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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

Introduction to Geospatial Databases

In the previous chapters, you learned how to set up your Python environment and learned about the different libraries available for working with geospatial data using Python. In this chapter, you will start working with data.

Databases provide one of the most popular ways to store large amounts of data, and one of the most popular open source databases is PostgreSQL. PostGIS extends PostgreSQL, adding geographic objects and the ability to query records spatially. When PostgreSQL and PostGIS are combined, they create a powerful geospatial data repository.

Geospatial databases improve on basic relational database queries by allowing you to query your data by location or by location to other features in the database. You can also perform geospatial operations such as measurements of features, distances between features, and converting between...

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