Python for Geospatial Data Analysis: Theory, Tools, and Practice for Location Intelligence - Brossura
Sinossi
In spatial data science, things in closer proximity to one another likely have more in common than things that are farther apart. With this practical book, geospatial professionals, data scientists, business analysts, geographers, geologists, and others familiar with data analysis and visualization will learn the fundamentals of spatial data analysis to gain a deeper understanding of their data questions.
Author Bonny P. McClain demonstrates why detecting and quantifying patterns in geospatial data is vital. Both proprietary and open source platforms allow you to process and visualize spatial information. This book is for people familiar with data analysis or visualization who are eager to explore geospatial integration with Python.
This book helps you:
- Understand the importance of applying spatial relationships in data science
- Select and apply data layering of both raster and vector graphics
- Apply location data to leverage spatial analytics
- Design informative and accurate maps
- Automate geographic data with Python scripts
- Explore Python packages for additional functionality
- Work with atypical data types such as polygons, shape files, and projections
- Understand the graphical syntax of spatial data science to stimulate curiosity
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Python for Geospatial Data Analysis: Theory, Tools, and Practice for Location Intelligence
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Python for Geospatial Data Analysis Theory, Tools, and Practice for Location Intelligence
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Paperback. Condizione: New. In spatial data science, things in closer proximity to one another likely have more in common than things that are farther apart. With this practical book, geospatial professionals, data scientists, business analysts, geographers, geologists, and others familiar with data analysis and visualization will learn the fundamentals of spatial data analysis to gain a deeper understanding of their data questions.Author Bonny P. McClain demonstrates why detecting and quantifying patterns in geospatial data is vital. Both proprietary and open source platforms allow you to process and visualize spatial information. This book is for people familiar with data analysis or visualization who are eager to explore geospatial integration with Python.This book helps you:Understand the importance of applying spatial relationships in data scienceSelect and apply data layering of both raster and vector graphicsApply location data to leverage spatial analyticsDesign informative and accurate mapsAutomate geographic data with Python scriptsExplore Python packages for additional functionalityWork with atypical data types such as polygons, shape files, and projectionsUnderstand the graphical syntax of spatial data science to stimulate curiosity. Codice articolo LU-9781098104795
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Python for Geospatial Data Analysis: Theory, Tools, and Practice for Location Intelligence
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Softcover. Condizione: As New. Leichte Kratzer / Abnutzungen / Druckstellen. In spatial data science, things in closer proximity to one another likely have more in common than things that are farther apart. With this practical book, geospatial professionals, data scientists, business analysts, geographers, geologists, and others familiar with data analysis and visualization will learn the fundamentals of spatial data analysis to gain a deeper understanding of their data questions. Author Bonny P. McClain demonstrates why detecting and quantifying patterns in geospatial data is vital. Both proprietary and open source platforms allow you to process and visualize spatial information. This book is for people familiar with data analysis or visualization who are eager to explore geospatial integration with Python. This book helps you: Understand the importance of applying spatial relationships in data science Select and apply data layering of both raster and vector graphics Apply location data to leverage spatial analytics Design informative and accurate maps Automate geographic data with Python scripts Explore Python packages for additional functionality Work with atypical data types such as polygons, shape files, and projections Understand the graphical syntax of spatial data science to stimulate curiosity. Codice articolo 9f3cff9d-10db-49d0-a26a-c8fe39a3bcc0
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