Which cell phone towers serve the most people?
Using H3 to calculate population statistics for areas of influence
Last updated
Was this helpful?
Using H3 to calculate population statistics for areas of influence
Last updated
Was this helpful?
In this tutorial, we will calculate the population living within 1km of cell towers in the District of Columbia. We will be using the following datasets, all of which can be found in the demo tables section of the CARTO Data Warehouse:
Cell towers worldwide
USA state boundaries
Derived Spatial Features H3 USA
In this step we will filter the cell towers to an area of interest (in this example, that's the District of Columbia), before converting them to a H3 index. For this, we'll follow the workflow below.
Create a workflow using the CARTO Data Warehouse connection and drag the three tables onto the canvas.
Connect the USA state boundaries table to a Simple Filter dataset, and set the filter condition for the name to equal the Colorado (or any state of your choosing!).
Next, connect the outcome of the Simple Filter to the bottom input (filter table) of a Spatial Filter component, and then connect the Cell towers table to the top input (source table). This should automatically detect the geometry columns in both tables. We'll keep the spatial predicate as the default "intersects"; this predicate filters the source table where any part of its geometry intersects with any part of the filter geometry.
Finally, connect the output of the Spatial Filter to a H3 from GeoPoint component to encode the point location as a H3 index. Ensure the resolution is the same as the Spatial Features population data; 8.
You can see in the image above that this generates a new table containing the K-rings; the kring_index is the H3 reference for the newly generated ring, which can be linked to the original, central H3 cell.
Next, use a Join to join the K-ring to the Spatial Features population data. Ensure the K-ring is the top input and the population data is the bottom field. Then set up the parameters so the main table column is kring_index, the secondary table column is h3 and the join type is Left.
You can see this visualized below.
Finally, we will calculate the total population within 1km of each individual cell tower.
Connect the result of your last Join component to a Group by component. Set the group by column to H3 and the aggregation to population_joined with the aggregation type SUM (see above).
You should now know the total population for each H3 cell which represents the cell towers. The final step is to join these results back to the cell tower data so we can identify individual towers. To do this, add a final Join component, connecting H3 from GeoPoint (created in Step 1, point 4) to its top input, and the result of Group by to the bottom input. The columns for both main and secondary table should be H3, and you will want to use a Left join type to ensure all cell tower records are retained.
Run!
Altogether, your workflow should look something like the example below. The final output (the second Join component) should be a table containing all of the original cell tower data, as well as a H3 index column and the population_joined_sum_joined field (you may wish to use Rename Column to rename this!).
And here are the results!
Next, we will use to calculate the population who live roughly within 1km of each cell tower.
Connect the result of H3 from Geopoint to a new H3 KRing component, and set the size. You can use and this to work out how many K-rings you need to approximate specific distances. We are working at resolution 8, where a H3 cell has a long-diagonal of approximately 1km, so we need a H3 of 1 to approximate 1km.
