H3 Quickstart (Databricks SQL)

The H3 geospatial functions quickstart on this page illustrates the following:

• How to load geolocation dataset(s) into the Unity Catalog.
• How to convert latitude and longitude columns to H3 cell columns.
• How to convert zip code polygon or multipolygon WKT columns to H3 cell columns.
• How to query for pickup and dropoff analysis from the LaGuardia Airport to Manhattan’s Financial District.
• How to render H3 aggregate counts on a map.

Example notebooks and queries

Prepare Unity Catalog Data

In this notebook we:

• Set up the public taxi dataset from Databricks Filesystem.
• Set up the NYC Zip Code dataset.

Prepare Unity Catalog data

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Databricks SQL Queries with Databricks Runtime 11.3 LTS and above

Query 1: Verify base data has been setup. See Notebook.

SQL

use catalog geospatial_docs;
use database nyc_taxi;
show tables;
-- Verify initial data is setup (see instructions in setup notebook)
-- select format_number(count(*),0) as count from yellow_trip;
-- select * from nyc_zipcode;

Query 2: H3 NYC Zip Code - Apply h3_polyfillash3 at resolution 12.

SQL

use catalog geospatial_docs;
use database nyc_taxi;
-- drop table if exists nyc_zipcode_h3_12;
create table if not exists nyc_zipcode_h3_12 as (
  select
    explode(h3_polyfillash3(geom_wkt, 12)) as cell,
    zipcode,
    po_name,
    county
  from
    nyc_zipcode
);
-- optional: zorder by `cell`
optimize nyc_zipcode_h3_12 zorder by (cell);
select
  *
from
  nyc_zipcode_h3_12;

Query 3: H3 Taxi Trips - Apply h3_longlatash3 at resolution 12.

SQL

use catalog geospatial_docs;
use database nyc_taxi;
-- drop table if exists yellow_trip_h3_12;
create table if not exists yellow_trip_h3_12 as (
  select
    h3_longlatash3(pickup_longitude, pickup_latitude, 12) as pickup_cell,
    h3_longlatash3(dropoff_longitude, dropoff_latitude, 12) as dropoff_cell,
    *
  except
    (
      rate_code_id,
      store_and_fwd_flag
    )
  from
    yellow_trip
);
-- optional: zorder by `pickup_cell`
-- optimize yellow_trip_h3_12 zorder by (pickup_cell);
select
  *
from
  yellow_trip_h3_12
 where pickup_cell is not null;

Query 4: H3 LGA Pickups - 25M pickups from LaGuardia (LGA)

SQL

use catalog geospatial_docs;
use database nyc_taxi;
create
or replace view lga_pickup_h3_12 as (
  select
    t.*
  except(cell),
    s.*
  from
    yellow_trip_h3_12 as s
    inner join nyc_zipcode_h3_12 as t on s.pickup_cell = t.cell
  where
    t.zipcode = '11371'
);
select
  format_number(count(*), 0) as count
from
  lga_pickup_h3_12;
-- select
  --   *
  -- from
  --   lga_pickup_h3_12;

Query 5: H3 Financial District Dropoffs - 34M total drop offs in Financial District

SQL

use catalog geospatial_docs;
use database nyc_taxi;
create
or replace view fd_dropoff_h3_12 as (
  select
    t.*
  except(cell),
    s.*
  from
    yellow_trip_h3_12 as s
    inner join nyc_zipcode_h3_12 as t on s.dropoff_cell = t.cell
  where
    t.zipcode in ('10004', '10005', '10006', '10007', '10038')
);
select
  format_number(count(*), 0) as count
from
  fd_dropoff_h3_12;
-- select * from fd_dropoff_h3_12;

Query 6: H3 LGA-FD - 827K drop offs in FD with pickup from LGA

SQL

use catalog geospatial_docs;
use database nyc_taxi;
create
or replace view lga_fd_dropoff_h3_12 as (
  select
    *
  from
    fd_dropoff_h3_12
  where
    pickup_cell in (
      select
        distinct pickup_cell
      from
        lga_pickup_h3_12
    )
);
select
  format_number(count(*), 0) as count
from
  lga_fd_dropoff_h3_12;
-- select * from lga_fd_dropoff_h3_12;

Query 7: LGA-FD by zip code - Count FD drop offs by zip code + bar chart

SQL

use catalog geospatial_docs;
use database nyc_taxi;
select
  zipcode,
  count(*) as count
from
  lga_fd_dropoff_h3_12
group by
  zipcode
order by
  zipcode;

Query 8: LGA-FD by H3 - Count FD drop offs by H3 cell + map marker visualization

SQL

use catalog geospatial_docs;
use database nyc_taxi;
select
  zipcode,
  dropoff_cell,
  h3_centerasgeojson(dropoff_cell) :coordinates [0] as dropoff_centroid_x,
  h3_centerasgeojson(dropoff_cell) :coordinates [1] as dropoff_centroid_y,
  format_number(count(*), 0) as count_disp,
  count(*) as `count`
from
  lga_fd_dropoff_h3_12
group by
  zipcode,
  dropoff_cell
order by
  zipcode,
  `count` DESC;

Notebooks for Databricks Runtime 11.3 LTS and above

Quickstart-Python: H3 NYC Taxi LaGuardia to Manhattan

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Same quickstart structure as in Databricks SQL, using Spark Python bindings within Notebooks + kepler.gl.

Quickstart-Scala: H3 NYC Taxi LaGuardia to Manhattan

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Same quickstart structure as in Databricks SQL, using Spark Scala bindings within Notebooks + kepler.gl via Python cells.

Quickstart-SQL: H3 NYC Taxi LaGuardia to Manhattan

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Same quickstart structure as in Databricks SQL, using Spark SQL bindings within Notebooks + kepler.gl via Python cells.