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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
Author Profile Icon Silas Toms
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

In this chapter, you learned how to set up a Hadoop environment. This required you to install Linux and Docker to download an image from Hortonworks, and to learn the ropes of that environment. Much of this chapter was spent on the environment and how to perform a spatial query using the GUI tools provided. This is because the Hadoop environment is complex and without a proper understanding, it would be hard to fully understand how to use it with Python. Lastly, you learned how to use HDFS and Hive in Python. The Python libraries for working with Hadoop, Hive, and HDFS are still developing. This chapter provided you with a foundation so that when these libraries improve, you will have enough knowledge of Hadoop and the accompanying technologies to implement these new Python libraries.

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