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Machine Learning on Geographical Data Using Python: Introduction into Geodata with Applications and
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Librairie Eyrolles - Paris 5e
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Machine Learning on Geographical Data Using Python: Introduction into Geodata with Applications and

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

Joos Korstanje

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312 pages, parution le 20/07/2022
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Résumé

Beginning user levelGet 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 environmentChapter 1: Introduction to GeodataChapter Goal: Presenting what geodata is, how to represent it, its difficultiesNo of pages 20Sub -Topics1. Geodata definitions2. Geographical Information Systems and common tools3. Standard formats of geographical data4. Overview of Python tools for geodata
Chapter 2: Coordinate Systems and ProjectionsChapter Goal: Introduce coordinate systems and projectionsNo of pages: 20Sub - Topics 1. Geographical coordinates2. Geographical coordinate systems3. Map projections4. Conversions between coordinate systems
Chapter 3: Geodata Data Types: Points, Lines, Polygons, RasterChapter Goal: Explain the four main data types in geodataNo of pages : 20Sub - Topics: 1. Points2. Lines3. Polygons4. Raster
Chapter 4: Creating MapsChapter Goal: Learn how to create maps in PythonNo of pages : 20Sub - Topics: 1. Discover mapping libraries2. See how to create maps with different data types
Chapter 5: Basic Operations 1: Clipping and Intersecting in PythonChapter Goal: Learn clipping and intersecting in PythonNo of pages: 20Sub - Topics: 1. What is clipping?2. How to do clipping in Python?3. What is intersecting4. How to do intersecting in Python?
Chapter 6: Basic Operations 2: Buffering in PythonChapter Goal: Learn how to create buffers in PythonNo of pages: 20Sub - Topics: 1. What are buffers?2. How to create buffers in Python
Chapter 7: Basic Operations 3: Merge and Dissolve in PythonChapter Goal: Learn how to merge and dissolve in PythonNo 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 PythonChapter Goal: Learn how to do an erase in PythonNo of pages: 20Sub - Topics: 1. What is the erase operation?2. How to apply the erase operation in Python
Chapter 9: Machine Learning: InterpolationChapter Goal: Learn how to do interpolation PythonNo of pages: 20Sub - Topics: 1.What is interpolation?2.How to do interpolation in Python3.Different methods for spatial interpolation in Python
Chapter 10: Machine Learning: ClassificationChapter Goal: Learn how to do classification on geodata in PythonNo of pages: 20Sub - 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: RegressionChapter Goal: Learn how to do regression on geodata in PythonNo of pages: 20Sub - 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: ClusteringChapter Goal: Learn how to do clustering on geodata in PythonNo of pages: 20Sub - 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: ConclusionChapter Goal: Regroup all the knowledge togetherNo of pages: 10Sub - Topics: 1.What have you learned?2.How to combine different practices together3. Other reflections for applying the topics in a real-world use case




 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.

Caractéristiques techniques

  PAPIER
Éditeur(s) Apress
Auteur(s) Joos Korstanje
Parution 20/07/2022
Nb. de pages 312
EAN13 9781484282861

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