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Machine learning on geographical data using Pythonintroduction into geodata with applications and use cases /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning on geographical data using Pythonby Joos Korstanje.
Reminder of title:	introduction into geodata with applications and use cases /
Author:	Korstanje, Joos.
Published:	Berkeley, CA :Apress :2022.
Description:	xv, 312 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-1-4842-8287-8
ISBN:	9781484282878$q(electronic bk.)

Machine learning on geographical data using Pythonintroduction into geodata with applications and use cases /
Korstanje, Joos.

Machine learning on geographical data using Pythonintroduction into geodata with applications and use cases /[electronic resource] :by Joos Korstanje. - Berkeley, CA :Apress :2022. - xv, 312 p. :ill. (some col.), digital ;24 cm.

Chapter 1: Introduction to Geodata -- Chapter 2: Coordinate Systems and Projections -- Chapter 3: Geodata Data Types: Points, Lines, Polygons, Raster -- Chapter 4: Creating Maps -- Chapter 5: Basic Operations 1: Clipping and Intersecting in Python -- Chapter 6: Basic Operations 2: Buffering in Python -- Chapter 7: Basic Operations 3: Merge and Dissolve in Python -- Chapter 8: Basic Operations 4: Erase in Python -- Chapter 9: Machine Learning: Interpolation -- Chapter 10: Machine Learning: Classification -- Chapter 11: Machine Learning: Regression -- Chapter 12: Machine Learning: Clustering -- Chapter 13: Conclusion.

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 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.

ISBN: 9781484282878$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-8287-8doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: QA76.73.P98 / K67 2022

Dewey Class. No.: 005.133

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505 0 $a Chapter 1: Introduction to Geodata -- Chapter 2: Coordinate Systems and Projections -- Chapter 3: Geodata Data Types: Points, Lines, Polygons, Raster -- Chapter 4: Creating Maps -- Chapter 5: Basic Operations 1: Clipping and Intersecting in Python -- Chapter 6: Basic Operations 2: Buffering in Python -- Chapter 7: Basic Operations 3: Merge and Dissolve in Python -- Chapter 8: Basic Operations 4: Erase in Python -- Chapter 9: Machine Learning: Interpolation -- Chapter 10: Machine Learning: Classification -- Chapter 11: Machine Learning: Regression -- Chapter 12: Machine Learning: Clustering -- Chapter 13: Conclusion.
520 $a 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 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.
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