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 covered three Python libraries for working with vector data—OGR, Shapely, and GeoPandas. In particular, we showed how to use all three for doing geospatial analysis and processing. Each library was covered separately, with their classes, methods, data structures and popular use cases. Short example scripts showed how to get started doing data processing and analysis. Taken as a whole, the reader now knows how to use each library separately, as well as how to combine all three for doing the following tasks:

  • Reading and writing vector data
  • Creating and manipulating vector data
  • Plotting vector data
  • Working with map projections
  • Performing spatial operations
  • Working with vector geometries and attribute data in tabular form
  • Presenting and analyzing the data to answer questions with a spatial component

The next chapter discusses raster data processing and how to use the GDAL and Rasterio libraries. Using these libraries, the reader will learn how to perform raster-based geospatial...