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Python for Geospatial Data Analysis (Theory, Tools, and Practice for Location Intelligence)

List Price: $79.99
SKU:
9781098104795
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  • Product Details

    Author:
    Bonny P. McClain
    Format:
    Paperback
    Pages:
    279
    Publisher:
    O'Reilly Media (November 29, 2022)
    Language:
    English
    ISBN-13:
    9781098104795
    ISBN-10:
    109810479X
    File:
    TWO RIVERS-PERSEUS-Metadata_Only_Perseus_Distribution_Customer_Group_Metadata_20251023163248-20251023.xml
    Folder:
    TWO RIVERS
    List Price:
    $79.99
    Case Pack:
    11
    As low as:
    $68.79
    Publisher Identifier:
    P-PER
    Discount Code:
    C
    Dimensions:
    7" x 9.19"
    Country of Origin:
    United States
    Pub Discount:
    60
    Weight:
    21.44oz
    Imprint:
    O'Reilly Media
  • Overview

    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