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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

Summary


This chapter covered the following topics. First, we introduced the CARTOframes Python library and discussed how it relates to other parts of the CARTO stack, such as CARTO Builder and CARTO Data Observatory. Next, we explained how to install the CARTOframes library, what other Python packages it depends on, and where to look for documentation. Because CARTOframes uses data from CARTO Builder, we explained how to set up a CARTO Builder account. In the example scripts that make up the rest of the chapter, we saw how the library integrates pandas dataframes, how to work with tables, and how to make maps and combine them with other geospatial libraries, such as Shapely and GeoPandas.

In the next chapter, we will cover another module that utilizes Jupyter Notebooks and cartographic visualizations, MapboxGL—Jupyter.

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