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

Case Study: Predicting Crop Yields

In our final case study, we will explore the real-world problem of crop yields. To do this, we will demonstrate an Extract, Transform, Load (ETL) workflow that uses many of the Python methods explained in previous chapters – ArcPy, ArcGIS API for Python, Pandas, and scikit-learn – as well as some of the web tools that Python allows you to use. The ETL process combines worldwide agricultural data into a format that can be used to predict crop yields using machine learning and loads it into ArcGIS Online. The resulting combined dataset is geographically enabled and can be updated with the latest data at any time using code.

To top it all off, we will display the final combined data in a simple web app built with HTML, CSS, and JavaScript, to illustrate the kinds of tooling that Python makes possible.

The following topics are covered in this chapter:

  • Introducing the problem, data, and study area
  • Downloading the...