Database Reference
In-Depth Information
Chapter 4. Working with Vector Data -
Advanced Recipes
In this chapter, we will cover:
• Improving proximity filtering with KNN
• Improving proximity filtering with KNN - advanced
• Rotating geometries
• Improving ST_Polygonize
• Translating, scaling, and rotating geometries - advanced
• Generating detailed building footprints from LiDAR
• UsingexternalscriptstoembednewfunctionalityinordertocalculateaVoro-
noi diagram
• UsingexternalscriptstoembedotherlibrariesinordertocalculateaVoronoi
diagram - advanced
Introduction
Beyond being a spatial database with the capacity to store and query spatial data,
PostGISisaverypowerfulanalyticaltool.Whatthismeanstotheuserisatremend-
ous capacity to expose and encapsulate deep spatial analyses right within a Post-
greSQL database.
The recipes in this chapter can roughly be divided into three main sections:
• Highly optimized queries::
• Improving proximity filtering with KNN
• Improving proximity filtering with KNN - advanced
• Using the database to create and modify geometries:
• Rotating geometries
• Improving ST_Polygonize
• Translating, scaling, and rotating geometries - advanced
• Generating detailed building footprints from LiDAR
