There are over 50 spatial databases on the market right now. If you’re trying to pick the right one for your next project, that number alone is enough to induce decision paralysis. But here’s the thing most of these tools fall into just six architectural categories. Once you understand those categories and what they’re optimized […]

For decades, we treated maps like snapshots: static images that were outdated the moment they were printed. Today, we are witnessing a fundamental shift from maps as orientation tools to maps as living, breathing ecosystems. AI and global collaboration are turning geospatial data into a “digital atlas” that doesn’t just show us where we are—it […]

We’ve all been there. You hit “run” on a spatial join query, walk away to get coffee, come back, and it’s still running. Or worse, your machine has crashed completely. Processing geospatial data isn’t just about volume; it’s about complexity. A single polygon can be megabytes in size, and checking spatial relationships (like intersections) is […]

Working with geospatial data used to require specialized desktop software. Today you can do almost everything directly in Python with open-source libraries. At the center of that ecosystem is GeoPandas, the foundational tool for handling vector data in Python. If you’re just getting started with geospatial Python, or you want a practical walkthrough you can […]

Every map traditionally starts with latitude and longitude. But if you are building modern data systems, raw coordinates are often too messy, too slow, and too heavy to scale effectively. There is a better framework that turns the planet into a manageable grid of unique identifiers: Discrete Global Grid Systems (DGGS). These frameworks—specifically H3, S2, […]