1. After selecting the Data Source, new fields will pop up. These are helper guides to join your spatial data onto your non-spatial data (in this case; in other cases you can start out with the spatial data and join on non-spatial data).
2. Under “Selected Data”, which automatically populates with your Selected Data Source (NYC Tree Census) you’ll see an option to select the geometry column. This will be the column that corresponds with or matches the geometry of your spatial file. In this case, since we know we want to visualize NYC zip codes, we know the geometry column is “zipcode”.
3. Under “Select a dataset to combine”, you will select the dataset you want to combine with or join with your source data. In this case, that is the geometry data on NYC zip codes. We select “NYC Zip Code GeoData” and choose the geometry column titled “geometry”.

Under Columns, press the “+” button to add the columns you need from each Data Source.

In addition to spatial join, you can also choose a number of other spatial operations, such as intersects, contains, within, and touches.

1. Intersects is a boolean spatial operator that returns true if the two objects intersect at some point, or if part of the first object is within the second object, or if the first object contains part of the second object.

2. Contains is a boolean spatial operator that evaluates whether an object, such as a polygon, contains all of another geometry.

3. Within is a boolean spatial operator that evaluates whether an object, such as a polygon, contains all of another geometry.

4. Within is a boolean spatial operator that evaluates whether an object, such as a polygon, touches part of another geometry.

5. When you’re ready, press Save Data View to generate your data endpoints and save for further analysis or visualization.