國立虎尾科技大學 |

Machine Learning on Geographical Data Using Python = Introduction into Geodata with Applications and Use Cases /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine Learning on Geographical Data Using Python/ by Joos Korstanje.
其他題名:	Introduction into Geodata with Applications and Use Cases /
作者:	Korstanje, Joos.
面頁冊數:	XV, 312 p. 227 illus., 123 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Python. -
電子資源:	https://doi.org/10.1007/978-1-4842-8287-8
ISBN:	9781484282878

Machine Learning on Geographical Data Using Python = Introduction 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. - 1st ed. 2022. - XV, 312 p. 227 illus., 123 illus. in color.online resource.

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

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

1115944
Python.

LC Class. No.: Q325.5-.7

Dewey Class. No.: 006.31

Machine Learning on Geographical Data Using Python = Introduction into Geodata with Applications and Use Cases /
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