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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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Toc

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

Summary

This chapter covered three Python libraries for working with vector data—OGR, Shapely, and GeoPandas. In particular, we showed how to use all three for doing geospatial analysis and processing. Each library was covered separately, with their classes, methods, data structures and popular use cases. Short example scripts showed how to get started doing data processing and analysis. Taken as a whole, the reader now knows how to use each library separately, as well as how to combine all three for doing the following tasks:

  • Reading and writing vector data
  • Creating and manipulating vector data
  • Plotting vector data
  • Working with map projections
  • Performing spatial operations
  • Working with vector geometries and attribute data in tabular form
  • Presenting and analyzing the data to answer questions with a spatial component

The next chapter discusses raster data processing and...

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