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PostGIS Cookbook

You're reading from   PostGIS Cookbook For web developers and software architects this book will provide a vital guide to the tools and capabilities available to PostGIS spatial databases. Packed with hands-on recipes and powerful concepts

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Product type Paperback
Published in Jan 2014
Publisher
ISBN-13 9781849518666
Length 484 pages
Edition Edition
Languages
Tools
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Toc

Table of Contents (18) Chapters Close

PostGIS Cookbook
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Moving Data In and Out of PostGIS FREE CHAPTER 2. Structures that Work 3. Working with Vector Data – The Basics 4. Working with Vector Data – Advanced Recipes 5. Working with Raster Data 6. Working with pgRouting 7. Into the Nth Dimension 8. PostGIS Programming 9. PostGIS and the Web 10. Maintenance, Optimization, and Performance Tuning 11. Using Desktop Clients Index

Performing 3D queries on a LiDAR point cloud


In the previous recipe, Importing LiDAR data, we brought a LiDAR 3D point cloud into PostGIS, creating an explicit 3D dataset from the input. With the data in 3D form, we have the ability to perform spatial queries against it. In this recipe, we will leverage 3D indexes such that our query works in all the dimensions our data are in.

How to do it...

We will use the LiDAR data imported in the previous recipe as our dataset of choice. We named that table chp07.lidar. To perform our query, we will require an index created on the dataset. Spatial indexes, much like ordinary database table indexes, are similar to book indexes insofar as they help us find what we are looking for faster. Ordinarily, such an index-creation step would look like the following (which we won't run this time):

CREATE INDEX chp07_lidar_the_geom_idx ON chp07.lidar USING gist(the_geom);

A 3D index does not perform as quickly as a 2D index for 2D queries, so a CREATE INDEX query...

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