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Geospatial Development By Example with Python

You're reading from   Geospatial Development By Example with Python Build your first interactive map and build location-aware applications using cutting-edge examples in Python

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785282355
Length 340 pages
Edition 1st Edition
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Author (1):
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Pablo Carreira Pablo Carreira
Author Profile Icon Pablo Carreira
Pablo Carreira
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Table of Contents (12) Chapters Close

Preface 1. Preparing the Work Environment 2. The Geocaching App FREE CHAPTER 3. Combining Multiple Data Sources 4. Improving the App Search Capabilities 5. Making Maps 6. Working with Remote Sensing Images 7. Extract Information from Raster Data 8. Data Miner App 9. Processing Big Images 10. Parallel Processing Index

Working with polygons

Supposing we want to filter our data by a given region, it's possible to assume that this region is represented by a polygon.

For example, the following image represents the world counties' borders, it was rendered from a Shapefile where each feature is a country and it's geometry is a polygon.

Working with polygons

Differently from the geocaching point, whose geometries are only a pair of coordinates, a polygon is a sequence of at least three-point coordinates beginning and ending at the same point.

By now, you can assume that we won't be able to store the polygon's coordinates with the same structure that we had with the geocaching point. We will need to store the whole OGR geometry or store something that can be transformed from or to it.

How these polygons are represented is an important subject, because mastering it allows you to manipulate them any way you need to do any kind of work. It also allows you to build polygons from point coordinates (from a GPS for...

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