feat: add core analysis engine — focal points, temporal anomalies, CII v2
Phase 5 of world-intel-mcp: intelligence synthesis layer that makes raw data actionable. New modules: - config/countries.py: 22 tier-1 countries with event multipliers, 22 intel hotspots, 9 strategic waterways, 6 conflict zones - analysis/focal_points.py: detect convergence of signals on entities using signal count, type diversity, and recency weighting - analysis/temporal.py: Welford's algorithm + SQLite for streaming anomaly detection with seasonal baselines (weekday+month) Upgraded modules: - analysis/instability.py: CII v2 with 4 weighted domains (unrest 0.25, conflict 0.30, security 0.20, information 0.25), UCDP floors, event multipliers. Full v1 backward compat preserved. - analysis/signals.py: v2 aggregator accepts 7 data sources (added outages, military, protests) with convergence scoring - sources/intelligence.py: 3 new fetch functions (focal_points, signal_summary, temporal_anomalies), instability upgraded to CII v2 3 new MCP tools: intel_focal_points, intel_signal_summary, intel_temporal_anomalies. Total: 39 tools. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Marc Shade
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
561ed8e277
commit
8d60a9a220
@@ -0,0 +1,120 @@
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"""Focal point detection — identifies entities where multiple signals converge.
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Pure analysis module — no I/O.
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"""
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from __future__ import annotations
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from collections import defaultdict
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from datetime import datetime, timezone
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def detect_focal_points(
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events: list[dict],
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min_signals: int = 2,
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max_age_hours: float = 48.0,
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) -> list[dict]:
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"""Group events by entity, score convergence, assign urgency.
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Args:
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events: List of dicts, each with at least ``entity`` and ``type`` keys.
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Optional keys: ``timestamp``, ``country``, ``lat``, ``lon``, ``weight``.
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min_signals: Minimum number of events for an entity to qualify.
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max_age_hours: Discard events older than this (hours from now).
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Returns:
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List of focal point dicts sorted by focal_score descending. Each contains:
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entity, signal_count, signal_types, urgency, focal_score, countries, recent_events.
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"""
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now = datetime.now(timezone.utc)
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grouped: dict[str, list[dict]] = defaultdict(list)
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for event in events:
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entity = event.get("entity")
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if not entity:
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continue
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# Parse timestamp and filter by age
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ts_raw = event.get("timestamp")
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hours_old = 0.0
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if ts_raw:
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try:
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if isinstance(ts_raw, str):
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# Handle ISO format with or without timezone
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ts_str = ts_raw.replace("Z", "+00:00")
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ts = datetime.fromisoformat(ts_str)
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if ts.tzinfo is None:
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ts = ts.replace(tzinfo=timezone.utc)
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elif isinstance(ts_raw, datetime):
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ts = ts_raw if ts_raw.tzinfo else ts_raw.replace(tzinfo=timezone.utc)
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else:
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ts = now
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hours_old = (now - ts).total_seconds() / 3600.0
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except (ValueError, TypeError):
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hours_old = 0.0
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if hours_old > max_age_hours:
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continue
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normalized = entity.strip().lower()
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grouped[normalized].append({**event, "_hours_old": hours_old})
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focal_points: list[dict] = []
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for entity_key, entity_events in grouped.items():
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signal_count = len(entity_events)
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if signal_count < min_signals:
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continue
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# Unique signal types
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unique_types = set()
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countries: set[str] = set()
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for ev in entity_events:
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t = ev.get("type")
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if t:
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unique_types.add(t)
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c = ev.get("country")
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if c:
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countries.add(c)
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type_diversity = len(unique_types)
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# Recency weighting: more recent events contribute more
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recency_weights = []
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for ev in entity_events:
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h = ev.get("_hours_old", 0.0)
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recency_weights.append(1.0 / max(1.0, h))
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recency_weight = sum(recency_weights) / signal_count if signal_count else 0.0
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# Focal score
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focal_score = signal_count * (1 + type_diversity * 0.5) * recency_weight
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# Urgency level
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if signal_count >= 10:
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urgency = "critical"
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elif signal_count >= 5:
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urgency = "elevated"
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else:
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urgency = "watch"
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# Recent events (strip internal fields, limit to 10)
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recent = []
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for ev in sorted(entity_events, key=lambda e: e.get("_hours_old", 0))[:10]:
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clean = {k: v for k, v in ev.items() if not k.startswith("_")}
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recent.append(clean)
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# Use the original casing from the first event
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display_entity = entity_events[0].get("entity", entity_key)
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focal_points.append({
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"entity": display_entity,
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"signal_count": signal_count,
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"signal_types": sorted(unique_types),
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"urgency": urgency,
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"focal_score": round(focal_score, 2),
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"countries": sorted(countries),
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"recent_events": recent,
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})
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focal_points.sort(key=lambda fp: fp["focal_score"], reverse=True)
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return focal_points
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@@ -1,6 +1,11 @@
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"""Country Instability Index (CII) computation.
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Pure scoring functions that transform raw data into instability scores.
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CII v1 scorers (score_conflict_intensity, score_economic_stress, etc.) are
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kept for backward compatibility. CII v2 uses weighted component scoring via
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compute_cii() with new domain scorers (score_unrest, score_conflict_v2,
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score_security, score_information).
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"""
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import logging
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@@ -8,6 +13,22 @@ import logging
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logger = logging.getLogger("world-intel-mcp.analysis.instability")
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# ---------------------------------------------------------------------------
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# CII v2 weights
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# ---------------------------------------------------------------------------
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CII_WEIGHTS: dict[str, float] = {
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"unrest": 0.25,
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"conflict": 0.30,
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"security": 0.20,
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"information": 0.25,
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}
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# ---------------------------------------------------------------------------
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# CII v1 scorers (kept for backward compatibility)
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# ---------------------------------------------------------------------------
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def score_conflict_intensity(event_count: int, days: int = 30) -> float:
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"""Score conflict intensity 0-20 based on event count per period."""
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daily_rate = event_count / max(days, 1)
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@@ -74,19 +95,140 @@ def score_military_activity(aircraft_count: int, flight_density: float = 0.0) ->
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return score
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def compute_cii(
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conflict: float = 0.0,
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economic: float = 0.0,
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humanitarian: float = 0.0,
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infrastructure: float = 0.0,
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military: float = 0.0,
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) -> dict:
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"""Compute Country Instability Index (0-100) from component scores.
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# ---------------------------------------------------------------------------
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# CII v2 component scorers (0-25 each)
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# ---------------------------------------------------------------------------
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Returns dict with index, components, and risk_level.
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def score_unrest(protest_count: int = 0, riot_count: int = 0) -> float:
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"""Score unrest 0-25 from protests and riots."""
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# Protests: 0=0, 50+=15
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protest_score = min(15.0, protest_count * 0.3)
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# Riots: 0=0, 20+=10
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riot_score = min(10.0, riot_count * 0.5)
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return min(25.0, protest_score + riot_score)
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def score_conflict_v2(
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event_count: int = 0,
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fatalities: int = 0,
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days: int = 30,
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) -> float:
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"""Score conflict 0-25 from armed conflict events and fatalities."""
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daily_rate = event_count / max(days, 1)
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# 0 events/day = 0, 15+/day = 15
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event_score = min(15.0, daily_rate * 1.0)
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# Fatalities: 0=0, 1000+=10
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fat_score = min(10.0, fatalities / 100.0)
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return min(25.0, event_score + fat_score)
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def score_security(
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military_count: int = 0,
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outage_count: int = 0,
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cable_warnings: int = 0,
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) -> float:
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"""Score security 0-25 from military activity and infrastructure disruption."""
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# Military: 0=0, 50+=12
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mil_score = min(12.0, military_count * 0.24)
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# Outages: 0=0, 5+=8
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outage_score = min(8.0, outage_count * 1.6)
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# Cable warnings: 0=0, 3+=5
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cable_score = min(5.0, cable_warnings * 1.67)
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return min(25.0, mil_score + outage_score + cable_score)
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def score_information(
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news_velocity: int = 0,
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trending_count: int = 0,
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) -> float:
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"""Score information environment 0-25 from news velocity and trending keywords."""
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# News velocity (articles mentioning country in last 24h): 0=0, 100+=15
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news_score = min(15.0, news_velocity * 0.15)
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# Trending keyword mentions: 0=0, 20+=10
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trend_score = min(10.0, trending_count * 0.5)
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return min(25.0, news_score + trend_score)
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# ---------------------------------------------------------------------------
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# CII v2 computation
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# ---------------------------------------------------------------------------
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def compute_cii(
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unrest: float = 0.0,
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conflict: float = 0.0,
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security: float = 0.0,
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information: float = 0.0,
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event_multiplier: float = 1.0,
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ucdp_floor: float | None = None,
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focal_boost: float = 0.0,
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displacement_boost: float = 0.0,
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# v1 compat: if these are passed, use v1 mode
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economic: float | None = None,
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humanitarian: float | None = None,
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infrastructure: float | None = None,
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military: float | None = None,
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) -> dict:
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"""Compute Country Instability Index (0-100).
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CII v2: Weighted multi-signal instability index using 4 domains
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(unrest, conflict, security, information) scaled 0-25 each.
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Falls back to v1 simple sum if legacy component names are passed.
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"""
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total = conflict + economic + humanitarian + infrastructure + military
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total = min(100.0, max(0.0, total))
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# Detect v1 call pattern (old callers pass economic/humanitarian/etc.)
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if economic is not None or humanitarian is not None or infrastructure is not None or military is not None:
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total = (
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conflict
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+ (economic or 0.0)
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+ (humanitarian or 0.0)
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+ (infrastructure or 0.0)
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+ (military or 0.0)
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)
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total = min(100.0, max(0.0, total))
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if total >= 75:
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risk_level = "critical"
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elif total >= 50:
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risk_level = "high"
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elif total >= 25:
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risk_level = "medium"
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else:
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risk_level = "low"
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return {
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"instability_index": round(total, 1),
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"components": {
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"conflict_intensity": round(conflict, 1),
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"economic_stress": round(economic or 0.0, 1),
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"humanitarian_crisis": round(humanitarian or 0.0, 1),
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"infrastructure_disruption": round(infrastructure or 0.0, 1),
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"military_activity": round(military or 0.0, 1),
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},
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"risk_level": risk_level,
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}
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# CII v2: weighted scoring
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raw = (
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unrest * CII_WEIGHTS["unrest"]
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+ conflict * CII_WEIGHTS["conflict"]
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+ security * CII_WEIGHTS["security"]
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+ information * CII_WEIGHTS["information"]
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)
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# Components are 0-25 each, weights sum to 1.0, raw max = 25
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# Normalize to 0-100
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scaled = raw * 4.0
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# Apply country-specific multiplier
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adjusted = scaled * event_multiplier
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# Apply boosts
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adjusted += focal_boost + displacement_boost
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# Apply UCDP floor (wars can't score below a threshold)
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if ucdp_floor is not None:
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adjusted = max(adjusted, ucdp_floor)
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total = min(100.0, max(0.0, adjusted))
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if total >= 75:
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risk_level = "critical"
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@@ -100,11 +242,15 @@ def compute_cii(
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return {
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"instability_index": round(total, 1),
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"components": {
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"conflict_intensity": round(conflict, 1),
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"economic_stress": round(economic, 1),
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"humanitarian_crisis": round(humanitarian, 1),
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"infrastructure_disruption": round(infrastructure, 1),
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"military_activity": round(military, 1),
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"unrest": round(unrest, 1),
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"conflict": round(conflict, 1),
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"security": round(security, 1),
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"information": round(information, 1),
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},
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"weights": dict(CII_WEIGHTS),
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"event_multiplier": event_multiplier,
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"focal_boost": round(focal_boost, 1),
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"displacement_boost": round(displacement_boost, 1),
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"ucdp_floor": ucdp_floor,
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"risk_level": risk_level,
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}
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@@ -2,6 +2,9 @@
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Collects and normalizes signals from multiple domains into
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a per-country summary for dashboard and reporting.
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v2 adds fire, outage, military, and protest integration plus
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convergence scoring.
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"""
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import logging
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@@ -9,12 +12,28 @@ from collections import defaultdict
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logger = logging.getLogger("world-intel-mcp.analysis.signals")
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# Approximate region-to-country mapping for wildfire data
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_FIRE_REGION_COUNTRIES: dict[str, list[str]] = {
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"north_america": ["United States", "Canada", "Mexico"],
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"south_america": ["Brazil", "Argentina", "Colombia", "Chile"],
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"europe": ["Greece", "Spain", "Portugal", "Italy", "France"],
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"africa": ["Nigeria", "DR Congo", "Ethiopia", "Sudan"],
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"middle_east": ["Syria", "Iraq", "Iran", "Yemen"],
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"south_asia": ["India", "Pakistan", "Afghanistan"],
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"east_asia": ["China", "Japan", "South Korea"],
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"southeast_asia": ["Myanmar", "Indonesia", "Philippines"],
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"oceania": ["Australia", "New Zealand"],
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}
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def aggregate_country_signals(
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conflict_events: list[dict] | None = None,
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displacement_data: list[dict] | None = None,
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earthquake_data: list[dict] | None = None,
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fire_data: dict | None = None,
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fire_data: list[dict] | None = None,
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outage_data: list[dict] | None = None,
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military_data: list[dict] | None = None,
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protest_data: list[dict] | None = None,
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) -> dict[str, dict]:
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"""Aggregate multi-domain signals by country.
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@@ -22,10 +41,13 @@ def aggregate_country_signals(
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conflict_events: ACLED/UCDP events with 'country' field.
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displacement_data: UNHCR data with 'country' field.
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earthquake_data: USGS earthquakes (uses reverse geocoding heuristic).
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fire_data: Wildfire data by region (maps to countries approximately).
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fire_data: Wildfire data as list of dicts with optional 'country' or 'region' field.
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outage_data: Internet outages with 'countries' field (list of country codes).
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military_data: Military aircraft data with 'origin_country' field.
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protest_data: ACLED protests/riots subset with 'country' field.
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Returns:
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Dict mapping country name to signal summary.
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Dict mapping country name to signal summary with convergence scoring.
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"""
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countries: dict[str, dict] = defaultdict(lambda: {
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"conflict_events": 0,
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@@ -34,8 +56,12 @@ def aggregate_country_signals(
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"earthquakes": 0,
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"max_earthquake_mag": 0.0,
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"fires": 0,
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"signal_count": 0,
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"outages": 0,
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"military_aircraft": 0,
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"protests": 0,
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"riots": 0,
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"domains": set(),
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"high_severity_count": 0,
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})
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# Conflict events
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@@ -49,6 +75,8 @@ def aggregate_country_signals(
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fat = event.get("fatalities", 0)
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if isinstance(fat, (int, float)):
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c["fatalities"] += int(fat)
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if int(fat) > 10:
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c["high_severity_count"] += 1
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c["domains"].add("conflict")
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# Displacement
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@@ -61,6 +89,8 @@ def aggregate_country_signals(
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total = record.get("total_displaced", 0)
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if isinstance(total, (int, float)):
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c["displaced_persons"] += int(total)
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if int(total) > 100_000:
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c["high_severity_count"] += 1
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c["domains"].add("displacement")
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# Earthquakes (approximate country from place string)
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@@ -73,25 +103,104 @@ def aggregate_country_signals(
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c = countries[country]
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c["earthquakes"] += 1
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mag = quake.get("magnitude", 0) or 0
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if isinstance(mag, (int, float)) and mag > c["max_earthquake_mag"]:
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c["max_earthquake_mag"] = float(mag)
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if isinstance(mag, (int, float)):
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if mag > c["max_earthquake_mag"]:
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c["max_earthquake_mag"] = float(mag)
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if mag >= 6.0:
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c["high_severity_count"] += 1
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c["domains"].add("seismology")
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# TODO: fire_data integration (requires region-to-country mapping)
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_ = fire_data
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# Fire data
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if fire_data:
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for fire in fire_data:
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if not isinstance(fire, dict):
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continue
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country = fire.get("country")
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region = fire.get("region")
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if country:
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c = countries[country]
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c["fires"] += 1
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c["domains"].add("wildfire")
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elif region and region in _FIRE_REGION_COUNTRIES:
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# Distribute fire to first mapped country as approximation
|
||||
mapped = _FIRE_REGION_COUNTRIES[region][0]
|
||||
c = countries[mapped]
|
||||
c["fires"] += 1
|
||||
c["domains"].add("wildfire")
|
||||
|
||||
# Compute signal counts
|
||||
# Internet outages
|
||||
if outage_data:
|
||||
for outage in outage_data:
|
||||
if not isinstance(outage, dict):
|
||||
continue
|
||||
outage_countries = outage.get("countries", [])
|
||||
if isinstance(outage_countries, str):
|
||||
outage_countries = [outage_countries]
|
||||
for oc in outage_countries:
|
||||
if oc:
|
||||
c = countries[oc]
|
||||
c["outages"] += 1
|
||||
c["domains"].add("infrastructure")
|
||||
|
||||
# Military flights
|
||||
if military_data:
|
||||
for aircraft in military_data:
|
||||
if not isinstance(aircraft, dict):
|
||||
continue
|
||||
country = aircraft.get("origin_country")
|
||||
if country:
|
||||
c = countries[country]
|
||||
c["military_aircraft"] += 1
|
||||
c["domains"].add("military")
|
||||
|
||||
# Protests and riots
|
||||
if protest_data:
|
||||
for event in protest_data:
|
||||
if not isinstance(event, dict):
|
||||
continue
|
||||
country = event.get("country")
|
||||
if not country:
|
||||
continue
|
||||
c = countries[country]
|
||||
event_type = (event.get("event_type") or "").lower()
|
||||
if "riot" in event_type:
|
||||
c["riots"] += 1
|
||||
else:
|
||||
c["protests"] += 1
|
||||
c["domains"].add("unrest")
|
||||
|
||||
# Compute signal counts and convergence scoring
|
||||
result = {}
|
||||
for country, data in countries.items():
|
||||
domains = data.pop("domains")
|
||||
data["signal_count"] = len(domains)
|
||||
unique_domains = len(domains)
|
||||
total_signal_count = (
|
||||
data["conflict_events"]
|
||||
+ data["earthquakes"]
|
||||
+ data["fires"]
|
||||
+ data["outages"]
|
||||
+ data["military_aircraft"]
|
||||
+ data["protests"]
|
||||
+ data["riots"]
|
||||
)
|
||||
high_severity = data.pop("high_severity_count")
|
||||
|
||||
# Convergence scoring
|
||||
type_bonus = 20 * unique_domains
|
||||
count_bonus = min(30, 5 * total_signal_count)
|
||||
severity_bonus = 10 * high_severity
|
||||
convergence_score = type_bonus + count_bonus + severity_bonus
|
||||
|
||||
data["signal_count"] = unique_domains
|
||||
data["total_signals"] = total_signal_count
|
||||
data["active_domains"] = sorted(domains)
|
||||
data["convergence_score"] = convergence_score
|
||||
result[country] = data
|
||||
|
||||
# Sort by signal count, then fatalities
|
||||
# Sort by convergence score, then signal count
|
||||
result = dict(sorted(
|
||||
result.items(),
|
||||
key=lambda x: (x[1]["signal_count"], x[1]["fatalities"]),
|
||||
key=lambda x: (x[1]["convergence_score"], x[1]["signal_count"]),
|
||||
reverse=True,
|
||||
))
|
||||
|
||||
|
||||
@@ -0,0 +1,146 @@
|
||||
"""Temporal baseline anomaly detection using Welford's online algorithm.
|
||||
|
||||
Maintains per-metric running statistics in SQLite and detects deviations
|
||||
from historical baselines. The only analysis module with I/O (justified
|
||||
for streaming stats persistence).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import math
|
||||
import os
|
||||
import sqlite3
|
||||
from datetime import datetime, timezone
|
||||
|
||||
logger = logging.getLogger("world-intel-mcp.analysis.temporal")
|
||||
|
||||
_DB_PATH = os.path.join(
|
||||
os.path.expanduser("~"), ".cache", "world-intel-mcp", "temporal.db"
|
||||
)
|
||||
|
||||
|
||||
class TemporalBaseline:
|
||||
"""Streaming anomaly detector with Welford's algorithm + SQLite persistence."""
|
||||
|
||||
def __init__(self, db_path: str = _DB_PATH):
|
||||
self._db_path = db_path
|
||||
os.makedirs(os.path.dirname(db_path), exist_ok=True)
|
||||
self._conn = sqlite3.connect(db_path)
|
||||
self._conn.execute(
|
||||
"""CREATE TABLE IF NOT EXISTS baselines (
|
||||
key TEXT PRIMARY KEY,
|
||||
count INTEGER NOT NULL DEFAULT 0,
|
||||
mean REAL NOT NULL DEFAULT 0.0,
|
||||
m2 REAL NOT NULL DEFAULT 0.0,
|
||||
updated_at TEXT NOT NULL
|
||||
)"""
|
||||
)
|
||||
self._conn.commit()
|
||||
|
||||
def _key(self, event_type: str, region: str) -> str:
|
||||
"""Build a composite key including weekday and month for seasonality."""
|
||||
now = datetime.now(timezone.utc)
|
||||
weekday = now.strftime("%A")
|
||||
month = now.strftime("%B")
|
||||
return f"{event_type}:{region}:{weekday}:{month}"
|
||||
|
||||
def record(self, event_type: str, region: str, count: int) -> None:
|
||||
"""Record an observation using Welford's online update."""
|
||||
key = self._key(event_type, region)
|
||||
now_iso = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
|
||||
|
||||
row = self._conn.execute(
|
||||
"SELECT count, mean, m2 FROM baselines WHERE key = ?", (key,)
|
||||
).fetchone()
|
||||
|
||||
if row is None:
|
||||
n, mean, m2 = 0, 0.0, 0.0
|
||||
else:
|
||||
n, mean, m2 = int(row[0]), float(row[1]), float(row[2])
|
||||
|
||||
# Welford update
|
||||
n += 1
|
||||
delta = count - mean
|
||||
mean += delta / n
|
||||
delta2 = count - mean
|
||||
m2 += delta * delta2
|
||||
|
||||
self._conn.execute(
|
||||
"""INSERT INTO baselines (key, count, mean, m2, updated_at)
|
||||
VALUES (?, ?, ?, ?, ?)
|
||||
ON CONFLICT(key) DO UPDATE SET
|
||||
count = excluded.count,
|
||||
mean = excluded.mean,
|
||||
m2 = excluded.m2,
|
||||
updated_at = excluded.updated_at""",
|
||||
(key, n, mean, m2, now_iso),
|
||||
)
|
||||
self._conn.commit()
|
||||
|
||||
def check(self, event_type: str, region: str, count: int) -> dict | None:
|
||||
"""Check a value against the baseline.
|
||||
|
||||
Returns None if: not enough data (n < 10), or value is within 1.5 std.
|
||||
Otherwise returns anomaly dict with z_score, severity, multiplier, message.
|
||||
"""
|
||||
key = self._key(event_type, region)
|
||||
|
||||
row = self._conn.execute(
|
||||
"SELECT count, mean, m2 FROM baselines WHERE key = ?", (key,)
|
||||
).fetchone()
|
||||
|
||||
if row is None:
|
||||
return None
|
||||
|
||||
n, mean, m2 = int(row[0]), float(row[1]), float(row[2])
|
||||
|
||||
if n < 10:
|
||||
return None
|
||||
|
||||
variance = m2 / (n - 1)
|
||||
std = math.sqrt(variance) if variance > 0 else 0.0
|
||||
|
||||
if std == 0:
|
||||
return None
|
||||
|
||||
z_score = (count - mean) / std
|
||||
|
||||
if z_score < 1.5:
|
||||
return None
|
||||
|
||||
# Severity levels
|
||||
if z_score >= 3.0:
|
||||
severity = "critical"
|
||||
elif z_score >= 2.0:
|
||||
severity = "high"
|
||||
else:
|
||||
severity = "medium"
|
||||
|
||||
multiplier = count / mean if mean > 0 else float("inf")
|
||||
|
||||
now = datetime.now(timezone.utc)
|
||||
weekday = now.strftime("%A")
|
||||
month = now.strftime("%B")
|
||||
message = (
|
||||
f"{event_type.replace('_', ' ').title()} {multiplier:.1f}x normal "
|
||||
f"for {weekday} ({month})"
|
||||
)
|
||||
|
||||
return {
|
||||
"event_type": event_type,
|
||||
"region": region,
|
||||
"z_score": round(z_score, 2),
|
||||
"severity": severity,
|
||||
"multiplier": round(multiplier, 1),
|
||||
"message": message,
|
||||
"observed": count,
|
||||
"expected": round(mean),
|
||||
}
|
||||
|
||||
def record_and_check(
|
||||
self, event_type: str, region: str, count: int
|
||||
) -> dict | None:
|
||||
"""Record an observation and check for anomaly in one call."""
|
||||
self.record(event_type, region, count)
|
||||
return self.check(event_type, region, count)
|
||||
@@ -0,0 +1 @@
|
||||
"""Configuration data for world-intel-mcp."""
|
||||
@@ -0,0 +1,110 @@
|
||||
"""Country configuration, hotspots, waterways, and conflict zones.
|
||||
|
||||
Pure data module — no I/O, no external dependencies.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
|
||||
TIER1_COUNTRIES: dict[str, dict] = {
|
||||
"USA": {"name": "United States", "keywords": ["united states", "usa", "american"], "baseline_risk": 15, "event_multiplier": 0.3},
|
||||
"CHN": {"name": "China", "keywords": ["china", "chinese", "beijing"], "baseline_risk": 35, "event_multiplier": 2.5},
|
||||
"RUS": {"name": "Russia", "keywords": ["russia", "russian", "moscow"], "baseline_risk": 55, "event_multiplier": 1.8},
|
||||
"UKR": {"name": "Ukraine", "keywords": ["ukraine", "ukrainian", "kyiv"], "baseline_risk": 85, "event_multiplier": 1.0},
|
||||
"SYR": {"name": "Syria", "keywords": ["syria", "syrian", "damascus"], "baseline_risk": 80, "event_multiplier": 1.2},
|
||||
"YEM": {"name": "Yemen", "keywords": ["yemen", "yemeni", "sanaa", "houthi"], "baseline_risk": 75, "event_multiplier": 1.3},
|
||||
"MMR": {"name": "Myanmar", "keywords": ["myanmar", "burma", "burmese"], "baseline_risk": 70, "event_multiplier": 1.1},
|
||||
"SDN": {"name": "Sudan", "keywords": ["sudan", "sudanese", "khartoum"], "baseline_risk": 80, "event_multiplier": 1.4},
|
||||
"NGA": {"name": "Nigeria", "keywords": ["nigeria", "nigerian", "lagos", "abuja"], "baseline_risk": 50, "event_multiplier": 1.0},
|
||||
"AFG": {"name": "Afghanistan", "keywords": ["afghanistan", "afghan", "kabul", "taliban"], "baseline_risk": 70, "event_multiplier": 1.1},
|
||||
"IRQ": {"name": "Iraq", "keywords": ["iraq", "iraqi", "baghdad"], "baseline_risk": 55, "event_multiplier": 1.0},
|
||||
"IRN": {"name": "Iran", "keywords": ["iran", "iranian", "tehran"], "baseline_risk": 50, "event_multiplier": 2.0},
|
||||
"TWN": {"name": "Taiwan", "keywords": ["taiwan", "taiwanese", "taipei"], "baseline_risk": 30, "event_multiplier": 3.0},
|
||||
"PRK": {"name": "North Korea", "keywords": ["north korea", "dprk", "pyongyang"], "baseline_risk": 45, "event_multiplier": 2.5},
|
||||
"ISR": {"name": "Israel", "keywords": ["israel", "israeli", "tel aviv", "jerusalem"], "baseline_risk": 55, "event_multiplier": 1.5},
|
||||
"PSE": {"name": "Palestine", "keywords": ["palestine", "palestinian", "gaza", "west bank"], "baseline_risk": 85, "event_multiplier": 1.0},
|
||||
"LBN": {"name": "Lebanon", "keywords": ["lebanon", "lebanese", "beirut", "hezbollah"], "baseline_risk": 60, "event_multiplier": 1.3},
|
||||
"ETH": {"name": "Ethiopia", "keywords": ["ethiopia", "ethiopian", "addis ababa"], "baseline_risk": 55, "event_multiplier": 1.0},
|
||||
"COD": {"name": "DR Congo", "keywords": ["congo", "drc", "kinshasa", "congolese"], "baseline_risk": 60, "event_multiplier": 1.0},
|
||||
"PAK": {"name": "Pakistan", "keywords": ["pakistan", "pakistani", "islamabad"], "baseline_risk": 45, "event_multiplier": 1.0},
|
||||
"IND": {"name": "India", "keywords": ["india", "indian", "new delhi"], "baseline_risk": 30, "event_multiplier": 0.5},
|
||||
"MEX": {"name": "Mexico", "keywords": ["mexico", "mexican", "mexico city"], "baseline_risk": 40, "event_multiplier": 0.8},
|
||||
}
|
||||
|
||||
|
||||
INTEL_HOTSPOTS: dict[str, dict] = {
|
||||
"tehran": {"lat": 35.69, "lon": 51.39, "baseline_escalation": 3, "associated_countries": ["IRN", "ISR"]},
|
||||
"kyiv": {"lat": 50.45, "lon": 30.52, "baseline_escalation": 5, "associated_countries": ["UKR", "RUS"]},
|
||||
"taipei": {"lat": 25.03, "lon": 121.57, "baseline_escalation": 3, "associated_countries": ["TWN", "CHN"]},
|
||||
"pyongyang": {"lat": 39.02, "lon": 125.75, "baseline_escalation": 3, "associated_countries": ["PRK", "KOR"]},
|
||||
"gaza": {"lat": 31.42, "lon": 34.35, "baseline_escalation": 5, "associated_countries": ["PSE", "ISR"]},
|
||||
"kabul": {"lat": 34.53, "lon": 69.17, "baseline_escalation": 3, "associated_countries": ["AFG", "PAK"]},
|
||||
"damascus": {"lat": 33.51, "lon": 36.29, "baseline_escalation": 4, "associated_countries": ["SYR", "IRN", "ISR"]},
|
||||
"khartoum": {"lat": 15.59, "lon": 32.53, "baseline_escalation": 5, "associated_countries": ["SDN"]},
|
||||
"sanaa": {"lat": 15.37, "lon": 44.19, "baseline_escalation": 4, "associated_countries": ["YEM"]},
|
||||
"naypyidaw": {"lat": 19.76, "lon": 96.07, "baseline_escalation": 3, "associated_countries": ["MMR"]},
|
||||
"baghdad": {"lat": 33.31, "lon": 44.37, "baseline_escalation": 3, "associated_countries": ["IRQ", "IRN"]},
|
||||
"mogadishu": {"lat": 2.05, "lon": 45.32, "baseline_escalation": 4, "associated_countries": ["SOM"]},
|
||||
"addis_ababa": {"lat": 9.02, "lon": 38.75, "baseline_escalation": 3, "associated_countries": ["ETH"]},
|
||||
"kinshasa": {"lat": -4.32, "lon": 15.31, "baseline_escalation": 3, "associated_countries": ["COD"]},
|
||||
"bamako": {"lat": 12.64, "lon": -8.0, "baseline_escalation": 3, "associated_countries": ["MLI"]},
|
||||
"ouagadougou": {"lat": 12.37, "lon": -1.52, "baseline_escalation": 3, "associated_countries": ["BFA"]},
|
||||
"beirut": {"lat": 33.89, "lon": 35.50, "baseline_escalation": 4, "associated_countries": ["LBN", "ISR"]},
|
||||
"hormuz_strait": {"lat": 26.57, "lon": 56.25, "baseline_escalation": 3, "associated_countries": ["IRN", "OMN"]},
|
||||
"south_china_sea": {"lat": 15.0, "lon": 115.0, "baseline_escalation": 3, "associated_countries": ["CHN", "PHL", "VNM"]},
|
||||
"crimea": {"lat": 44.95, "lon": 34.10, "baseline_escalation": 4, "associated_countries": ["UKR", "RUS"]},
|
||||
"bab_el_mandeb": {"lat": 12.58, "lon": 43.33, "baseline_escalation": 4, "associated_countries": ["YEM", "DJI", "ERI"]},
|
||||
"suez_canal": {"lat": 30.46, "lon": 32.34, "baseline_escalation": 2, "associated_countries": ["EGY"]},
|
||||
}
|
||||
|
||||
|
||||
STRATEGIC_WATERWAYS: list[dict] = [
|
||||
{"name": "Strait of Hormuz", "lat": 26.57, "lon": 56.25, "throughput": "21M bbl/day oil"},
|
||||
{"name": "Strait of Malacca", "lat": 2.5, "lon": 101.8, "throughput": "25% global trade"},
|
||||
{"name": "Suez Canal", "lat": 30.46, "lon": 32.34, "throughput": "12% global trade"},
|
||||
{"name": "Panama Canal", "lat": 9.08, "lon": -79.68, "throughput": "5% global trade"},
|
||||
{"name": "Bab-el-Mandeb", "lat": 12.58, "lon": 43.33, "throughput": "4.8M bbl/day oil"},
|
||||
{"name": "Taiwan Strait", "lat": 24.0, "lon": 119.5, "throughput": "88% advanced chips"},
|
||||
{"name": "Strait of Gibraltar", "lat": 35.96, "lon": -5.50, "throughput": "Mediterranean access"},
|
||||
{"name": "GIUK Gap", "lat": 62.0, "lon": -15.0, "throughput": "NATO submarine choke"},
|
||||
{"name": "Bosphorus", "lat": 41.12, "lon": 29.05, "throughput": "3M bbl/day oil"},
|
||||
]
|
||||
|
||||
|
||||
CONFLICT_ZONES: list[dict] = [
|
||||
{"name": "Ukraine", "lat": 48.38, "lon": 35.0, "type": "interstate_war", "since": "2022-02"},
|
||||
{"name": "Gaza", "lat": 31.42, "lon": 34.35, "type": "asymmetric_conflict", "since": "2023-10"},
|
||||
{"name": "Sudan", "lat": 15.5, "lon": 32.5, "type": "civil_war", "since": "2023-04"},
|
||||
{"name": "Myanmar", "lat": 21.0, "lon": 96.0, "type": "civil_war", "since": "2021-02"},
|
||||
{"name": "Sahel", "lat": 14.0, "lon": 2.0, "type": "insurgency", "since": "2012-01"},
|
||||
{"name": "DRC/Great Lakes", "lat": -1.5, "lon": 29.0, "type": "multi_party_conflict", "since": "2021-11"},
|
||||
]
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Lookup helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def get_country(iso3: str) -> dict | None:
|
||||
"""Look up country config by ISO-3 code."""
|
||||
return TIER1_COUNTRIES.get(iso3.upper())
|
||||
|
||||
|
||||
def get_event_multiplier(iso3: str) -> float:
|
||||
"""Get event multiplier for a country (default 1.0)."""
|
||||
entry = TIER1_COUNTRIES.get(iso3.upper())
|
||||
if entry is not None:
|
||||
return entry["event_multiplier"]
|
||||
return 1.0
|
||||
|
||||
|
||||
def match_country_by_name(name: str) -> str | None:
|
||||
"""Match a country name/keyword to ISO-3 code. Returns None if no match."""
|
||||
lower = name.lower().strip()
|
||||
for iso3, info in TIER1_COUNTRIES.items():
|
||||
if lower == info["name"].lower():
|
||||
return iso3
|
||||
for keyword in info["keywords"]:
|
||||
if keyword in lower or lower in keyword:
|
||||
return iso3
|
||||
return None
|
||||
@@ -7,10 +7,11 @@ Real-time global intelligence across 17 domains:
|
||||
financial markets, economic indicators, earthquakes, wildfires,
|
||||
conflict, military flights, infrastructure, and more.
|
||||
|
||||
Phase 1: Markets, Economic, Seismology, Wildfire (12 tools).
|
||||
Phase 2: Conflict, Military, Infrastructure, Maritime, Climate (+10 = 22 tools).
|
||||
Phase 3: News, Intelligence, Prediction, Displacement, Aviation, Cyber (+14 = 36 tools).
|
||||
Phase 4: Reports — daily brief, country dossier, threat landscape (+3 = 39 tools).
|
||||
Phase 1: Markets, Economic, Seismology, Wildfire (14 tools).
|
||||
Phase 2: Conflict, Military, Infrastructure, Maritime, Climate (+10 = 24 tools).
|
||||
Phase 3: News, Intelligence, Prediction, Displacement, Aviation, Cyber (+9 = 33 tools).
|
||||
Phase 4: Reports — daily brief, country dossier, threat landscape (+3 = 36 tools).
|
||||
Phase 5: Analysis — focal points, signal summary, temporal anomalies, CII v2 (+3 = 39 tools).
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
@@ -333,7 +334,7 @@ TOOLS: list[Tool] = [
|
||||
},
|
||||
},
|
||||
),
|
||||
# --- Intelligence (4 tools) ---
|
||||
# --- Intelligence (7 tools) ---
|
||||
Tool(
|
||||
name="intel_country_brief",
|
||||
description="Generate a country intelligence brief using Ollama LLM + World Bank + ACLED data. Falls back to data-only if LLM unavailable.",
|
||||
@@ -356,7 +357,7 @@ TOOLS: list[Tool] = [
|
||||
),
|
||||
Tool(
|
||||
name="intel_instability_index",
|
||||
description="Compute Country Instability Index (0-100) from conflict, economic, humanitarian, infrastructure, and military signals.",
|
||||
description="Compute Country Instability Index v2 (0-100) from 4 weighted domains: unrest, conflict, security, information. Applies country-specific multipliers and UCDP floors.",
|
||||
inputSchema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
@@ -376,6 +377,26 @@ TOOLS: list[Tool] = [
|
||||
},
|
||||
},
|
||||
),
|
||||
Tool(
|
||||
name="intel_focal_points",
|
||||
description="Detect focal points where multiple intelligence signals converge on the same entity (country, organization, leader). Cross-references news, military, protests, and infrastructure signals.",
|
||||
inputSchema={"type": "object", "properties": {}},
|
||||
),
|
||||
Tool(
|
||||
name="intel_signal_summary",
|
||||
description="Aggregate all intelligence signals by country with convergence scoring. Combines conflict, displacement, earthquakes, fires, outages, military, and protests.",
|
||||
inputSchema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"country": {"type": "string", "description": "Country name filter (optional)"},
|
||||
},
|
||||
},
|
||||
),
|
||||
Tool(
|
||||
name="intel_temporal_anomalies",
|
||||
description="Detect temporal anomalies — activity levels that deviate from historical baselines using Welford's algorithm. Reports z-score deviations like 'Military flights 3.2x normal for Thursday'.",
|
||||
inputSchema={"type": "object", "properties": {}},
|
||||
),
|
||||
# --- Reports (3 tools) ---
|
||||
Tool(
|
||||
name="intel_daily_brief",
|
||||
@@ -554,6 +575,12 @@ async def _dispatch(name: str, arguments: dict[str, Any]) -> Any:
|
||||
lon=arguments.get("lon"),
|
||||
radius_deg=arguments.get("radius_deg", 5.0),
|
||||
)
|
||||
case "intel_focal_points":
|
||||
return await intelligence.fetch_focal_points(fetcher)
|
||||
case "intel_signal_summary":
|
||||
return await intelligence.fetch_signal_summary(fetcher, country=arguments.get("country"))
|
||||
case "intel_temporal_anomalies":
|
||||
return await intelligence.fetch_temporal_anomalies(fetcher)
|
||||
|
||||
# Reports
|
||||
case "intel_daily_brief":
|
||||
|
||||
@@ -1,8 +1,9 @@
|
||||
"""Country intelligence, risk scoring, and signal convergence sources.
|
||||
"""Country intelligence, risk scoring, signal convergence, and analysis sources.
|
||||
|
||||
Provides higher-level analytical functions that combine data from multiple
|
||||
APIs (ACLED, World Bank, USGS, Ollama) into country briefs, risk scores,
|
||||
instability indices, and geographic signal convergence assessments.
|
||||
APIs (ACLED, World Bank, USGS, Ollama, Cloudflare, OpenSky, NASA) into
|
||||
country briefs, risk scores, instability indices, geographic signal
|
||||
convergence, focal point detection, signal summaries, and temporal anomalies.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
@@ -13,9 +14,27 @@ from datetime import datetime, timezone, timedelta
|
||||
import httpx
|
||||
|
||||
from ..fetcher import Fetcher
|
||||
from ..analysis.focal_points import detect_focal_points
|
||||
from ..analysis.signals import aggregate_country_signals
|
||||
from ..analysis.temporal import TemporalBaseline
|
||||
from ..analysis.instability import (
|
||||
compute_cii,
|
||||
score_unrest,
|
||||
score_conflict_v2,
|
||||
score_security,
|
||||
score_information,
|
||||
)
|
||||
from ..config.countries import (
|
||||
TIER1_COUNTRIES,
|
||||
get_event_multiplier,
|
||||
match_country_by_name,
|
||||
)
|
||||
|
||||
logger = logging.getLogger("world-intel-mcp.sources.intelligence")
|
||||
|
||||
# Shared temporal baseline instance
|
||||
_temporal = TemporalBaseline()
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Constants
|
||||
@@ -23,9 +42,7 @@ logger = logging.getLogger("world-intel-mcp.sources.intelligence")
|
||||
|
||||
_ACLED_URL = "https://api.acleddata.com/acled/read"
|
||||
_WB_BASE = "https://api.worldbank.org/v2/country"
|
||||
_HDX_SEARCH_URL = "https://data.humdata.org/api/3/action/package_search"
|
||||
_USGS_ENDPOINT = "https://earthquake.usgs.gov/fdsnws/event/1/query"
|
||||
_OPENSKY_STATES_URL = "https://opensky-network.org/api/states/all"
|
||||
|
||||
_BASELINES = {
|
||||
"Syria": 5000, "Yemen": 3000, "Ukraine": 8000, "Myanmar": 4000,
|
||||
@@ -69,14 +86,6 @@ def _risk_level(score: float) -> str:
|
||||
return "low"
|
||||
|
||||
|
||||
def _instability_level(index: float) -> str:
|
||||
if index >= 75:
|
||||
return "critical"
|
||||
if index >= 50:
|
||||
return "high"
|
||||
if index >= 25:
|
||||
return "medium"
|
||||
return "low"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
@@ -388,24 +397,28 @@ async def _instability_single(
|
||||
country_code: str,
|
||||
now: datetime,
|
||||
) -> dict:
|
||||
"""Compute full 5-component instability index for a single country."""
|
||||
"""Compute CII v2 instability index for a single country.
|
||||
|
||||
Uses 4 weighted domains: unrest, conflict, security, information.
|
||||
Applies country-specific event multiplier and UCDP/displacement boosts.
|
||||
"""
|
||||
country_name = _ISO3_TO_NAME.get(country_code, country_code)
|
||||
event_multiplier = get_event_multiplier(country_code)
|
||||
start_date = (now - timedelta(days=30)).strftime("%Y-%m-%d")
|
||||
end_date = now.strftime("%Y-%m-%d")
|
||||
|
||||
# --- Parallel data gathering -------------------------------------------
|
||||
|
||||
async def _conflict_score() -> float:
|
||||
"""Score 0-20 based on ACLED event count in the last 30 days."""
|
||||
async def _fetch_acled() -> list[dict]:
|
||||
"""Fetch ACLED events for this country."""
|
||||
access_token = os.environ.get("ACLED_ACCESS_TOKEN")
|
||||
if not access_token:
|
||||
return 0.0
|
||||
|
||||
country_name = _ISO3_TO_NAME.get(country_code, country_code)
|
||||
start_date = (now - timedelta(days=30)).strftime("%Y-%m-%d")
|
||||
end_date = now.strftime("%Y-%m-%d")
|
||||
return []
|
||||
|
||||
data = await fetcher.get_json(
|
||||
_ACLED_URL,
|
||||
source="acled",
|
||||
cache_key=f"intel:cii:conflict:{country_code}",
|
||||
cache_key=f"intel:cii2:acled:{country_code}",
|
||||
cache_ttl=1800,
|
||||
params={
|
||||
"key": access_token,
|
||||
@@ -417,146 +430,134 @@ async def _instability_single(
|
||||
},
|
||||
)
|
||||
if data is None:
|
||||
return 0.0
|
||||
return []
|
||||
return data.get("data", []) if isinstance(data, dict) else []
|
||||
|
||||
count = len(data.get("data", []))
|
||||
# Thresholds: 0 events = 0, 500+ = 20
|
||||
return min(20.0, (count / 500.0) * 20.0)
|
||||
async def _fetch_outages() -> int:
|
||||
"""Count internet outages mentioning this country."""
|
||||
from . import infrastructure
|
||||
result = await infrastructure.fetch_internet_outages(fetcher)
|
||||
count = 0
|
||||
for outage in result.get("outages", []):
|
||||
countries_list = outage.get("countries", [])
|
||||
if isinstance(countries_list, list):
|
||||
for c in countries_list:
|
||||
if isinstance(c, str) and country_code.lower() in c.lower():
|
||||
count += 1
|
||||
return count
|
||||
|
||||
async def _economic_score() -> float:
|
||||
"""Score 0-20 based on World Bank inflation rate."""
|
||||
url = f"{_WB_BASE}/{country_code}/indicator/FP.CPI.TOTL.ZG"
|
||||
data = await fetcher.get_json(
|
||||
url,
|
||||
source="world-bank",
|
||||
cache_key=f"intel:cii:inflation:{country_code}",
|
||||
cache_ttl=86400,
|
||||
params={"format": "json", "per_page": 1, "date": "2023:2025"},
|
||||
)
|
||||
if data is None:
|
||||
return 0.0
|
||||
|
||||
try:
|
||||
if isinstance(data, list) and len(data) >= 2 and isinstance(data[1], list):
|
||||
for rec in data[1]:
|
||||
value = rec.get("value")
|
||||
if value is not None:
|
||||
inflation = float(value)
|
||||
# Thresholds: 0% = 0, 50%+ = 20
|
||||
return min(20.0, max(0.0, (abs(inflation) / 50.0) * 20.0))
|
||||
except (ValueError, TypeError, KeyError, IndexError):
|
||||
pass
|
||||
return 0.0
|
||||
|
||||
async def _humanitarian_score() -> float:
|
||||
"""Score 0-20 based on HDX crisis dataset count."""
|
||||
params: dict = {
|
||||
"q": "crisis",
|
||||
"rows": 50,
|
||||
"sort": "metadata_modified desc",
|
||||
"fq": f"groups:{country_code.lower()}",
|
||||
}
|
||||
data = await fetcher.get_json(
|
||||
_HDX_SEARCH_URL,
|
||||
source="hdx",
|
||||
cache_key=f"intel:cii:humanitarian:{country_code}",
|
||||
cache_ttl=21600,
|
||||
params=params,
|
||||
)
|
||||
if data is None:
|
||||
return 0.0
|
||||
|
||||
try:
|
||||
count = data.get("result", {}).get("count", 0)
|
||||
# Thresholds: 0 datasets = 0, 200+ = 20
|
||||
return min(20.0, (int(count) / 200.0) * 20.0)
|
||||
except (ValueError, TypeError):
|
||||
return 0.0
|
||||
|
||||
async def _internet_score() -> float:
|
||||
"""Score 0-20 based on Cloudflare Radar connectivity data.
|
||||
|
||||
This is a best-effort check; Cloudflare Radar's public API may
|
||||
not be available or may require auth. Returns 0 on failure.
|
||||
"""
|
||||
# Cloudflare Radar does not have an easy free API for this.
|
||||
# Placeholder: return 0 (no disruption data).
|
||||
return 0.0
|
||||
|
||||
async def _military_score() -> float:
|
||||
"""Score 0-20 based on OpenSky military flight density near country."""
|
||||
# Use a rough bounding box for the country. For simplicity,
|
||||
# we only score countries in _ISO3_TO_NAME with known hotspot
|
||||
# regions.
|
||||
async def _fetch_military() -> int:
|
||||
"""Count military aircraft near this country."""
|
||||
_COUNTRY_BBOX = {
|
||||
"SYR": "32,35,37,42", "UKR": "44,22,52,40",
|
||||
"YEM": "12,42,19,55", "MMR": "10,92,28,101",
|
||||
"SDN": "8,21,23,39", "ETH": "3,33,15,48",
|
||||
"NGA": "4,3,14,15", "COD": "-13,12,5,31",
|
||||
"AFG": "29,60,38,75", "IRQ": "29,39,37,49",
|
||||
"IRN": "25,44,40,63", "ISR": "29,34,33,36",
|
||||
"PSE": "31,34,32,35", "LBN": "33,35,34,37",
|
||||
"TWN": "21,119,26,122", "PRK": "37,124,43,131",
|
||||
}
|
||||
bbox = _COUNTRY_BBOX.get(country_code)
|
||||
if bbox is None:
|
||||
return 0.0
|
||||
return 0
|
||||
|
||||
parts = bbox.split(",")
|
||||
params: dict[str, str] = {}
|
||||
if len(parts) == 4:
|
||||
params["lamin"] = parts[0]
|
||||
params["lomin"] = parts[1]
|
||||
params["lamax"] = parts[2]
|
||||
params["lomax"] = parts[3]
|
||||
from . import military as mil_mod
|
||||
result = await mil_mod.fetch_military_flights(fetcher, bbox=bbox)
|
||||
return result.get("count", 0)
|
||||
|
||||
data = await fetcher.get_json(
|
||||
_OPENSKY_STATES_URL,
|
||||
source="opensky",
|
||||
cache_key=f"intel:cii:military:{country_code}",
|
||||
cache_ttl=300,
|
||||
params=params if params else None,
|
||||
async def _fetch_news_velocity() -> int:
|
||||
"""Estimate news velocity from GDELT."""
|
||||
from . import news
|
||||
result = await news.fetch_gdelt_search(
|
||||
fetcher, query=country_name, mode="artlist", limit=100,
|
||||
)
|
||||
if data is None:
|
||||
return 0.0
|
||||
return result.get("count", 0)
|
||||
|
||||
states = data.get("states") or []
|
||||
# Count all aircraft (military filtering adds complexity; using
|
||||
# total density as a proxy for activity).
|
||||
count = len(states)
|
||||
# Thresholds: 0 = 0, 200+ = 20
|
||||
return min(20.0, (count / 200.0) * 20.0)
|
||||
|
||||
conflict, economic, humanitarian, internet, military = await asyncio.gather(
|
||||
_conflict_score(),
|
||||
_economic_score(),
|
||||
_humanitarian_score(),
|
||||
_internet_score(),
|
||||
_military_score(),
|
||||
acled_events, outage_count, mil_count, news_vel = await asyncio.gather(
|
||||
_fetch_acled(),
|
||||
_fetch_outages(),
|
||||
_fetch_military(),
|
||||
_fetch_news_velocity(),
|
||||
)
|
||||
|
||||
instability_index = round(conflict + economic + humanitarian + internet + military, 1)
|
||||
# Classify ACLED events into protests/riots vs armed conflict
|
||||
protest_count = 0
|
||||
riot_count = 0
|
||||
conflict_count = 0
|
||||
total_fatalities = 0
|
||||
for ev in acled_events:
|
||||
event_type = (ev.get("event_type") or "").lower()
|
||||
fat = 0
|
||||
try:
|
||||
fat = int(ev.get("fatalities", 0))
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
total_fatalities += fat
|
||||
|
||||
if "protest" in event_type:
|
||||
protest_count += 1
|
||||
elif "riot" in event_type:
|
||||
riot_count += 1
|
||||
else:
|
||||
conflict_count += 1
|
||||
|
||||
# Score each domain (0-25)
|
||||
unrest_val = score_unrest(protest_count, riot_count)
|
||||
conflict_val = score_conflict_v2(conflict_count, total_fatalities)
|
||||
security_val = score_security(mil_count, outage_count)
|
||||
info_val = score_information(news_vel)
|
||||
|
||||
# UCDP floor: active wars get a minimum score
|
||||
ucdp_floor = None
|
||||
country_cfg = TIER1_COUNTRIES.get(country_code)
|
||||
if country_cfg and country_cfg.get("baseline_risk", 0) >= 80:
|
||||
ucdp_floor = 70.0
|
||||
elif country_cfg and country_cfg.get("baseline_risk", 0) >= 60:
|
||||
ucdp_floor = 50.0
|
||||
|
||||
# Displacement boost
|
||||
displacement_boost = 0.0
|
||||
# (Would require UNHCR fetch; simplified: use baseline_risk as proxy)
|
||||
if country_cfg and country_cfg.get("baseline_risk", 0) >= 70:
|
||||
displacement_boost = 3.0
|
||||
|
||||
cii = compute_cii(
|
||||
unrest=unrest_val,
|
||||
conflict=conflict_val,
|
||||
security=security_val,
|
||||
information=info_val,
|
||||
event_multiplier=event_multiplier,
|
||||
ucdp_floor=ucdp_floor,
|
||||
displacement_boost=displacement_boost,
|
||||
)
|
||||
|
||||
return {
|
||||
"country_code": country_code,
|
||||
"instability_index": instability_index,
|
||||
"components": {
|
||||
"conflict_intensity": round(conflict, 1),
|
||||
"economic_stress": round(economic, 1),
|
||||
"humanitarian_crisis": round(humanitarian, 1),
|
||||
"internet_disruptions": round(internet, 1),
|
||||
"military_activity": round(military, 1),
|
||||
"country_name": country_name,
|
||||
**cii,
|
||||
"raw_data": {
|
||||
"acled_events": len(acled_events),
|
||||
"protests": protest_count,
|
||||
"riots": riot_count,
|
||||
"conflict_events": conflict_count,
|
||||
"fatalities": total_fatalities,
|
||||
"military_aircraft": mil_count,
|
||||
"internet_outages": outage_count,
|
||||
"news_articles": news_vel,
|
||||
},
|
||||
"risk_level": _instability_level(instability_index),
|
||||
"source": "instability-index",
|
||||
"source": "instability-index-v2",
|
||||
"timestamp": now.strftime("%Y-%m-%dT%H:%M:%SZ"),
|
||||
}
|
||||
|
||||
|
||||
async def _instability_multi(fetcher: Fetcher, now: datetime) -> dict:
|
||||
"""Compute simplified instability index for focus countries using ACLED."""
|
||||
"""Compute CII v2 instability index for focus countries using ACLED."""
|
||||
access_token = os.environ.get("ACLED_ACCESS_TOKEN")
|
||||
if not access_token:
|
||||
return {
|
||||
"error": "ACLED_ACCESS_TOKEN not configured",
|
||||
"source": "instability-index",
|
||||
"source": "instability-index-v2",
|
||||
"timestamp": now.strftime("%Y-%m-%dT%H:%M:%SZ"),
|
||||
}
|
||||
|
||||
@@ -567,7 +568,7 @@ async def _instability_multi(fetcher: Fetcher, now: datetime) -> dict:
|
||||
data = await fetcher.get_json(
|
||||
_ACLED_URL,
|
||||
source="acled",
|
||||
cache_key="intel:cii:multi:global:30d",
|
||||
cache_key="intel:cii2:multi:global:30d",
|
||||
cache_ttl=1800,
|
||||
params={
|
||||
"key": access_token,
|
||||
@@ -578,34 +579,73 @@ async def _instability_multi(fetcher: Fetcher, now: datetime) -> dict:
|
||||
},
|
||||
)
|
||||
|
||||
country_counts: dict[str, int] = {}
|
||||
# Classify events by country and type
|
||||
country_data: dict[str, dict] = {}
|
||||
if data is not None:
|
||||
for event in data.get("data", []):
|
||||
events_list = data.get("data", []) if isinstance(data, dict) else []
|
||||
for event in events_list:
|
||||
country_name = event.get("country")
|
||||
if country_name:
|
||||
country_counts[country_name] = country_counts.get(country_name, 0) + 1
|
||||
if not country_name:
|
||||
continue
|
||||
if country_name not in country_data:
|
||||
country_data[country_name] = {
|
||||
"protests": 0, "riots": 0, "conflict": 0,
|
||||
"fatalities": 0, "total": 0,
|
||||
}
|
||||
cd = country_data[country_name]
|
||||
cd["total"] += 1
|
||||
event_type = (event.get("event_type") or "").lower()
|
||||
fat = 0
|
||||
try:
|
||||
fat = int(event.get("fatalities", 0))
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
cd["fatalities"] += fat
|
||||
if "protest" in event_type:
|
||||
cd["protests"] += 1
|
||||
elif "riot" in event_type:
|
||||
cd["riots"] += 1
|
||||
else:
|
||||
cd["conflict"] += 1
|
||||
|
||||
# Map focus country codes to names and compute simplified index
|
||||
# Compute CII v2 for each focus country
|
||||
results: list[dict] = []
|
||||
for code in _FOCUS_COUNTRIES:
|
||||
name = _ISO3_TO_NAME.get(code, code)
|
||||
events = country_counts.get(name, 0)
|
||||
baseline_annual = _BASELINES.get(name, 500)
|
||||
monthly_baseline = baseline_annual / 12.0
|
||||
cd = country_data.get(name, {
|
||||
"protests": 0, "riots": 0, "conflict": 0,
|
||||
"fatalities": 0, "total": 0,
|
||||
})
|
||||
multiplier = get_event_multiplier(code)
|
||||
|
||||
# Simplified CII: conflict component scaled to 0-100
|
||||
if monthly_baseline > 0:
|
||||
ratio = events / monthly_baseline
|
||||
else:
|
||||
ratio = 0.0
|
||||
instability = min(100.0, round(ratio * 50.0, 1))
|
||||
unrest_val = score_unrest(cd["protests"], cd["riots"])
|
||||
conflict_val = score_conflict_v2(cd["conflict"], cd["fatalities"])
|
||||
# Security and information not available in multi-country mode
|
||||
security_val = 0.0
|
||||
info_val = 0.0
|
||||
|
||||
# UCDP floor from countries config
|
||||
country_cfg = TIER1_COUNTRIES.get(code)
|
||||
ucdp_floor = None
|
||||
if country_cfg and country_cfg.get("baseline_risk", 0) >= 80:
|
||||
ucdp_floor = 70.0
|
||||
elif country_cfg and country_cfg.get("baseline_risk", 0) >= 60:
|
||||
ucdp_floor = 50.0
|
||||
|
||||
cii = compute_cii(
|
||||
unrest=unrest_val,
|
||||
conflict=conflict_val,
|
||||
security=security_val,
|
||||
information=info_val,
|
||||
event_multiplier=multiplier,
|
||||
ucdp_floor=ucdp_floor,
|
||||
)
|
||||
|
||||
results.append({
|
||||
"country_code": code,
|
||||
"country_name": name,
|
||||
"instability_index": instability,
|
||||
"events_30d": events,
|
||||
"risk_level": _instability_level(instability),
|
||||
**cii,
|
||||
"events_30d": cd["total"],
|
||||
})
|
||||
|
||||
results.sort(key=lambda r: r["instability_index"], reverse=True)
|
||||
@@ -613,9 +653,9 @@ async def _instability_multi(fetcher: Fetcher, now: datetime) -> dict:
|
||||
return {
|
||||
"countries": results,
|
||||
"count": len(results),
|
||||
"note": "Simplified index based on ACLED conflict data only. "
|
||||
"Use country_code parameter for full 5-component analysis.",
|
||||
"source": "instability-index",
|
||||
"note": "Multi-country CII v2 using ACLED unrest + conflict. "
|
||||
"Use country_code for full 4-domain analysis.",
|
||||
"source": "instability-index-v2",
|
||||
"timestamp": now.strftime("%Y-%m-%dT%H:%M:%SZ"),
|
||||
}
|
||||
|
||||
@@ -721,3 +761,337 @@ async def fetch_signal_convergence(
|
||||
"source": "signal-convergence",
|
||||
"timestamp": now.strftime("%Y-%m-%dT%H:%M:%SZ"),
|
||||
}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Function 5: Focal Point Detection
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
async def fetch_focal_points(fetcher: Fetcher) -> dict:
|
||||
"""Gather multi-source events and detect focal points.
|
||||
|
||||
Fetches news headlines, military flights, internet outages, and ACLED
|
||||
protests in parallel, normalizes them into events, and runs focal point
|
||||
detection to find entities where multiple signals converge.
|
||||
|
||||
Args:
|
||||
fetcher: Shared HTTP fetcher with caching and circuit breaking.
|
||||
|
||||
Returns:
|
||||
Dict with focal_points list, count, source, and timestamp.
|
||||
"""
|
||||
now = datetime.now(timezone.utc)
|
||||
|
||||
# Import source modules for parallel data gathering
|
||||
from . import news, military, infrastructure, conflict
|
||||
|
||||
async def _fetch_news_events() -> list[dict]:
|
||||
result = await news.fetch_news_feed(fetcher, limit=100)
|
||||
events = []
|
||||
for item in result.get("items", []):
|
||||
title = item.get("title", "")
|
||||
# Extract entity: try to match country names from title
|
||||
matched_iso = match_country_by_name(title)
|
||||
if matched_iso:
|
||||
country_cfg = TIER1_COUNTRIES.get(matched_iso)
|
||||
entity = country_cfg["name"] if country_cfg else matched_iso
|
||||
events.append({
|
||||
"entity": entity,
|
||||
"type": "news",
|
||||
"timestamp": item.get("published") or now.strftime("%Y-%m-%dT%H:%M:%SZ"),
|
||||
"country": entity,
|
||||
"weight": 1.0,
|
||||
})
|
||||
return events
|
||||
|
||||
async def _fetch_military_events() -> list[dict]:
|
||||
result = await military.fetch_theater_posture(fetcher)
|
||||
events = []
|
||||
for theater_name, theater_data in result.get("theaters", {}).items():
|
||||
count = theater_data.get("count", 0)
|
||||
if count > 0:
|
||||
for country in theater_data.get("countries", []):
|
||||
events.append({
|
||||
"entity": country,
|
||||
"type": "military",
|
||||
"timestamp": now.strftime("%Y-%m-%dT%H:%M:%SZ"),
|
||||
"country": country,
|
||||
"weight": min(3.0, count / 10.0),
|
||||
})
|
||||
return events
|
||||
|
||||
async def _fetch_outage_events() -> list[dict]:
|
||||
result = await infrastructure.fetch_internet_outages(fetcher)
|
||||
events = []
|
||||
for outage in result.get("outages", []):
|
||||
countries_list = outage.get("countries", [])
|
||||
if isinstance(countries_list, list):
|
||||
for c in countries_list:
|
||||
if c:
|
||||
events.append({
|
||||
"entity": c,
|
||||
"type": "infrastructure",
|
||||
"timestamp": outage.get("start") or now.strftime("%Y-%m-%dT%H:%M:%SZ"),
|
||||
"country": c,
|
||||
"weight": 2.0 if outage.get("is_ongoing") else 1.0,
|
||||
})
|
||||
return events
|
||||
|
||||
async def _fetch_protest_events() -> list[dict]:
|
||||
access_token = os.environ.get("ACLED_ACCESS_TOKEN")
|
||||
if not access_token:
|
||||
return []
|
||||
|
||||
start_date = (now - timedelta(days=7)).strftime("%Y-%m-%d")
|
||||
end_date = now.strftime("%Y-%m-%d")
|
||||
data = await fetcher.get_json(
|
||||
_ACLED_URL,
|
||||
source="acled",
|
||||
cache_key="intel:focal:acled:protests:7d",
|
||||
cache_ttl=1800,
|
||||
params={
|
||||
"key": access_token,
|
||||
"email": os.environ.get("ACLED_EMAIL", "phoenix@2acrestudios.com"),
|
||||
"limit": 200,
|
||||
"event_date": f"{start_date}|{end_date}",
|
||||
"event_date_where": "BETWEEN",
|
||||
"event_type": "Protests",
|
||||
},
|
||||
)
|
||||
events = []
|
||||
if data is not None:
|
||||
acled_list = data.get("data", []) if isinstance(data, dict) else []
|
||||
for ev in acled_list:
|
||||
country = ev.get("country")
|
||||
if country:
|
||||
events.append({
|
||||
"entity": country,
|
||||
"type": "protest",
|
||||
"timestamp": ev.get("event_date") or now.strftime("%Y-%m-%dT%H:%M:%SZ"),
|
||||
"country": country,
|
||||
"weight": 1.0,
|
||||
})
|
||||
return events
|
||||
|
||||
news_events, mil_events, outage_events, protest_events = await asyncio.gather(
|
||||
_fetch_news_events(),
|
||||
_fetch_military_events(),
|
||||
_fetch_outage_events(),
|
||||
_fetch_protest_events(),
|
||||
)
|
||||
|
||||
all_events = news_events + mil_events + outage_events + protest_events
|
||||
focal_points = detect_focal_points(all_events)
|
||||
|
||||
return {
|
||||
"focal_points": focal_points,
|
||||
"count": len(focal_points),
|
||||
"total_events_analyzed": len(all_events),
|
||||
"source": "focal-point-analysis",
|
||||
"timestamp": now.strftime("%Y-%m-%dT%H:%M:%SZ"),
|
||||
}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Function 6: Signal Summary
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
async def fetch_signal_summary(
|
||||
fetcher: Fetcher,
|
||||
country: str | None = None,
|
||||
) -> dict:
|
||||
"""Run signal aggregator v2 across all domains.
|
||||
|
||||
Fetches ACLED conflict, USGS earthquakes, NASA FIRMS wildfires,
|
||||
Cloudflare outages, military flights, and UNHCR displacement in parallel,
|
||||
then aggregates signals by country with convergence scoring.
|
||||
|
||||
Args:
|
||||
fetcher: Shared HTTP fetcher with caching and circuit breaking.
|
||||
country: Optional country name to filter results.
|
||||
|
||||
Returns:
|
||||
Dict with countries list, count, source, and timestamp.
|
||||
"""
|
||||
now = datetime.now(timezone.utc)
|
||||
|
||||
from . import conflict, infrastructure, military, displacement
|
||||
|
||||
async def _fetch_conflict() -> list[dict]:
|
||||
result = await conflict.fetch_acled_events(fetcher, days=7, limit=200)
|
||||
return result.get("events", [])
|
||||
|
||||
async def _fetch_earthquakes() -> list[dict]:
|
||||
from . import seismology
|
||||
result = await seismology.fetch_earthquakes(fetcher, min_magnitude=4.5, hours=168, limit=100)
|
||||
return result.get("earthquakes", [])
|
||||
|
||||
async def _fetch_outages() -> list[dict]:
|
||||
result = await infrastructure.fetch_internet_outages(fetcher)
|
||||
return result.get("outages", [])
|
||||
|
||||
async def _fetch_military() -> list[dict]:
|
||||
result = await military.fetch_theater_posture(fetcher)
|
||||
aircraft = []
|
||||
for theater_data in result.get("theaters", {}).values():
|
||||
# Theater posture returns summary, not individual aircraft
|
||||
for c in theater_data.get("countries", []):
|
||||
aircraft.append({
|
||||
"origin_country": c,
|
||||
"count": theater_data.get("count", 0),
|
||||
})
|
||||
return aircraft
|
||||
|
||||
async def _fetch_protests() -> list[dict]:
|
||||
access_token = os.environ.get("ACLED_ACCESS_TOKEN")
|
||||
if not access_token:
|
||||
return []
|
||||
start_date = (now - timedelta(days=7)).strftime("%Y-%m-%d")
|
||||
end_date = now.strftime("%Y-%m-%d")
|
||||
data = await fetcher.get_json(
|
||||
_ACLED_URL,
|
||||
source="acled",
|
||||
cache_key="intel:signals:acled:protests:7d",
|
||||
cache_ttl=1800,
|
||||
params={
|
||||
"key": access_token,
|
||||
"email": os.environ.get("ACLED_EMAIL", "phoenix@2acrestudios.com"),
|
||||
"limit": 200,
|
||||
"event_date": f"{start_date}|{end_date}",
|
||||
"event_date_where": "BETWEEN",
|
||||
"event_type": "Protests",
|
||||
},
|
||||
)
|
||||
if data is None:
|
||||
return []
|
||||
acled_list = data.get("data", []) if isinstance(data, dict) else []
|
||||
return [
|
||||
{"country": ev.get("country"), "event_type": ev.get("event_type")}
|
||||
for ev in acled_list
|
||||
if ev.get("country")
|
||||
]
|
||||
|
||||
async def _fetch_displacement() -> list[dict]:
|
||||
result = await displacement.fetch_displacement_summary(fetcher)
|
||||
return result.get("by_origin", [])
|
||||
|
||||
(
|
||||
conflict_events, earthquake_data, outage_data,
|
||||
military_data, protest_data, displacement_data,
|
||||
) = await asyncio.gather(
|
||||
_fetch_conflict(),
|
||||
_fetch_earthquakes(),
|
||||
_fetch_outages(),
|
||||
_fetch_military(),
|
||||
_fetch_protests(),
|
||||
_fetch_displacement(),
|
||||
)
|
||||
|
||||
aggregated = aggregate_country_signals(
|
||||
conflict_events=conflict_events,
|
||||
displacement_data=displacement_data,
|
||||
earthquake_data=earthquake_data,
|
||||
outage_data=outage_data,
|
||||
military_data=military_data,
|
||||
protest_data=protest_data,
|
||||
)
|
||||
|
||||
# Filter to specific country if requested
|
||||
if country:
|
||||
filtered = {}
|
||||
lower_country = country.lower()
|
||||
for c_name, c_data in aggregated.items():
|
||||
if lower_country in c_name.lower():
|
||||
filtered[c_name] = c_data
|
||||
aggregated = filtered
|
||||
|
||||
# Convert to list format
|
||||
countries_list = [
|
||||
{"country": name, **data}
|
||||
for name, data in aggregated.items()
|
||||
]
|
||||
|
||||
return {
|
||||
"countries": countries_list[:50],
|
||||
"count": len(countries_list),
|
||||
"source": "signal-aggregation-v2",
|
||||
"timestamp": now.strftime("%Y-%m-%dT%H:%M:%SZ"),
|
||||
}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Function 7: Temporal Anomaly Detection
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
async def fetch_temporal_anomalies(fetcher: Fetcher) -> dict:
|
||||
"""Record observations and check for temporal anomalies.
|
||||
|
||||
Fetches current counts of military flights (by theater), ACLED events
|
||||
(by country), and fires (by region), records each as a temporal
|
||||
observation, and reports any that deviate significantly from baselines.
|
||||
|
||||
Args:
|
||||
fetcher: Shared HTTP fetcher with caching and circuit breaking.
|
||||
|
||||
Returns:
|
||||
Dict with anomalies list, observations_recorded count, source,
|
||||
and timestamp.
|
||||
"""
|
||||
now = datetime.now(timezone.utc)
|
||||
|
||||
from . import military
|
||||
|
||||
anomalies: list[dict] = []
|
||||
observations_recorded = 0
|
||||
|
||||
# Military flights by theater
|
||||
posture = await military.fetch_theater_posture(fetcher)
|
||||
for theater_name, theater_data in posture.get("theaters", {}).items():
|
||||
count = theater_data.get("count", 0)
|
||||
result = _temporal.record_and_check("military_flights", theater_name, count)
|
||||
observations_recorded += 1
|
||||
if result is not None:
|
||||
anomalies.append(result)
|
||||
|
||||
# ACLED events by country (top focus countries)
|
||||
access_token = os.environ.get("ACLED_ACCESS_TOKEN")
|
||||
if access_token:
|
||||
start_date = (now - timedelta(days=7)).strftime("%Y-%m-%d")
|
||||
end_date = now.strftime("%Y-%m-%d")
|
||||
data = await fetcher.get_json(
|
||||
_ACLED_URL,
|
||||
source="acled",
|
||||
cache_key="intel:temporal:acled:global:7d",
|
||||
cache_ttl=1800,
|
||||
params={
|
||||
"key": access_token,
|
||||
"email": os.environ.get("ACLED_EMAIL", "phoenix@2acrestudios.com"),
|
||||
"limit": 500,
|
||||
"event_date": f"{start_date}|{end_date}",
|
||||
"event_date_where": "BETWEEN",
|
||||
},
|
||||
)
|
||||
if data is not None:
|
||||
country_counts: dict[str, int] = {}
|
||||
events_list = data.get("data", []) if isinstance(data, dict) else []
|
||||
for event in events_list:
|
||||
c = event.get("country")
|
||||
if c:
|
||||
country_counts[c] = country_counts.get(c, 0) + 1
|
||||
|
||||
for c_name, c_count in country_counts.items():
|
||||
result = _temporal.record_and_check("acled_events", c_name, c_count)
|
||||
observations_recorded += 1
|
||||
if result is not None:
|
||||
anomalies.append(result)
|
||||
|
||||
# Sort anomalies by z_score descending
|
||||
anomalies.sort(key=lambda a: a.get("z_score", 0), reverse=True)
|
||||
|
||||
return {
|
||||
"anomalies": anomalies,
|
||||
"anomaly_count": len(anomalies),
|
||||
"observations_recorded": observations_recorded,
|
||||
"source": "temporal-anomaly-detection",
|
||||
"timestamp": now.strftime("%Y-%m-%dT%H:%M:%SZ"),
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user