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Mastering Geospatial Analysis with Python

Mastering Geospatial Analysis with Python

By : Toms, Paul Crickard, Eric van Rees
3 (4)
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Mastering Geospatial Analysis with Python

Mastering Geospatial Analysis with Python

3 (4)
By: 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 (18 chapters)
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7
Geoprocessing with Geodatabases

Reading and writing vector data with OGR

Now, let's turn to OGR for reading and writing a vector so that you can compare both OGR and GeoPandas functionality for performing the same kind of tasks. To follow the instructions that are mentioned as we proceed, you can download the MTBS wildfire data from: https://edcintl.cr.usgs.gov/downloads/sciweb1/shared/MTBS_Fire/data/composite_data/fod_pt_shapefile/mtbs_fod_pts_data.zip and store them on your PC. The file that will be analyzed here is the mtbs_fod_pts_20170501 shapefile's attribute table, which has 20,340 rows and 30 columns.

We'll start with the ogrinfo command which works in a terminal window and can be used for describing vector data. These are not Python commands, but we'll include them here as you can easily run them in a Jupyter Notebook with a simple prefix (adding an exclamation mark before...

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