Exploring Geospatial Data with Google S2

Geolocation data powers everything from maps to delivery apps, but handling it efficiently can be tricky. In this series, I’ll share my hands-on journey building simple implementations of Quad Trees. Let’s start with a problem statement.

Problem Statement:

Why isn’t a standard database index on latitude and longitude enough for handling geolocation data effectively?

• A regular index treats latitude and longitude as plain numbers, but finding nearby locations requires complex math (like calculating distances on a sphere). Standard indexes don’t handle this well, so searches can be slow or inaccurate. Distance Calculations Are Tricky
• With just a basic index, the database might still need to check tons of rows to find points within a specific area (like a 5-mile radius). This makes queries sluggish, especially with big datasets. Too Much Data to Scan
• Latitude and longitude represent spots on a curved surface (the Earth), not a flat grid. A regular index doesn’t account for this curvature, so it can miss nearby points or include ones that are too far away. Earth’s Shape Isn’t Flat

Solution Overview:

To fix these issues, special tools like geospatial indexes (e.g., R-trees or quad-trees) are used instead…