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Overview

Intelligence hotspots receive dynamic escalation scores that blend four normalized signals (0-100) with 48-hour trend detection. The system correlates news activity, country instability, geographic convergence, and military presence to identify escalating situations.
27 predefined intelligence hotspots span conflict zones, strategic waterways, and critical cities. Scores update every 5-15 minutes as new data arrives.

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Hotspot List

  • Tehran, Iran — IRGC HQ, nuclear facilities, strike targets
  • Kyiv, Ukraine — Active war zone, Russian invasion
  • Gaza Strip — Israel-Hamas conflict, humanitarian crisis
  • Damascus, Syria — Civil war, foreign interventions
  • Sanaa, Yemen — Houthi controlled, Red Sea shipping threats
  • Caracas, Venezuela — Political instability, economic collapse
  • Sahel Region — Mali, Niger, Burkina Faso coups and insurgency
  • Horn of Africa — Ethiopia, Somalia, Sudan conflicts
  • Strait of Hormuz — 21% of global oil transit
  • Taiwan Strait — US-China flashpoint
  • Bab el-Mandeb — Red Sea chokepoint, Houthi attacks
  • Suez Canal — Europe-Asia trade artery
  • South China Sea — Territorial disputes, naval buildup
  • Turkish Straits — Bosphorus & Dardanelles, Black Sea access
  • Malacca Strait — 25% of traded goods, piracy risks
  • Moscow, Russia — Kremlin, Wagner mutiny risks
  • Beijing, China — CCP leadership, Taiwan policy
  • Taipei, Taiwan — PLA encirclement threat
  • Tel Aviv, Israel — Military HQ, population center
  • Pyongyang, North Korea — Nuclear program, missile tests
  • Riyadh, Saudi Arabia — Oil markets, Gulf stability
  • Ankara, Turkey — NATO member, Syria border
  • Washington DC, USA — Pentagon, policy epicenter
  • London, UK — NATO, intelligence hub
  • Brussels, Belgium — NATO HQ, EU capital
  • Baghdad, Iraq — Iranian influence, US presence
  • Beirut, Lebanon — Hezbollah, political collapse
See full list in src/config/geo.ts (INTEL_HOTSPOTS)

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Scoring Formula

Escalation scores blend static baseline (30%) with dynamic signals (70%): 
const dynamicRaw = (
  components.newsActivity * 0.35 +
  components.ciiContribution * 0.25 +
  components.geoConvergence * 0.25 +
  components.militaryActivity * 0.15
);

const dynamicScore = 1 + (dynamicRaw / 100) * 4; // Scale 0-100 → 1-5
const combinedScore = staticBaseline * 0.3 + dynamicScore * 0.7;
Output range: 1.0 (minimal) to 5.0 (critical) Source: src/services/hotspot-escalation.ts:92-107

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Component Weights

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newsActivity
number
default:"0.35"
35% weight — highest priority because news coverage indicates attention and information velocity.
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ciiContribution
number
default:"0.25"
25% weight — country-level instability scores provide structural context.
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geoConvergence
number
default:"0.25"
25% weight — multiple event types in same area indicate complex situation.
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militaryActivity
number
default:"0.15"
15% weight — lowest priority because military presence is often routine (patrols, exercises).

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Component Normalization

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News Activity (0-100)

Measures news coverage intensity and breaking status: 
function normalizeNewsActivity(matches: number, hasBreaking: boolean, velocity: number): number {
  return Math.min(100,
    matches * 15 +
    (hasBreaking ? 30 : 0) +
    velocity * 5
  );
}
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matches
number
Count of news clusters mentioning hotspot (×15 multiplier).
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hasBreaking
boolean
+30 if any cluster has isAlert flag.
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velocity
number
Average sources-per-hour across clusters (×5 multiplier).
Source: src/services/hotspot-escalation.ts:75-77

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CII Contribution (0-100)

Uses the maximum CII score among hotspot-linked countries: 
function getCIIForHotspot(hotspotId: string): number | null {
  const countryCodes = getHotspotCountries(hotspotId);
  if (countryCodes.length === 0) return null;

  const scores = countryCodes.map(code => ciiGetter!(code)).filter((s): s is number => s !== null);
  return scores.length > 0 ? Math.max(...scores) : null;
}

function normalizeCII(score: number | null): number {
  return score ?? 30; // Default if no CII data
}
Example: Tehran hotspot is linked to Iran (IR), so it uses Iran’s CII score. Source: src/services/hotspot-escalation.ts:60-68, 79-81

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Geo Convergence (0-100)

Detects multiple event types co-occurring in 1°×1° cells within 150km radius: 
function normalizeGeo(alertScore: number, alertTypes: number): number {
  if (alertScore === 0) return 0;
  return Math.min(100, alertScore + alertTypes * 10);
}
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alertScore
number
Raw convergence score from geographic binning: typeCount * 25 + eventCount * 2
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alertTypes
number
Number of distinct event types (protests, military flights, vessels, earthquakes). +10 per type.
Example: If Tehran area has protests + military flights + naval vessels within 150km, alertTypes = 3, contributing +30 to geo score. Source: src/services/hotspot-escalation.ts:83-86, geo-convergence.ts:100-123

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Military Activity (0-100)

Counts military assets within 200km radius: 
function normalizeMilitary(flights: number, vessels: number): number {
  return Math.min(100, flights * 10 + vessels * 15);
}

function countMilitaryNearHotspot(
  hotspot: Hotspot,
  flights: MilitaryFlight[],
  vessels: MilitaryVessel[],
  radiusKm: number = 200
): { flights: number; vessels: number } {
  let flightCount = 0;
  let vesselCount = 0;

  for (const f of flights) {
    if (haversineKm(hotspot.lat, hotspot.lon, f.lat, f.lon) <= radiusKm) {
      flightCount++;
    }
  }

  for (const v of vessels) {
    if (haversineKm(hotspot.lat, hotspot.lon, v.lat, v.lon) <= radiusKm) {
      vesselCount++;
    }
  }

  return { flights: flightCount, vessels: vesselCount };
}
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flights
number
Military aircraft within 200km (×10 multiplier).
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vessels
number
Naval vessels within 200km (×15 multiplier, higher weight for capital ships).
Source: src/services/hotspot-escalation.ts:88-90, 241-263

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Trend Detection

Linear regression on 48-hour score history detects escalation patterns: 
function detectTrend(history: Array<{ timestamp: number; score: number }>): EscalationTrend {
  if (history.length < 3) return 'stable';

  // Least squares regression
  let sumX = 0, sumY = 0, sumXY = 0, sumX2 = 0;
  for (let i = 0; i < history.length; i++) {
    sumX += i;
    sumY += history[i].score;
    sumXY += i * history[i].score;
    sumX2 += i * i;
  }

  const slope = (history.length * sumXY - sumX * sumY) / (history.length * sumX2 - sumX * sumX);

  if (slope > 0.1) return 'escalating';
  if (slope < -0.1) return 'de-escalating';
  return 'stable';
}
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escalating
string
Slope > 0.1 → situation worsening over 48h.
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stable
string
Slope between -0.1 and 0.1 → no significant change.
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de-escalating
string
Slope < -0.1 → situation improving.
History pruning: Scores older than 24h are removed. Max 48 data points stored. Source: src/services/hotspot-escalation.ts:117-144

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Signal Emission

Signals fire when escalation crosses thresholds or changes rapidly:
Integer score increases (2.3 → 3.1) trigger signals:
const oldInt = Math.floor(oldScore); // 2
const newInt = Math.floor(newScore); // 3
if (newInt > oldInt && newScore >= 2) {
  return { type: 'threshold_crossed', oldScore, newScore, threshold: newInt };
}
Minimum score: Must be ≥2 to emit (prevents noise from low-activity hotspots).
+0.5 or more in one update cycle:
if (newScore - oldScore >= 0.5) {
  return { type: 'rapid_increase', oldScore, newScore };
}
Example: Tehran jumps from 3.2 → 3.8 in 5 minutes due to breaking news + military surge.
Score crosses 4.5 threshold:
if (newScore >= 4.5 && oldScore < 4.5) {
  return { type: 'critical_reached', oldScore, newScore };
}
Critical threshold: 4.5/5.0 indicates imminent crisis.
Cooldown: 2-hour cooldown after signal emission prevents alert fatigue. 
const SIGNAL_COOLDOWN_MS = 2 * 60 * 60 * 1000;

function shouldEmitSignal(hotspotId: string, oldScore: number | null, newScore: number): EscalationSignalReason | null {
  const lastSignal = lastSignalTime.get(hotspotId) ?? 0;
  if (Date.now() - lastSignal < SIGNAL_COOLDOWN_MS) return null;
  // ... threshold checks
}
Source: src/services/hotspot-escalation.ts:206-227

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Example Scores

{
  "hotspotId": "tehran",
  "staticBaseline": 4.0,
  "dynamicScore": 4.8,
  "combinedScore": 4.6,
  "trend": "escalating",
  "components": {
    "newsActivity": 92,
    "ciiContribution": 72,
    "geoConvergence": 85,
    "militaryActivity": 68
  },
  "history": [
    { "timestamp": 1709294400000, "score": 4.2 },
    { "timestamp": 1709298000000, "score": 4.4 },
    { "timestamp": 1709301600000, "score": 4.6 }
  ],
  "lastUpdated": "2026-03-01T12:00:00Z"
}

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Integration with Other Systems

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CII Scoring

Hotspot proximity boosts country instability scores: 
function trackHotspotActivity(lat: number, lon: number, weight: number = 1): void {
  for (const hotspot of INTEL_HOTSPOTS) {
    const dist = haversineKm(lat, lon, hotspot.lat, hotspot.lon);
    if (dist < 150) {
      const countryCodes = getHotspotCountries(hotspot.id);
      for (const countryCode of countryCodes) {
        hotspotActivityMap.set(countryCode, (hotspotActivityMap.get(countryCode) || 0) + weight);
      }
    }
  }
}

function getHotspotBoost(countryCode: string): number {
  const activity = hotspotActivityMap.get(countryCode) || 0;
  return Math.min(10, activity * 1.5);
}
Source: src/services/country-instability.ts:304-348

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Focal Point Detection

Hotspot escalation feeds into focal point urgency calculations: 
const focalUrgency = focalUrgencies.get(code);
const focalBoost = focalUrgency === 'critical' ? 8
  : focalUrgency === 'elevated' ? 4
  : 0;

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Signal Aggregation

Hotspot data is included in geographic signal context for AI summarization: 
const aiContext = `
[INTELLIGENCE HOTSPOTS]
Tehran: Score 4.6/5.0 (escalating) — 92 news mentions, CII 72, convergence detected
Taiwan Strait: Score 3.3/5.0 (stable) — 45 news mentions, 12 military flights, 8 naval vessels
`;

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Key Files

  • src/services/hotspot-escalation.ts — Main escalation scoring engine
  • src/services/geo-convergence.ts — Geographic event binning
  • src/config/geo.ts — Hotspot definitions and coordinates
  • src/components/HotspotMarker.tsx — Map visualization

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