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Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems

(ebook) (audiobook) (audiobook) Książka w języku 1
Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems David S. Jordan - okladka książki

Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems David S. Jordan - okladka książki

Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems David S. Jordan - audiobook MP3

Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems David S. Jordan - audiobook CD

Serie wydawnicze:
Learning
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
308
Dostępne formaty:
     PDF
     ePub

Ebook (29,90 zł najniższa cena z 30 dni)

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Do przechowalni

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.
By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.

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O autorze książki

David S. Jordan has made a career out of applying spatial thinking to tough problem spaces in the domains of real estate planning, disaster response, social equity, and climate change. He currently leads distribution and geospatial data science at JPMorgan Chase & Co. In addition to leading and building out geospatial data science teams, David is a patented inventor of new geospatial analytics processes, a winner of a Special Achievement in GIS (SAG) Award from Esri, and a conference speaker on topics including banking deserts and how great businesses leverage GIS.

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