Book Image

Python Geospatial Analysis Cookbook

Book Image

Python Geospatial Analysis Cookbook

Overview of this book

Table of Contents (20 chapters)
Python Geospatial Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Other Geospatial Python Libraries
Mapping Icon Libraries
Index

Introduction


Geospatial data comes in hundreds of formats and massaging this data from one format to another is a simple task. The ability to convert between data types, such as rasters or vectors, belongs to data wrangling tasks and can be used for better geospatial analysis. Here is an example of a raster and vector dataset so that you can take a look at what I am talking about:

The best practice methodology is to run analysis functions or models over data stored in a common format, such as a PostgreSQL PostGIS database, or a set of Shapefiles in a common coordinate system. For example, running an analysis on input data stored in multiple formats is also possible, but you can expect to find the devil in the detail if something goes wrong or your results are not as you expected them to be.

This chapter looks at some common data formats and demonstrates how to move these from one format to another with the help of the most common tools.