Geospatial Data Engineering: Spatial Indexing

<h1><strong>Intro: why is a spatial index useful?</strong></h1> <p>In doing geospatial data science work, it is very important to think about optimizing the code you are writing. How can you make datasets with hundreds of millions of rows aggregate or join faster? This is where concepts such as spatial indices come in. In this post, I will talk about how a spatial index gets implemented, what its benefits and limitations are, and take a look at Uber’s open source H3 indexing library for some cool spatial data science applications. Let’s get started!</p> <h1>What’s a spatial index?</h1> <p>A regular index is the kind of thing you might find at the end of a book: a list of words and where they have shown up in the text. This helps you quickly look up any reference to a word you’re interested in within a certain text. Without this handy tool, you would need to manually look through every page of your book, searching for that one mention you wanted to read about.</p> <p><a href="https://towardsdatascience.com/geospatial-data-engineering-spatial-indexing-18200ef9160b"><strong>Click Here</strong></a></p>