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Mastering Geospatial Analysis with Python

You're reading from   Mastering Geospatial Analysis with Python Explore GIS processing and learn to work with GeoDjango, CARTOframes and MapboxGL-Jupyter

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788293334
Length 440 pages
Edition 1st Edition
Languages
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Authors (3):
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Silas Toms Silas Toms
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Silas Toms
Paul Crickard Paul Crickard
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Paul Crickard
Eric van Rees Eric van Rees
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Eric van Rees
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Toc

Table of Contents (18) Chapters Close

Preface 1. Package Installation and Management 2. Introduction to Geospatial Code Libraries FREE CHAPTER 3. Introduction to Geospatial Databases 4. Data Types, Storage, and Conversion 5. Vector Data Analysis 6. Raster Data Processing 7. Geoprocessing with Geodatabases 8. Automating QGIS Analysis 9. ArcGIS API for Python and ArcGIS Online 10. Geoprocessing with a GPU Database 11. Flask and GeoAlchemy2 12. GeoDjango 13. Geospatial REST API 14. Cloud Geodatabase Analysis and Visualization 15. Automating Cloud Cartography 16. Python Geoprocessing with Hadoop 17. Other Books You May Enjoy

Vector Data Analysis

This chapter will cover geospatial analysis and processing of vector data. The following three Python libraries will be covered—Shapely, OGR, and GeoPandas. The reader will learn how to use these Python libraries to perform geospatial analysis, including the writing of basic and advanced analysis scripts.

Each library is covered separately, with an overview of its data structures, methods, and classes where appropriate. We'll discuss the best use cases for each library and how to use them together for geospatial workflows. Short example scripts illustrate how to perform the basic geographical analysis. The GeoPandas library enables more complex functionality for doing data science tasks and incorporating geospatial analysis.

In this chapter, we'll cover the following topics:

  • Reading and writing vector data
  • Creating and manipulating vector...
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