Applied Geospatial Data Science with Python: Leverage geospatial data analysis and modeling to find unique solutions to environmental problems by David S. Jordan
Key Features
- Learn how to integrate spatial data and spatial thinking into traditional data science workflows
- Develop a spatial perspective and learn to avoid common pitfalls along the way
- Gain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expanded
Table of Contents
1. Introducing Geographic Information Systems and Geospatial Data Science
2. What Is Geospatial Data and Where Can I Find It?
3. Working with Geographic and Projected Coordinate Systems
4. Exploring Geospatial Data Science Packages
5. Exploratory Data Visualization
6. Hypothesis Testing and Spatial Randomness
7. Spatial Feature Engineering
8. Spatial Clustering and Regionalization
9. Developing Spatial Regression Models
10. Developing Solutions for Spatial Optimization Problems
11. Advanced Topics in Spatial Data Science
Links:
- Amazon
- Packt
- GitHub
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #datascience #geo #geospatial
@data_science_weekly
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