Applied Geospatial Data Science with Python
eBook Details:
- Paperback: 308 pages
- Publisher: WOW! eBook (February 28, 2023)
- Language: English
- ISBN-10: 1803238127
- ISBN-13: 978-1803238128
eBook Description:
Applied Geospatial Data Science with Python: Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in Python
Data scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python.
Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries.
- Understand the fundamentals needed to work with geospatial data
- Transition from tabular to geo-enabled data in your workflows
- Develop an introductory portfolio of spatial data science work using Python
- Gain hands-on skills with case studies relevant to different industries
- Discover best practices focusing on geospatial data to bring a positive change in your environment
- Explore solving use cases, such as traveling salesperson and vehicle routing problems
By the end of the Applied Geospatial Data Science with Python book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.
