The clearance rate for violent crime in North America is only about 50%, but for minority and underserved communities, clearance rates are often lower due to case deprioritization and limited investigative resources. As a result, many families are left without answers for years, sometimes decades.

Our mission is to democratize investigative intelligence, giving community advocates, independent researchers, and under-resourced agencies the same pattern-recognition capabilities that well-funded departments take for granted. By making cold case analysis 10x more efficient, we aim to give voice to the voiceless and bring attention back to the cases that have been forgotten.

EchoCases is an AI-powered cold case intelligence platform that functions as an investigative pattern-recognition system, analyzing case narratives to surface hidden connections across time and geography.

Its core features include:

• Interactive Case Mapping: A Mapbox-powered interface displays cold cases geographically, allowing investigators to visualize spatial patterns and filter by date, category, and location.

• Semantic Pattern Matching: Using sentence embeddings and cosine similarity, EchoCases identifies cases with similar narratives, MOs, and circumstances even when they occur in different jurisdictions or use different terminology.

• AI-Powered Investigation: Detailed investigative analysis including behavioral patterns, victimology assessment, geographic profiling, temporal analysis, overlooked angles, and actionable next steps.

• Crime Series Detection: HDBSCAN clustering automatically identifies potential serial patterns, grouping cases that may be linked but were never connected.

• Dual Interface Modes: Switch between Tactical (hacker aesthetic) and Standard (government/professional) themes.

• Temporal Animation: An animated timeline reveals how cases unfold over time, exposing escalation patterns and cooling-off periods that might indicate serial behavior

EchoCases is a full-stack application built on a technology stack that supports scalable analysis and interactive visualization.

Backend:

• Python
• Flask (RESTful API)
• Sentence-Transformers (all-MiniLM-L6-v2 for semantic embeddings)
• scikit-learn (cosine similarity, HDBSCAN clustering)
• Pandas & NumPy (data processing)
• Google Gemini AI (advanced investigative analysis)

Frontend:

• React 18
• Mapbox GL JS (interactive mapping)
• Lucide React (iconography)
• Custom CSS with dual-theme support

Prerequisites

• Python 3.8+
• Node.js 16+
• Mapbox API token

Backend Setup git clone https://github.com/alishaaaaaaaa/EchoCases cd echocases/backend

python -m venv venv source venv/bin/activate # Windows: venv\Scripts\activate

pip install flask flask-cors pandas numpy scikit-learn sentence-transformers python-dateutil google-generativeai python-dotenv

cp .env.example .env

python app.py

Frontend Setup cd frontend

npm install

echo "REACT_APP_MAPBOX_TOKEN=your_token_here" > .env

npm start

Usage

1. Upload your dataset
2. Explore cases on the map
3. View pattern matches
4. Request AI analysis
5. Explore clusters
6. Animate timelines