Название: Python for Geospatial Data Analysis: Theory, Tools, and Practice for Location Intelligence (Eighth Early Release)
Автор: Bonny P. McClain
Издательство: O’Reilly Media, Inc.
Год: 2022-10-03
Страниц: 200
Язык: английский
Формат: epub (true)
Размер: 113.2 MB

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.

There are a variety of options in geographic information systems (GIS) software with pros and cons associated with all of them. I will mention a few when they come up, but although I have access to ArcGIS and QGIS (Quantum GIS), I like to give QGIS the main stage. It is truly open source, meaning that you don’t need different levels of licensing for access to all of the available tools. Since this book is intended for a wide level of interests, I want you to be able to explore all of the tools. Why explore geospatial data analysis with Python programming? Python has been embraced by the geospatial community and can be found integrated with a wide variety of commercial products such as ESRI, backend for other software packages such as QGIS and Geographic Resources Analysis Support System (GRASS), and Google Earth.

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