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

Raster and vector data

Before diving into some of the most used GIS data types, a little background is required about what type of information geographical data represents. Earlier in this book, the distinction between raster and vector data was mentioned. All GIS data is comprised of one or the other, but a combination of both vectors and rasters is also possible. When deciding on which data type to use, consider the scale and type of geographical information represented by the data, which in turn determines what Python data libraries to use. As is illustrated in the following examples, the choice for a certain Python library can also depend on personal preference, and there may be various ways to do the same task.

In the geospatial world, raster data comes in the form of aerial imagery or satellite data, where each pixel has an associated value that corresponds to a different color or shade. Raster data is used for large continuous areas, such as differentiating between different temperature...