How shagunpathak1508-create/spidernet works
SpiderNet is a Streamlit crime-intelligence dashboard for San Francisco incident data. Users load the dataset, apply year/category filters, and explore weekly anomalies, district hotspots, crime breakdowns, geographic clusters, and recommended patrol actions. The sampled source shows data preparation, weekly Z-score detection, district aggregation, clustering, and map construction; the README documents the dashboard’s other panels. The implementation is concentrated in app.py, so feature nodes share that path. The expected input data is described in the README but is not present in the tree.
Main entry points: app.py Streamlit dashboard
Components 12
Data preparation
- Crime dataset
- Load and clean
app.py - Year and category filters
app.py
Crime analysis
- Weekly spike detection
app.py - District aggregation
app.py - Crime breakdowns
app.py - Geographic clustering
app.py
Intelligence views
- District crime map
app.py - Actionable explainer
app.py - Weekly summary CSV
app.py
Other components
- Analyst
- Streamlit dashboard
app.py
Connections 12
- Analyst → Streamlit dashboard: opens
- Streamlit dashboard → Crime dataset: reads
- Streamlit dashboard → Load and clean: loads
- Load and clean → Crime dataset: reads
- Streamlit dashboard → Year and category filters: filters
- Streamlit dashboard → Weekly spike detection: computes
- Streamlit dashboard → District aggregation: aggregates
- Streamlit dashboard → Crime breakdowns: summarizes
- Streamlit dashboard → Geographic clustering: clusters
- Streamlit dashboard → District crime map: renders
- Streamlit dashboard → Actionable explainer: presents
- Weekly spike detection → Weekly summary CSV: exports