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Otutu11/Real-Time-Disaster-Impact-Analytics-Platform

Domaine:

climategeospatial

Type de record:

project
Créateur:
Otu
Hôte:
This project simulates a real-time disaster impact analytics platform, using synthetic geospatial data to model hazard events, assess asset-level impacts, detect anomalies, and visualize hotspots, supporting early warning, climate resilience, and risk mitigation in vulnerable regions like the Niger Delta. Real-Time Disaster Impact Analytics Platform 📌 Overview This project is a synthetic data demonstration of a real-time disaster impact analytics system. It simulates natural hazard events (e.g., floods and windstorms) and computes their spatiotemporal impacts on vulnerable assets within the Niger Delta region. It integrates: 🛰️ Geospatial data modeling 🤖 Impact estimation using vulnerability, population, and criticality factors 📈 Rolling real-time analytics and anomaly detection 🗺️ Visualization of disaster impact hotspots This framework serves as a foundation for building operational disaster monitoring systems for climate resilience, early warning, and risk mitigation. ⚙️ Features Generate >1,000 synthetic asset points with attributes (location, population, vulnerability). Simulate real-time disaster event streams (flood/wind). Compute impact severity scores based on exposure and vulnerability. Perform rolling-minute analytics and z-score anomaly detection. Output: assets.csv — synthetic assets database hazard_events.csv — simulated disaster events impacts_stream.csv — asset-level event impacts impact_summary_by_minute.csv — aggregated rolling analytics latest_impact_map.png — visualization of recent impacts 🧠 Methodology Impact Score Formula Impact = hazard_intensity × sqrt(population) × vulnerability × critical_boost hazard_intensity: Decays with distance from event center (Gaussian-like) critical_boost: +25% for critical infrastructure Normalized to 0–100 and categorized into severity bands: Minimal, Minor, Moderate, Severe, Extreme Anomaly Detection Rolling 15-minute window Z-score ≥ 2.5 triggers anomaly flags on: Number of affected assets Extreme events 90th percentile impact 📂 Project Structure real_time_disaster_platform.py outputs/ ├─ assets.csv ├─ hazard_events.csv ├─ impacts_stream.csv ├─ impact_summary_by_minute.csv └─ latest_impact_map.png README.md 🚀 Usage 1. Requirements Python 3.9+ Libraries: numpy, pandas, matplotl …