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Mastering Geospatial Analysis with Python - Explore GIS processing and learn to work with GeoDjango, CARTOframes and MapboxGL-Jupyter (Paperback)
Loot Price: R1,449
Discovery Miles 14 490
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Mastering Geospatial Analysis with Python - Explore GIS processing and learn to work with GeoDjango, CARTOframes and MapboxGL-Jupyter (Paperback)
Expected to ship within 10 - 15 working days
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Explore GIS processing and learn to work with various tools and
libraries in Python. 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
DescriptionPython 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. 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. Who this
book is forThe 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.
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