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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

Raster operations using PostgreSQL

In the first section of this chapter, you were able to load, display, and query rasters using gdal. In this section, you will learn how to load and query rasters using a spatial database—PostgreSQL. As you start to model your data, you will most likely hold it in a spatial database. You can leverage your database to perform the queries on your rasters.

Loading rasters into PostgreSQL

To load a raster into PostgreSQL, you can use the raster2pgsql binary. If it is not in your path, you may need to add it. You should be able to find the binary in your PostgreSQL install directory on Windows at \PostgreSQL\10\bin.

The following command should be executed from your operating system...

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