# Spatial Extension for BigQuery

## An H3 grid of Starbucks locations and simple cannibalization analysis

### Bulding the H3 grid

We are going to demonstrate how fast and easy it is to make a visualization of an H3 grid to identify the concentration of Starbucks locations in the US.

The first step is to import the Starbucks locations dataset into a BigQuery table called `starbucks_locations_usa`. Then, with a single query, we are going to calculate how many Starbucks locations fall within each H3 grid cell of resolution 4.

 `````` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 `````` ``````WITH data AS ( SELECT bqcarto.h3.ST_ASH3(geog, 4) AS h3id, COUNT(*) AS agg_total FROM `cartobq.docs.starbucks_locations_usa` GROUP BY h3id ) SELECT h3id, agg_total, bqcarto.h3.ST_BOUNDARY(h3id) AS geom FROM data ``````

This query adds two new columns to our dataset: `geom`, representing the boundary of each of the H3 grid cells where there’s at least one Starbucks, and `agg_total`, containing the total number of locations that fall within each cell. Finally, we can visualize the result.

Note: this visualization is made using Builder, where you can easily import your BigQuery data using our connector, but you can also create a quick visualization using BigQuery Geo Viz.

### Using finer resolution H3 for simple cannibalization analysis

Next, we will analyze in finer detail the grid cell that we have identified contains the highest concentration of Starbucks locations, with ID `8428d55ffffffff`.

 `````` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 `````` ``````WITH data AS ( SELECT bqcarto.h3.ST_ASH3(geog, 9) AS h3id, COUNT(*) AS agg_total FROM `cartobq.docs.starbucks_locations_usa` WHERE ST_INTERSECTS(geog, bqcarto.h3.ST_BOUNDARY('8428d55ffffffff')) GROUP BY h3id ) SELECT h3id, agg_total, bqcarto.h3.ST_BOUNDARY(h3id) AS geom FROM data ``````

We can clearly identify that there are two H3 cells with the highest concentration of Starbucks locations, and therefore at risk of suffering cannibalization. These are cells with IDs `8928d542c17ffff` and `8928d542c87ffff` respectively. Finally, to complete our analysis, we can calculate how many locations are within one cell distance of this first cell:

 `````` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 `````` ``````WITH data AS ( SELECT bqcarto.h3.ST_ASH3(geog, 9) AS h3id, COUNT(*) AS agg_total FROM `cartobq.docs.starbucks_locations_usa` WHERE ST_INTERSECTS(geog, bqcarto.h3.ST_BOUNDARY('8428d55ffffffff')) GROUP BY h3id ) SELECT SUM(agg_total) FROM data WHERE h3id IN UNNEST(bqcarto.h3.KRING('8928d542c17ffff', 1)) -- 13 ``````