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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 FREE CHAPTER 2. Introduction to Geospatial Code Libraries 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 the installation of PostgreSQL and PostGIS as well as psycogp2 and Shapely. Then, we gave a brief overview of the major functions used when working with a spatial database. You should now be familiar with connecting to the database, executing queries to insert data, and how to get your data out. Furthermore, we covered functions that return new geometries, distances, and areas of geometry. Understanding how these functions work should allow you to read the PostGIS documents and be comfortable with forming the SQL statement for that function.

In the next chapter, you will learn about the major data types in GIS and how to use Python code libraries to read and write geospatial data. You will learn how to convert between data types, and how to upload and download data from geospatial databases and remote data sources.

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