Book Image

Mastering Geospatial Analysis with Python

By : Silas Toms, Paul Crickard, Eric van Rees
Book Image

Mastering Geospatial Analysis with Python

By: Silas Toms, Paul Crickard, Eric van Rees

Overview of this book

Python comes with a host of open source libraries and tools that help you work on professional geoprocessing tasks without investing in expensive tools. This book will introduce Python developers, both new and experienced, to a variety of new code libraries that have been developed to perform geospatial analysis, statistical analysis, and data management. This book will use examples and code snippets that will help explain how Python 3 differs from Python 2, and how these new code libraries can be used to solve age-old problems in geospatial analysis. You will begin by understanding what geoprocessing is and explore the tools and libraries that Python 3 offers. You will then learn to use Python code libraries to read and write geospatial data. You will then learn to perform geospatial queries within databases and learn PyQGIS to automate analysis within the QGIS mapping suite. Moving forward, you will explore the newly released ArcGIS API for Python and ArcGIS Online to perform geospatial analysis and create ArcGIS Online web maps. Further, you will deep dive into Python Geospatial web frameworks and learn to create a geospatial REST API.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
Packt Upsell
Geoprocessing with Geodatabases


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.