Geospatial Development By Example with Python

Key Features

• Learn the full geo-processing workflow using Python with open source packages
• Create press-quality styled maps and data visualization with high-level and reusable code
• Process massive datasets efficiently using parallel processing

Book Description

From Python programming good practices to the advanced use of analysis packages, this book teaches you how to write applications that will perform complex geoprocessing tasks that can be replicated and reused.

Much more than simple scripts, you will write functions to import data, create Python classes that represent your features, and learn how to combine and filter them.

With pluggable mechanisms, you will learn how to visualize data and the results of analysis in beautiful maps that can be batch-generated and embedded into documents or web pages.

Finally, you will learn how to consume and process an enormous amount of data very efficiently by using advanced tools and modern computers' parallel processing capabilities.

What you will learn

• Prepare a development environment with all the tools needed for geo-processing with Python
• Import point data and structure an application using Python's resources
• Combine point data from multiple sources, creating intuitive and functional representations of geographic objects
• Filter data by coordinates or attributes easily using pure Python
• Make press-quality and replicable maps from any data
• Download, transform, and use remote sensing data in your maps
• Make calculations to extract information from raster data and show the results on beautiful maps
• Handle massive amounts of data with advanced processing techniques
• Process huge satellite images in an efficient way
• Optimize geo-processing times with parallel processing

About the Author

Pablo Carreira is a Python programmer and a full stack developer living in Sao Paulo state, Brazil. He is now the lead developer of an advanced web platform for precision agriculture and actively uses Python as a backend solution for efficient geoprocessing.

Born in 1980, Brazil, Pablo graduated as an agronomical engineer. Being a programming enthusiast and self-taught since childhood, he learned programming as a hobby and later honored his techniques in order to solve work tasks.

Having 8 years of professional experience in geoprocessing, he uses Python along with geographic information systems in order to automate processes and solve problems related to precision agriculture, environmental analysis, and land division.

Table of Contents

1. Preparing the Work Environment
2. The Geocaching App
3. Combining Multiple Data Sources
4. Improving the App Search Capabilities
5. Making Maps
6. Working with Remote Sensing Images
7. Extract Information from Raster Data
8. Data Miner App
9. Processing Big Images
10. Parallel Processing