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Beschreibung
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 [...] 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 ForWork with multiple geographical data types and file formats in Python
Create maps in Python
Apply machine learning on geographical data
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
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 [...] 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 ForWork with multiple geographical data types and file formats in Python
Create maps in Python
Apply machine learning on geographical data
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
Über den Autor
Joos Korstanje is a data scientist, with over five years of industry experience in developing machine learning tools. He has a double MSc in Applied Data Science and in Environmental Science and has extensive experience working with geodata use cases. He currently works at Disneyland Paris where he develops machine learning for a variety of tools. His experience in writing and teaching have motivated him to write this book on machine learning for geodata with Python.
Zusammenfassung
Provides a comprehensive introduction to working with geodata in Python
Presents ML approaches-such as interpolation, classification, and clustering-to geographical data in Python
Starts with basic operations, topics increase in complexity, and covers advanced use cases
Inhaltsverzeichnis
Chapter 1: Introduction to Geodata
Chapter Goal: Presenting what geodata is, how to represent it, its difficulties
No of pages 20
Sub -Topics
1. Geodata definitions
2. Geographical Information Systems and common tools
3. Standard formats of geographical data
4. Overview of Python tools for geodata
Chapter 2: Coordinate Systems and Projections
Chapter Goal: Introduce coordinate systems and projections
No of pages: 20
Sub - Topics 1. Geographical coordinates
2. Geographical coordinate systems
3. Map projections
4. Conversions between coordinate systems
Chapter 3: Geodata Data Types: Points, Lines, Polygons, RasterChapter Goal: Explain the four main data types in geodata
No of pages : 20
Sub - Topics:
1. Points
2. Lines
3. Polygons
4. Raster
Chapter 4: Creating Maps
Chapter Goal: Learn how to create maps in Python
No of pages : 20
Sub - Topics:
1. Discover mapping libraries
2. See how to create maps with different data types
Chapter 5: Basic Operations 1: Clipping and Intersecting in Python
Chapter Goal: Learn clipping and intersecting in Python
No of pages: 20
Sub - Topics:
1. What is clipping?
2. How to do clipping in Python?
3. What is intersecting
4. How to do intersecting in Python?
Chapter 6: Basic Operations 2: Buffering in Python
Chapter Goal: Learn how to create buffers in Python
No of pages: 20
Sub - Topics:
1. What are buffers?
2. How to create buffers in Python
Chapter 7: Basic Operations 3: Merge and Dissolve in Python
Chapter Goal: Learn how to merge and dissolve in Python
No of pages: 20Sub - Topics:
1. What is the merge operation?
2. How to do the merge operation in Python?
3. What is the dissolve operation?
4. How to do the dissolve operation in Python?
Chapter 8: Basic Operations 4: Erase in Python
Chapter Goal: Learn how to do an erase in Python
No of pages: 20
Sub - Topics:
1. What is the erase operation?
2. How to apply the erase operation in Python
Chapter 9: Machine Learning: Interpolation
Chapter Goal: Learn how to do interpolation Python
No of pages: 20
Sub - Topics:
[...] is interpolation?
[...] to do interpolation in Python
3.Different methods for spatial interpolation in PythonChapter 10: Machine Learning: Classification
Chapter Goal: Learn how to do classification on geodata in Python
No of pages: 20
Sub - Topics:
[...] is classification?
[...] to do classification on geodata in Python?
[...] depth example application of classification on geodata.
Chapter 11: Machine Learning: Regression
Chapter Goal: Learn how to do regression on geodata in Python
No of pages: 20
Sub - Topics:
[...] is regression?
[...] to do regression on geodata in Python?
[...] depth example application of regression on geodata.
Chapter 12: Machine Learning: Clustering
Chapter Goal: Learn how to do clustering on geodata in Python
No of pages: 20
Sub - Topics:
[...] is clustering?
[...] to do clustering on geodata in Python?
[...] depth example application of clustering on geodata.
Chapter 13: Conclusion
Chapter Goal: Regroup all the knowledge together
No of pages: 10
Sub - Topics:
[...] have you learned?
[...] to combine different practices together
3. Other reflections for applying the topics in a real-world use case
Details
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Programmiersprachen |
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xv
312 S. 104 s/w Illustr. 123 farbige Illustr. 312 p. 227 illus. 123 illus. in color. |
| ISBN-13: | 9781484282861 |
| ISBN-10: | 1484282868 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Korstanje, Joos |
| Hersteller: |
Apress
Apress L.P. |
| Verantwortliche Person für die EU: | APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com |
| Maße: | 254 x 178 x 17 mm |
| Von/Mit: | Joos Korstanje |
| Erscheinungsdatum: | 21.07.2022 |
| Gewicht: | 0,619 kg |
Über den Autor
Joos Korstanje is a data scientist, with over five years of industry experience in developing machine learning tools. He has a double MSc in Applied Data Science and in Environmental Science and has extensive experience working with geodata use cases. He currently works at Disneyland Paris where he develops machine learning for a variety of tools. His experience in writing and teaching have motivated him to write this book on machine learning for geodata with Python.
Zusammenfassung
Provides a comprehensive introduction to working with geodata in Python
Presents ML approaches-such as interpolation, classification, and clustering-to geographical data in Python
Starts with basic operations, topics increase in complexity, and covers advanced use cases
Inhaltsverzeichnis
Chapter 1: Introduction to Geodata
Chapter Goal: Presenting what geodata is, how to represent it, its difficulties
No of pages 20
Sub -Topics
1. Geodata definitions
2. Geographical Information Systems and common tools
3. Standard formats of geographical data
4. Overview of Python tools for geodata
Chapter 2: Coordinate Systems and Projections
Chapter Goal: Introduce coordinate systems and projections
No of pages: 20
Sub - Topics 1. Geographical coordinates
2. Geographical coordinate systems
3. Map projections
4. Conversions between coordinate systems
Chapter 3: Geodata Data Types: Points, Lines, Polygons, RasterChapter Goal: Explain the four main data types in geodata
No of pages : 20
Sub - Topics:
1. Points
2. Lines
3. Polygons
4. Raster
Chapter 4: Creating Maps
Chapter Goal: Learn how to create maps in Python
No of pages : 20
Sub - Topics:
1. Discover mapping libraries
2. See how to create maps with different data types
Chapter 5: Basic Operations 1: Clipping and Intersecting in Python
Chapter Goal: Learn clipping and intersecting in Python
No of pages: 20
Sub - Topics:
1. What is clipping?
2. How to do clipping in Python?
3. What is intersecting
4. How to do intersecting in Python?
Chapter 6: Basic Operations 2: Buffering in Python
Chapter Goal: Learn how to create buffers in Python
No of pages: 20
Sub - Topics:
1. What are buffers?
2. How to create buffers in Python
Chapter 7: Basic Operations 3: Merge and Dissolve in Python
Chapter Goal: Learn how to merge and dissolve in Python
No of pages: 20Sub - Topics:
1. What is the merge operation?
2. How to do the merge operation in Python?
3. What is the dissolve operation?
4. How to do the dissolve operation in Python?
Chapter 8: Basic Operations 4: Erase in Python
Chapter Goal: Learn how to do an erase in Python
No of pages: 20
Sub - Topics:
1. What is the erase operation?
2. How to apply the erase operation in Python
Chapter 9: Machine Learning: Interpolation
Chapter Goal: Learn how to do interpolation Python
No of pages: 20
Sub - Topics:
[...] is interpolation?
[...] to do interpolation in Python
3.Different methods for spatial interpolation in PythonChapter 10: Machine Learning: Classification
Chapter Goal: Learn how to do classification on geodata in Python
No of pages: 20
Sub - Topics:
[...] is classification?
[...] to do classification on geodata in Python?
[...] depth example application of classification on geodata.
Chapter 11: Machine Learning: Regression
Chapter Goal: Learn how to do regression on geodata in Python
No of pages: 20
Sub - Topics:
[...] is regression?
[...] to do regression on geodata in Python?
[...] depth example application of regression on geodata.
Chapter 12: Machine Learning: Clustering
Chapter Goal: Learn how to do clustering on geodata in Python
No of pages: 20
Sub - Topics:
[...] is clustering?
[...] to do clustering on geodata in Python?
[...] depth example application of clustering on geodata.
Chapter 13: Conclusion
Chapter Goal: Regroup all the knowledge together
No of pages: 10
Sub - Topics:
[...] have you learned?
[...] to combine different practices together
3. Other reflections for applying the topics in a real-world use case
Details
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Programmiersprachen |
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xv
312 S. 104 s/w Illustr. 123 farbige Illustr. 312 p. 227 illus. 123 illus. in color. |
| ISBN-13: | 9781484282861 |
| ISBN-10: | 1484282868 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Korstanje, Joos |
| Hersteller: |
Apress
Apress L.P. |
| Verantwortliche Person für die EU: | APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com |
| Maße: | 254 x 178 x 17 mm |
| Von/Mit: | Joos Korstanje |
| Erscheinungsdatum: | 21.07.2022 |
| Gewicht: | 0,619 kg |
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