From the course: Using Spatial Data in AI Workflows
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Prepare and format spatial data for MCP analysis
From the course: Using Spatial Data in AI Workflows
Prepare and format spatial data for MCP analysis
To build a spatial AI agent, you need high-quality data. We are using the Foursquare Open Places dataset. It is an incredible resource with millions of points of interest globally, but it is also massive. The full dataset is over 60 gigabytes. We cannot simply feed a 60 gigabyte file into an LLM. It won't fit. Instead, we need to load just the data we need into our engine. For this project, we'll focus on Portland, Oregon. This strategy, filtering data before the AI sees it, is critical for performance and context. Let's look at how we handle this in our Python server. We are using a function called ensure underscore data underscore loaded. This is the bridge between the raw data on Amazon S3 and our local memory. First, we connect SedonaDB. This acts as our spatial database. It allows us to run SQL queries on the data instantly. It's also known as an in-memory database, meaning it can query remote data and does not need any additional setup or servers to run. In short, it is simple…
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