this.dataManager.identifyActiveAnomalies();
const recent = anomalies.slice(0, 5);
container.innerHTML = '';
if (recent.length === 0) {
container.innerHTML = '<p>No recent anomalies detected</p>';
return;
}
recent.forEach((record) => {
const item = document.createElement('div');
item.className = `anomaly-feed-item ${(record.anomaly_severity as string).toLowerCase()}`;
const severity = record.anomaly_severity as string;
const icon = ALERT_CONFIG[severity]?.icon || '⚪';
const zScore = parseFloat(record.max_z_score as string).toFixed(2);
let detailsText = '';
if (record.anomaly_type === 'BALLOT_ANOMALY') {
detailsText = `${record.total_ballots} ballots vs ${Math.round(parseFloat(record.q_baseline_ballots as string))} baseline`;
} else if (record.anomaly_type === 'DOCUMENT_ANOMALY') {
detailsText = `${record.documents_produced} documents vs ${Math.round(parseFloat(record.q_baseline_docs as string))} baseline`;
}
const header = document.createElement('div');
header.style.cssText = 'display: flex; align-items: center; gap: 10px; margin-bottom: 5px;';
const iconSpan = document.createElement('span');
iconSpan.style.fontSize = '1.5rem';
iconSpan.textContent = icon;
header.appendChild(iconSpan);
const badge = document.createElement('span');
badge.className = `severity-badge ${severity.toLowerCase()}`;
badge.textContent = severity;
header.appendChild(badge);
const period = document.createElement('span');
const periodStrong = document.createElement('strong');
periodStrong.textContent = `${record.year} Q${record.quarter}`;
period.appendChild(periodStrong);
header.appendChild(period);
item.appendChild(header);
const details = document.createElement('div');
details.style.marginLeft = '2.5rem';
const lines: [string, string][] = [
['Type:', record.anomaly_type as string],
['Z-Score:', `${parseFloat(zScore) > 0 ? '+' : ''}${zScore}`],
['Direction:', record.anomaly_direction as string],
['Details:', detailsText],
];
lines.forEach(([label, value]) => {
const p = document.createElement('p');
p.style.margin = '2px 0';
const strong = document.createElement('strong');
strong.textContent = label;
p.appendChild(strong);
p.appendChild(document.createTextNode(` ${value}`));
details.appendChild(p);
});
item.appendChild(details);
container.appendChild(item);
});
}
// --- colour helpers ---
private getSeverityColor(severity: string): string {
const colors: Record<string, string> = {
CRITICAL: '#d32f2f',
HIGH: '#f57c00',
MODERATE: '#fbc02d',
LOW: '#388e3c',
};
return colors[severity] || '#666';
}
private getSeverityRadius(severity: string): number {
const sizes: Record<string, number> = {
CRITICAL: 8,
HIGH: 7,
MODERATE: 6,
LOW: 5,
};
return sizes[severity] || 5;
}
private getHeatmapColor(severity: string): string {
const colors: Record<string, string> = {
CRITICAL: '#d32f2f',
HIGH: '#f57c00',
MODERATE: '#fbc02d',
LOW: '#388e3c',
NO_ANOMALY: '#2e3b4e',
};
return colors[severity] || colors.NO_ANOMALY;
}
}
// ============================================================================
// LOADING / ERROR UI
// ============================================================================
function showLoading(translations: AnomalyTranslationsFull): void {
const sections = document.querySelectorAll('#anomaly-detection-dashboard .chart-card');
sections.forEach((section) => {
const canvas = section.querySelector('canvas');
const container = section.querySelector<HTMLElement>(
'div[id$="-heatmap"], div[id$="-feed"]',
);
if (canvas || container) {
const loading = document.createElement('div');
loading.className = 'loading-indicator';
loading.textContent = translations.loading;
loading.style.padding = '20px';
loading.style.textAlign = 'center';
loading.style.color = '#00d9ff';
if (canvas) {
canvas.classList.add('hidden');
section.appendChild(loading);
} else if (container) {
container.innerHTML = '';
container.appendChild(loading);
}
}
});
}
function hideLoading(): void {
const loadingIndicators = document.querySelectorAll('.loading-indicator');
loadingIndicators.forEach((indicator) => indicator.remove());
const canvases = document.querySelectorAll('#anomaly-detection-dashboard canvas');
canvases.forEach((canvas) => canvas.classList.remove('hidden'));
}
function showDashboardError(message: string): void {
const dashboard = document.getElementById('anomaly-detection-dashboard');
if (!dashboard) return;
const errorDiv = document.createElement('div');
errorDiv.className = 'error-message';
errorDiv.style.padding = '20px';
errorDiv.style.backgroundColor = 'rgba(211, 47, 47, 0.2)';
errorDiv.style.border = '2px solid #d32f2f';
errorDiv.style.borderRadius = '8px';
errorDiv.style.margin = '20px 0';
errorDiv.style.color = '#fff';
const heading = document.createElement('h3');
heading.textContent = '⚠️ Error Loading Dashboard';
errorDiv.appendChild(heading);
const msgP = document.createElement('p');
msgP.textContent = message;
errorDiv.appendChild(msgP);
const helpP = document.createElement('p');
helpP.textContent =
'Please try refreshing the page or contact support if the issue persists.';
errorDiv.appendChild(helpP);
dashboard.insertBefore(errorDiv, dashboard.firstChild);
}
// ============================================================================
// PUBLIC API
// ============================================================================
/**
* Initialise the Anomaly Detection & Early Warning dashboard.
*
* Loads CSV data (local-first with remote fallback), processes anomaly
* statistics, renders all six Chart.js/D3 visualizations, and activates the
* alert banner for CRITICAL/HIGH anomalies in recent quarters.
*/
export async function init(): Promise<void> {
logger.debug('Initializing Anomaly Detection Dashboard...');
const dataManager = new AnomalyDetectionDataManager();
const alertSystem = new AnomalyAlertSystem(dataManager);
const charts = new AnomalyDetectionCharts(dataManager);
const t = dataManager.getTranslations();
showLoading(t);
try {
await dataManager.fetchData();
const dashboard = document.getElementById('anomaly-detection-dashboard');
if (dashboard) {
showDataSourceDisclaimer(dashboard, dataManager.dataSourceType);
}
const criticalAnomaly = dataManager.checkForCriticalAnomalies();
if (criticalAnomaly) {
alertSystem.checkAndDisplayAlert(criticalAnomaly);
}
await charts.renderAll();
const stats = dataManager.calculateAnomalyStats();
logger.debug('Anomaly Statistics:', stats);
hideLoading();
logger.debug('✅ Anomaly Detection Dashboard initialized successfully');
} catch (error) {
logger.error('Failed to initialize dashboard:', error);
hideLoading();
showDashboardError((error as Error).message);
}
}
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