Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788293334
Length 440 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (3):
Arrow left icon
Silas Toms Silas Toms
Author Profile Icon Silas Toms
Silas Toms
Paul Crickard Paul Crickard
Author Profile Icon Paul Crickard
Paul Crickard
Eric van Rees Eric van Rees
Author Profile Icon Eric van Rees
Eric van Rees
Arrow right icon
View More author details
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

Summary


This chapter introduced the brand new ArcGIS API for Python, which is built on Python 3.5. You learned how to make use of the API, Jupyter Notebooks, and data processing with data stored in the cloud-based ArcGIS Online system. We covered how the API is organized into different modules, how to install the API, how to use the map widget, how to log in to ArcGIS Online using different user accounts, and working with vector and raster data. Using some of the API modules, we learned how to use the API for Python to perform basic geospatial analysis and to create ArcGIS Online web maps.

The next chapter will introduce Python tools for interacting with cloud-based data for search and fast data processing. In particular, it focuses on the use of Elasticsearch and MapD GPU databases, both of which are based on the AWS cloud infrastructure. The reader will learn to create cloud services for geospatial search, geolocated data processing, geolocated data, and learn how to use Python libraries...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime