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Learning Geospatial Analysis with Python

You're reading from   Learning Geospatial Analysis with Python Understand GIS fundamentals and perform remote sensing data analysis using Python 3.7

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Product type Paperback
Published in Sep 2019
Publisher
ISBN-13 9781789959277
Length 456 pages
Edition 3rd Edition
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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 (15) Chapters Close

Preface 1. Section 1: The History and the Present of the Industry FREE CHAPTER
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

Getting an overview of common data formats

As a geospatial analyst, you may frequently encounter the following general data types:

  • Spreadsheets and comma-separated values (CSV files) or tab-separated values (TSV files)
  • Geotagged photos
  • Lightweight binary points, lines, and polygons
  • Multi-gigabyte satellite or aerial images
  • Elevation data such as grids, point clouds, or integer-based images
  • XML files
  • JSON files
  • Databases (both servers and file databases)
  • Web services
  • Geodatabases

Each format contains its own challenges for access and processing. When you perform analysis on data, you usually have to do some form of preprocessing first. You might clip or subset a satellite image of a large area down to just your area of interest, or you might reduce the number of points in a collection to just the ones meeting certain criteria in your data model. A good example of this type of...

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