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Mastering Geospatial Analysis with Python

You're reading from   Mastering Geospatial Analysis with Python Explore GIS processing and learn to work with GeoDjango, CARTOframes and MapboxGL-Jupyter

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788293334
Length 440 pages
Edition 1st Edition
Languages
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Authors (3):
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Silas Toms Silas Toms
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Silas Toms
Paul Crickard Paul Crickard
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Paul Crickard
Eric van Rees Eric van Rees
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Eric van Rees
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Table of Contents (18) Chapters Close

Preface 1. Package Installation and Management 2. Introduction to Geospatial Code Libraries FREE CHAPTER 3. Introduction to Geospatial Databases 4. Data Types, Storage, and Conversion 5. Vector Data Analysis 6. Raster Data Processing 7. Geoprocessing with Geodatabases 8. Automating QGIS Analysis 9. ArcGIS API for Python and ArcGIS Online 10. Geoprocessing with a GPU Database 11. Flask and GeoAlchemy2 12. GeoDjango 13. Geospatial REST API 14. Cloud Geodatabase Analysis and Visualization 15. Automating Cloud Cartography 16. Python Geoprocessing with Hadoop 17. Other Books You May Enjoy

OGR Simple Features Library

OGR Simple Features Library (part of the Geospatial Data Abstraction Library (GDAL)) offers a set of tools for dealing with vector data. Although both GDAL and OGR are now more integrated than they used to be, we can still divide GDAL between a vector part (OGR) and a raster part (GDAL). While OGR was written in C++ and the documentation is also in C++, with Python bindings we can access all of GDAL's functionality using Python.

We can distinguish the following components of OGR:

  • OGR batch commands for describing and processing vector data
  • ogrmerge, an instant Python script for merging multiple vector data files
  • The OGR library itself

We'll briefly cover these components first, before moving on to some examples of how to use all three.

OGR...

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