Take a moment and think about the last administrative task you completed in Workforce for ArcGIS. Perhaps you imported new workers into a project, or maybe you assigned workers to assignments based on their current location. Regardless of what the task was, chances are it can be automated using ArcGIS API for Python.
The Python scripts found in our Github repository allow you to automate key tasks for Workforce. Automation is especially useful when managing Workforce projects that contain a large number of workers and assignments. Import workers from a CSV file, assign work, create a dashboard, delete assignments, reset worker status, and more with ArcGIS API for Python.
Whether you are new to Python or are a seasoned programmer, the following blog posts are designed to help you start automating Workforce today:
- Automate Workforce with ArcGIS API for Python: Configure and Assign
- Automate Workforce with ArcGIS API for Python: Monitor
- Automate Workforce with ArcGIS API for Python: Clean and Maintain
Below, you will find a short description for each blog post.
Configure and assign
Configuring a Workforce project and assigning work are essential tasks for every project owner. While you can manually add workers and assignments one by one in the Workforce web app, this can become tedious depending on the size of your project.
This blog post offers a scenario-based tutorial that teaches you how to automate the following tasks using ArcGIS API for Python:
- Import workers from a CSV file
- Add assignment types
- Create assignments based on an existing feature layer
- Assign work based on location
It’s important to stay well informed on worker status and assignment completion. This blog post teaches you the following methods for monitoring your Workforce project using ArcGIS API for Python:
- Create a dashboard using ArcGIS Dashboards
- Monitor assignments in Slack
- Check assignment completion location using Tracker for ArcGIS
Clean and maintain
Over time, projects in Workforce may become cluttered with assignments that are no longer needed in the regular working view of the Workforce project. The project may also contain workers who, for various reasons, have not correctly updated their working status. This blog post teaches you how to keep your projects clean and maintained using the following Python scripts:
- Delete assignments
- Delete assignment types
- Copy assignments to a feature layer
- Reset stale workers
- Report completed assignments that have incomplete work orders