E-Books →Python Geospatial Analysis Essentials

About This Book
- Learn to build a complete geospatial application from scratch using Python
- Create good-looking maps based on the results of your analysis
- This is a fast-paced guide to help you explore the key concepts of geospatial to obtain high quality spatial data
Who This Book Is For
If you are an experienced Python developer and wish to get up-to-speed with geospatial programming, then this book is for you. While familiarity with installing third-party Python libraries would be an advantage, no prior knowledge of geospatial programming is required.
What You Will Learn
- Understand the key geospatial concepts and techniques needed to analyze and work with geospatial data
- Learn how to read and write geospatial data from within your Python code
- Use PostGIS to store spatial data and perform spatial queries
- Use Python libraries to analyze and manipulate geospatial data
- Generate maps based on your spatial data
- Implement complete geospatial analysis systems using Python
- Use the Shapely and NetworkX libraries to solve problems such as distance-area calculations, finding the shortest path between two points, buffering polygons, and much more
In Detail
Python is a highly expressive language that makes it easy to write sophisticated programs. Combining high-quality geospatial data with Python geospatial libraries will give you a powerful toolkit for solving a range of geospatial programming tasks.
The book begins with an introduction to geospatial analysis and programming and explains the ideas behind geospatial data. You will explore Python libraries for building your own geospatial applications. You will learn to create a geospatial database for your application using PostGIS and the psycopg2 library, and see how the Mapnik library can be used to create attractive and useful maps.
Finally, you will learn to use the Shapely and NetworkX libraries to create, analyze, and manipulate complex geometric objects, before implementing a system to match GPS recordings against a database of roads to produce a heatmap of the most frequently used roads.