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
Contributors
Preface
7
Geoprocessing with Geodatabases
Index

Summary


This chapter provided an overview of major data types in GIS. After explaining the difference between vector and raster data, the following vector and raster data types were covered—Esri shapefiles, GeoJSON, KML, GeoPackages, and GeoTIFF files. Next, we explained how to use some of the earlier described Python code libraries to read and write geospatial data. The following geospatial Python libraries for reading and writing raster and vector data were covered in particular—GeoPandas, OGR, GDAL, and Rasterio. Apart from reading and writing different geospatial data types, you learned how to use these libraries to perform file conversion between different data types and how to upload and download data from geospatial databases and remote sources.

The next chapter will cover geospatial analysis and processing. Python libraries covered are OGR, Shapely and GeoPandas. The reader will learn how to use these libraries and write scripts for geospatial analysis, using real-world examples.

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