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Mastering PostGIS

You're reading from   Mastering PostGIS Modern ways to create, analyze, and implement spatial data

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Product type Paperback
Published in May 2017
Publisher Packt
ISBN-13 9781784391645
Length 328 pages
Edition 1st Edition
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Authors (4):
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Dominik Mikiewicz Dominik Mikiewicz
Author Profile Icon Dominik Mikiewicz
Dominik Mikiewicz
Michal Mackiewicz Michal Mackiewicz
Author Profile Icon Michal Mackiewicz
Michal Mackiewicz
Tomasz Nycz Tomasz Nycz
Author Profile Icon Tomasz Nycz
Tomasz Nycz
George Silva George Silva
Author Profile Icon George Silva
George Silva
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Toc

Table of Contents (10) Chapters Close

Preface 1. Importing Spatial Data FREE CHAPTER 2. Spatial Data Analysis 3. Data Processing - Vector Ops 4. Data Processing - Raster Ops 5. Exporting Spatial Data 6. ETL Using Node.js 7. PostGIS – Creating Simple WebGIS Applications 8. PostGIS Topology 9. pgRouting

Geometry simplification


In surveying, we strive for the highest accuracy and precision possible. However, this is not always the case in mapmaking. Too-detailed geometries on small scale (zoomed-out) maps are bad for human perception; edges or lines appear jagged, and cause an unnecessary burden on the computer in trying to render them. For that reason, cartographers generalize the geometries used for mapmaking. There are a few algorithms designed for automated generalization. PostGIS provides an implementation of a widely used Douglas-Peucker algorithm in a ST_Simplify function.

The function accepts two arguments, the first being a geometry to be simplified, and the second being a tolerance parameter defining how aggressive the simplification can be. Tolerance is given in the same units as the geometry's coordinate system, and the bigger it is, the more simplified the output geometry becomes.

Here are some examples of simplification:

River meanders simplified with 50 (green) and 100 (red)...

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