Mastering Geospatial Analysis with Python

Explore GIS processing and learn to work with various tools and libraries in Python

Key Features

• Read, analyze, and present geospatial data programmatically
• Understand the powerful geoprocessing tools of Python
• Automate your Geospatial workflows using Python

Book Description

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. As geospatial programming is a specialized subset of Python programming, 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. From here, you will move on to working with rasters and vector analysis. 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. In the last module, you will work with machine learning Python libraries and also perform geospatial analysis using distributed servers.

What you will learn

• Manage code libraries and abstract geospatial analysis techniques using Python 3.
• Explore popular code libraries that perform specific tasks for geospatial analysis.
• Utilize these libraries for data conversion, data management, web map and REST API creation.
• Learn techniques related to processing geospatial data in the cloud.
• Leverage features of Python 3 with geospatial databases such as PostGIS, SQL Server, and Spatialite.
• Explore the use of machine learning and cluster computing with geospatial data..

Who This Book Is For

The audience for this book includes students, developers, and geospatial professionals who need a reference book that covers GIS data management, analysis, and automation techniques with code libraries built in Python 3.

Table of Contents

Chapter 1. Package Installation and Management
Chapter 2. Introduction to Geospatial Code Libraries
Chapter 3. Introduction to Geospatial Databases
Chapter 4. Data Types, Storage, and Conversion
Chapter 5. Vector Data Analysis
Chapter 6. Raster Data Processing
Chapter 7. Geoprocessing with Geodatabases
Chapter 8. Automating QGIS Analysis
Chapter 9. ArcGIS API for Python and ArcGIS Online
Chapter 10. Geoprocessing with a GPU Database
Chapter 11. Flask and GeoAlchemy2
Chapter 12. GeoDjango
Chapter 13. Geospatial REST API
Chapter 14. Cloud Geodatabase Analysis and Visualization
Chapter 15. Automating Cloud Cartography
Chapter 16. Python Geoprocessing with Hadoop