In this chapter, you learned how to set up a Hadoop environment. This required you to install Linux and Docker to download an image from Hortonworks, and to learn the ropes of that environment. Much of this chapter was spent on the environment and how to perform a spatial query using the GUI tools provided. This is because the Hadoop environment is complex and without a proper understanding, it would be hard to fully understand how to use it with Python. Lastly, you learned how to use HDFS and Hive in Python. The Python libraries for working with Hadoop, Hive, and HDFS are still developing. This chapter provided you with a foundation so that when these libraries improve, you will have enough knowledge of Hadoop and the accompanying technologies to implement these new Python libraries.
Mastering Geospatial Analysis with Python
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Mastering Geospatial Analysis with Python
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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.
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Current Title:
Mastering Geospatial Analysis with Python
Table of Contents (23 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Free Chapter
Package Installation and Management
Introduction to Geospatial Code Libraries
Introduction to Geospatial Databases
Data Types, Storage, and Conversion
Vector Data Analysis
Raster Data Processing
Geoprocessing with Geodatabases
Automating QGIS Analysis
ArcGIS API for Python and ArcGIS Online
Geoprocessing with a GPU Database
Flask and GeoAlchemy2
GeoDjango
Geospatial REST API
Cloud Geodatabase Analysis and Visualization
Automating Cloud Cartography
Python Geoprocessing with Hadoop
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