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Machine Learning on Geographical Data Using Python - Introduction into Geodata with Applications and Use Cases (Paperback, 1st ed.)
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Machine Learning on Geographical Data Using Python - Introduction into Geodata with Applications and Use Cases (Paperback, 1st ed.)
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Get up and running with the basics of geographic information
systems (GIS), geospatial analysis, and machine learning on spatial
data in Python. This book starts with an introduction to geodata
and covers topics such as GIS and common tools, standard formats of
geographical data, and an overview of Python tools for geodata.
Specifics and difficulties one may encounter when using
geographical data are discussed: from coordinate systems and map
projections to different geodata formats and types such as points,
lines, polygons, and rasters. Analytics operations typically
applied to geodata are explained such as clipping, intersecting,
buffering, merging, dissolving, and erasing, with implementations
in Python. Use cases and examples are included. The book also
focuses on applying more advanced machine learning approaches to
geographical data and presents interpolation, classification,
regression, and clustering via examples and use cases. This book is
your go-to resource for machine learning on geodata. It presents
the basics of working with spatial data and advanced applications.
Examples are presented using code (accessible at
github.com/Apress/machine-learning-geographic-data-python) and
facilitate learning by application. What You Will Learn Understand
the fundamental concepts of working with geodata Work with multiple
geographical data types and file formats in Python Create maps in
Python Apply machine learning on geographical data Who This Book Is
For Readers with a basic understanding of machine learning who wish
to extend their skill set to analysis of and machine learning on
spatial data while remaining in a common data science Python
environment
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