To help get started with using Data Pipelines, here is an example workflow you can follow to learn about a variety of different tools and features. In this workflow you will create a feature layer in ArcGIS Online containing new housing building permits enhanced with median household income in Seattle, Washington, USA.
The steps to complete this workflow include loading data from two different types of sources, cleaning up and filtering the data, performing a spatial join, and finally writing to a feature layer in ArcGIS Online. To learn more about Data Pipelines, visit the documentation.
Let's get started!
Step 1: To begin building the data pipeline, you’ll connect to the building permit dataset from Seattle’s open data website.
Step 2: Filter the dataset to only include new residential building permits.
PermitClassMapped is Residential
PermitTypeMapped is Building
PermitTypeDesc is New
6. Click Add to save the filter expression
7. Click the Preview button on the Filter by attribute tool pane to inspect the results
Step 3: Create a new point geometry from longitude and latitude fields to spatially enable the dataset.
Step 4: Add the census income survey dataset to the data pipeline. This dataset is hosted in Living Atlas.
Step 5: Keep only the required data by selecting and renaming the median household income field.
Step 6: Now join the two datasets together with a spatial join using the building permit dataset’s newly created Point field, and the census dataset’s existing Polygon field.
Step 7: Finally, write the result of the join to a feature layer.
Here's an example of what your final diagram will look like if you have been following along:
To recap, we've loaded data from two different sources, done some basic transformations, filtered and spatially enhanced a dataset, and spatially joined the two datasets. We then outputted the results of all this work to a new feature layer, ready for further use in Online. Nice!
Thanks for following along, and let us know if you would like to see additional content like this. We can't wait to see what you do with Data Pipelines!
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