Book Image

Python for ArcGIS Pro

By : Silas Toms, Bill Parker
Book Image

Python for ArcGIS Pro

By: Silas Toms, Bill Parker

Overview of this book

Integrating Python into your day-to-day ArcGIS work is highly recommended when dealing with large amounts of geospatial data. Python for ArcGIS Pro aims to help you get your work done faster, with greater repeatability and higher confidence in your results. Starting from programming basics and building in complexity, two experienced ArcGIS professionals-turned-Python programmers teach you how to incorporate scripting at each step: automating the production of maps for print, managing data between ArcGIS Pro and ArcGIS Online, creating custom script tools for sharing, and then running data analysis and visualization on top of the ArcGIS geospatial library, all using Python. You’ll use ArcGIS Pro Notebooks to explore and analyze geospatial data, and write data engineering scripts to manage ongoing data processing and data transfers. This exercise-based book also includes three rich real-world case studies, giving you an opportunity to apply and extend the concepts you studied earlier. Irrespective of your expertise level with Esri software or the Python language, you’ll benefit from this book’s hands-on approach, which takes you through the major uses of Python for ArcGIS Pro to boost your ArcGIS productivity.
Table of Contents (20 chapters)
1
Part I: Introduction to Python Modules for ArcGIS Pro
5
Part II: Applying Python Modules to Common GIS Tasks
10
Part III: Geospatial Data Analysis
14
Part IV: Case Studies
18
Other Books You May Enjoy
19
Index

Summary

In this chapter, you solved a real-world problem by creating an ETL workflow to predict agricultural crop yields using all the concepts and tools you’ve learned about throughout the book.

Firstly, you learned about the problem, understanding the data and tools needed to complete the tasks at hand. You then downloaded all the data, utilizing the Requests module, ArcGIS Online, and the World Bank API. You cleaned up all those individual datasets to ensure that a merge was possible and then completed the merge.

You took the merged data frame and used it to fit the random forest model, demonstrating its ability to predict yields quickly and efficiently. This Notebook can be used to update all the data to predict crop yields when needed by simply rerunning the whole Notebook again. Lastly, you created a web application to display the shapefile created in the Notebook using HTML, CSS, and JavaScript.

This process could form the framework to provide additional...