東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Machine learning on geographical dat...

Korstanje, Joos.

Linked to FindBook

Google Book

Amazon

博客來

Machine learning on geographical data using Python : = introduction into geodata with applications and use cases /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine learning on geographical data using Python :/ Joos Korstanje.
Reminder of title:	introduction into geodata with applications and use cases /
Author:	Korstanje, Joos.
Published:	New York, NY :Apress, : c2022.,
Description:	xv, 312 p. :ill. (some col.) ;26 cm.
Notes:	Includes index.
[NT 15003449]:	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
Subject:	Machine learning. -
ISBN:	9781484282861

Machine learning on geographical data using Python : = introduction into geodata with applications and use cases /
Korstanje, Joos.

Machine learning on geographical data using Python :introduction into geodata with applications and use cases /Joos Korstanje. - New York, NY :Apress,c2022. - xv, 312 p. :ill. (some col.) ;26 cm.

Includes index.

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 (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

ISBN: 9781484282861US19.99Subjects--Topical Terms:

533906
Machine learning.

LC Class. No.: QA76.73.P98

Dewey Class. No.: 005.13/3

Machine learning on geographical data using Python : = introduction into geodata with applications and use cases /
LDR:03007cam a2200205 a 4500 001 2288455
008 220915s2022 nyua 001 0 eng d
020 $a 9781484282861 $q (pbk.) : $c US19.99
020 $a 1484282868 $q (pbk.)
020 $z 1484282876 $q (ebk.)
020 $z 9781484282878 $q (ebk.)
035 $a (OCoLC)1336986830
040 $a YDX $b eng $e aacr2 $e pn $c YDX $d ORMDA $d GW5XE
050 # 4 $a QA76.73.P98
082 0 4 $a 005.13/3 $2 23/eng/20220727
100 1 $a Korstanje, Joos. $3 3506460
245 1 0 $a Machine learning on geographical data using Python : $b introduction into geodata with applications and use cases / $c Joos Korstanje.
260 # $a New York, NY : $b Apress, $c c2022.
300 $a xv, 312 p. : $b ill. (some col.) ; $c 26 cm.
500 $a Includes index.
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 (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
650 # 0 $a Machine learning. $3 533906
650 # 0 $a Python (Computer program language) $3 729789
650 # 0 $a Geodatabases. $3 603971