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Book details
File Size: 33 MB
Format: epub
Print Length: 440 pages
Publisher: Packt Publishing; 1 edition (April 27, 2018)
Publication Date: April 27, 2018
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B07BWJRN54

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

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.

Key Features

Analyze and process geospatial data using Python libraries such as; Anaconda, GeoPandas
Leverage new ArcGIS API to process geospatial data for the cloud.
Explore various Python geospatial web and machine learning frameworks.

Book Description

Python comes with a host of open source libraries and tools that help you work on professional geoprocessing tasks without investingin 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.

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 code libraries for data conversion, data management, web maps, 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.
Table of Contents

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 GeoAlchemy
GeoDjango
Creating a geospatial REST API
Cloud Geodatabase Analysis and Visualization
Automating Cloud Cartography
Python geoprocessing with Hadoop