Python for Geospatial Data Analysis

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 McClain demonstrates why data mapping is important for looking at outliers, distribution, variables, and temporal charts. You'll learn how visual exploration can reveal inaccurate geocodes or location statistics. The application of geospatial analysis is relevant for professionals working with pattern detection, clustering, and deep learning.

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



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