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

ISBN-13: 9781484282861 / Angielski / Miękka / 2022 / 312 str.

Joos Korstanje

Opis

Beginning user level

Spis treści

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, Raster

Chapter 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: 20

Sub - 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: 

1.What is interpolation?

2.How to do interpolation in Python

3.Different methods for spatial interpolation in Python

Chapter 10: Machine Learning: Classification

Chapter Goal: Learn how to do classification on geodata in Python

No of pages: 20

Sub - Topics: 

1.What is classification?

2.How to do classification on geodata in Python?

3.In 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: 

1.What is regression?

2.How to do regression on geodata in Python?

3.In 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: 

1.What is clustering?

2.How to do clustering on geodata in Python?

3.In depth example application of clustering on geodata.

Chapter 13: Conclusion

Chapter Goal: Regroup all the knowledge together

No of pages: 10

Sub - Topics: 

1.What have you learned?

2.How to combine different practices together

3. Other reflections for applying the topics in a real-world use case

O autorze

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.

Blurb

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