Machine Learning on Geographical Data Using Python: Introduction into Geodata with Applications and Use Cases
- Length: 327 pages
- Edition: 1
- Language: English
- Publisher: Apress
- Publication Date: 2022-08-31
- ISBN-10: 1484282868
- ISBN-13: 9781484282861
- Sales Rank: #0 (See Top 100 Books)
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
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
Cover Front Matter Part I. General Introduction 1. Introduction to Geodata 2. Coordinate Systems and Projections 3. Geodata Data Types 4. Creating Maps Part II. GIS Operations 5. Clipping and Intersecting 6. Buffers 7. Merge and Dissolve 8. Erase Part III. Machine Learning and Mathematics 9. Interpolation 10. Classification 11. Regression 12. Clustering 13. Conclusion Back Matter
Donate to keep this site alive
How to download source code?
1. Go to: https://github.com/Apress
2. In the Find a repository… box, search the book title: Machine Learning on Geographical Data Using Python: Introduction into Geodata with Applications and Use Cases
, sometime you may not get the results, please search the main title.
3. Click the book title in the search results.
3. Click Code to download.
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.