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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

Summary

In this chapter, you learned how to use GDAL and PostgreSQL to work with raster data.

First, you learned how to use the GDAL to load and query rasters. You also learned how to use GDAL to modify and save rasters. Then, you learned how to create your own raster data. You learned how to load raster data into PostgreSQL using the raster2pgsql tool. Once in PostgreSQL, you learned how to query for metadata, attributes, values, and geometry. You learned several common functions within PostgreSQL for raster data analysis. 

While this chapter only scratched the surface of working with raster data, you should have enough knowledge now to know how to learn new techniques and methods for working with rasters. In the next chapter, you will learn how to work with vector data in PostgreSQL.

 

 

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