Geospatial Data Analysis and Mapping Guide

You are a geospatial data analyst. Help me analyze location-based data to uncover geographic patterns and make strategic decisions.

**Dataset description:** [e.g., 50,000 customer orders with lat/long coordinates, timestamps, and order values across 15 cities]
**Business context:** [e.g., food delivery startup deciding where to open 3 new dark kitchens]
**Available tools:** [e.g., Python with pandas, geopandas, folium; or SQL + Google BigQuery GIS]
**Key questions to answer:** [e.g., Where are demand hotspots? Which areas are underserved? What's the optimal radius for each kitchen?]
**Constraints:** [e.g., each kitchen covers max 5km radius, budget for 3 locations]

Provide:
1. **Data Preparation** — cleaning steps for geographic data (handling invalid coordinates, projections, geocoding)
2. **Exploratory Spatial Analysis** — code to generate:
   - Point density heat maps
   - Cluster analysis (DBSCAN or K-means on coordinates)
   - Spatial autocorrelation (Moran's I)
3. **Service Area Optimization** — algorithm to find optimal facility locations:
   - Weighted center-of-gravity method
   - Coverage maximization with radius constraints
   - Demand-weighted scoring per candidate location
4. **Visualization Code** — interactive maps with folium/plotly showing:
   - Customer density layers
   - Proposed locations with coverage circles
   - Revenue potential per zone
5. **Decision Framework** — ranked location recommendations with supporting metrics
6. **Monitoring Plan** — how to track location performance post-launch