Featured Topic: Spatial Indexing Techniques
Spatial indexing methods help speed up spatial queries. Most GIS software and databases provide a mechanism to compute and use spatial index for your data layers.
University of Helsinki’s AutoGIS course has a thorough introduction to spatial indexing
using R-Tree data structure which is used by QGIS, PostGIS, GeoPandas and many more. Mapbox blog has a visual deep dive into spatial search algorithms
that explains the concepts quite well.
: You can read this new module
of my Advanced QGIS course material, where I show how to build and use spatial index in QGIS which helps run your queries much faster.
: Geooff Boeing has a great article
that explains the background and techniques for using R-Tree spatial index in Python. Zendrive - a ride-sharing analytics company - recently shared a blog post
with details on how leveraging R-Tree helped them to scale up their analytics.
An alternative to R-Tree are grid-based indexing schemes - which are used by Google, Uber and many companies that deal with big datasets. I wrote a new blog post
exploring Uber's H3 Hexagonal Spatial Index - which gives a big performance boost to certain types of analysis compared to R-Tree.