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Learning Geospatial Analysis with Python-Second Edition

You're reading from   Learning Geospatial Analysis with Python-Second Edition An effective guide to geographic information systems and remote sensing analysis using Python 3

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Product type Paperback
Published in Dec 2015
Publisher Packt
ISBN-13 9781783552429
Length 394 pages
Edition 1st Edition
Languages
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Author (1):
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Joel Lawhead Joel Lawhead
Author Profile Icon Joel Lawhead
Joel Lawhead
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Table of Contents (17) Chapters Close

Learning Geospatial Analysis with Python Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Learning Geospatial Analysis with Python FREE CHAPTER 2. Geospatial Data 3. The Geospatial Technology Landscape 4. Geospatial Python Toolbox 5. Python and Geographic Information Systems 6. Python and Remote Sensing 7. Python and Elevation Data 8. Advanced Geospatial Python Modeling 9. Real-Time Data 10. Putting It All Together Index

Raster data


Raster data consists of rows and columns of cells or pixels, with each cell representing a single value. The easiest way to think of raster data is as images, which is how they are typically represented by software. However, raster datasets are not necessarily stored as images. They can also be ASCII text files or Binary Large Objects (BLOBs) in databases.

Another difference between geospatial raster data and regular digital images is resolution. Digital images express resolution as dots-per-inch if printed in full size. Resolution can also be expressed as the total number of pixels in the image defined as megapixels. However, geospatial raster data uses the ground distance that each cell represents. For example, a raster dataset with a two-foot resolution means that a single cell represents two feet on the ground, which also means that only objects larger than two feet can be identified visually in the dataset.

Raster datasets may contain multiple bands, meaning that different...

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Visually different images