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Learning Geospatial Analysis with Python - Third Edition

You're reading from  Learning Geospatial Analysis with Python - Third Edition

Product type Book
Published in Sep 2019
Publisher Packt
ISBN-13 9781789959277
Pages 456 pages
Edition 3rd Edition
Languages
Author (1):
Joel Lawhead Joel Lawhead
Profile icon Joel Lawhead
Toc

Table of Contents (15) Chapters close

Preface 1. Section 1: The History and the Present of the Industry
2. Learning about Geospatial Analysis with Python 3. Learning Geospatial Data 4. The Geospatial Technology Landscape 5. Section 2: Geospatial Analysis Concepts
6. Geospatial Python Toolbox 7. Python and Geographic Information Systems 8. Python and Remote Sensing 9. Python and Elevation Data 10. Section 3: Practical Geospatial Processing Techniques
11. Advanced Geospatial Python Modeling 12. Real-Time Data 13. Putting It All Together 14. Other Books You May Enjoy

Understanding raster data types

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 their 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, and are 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...

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