DatabricksのビルトインH3エクスプレッションのウォークスルー

こちらの記事で説明されているサンプルノートブックのウォークスルーです。

https://qiita.com/taka_yayoi/items/f3eb72e5a52c6ef1a297

ノートブックの翻訳はこちらにあります。

https://github.com/taka-yayoi/public_repo/tree/main/H3_expressions

# クラスターの準備

[こちら](https://qiita.com/taka_yayoi/items/f3eb72e5a52c6ef1a297#h3%E3%82%92%E4%BD%BF%E3%81%84%E5%A7%8B%E3%82%81%E3%81%A6%E3%81%BF%E3%82%8B)で説明されている様に、Databricksランタイム11.2以降かつPhotonのアクセラレーションを有効にします。Photonは機械学習ランタイムをサポートしてないため、機械学習ランタイムは使用できません。
![Screen Shot 2022-09-18 at 13.08.16.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/730d55cc-5bda-3647-e7dd-6924b3fc99cd.png)

# データエンジニアリング

こちらのノートブックを使用してH3のインデクシングがされたデータを準備します。

https://github.com/taka-yayoi/public_repo/blob/main/H3_expressions/nb-01:%20h3%20data%20engineering%20(dbr%2011.2).scala

データベースを作成します。データベース名は適宜変更してください。

```sql:SQL
%sql CREATE DATABASE IF NOT EXISTS h3_prod_api_demo;
USE h3_prod_api_demo;
```

## トリップデータのセットアップ

タクシーのデータをセットアップします。

```scala:Scala
// Load NYC Taxi trip data as `tripsTable`
val tripsTable = spark.table("delta.`/databricks-datasets/nyctaxi/tables/nyctaxi_yellow`")
```

SQLを含む他の言語でデータフレームを使える様に一時ビュー `trips` を作成します。

> このビューはこのノートブックでのみ使用します。

```scala:Scala
tripsTable.createOrReplaceTempView("trips")
```

解像度12のH3セルにNYCタクシー移動の乗降位置をマッピングします。

> `h3_longlatash3(pickup_longitude, pickup_latitude, 12) as pickup_cell`  
> `h3_longlatash3(dropoff_longitude, dropoff_latitude, 12) as dropoff_cell`

```sql:SQL
%sql -- drop table if exists trips_h3;
create table if not exists trips_h3 as (
  select
    h3_longlatash3(pickup_longitude, pickup_latitude, 12) as pickup_cell,
    h3_longlatash3(dropoff_longitude, dropoff_latitude, 12) as dropoff_cell,
    passenger_count,
    fare_amount
  from
    trips
)
```

情報を得るために、H3セルにエンコードした移動の数をカウントします。

:::note
このデータには16億の移動が含まれています。
:::

```sql:SQL
%sql
select
  format_number(count(*),0) as count
from
  trips_h3
```
![Screen Shot 2022-09-18 at 13.13.00.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/c4554492-70df-c09a-7581-62747a83dc5a.png)

H3解像度15で位置が計算される`trips_h3_15`テーブルを新たに作成しましょう。

> `h3_longlatash3(pickup_longitude, pickup_latitude, 15) as pickup_cell`  
> `h3_longlatash3(dropoff_longitude, dropoff_latitude, 15) as dropoff_cell`

```sql:SQL
%sql -- drop table if exists trips_h3_15;
create table if not exists trips_h3_15 as (
  select
    h3_longlatash3(pickup_longitude, pickup_latitude, 15) as pickup_cell,
    h3_longlatash3(dropoff_longitude, dropoff_latitude, 15) as dropoff_cell,
    passenger_count,
    fare_amount
  from
    trips
)
```

## タクシーゾーンのGeoJSONのセットアップ

> Databricks Labsのプロジェクト[Mosaic](https://databrickslabs.github.io/mosaic/index.html)の機能を用いることで、Sparkを用いて容易にGeoJSONをロードすることができます。

```py:Python
%python
user_name = dbutils.notebook.entry_point.getDbutils().notebook().getContext().userName().get()

raw_path = f"dbfs:/tmp/mosaic/{user_name}"
raw_taxi_zones_path = f"{raw_path}/taxi_zones"

print(f"The raw data will be stored in {raw_path}")
```

タクシーゾーンGeoJSONをダウンロードします。

```py:Python
%python
import requests
import pathlib

taxi_zones_url = 'https://data.cityofnewyork.us/api/geospatial/d3c5-ddgc?method=export&format=GeoJSON'

# The DBFS file system is mounted under /dbfs/ directory on Databricks cluster nodes
local_taxi_zones_path = pathlib.Path(raw_taxi_zones_path.replace('dbfs:/', '/dbfs/'))
local_taxi_zones_path.mkdir(parents=True, exist_ok=True)

req = requests.get(taxi_zones_url)
with open(local_taxi_zones_path / f'nyc_taxi_zones.geojson', 'wb') as f:
  f.write(req.content)
  
display(dbutils.fs.ls(raw_taxi_zones_path))
```
![Screen Shot 2022-09-18 at 13.14.46.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/6209805e-76e8-5c73-32cd-120562671ec6.png)

テーブル`taxi_zones`を作成します。

> Mosaicの関数`st_geomfromgeojson`とSpark関数`to_json`を使うことに注意してください

```py:Python
%python
# Note: Here we are using python as a proxy to programmatically
# pass the location of our data source for taxi zones.
spark.sql(f"drop table if exists taxi_zones;")
spark.sql(
  f"""
     create table if not exists taxi_zones
     using json
     options (multiline = true)
     location "{raw_taxi_zones_path}";
     """
)
```

テーブル`taxi_zone_explode`を作成します。

> [Mosaic](https://databrickslabs.github.io/mosaic/index.html)で使用できる`st_astext`と`st_geomfromgeojson`を使います

```sql:SQL
%sql -- drop table if exists taxi_zone_explode;
create table if not exists taxi_zone_explode as (
  select
    type,
    feature.properties as properties,
    st_astext(st_geomfromgeojson(to_json(feature.geometry))) as geometry
  from
    (
      select
        type,
        explode(features) as feature
      from
        taxi_zones
    )
);

select
  *
from
  taxi_zone_explode;
```
![Screen Shot 2022-09-18 at 13.16.11.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/640d65b2-0f49-46c8-5711-22d547500231.png)

テーブル`taxi_zone_explode_h3`を作成します。

> 他のテーブルと同様H3解像度12を使用します

```sql:SQL
%sql -- drop table if exists taxi_zone_explode_h3
create table if not exists taxi_zone_explode_h3 as (
  select
    cast(properties.location_id as int) as locationid,
    properties.borough,
    properties.zone,
    explode(h3_polyfillash3(geometry, 12)) as cell
  from
    taxi_zone_explode
);
select
  *
from
  taxi_zone_explode_h3;
```

![Screen Shot 2022-09-18 at 13.16.57.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/a4ae4b59-d65a-cb59-e79b-9ac0ead6041f.png)

## 空港データのセットアップ

> このデモンストレーションではNewark (EWR)とLaGuardia (LGA)空港にフォーカスします。

:::note
**注意**
ポリゴンの定義はTax Zone GeoJSONにあります。簡単にするために取り出しています。
:::

Newark空港のデータフレーム`ewrDF`を作成します。

```scala:Scala
val ewrDF = Seq[(Int, String, String, Int, String)]((
  1, "MULTIPOLYGON (((-74.18445299999996 40.694995999999904, -74.18448899999999 40.69509499999987, -74.18449799999996 40.69518499999987, -74.18438099999997 40.69587799999989, -74.18428199999994 40.6962109999999, -74.18402099999997 40.697074999999884, -74.18391299999996 40.69750699999986, -74.18375099999997 40.69779499999988, -74.18363399999998 40.6983259999999, -74.18356199999994 40.698451999999875, -74.18354399999998 40.69855999999988, -74.18350799999996 40.69870399999992, -74.18327399999998 40.70008999999988, -74.18315699999994 40.701214999999884, -74.18316599999997 40.702384999999886, -74.18313899999998 40.7026279999999, -74.18309399999998 40.7028529999999, -74.18299499999995 40.70315899999985, -74.18284199999994 40.70346499999989, -74.18264399999998 40.70373499999988, -74.18242799999996 40.70395099999992, -74.18220299999996 40.704139999999896, -74.18203199999994 40.70425699999987, -74.18180699999994 40.7043919999999, -74.18157299999996 40.70449999999988, -74.18132099999997 40.70460799999991, -74.18080799999996 40.7047879999999, -74.179467 40.70534599999992, -74.17887299999995 40.70554399999987, -74.17831499999994 40.70572399999987, -74.17776599999996 40.70589499999988, -74.17709099999996 40.706092999999896, -74.17699199999998 40.70613799999988, -74.17689299999995 40.70619199999988, -74.17664999999994 40.70641699999988, -74.17642499999994 40.706695999999916, -74.17628999999994 40.70689399999988, -74.17608299999995 40.70710999999989, -74.17599299999995 40.70719099999991, -74.17589399999997 40.707262999999905, -74.17565999999994 40.70737999999988, -74.17538099999996 40.707469999999915, -74.17515599999996 40.707514999999894, -74.17475999999994 40.707595999999924, -74.17417499999993 40.70766799999991, -74.17388699999998 40.70773099999992, -74.17347299999994 40.707748999999865, -74.17275299999994 40.707802999999906, -74.17188899999996 40.707910999999854, -74.17163699999998 40.70795599999986, -74.17133999999999 40.707964999999895, -74.17120499999999 40.70795599999986, -74.16994499999998 40.707973999999886, -74.16888299999994 40.7079379999999, -74.16681299999993 40.70785699999989, -74.16442799999999 40.70779399999987, -74.16401399999995 40.70777599999992, -74.16233999999997 40.707721999999876, -74.16081899999995 40.70764099999991, -74.16057599999993 40.70760499999988, -74.16033299999998 40.70756899999987, -74.160063 40.7074879999999, -74.15938799999998 40.707262999999905, -74.15904599999999 40.707145999999916, -74.15891999999997 40.70710999999989, -74.15827199999995 40.70687599999993, -74.15459099999998 40.705651999999894, -74.15409599999998 40.70544499999989, -74.15401499999997 40.70538199999988, -74.15387999999996 40.705327999999895, -74.15376299999997 40.705408999999875, -74.15323199999995 40.70524699999987, -74.15317799999997 40.70531899999989, -74.15306999999996 40.7052829999999, -74.15359199999995 40.70437399999987, -74.15386199999995 40.7038429999999, -74.15513999999996 40.70155699999987, -74.15544599999998 40.70108899999988, -74.15575199999995 40.7006659999999, -74.15600399999994 40.70026099999991, -74.15635499999996 40.69975699999986, -74.15745299999998 40.69809199999988, -74.15754299999998 40.6979389999999, -74.15758799999998 40.69781299999988, -74.15762399999994 40.69767799999991, -74.15829899999994 40.696705999999885, -74.15951399999994 40.69488799999988, -74.15958599999993 40.69476199999984, -74.16014399999995 40.69410499999988, -74.16057599999993 40.693222999999875, -74.16262799999998 40.69028899999989, -74.16279899999995 40.69002799999989, -74.16290699999996 40.68987499999987, -74.16292499999997 40.689874999999866, -74.16295199999996 40.689874999999866, -74.16306899999995 40.68989299999988, -74.16309599999994 40.689928999999886, -74.16322199999996 40.68998299999989, -74.16331199999996 40.68999199999993, -74.16341099999994 40.69000099999988, -74.16352799999999 40.69000999999986, -74.16380699999996 40.69004599999989, -74.16410399999995 40.690081999999904, -74.16417599999994 40.690081999999904, -74.16422999999998 40.69005499999988, -74.16436499999998 40.69003699999991, -74.16450899999995 40.68998299999986, -74.16467099999994 40.68988399999989, -74.16479699999996 40.689757999999884, -74.16491399999995 40.689586999999904, -74.16499499999998 40.689388999999885, -74.16528299999999 40.68891199999991, -74.16542699999997 40.6887589999999, -74.16548099999994 40.68863299999987, -74.16560699999997 40.68842599999988, -74.16576899999995 40.68802999999986, -74.16587699999997 40.68787699999991, -74.16583199999997 40.68757999999987, -74.16582299999999 40.68748999999987, -74.16580499999998 40.687156999999914, -74.16582299999999 40.68703999999986, -74.16589499999998 40.6868419999999, -74.16604799999999 40.68655399999988, -74.16639899999996 40.686022999999864, -74.16650699999997 40.68588799999986, -74.16674099999994 40.685491999999925, -74.16695699999997 40.68523099999988, -74.16738899999996 40.684546999999924, -74.16781199999997 40.6839439999999, -74.16791099999995 40.68379099999988, -74.16804599999995 40.68360199999991, -74.16816299999994 40.683475999999885, -74.16822599999995 40.68334999999991, -74.16848699999997 40.68299899999991, -74.16886499999998 40.68239599999987, -74.16916199999997 40.68199999999991, -74.16929699999997 40.68178399999989, -74.16947699999997 40.68155899999991, -74.16981899999996 40.681018999999885, -74.16995399999996 40.680874999999915, -74.17005299999994 40.68066799999987, -74.17041299999994 40.6801549999999, -74.17051199999997 40.67999299999987, -74.17067399999996 40.679650999999886, -74.17093499999999 40.679290999999864, -74.17144799999994 40.67847199999989, -74.17151999999999 40.678381999999885, -74.17160999999999 40.678255999999884, -74.17193399999996 40.67782399999988, -74.17200599999995 40.67773399999988, -74.17283399999997 40.67656399999988, -74.17314899999997 40.67619499999991, -74.17322999999999 40.6760779999999, -74.17329299999994 40.67601499999989, -74.17358999999993 40.67571799999991, -74.17423799999995 40.67493499999991, -74.17437299999995 40.674817999999895, -74.17484999999994 40.67432299999992, -74.17500299999995 40.6741699999999, -74.17538999999995 40.67375599999987, -74.17604699999998 40.673044999999895, -74.17630799999995 40.67276599999986, -74.17641599999996 40.672621999999876, -74.17663199999998 40.67239699999989, -74.17678499999994 40.67218099999991, -74.17697399999997 40.6719379999999, -74.17709099999996 40.671784999999886, -74.17734299999995 40.67155999999988, -74.17754999999994 40.67142499999989, -74.17778399999997 40.671316999999874, -74.17802699999999 40.671208999999884, -74.17862999999994 40.671037999999896, -74.17888199999999 40.671001999999895, -74.17912499999994 40.67099299999991, -74.17933199999999 40.67101099999992, -74.17979099999997 40.67115499999989, -74.17997999999994 40.671208999999884, -74.18010599999997 40.671262999999904, -74.18030399999998 40.67129899999986, -74.18133899999998 40.67170399999986, -74.18213999999996 40.67202799999989, -74.18384999999995 40.672648999999886, -74.18437199999994 40.67290999999989, -74.18458799999996 40.67302699999988, -74.18492099999997 40.673269999999896, -74.18503799999996 40.67335999999989, -74.18513699999994 40.673458999999866, -74.18547899999999 40.67390899999987, -74.18594699999994 40.674664999999905, -74.18670299999997 40.67578999999992, -74.18733299999997 40.67674399999987, -74.18767499999996 40.67729299999991, -74.18795399999995 40.67761699999989, -74.18819699999995 40.67792299999992, -74.18852099999998 40.67848099999987, -74.18877299999997 40.67885899999989, -74.18905199999995 40.67933599999985, -74.18935799999997 40.67975899999988, -74.18949299999997 40.680091999999895, -74.18969999999996 40.680793999999885, -74.18977199999995 40.68113599999987, -74.189781 40.681198999999886, -74.18983499999996 40.68131599999987, -74.18991599999998 40.68154099999988, -74.18996999999996 40.6818019999999, -74.18999699999995 40.6822519999999, -74.18999699999995 40.68262999999992, -74.18996999999996 40.68295399999989, -74.18998799999997 40.68317899999989, -74.18995199999995 40.683520999999885, -74.18993399999994 40.68370999999992, -74.189871 40.684078999999876, -74.189781 40.68481699999991, -74.18976299999997 40.68503299999986, -74.18962799999997 40.686103999999915, -74.18955599999998 40.68689599999987, -74.18951999999996 40.6872019999999, -74.18947499999996 40.68748999999985, -74.18939399999994 40.68773299999988, -74.18939399999994 40.68783199999991, -74.18941199999995 40.687939999999855, -74.18940299999997 40.68809299999987, -74.18934899999994 40.68826399999989, -74.18922299999997 40.68862399999989, -74.18898899999994 40.68904699999991, -74.18870099999998 40.689442999999876, -74.18779199999994 40.690189999999866, -74.18723399999999 40.69059499999986, -74.18636999999995 40.69118899999991, -74.18591099999998 40.69144999999988, -74.18563199999994 40.69164799999987, -74.18445299999996 40.694995999999904)))", "Newark Airport", 1, "EWR")
).toDF("OBJECTID", "the_geom", "zone", "LocationID", "borough")
```

LaGuardia空港のデータフレーム`lgaDF`を作成します。

```scala:Scala
val lgaDF = Seq[(Int, String, String, Int, String)]((
 132, "MULTIPOLYGON (((-73.8712485624251 40.78603815125499, -73.86978081192638 40.78533793332583, -73.86923491782913 40.78596991495815, -73.86918113468879 40.785943489784934, -73.86969325713956 40.78529616325478, -73.86987007434347 40.78508590706814, -73.86943751657091 40.78363839981371, -73.86871414175994 40.783718086052126, -73.86871058864548 40.783733500520235, -73.86870358701877 40.78374821705438, -73.86869334724 40.78376179349807, -73.868680176961 40.78377382194827, -73.86866447188046 40.783783941011144, -73.86864670385509 40.78379184665982, -73.86862740672426 40.783797301369724, -73.8686071602691 40.78380014125412, -73.86858657279326 40.78380028098891, -73.86856626284758 40.78379771637564, -73.86854684064295 40.783792524468076, -73.86852888971897 40.783784861256905, -73.86851294940965 40.78377495698295, -73.86849949863941 40.783763109219564, -73.86848920723403 40.783750088384004, -73.86848193339068 40.78373594038316, -73.86847788300818 40.783721065704746, -73.86847717073857 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-73.89034195999996 40.77323760499998, -73.89183244902246 40.77488019994431, -73.8917254778065 40.77505203213404, -73.89171751335807 40.77516363289303, -73.89168935121086 40.775254737843575, -73.89152759083345 40.775478135367386, -73.89144361875265 40.77562269470968, -73.89135801404697 40.77604940793021, -73.8913676627334 40.77634299857849, -73.89145185712488 40.776512769270866, -73.89126414584786 40.77685585537462, -73.89110422665516 40.77702320697468, -73.89112044339463 40.77712469973607, -73.89106681416045 40.77721292530485, -73.89094860635713 40.77728384517263, -73.89073132114231 40.77733204986777, -73.89056250595364 40.77740392096429, -73.89049834642208 40.77746263585198, -73.89045915776707 40.77755049725344, -73.89046537395133 40.777703219287076, -73.89035193082609 40.77780032027483, -73.89017369508576 40.77785081277785, -73.89008729416794 40.77778419360695, -73.89002369095195 40.77724712640633, -73.88951644380981 40.775936714445564, -73.88947151143196 40.775738056523394, -73.8894710539975 40.77553555096718, -73.88955512480328 40.77527870961293, -73.88956149685701 40.77509223860254, -73.88958184665768 40.77503504961379, -73.88958935733879 40.77495871069513, -73.88961448773941 40.77484568714223, -73.88978900756075 40.774236861169776, -73.88984246558316 40.77410037855676, -73.88988671629585 40.77401734907423, -73.8900676015257 40.77396248425602, -73.89019171520681 40.773646876224944, -73.89008921683674 40.773626435762374)))", "LaGuardia Airport", 132, "Queens")
).toDF("OBJECTID", "the_geom", "zone", "LocationID", "borough")
```

このサンプルで使用する二つの空港Newark (EWR) and LaGuardia (LAG)のデータフレーム`nycAirportsDF`を作成します。

```scala:Scala
val nycAirportsDF = ewrDF.unionAll(lgaDF).toDF("OBJECTID", "the_geom", "zone", "LocationID", "borough")
```

以降の分析で再計算しなくて済む様に、テーブル`airports`を作成します。

```scala:Scala
nycAirportsDF
  .write
    .mode("overwrite")
    .saveAsTable("airports")
```

解像度12のH3セルで空港をポリゴンで埋めたH3セルを格納するテーブルを作成します。

> `explode(h3_polyfillash3(the_geom, 12)) as cell`

:::note
関数`h3_polyfillash3`は、入力ポリゴンを完全にカバー _しない_ 場合があるセルを生成することに注意してください。H3のライブラリがポリゴンで埋める動作においては、H3セルの中央がポリゴンの内面にある場合には、セルがポリゴンあるいはマルチポリゴンを(部分的に)カバーするとみなすというものです。これは、Mosaicの`mosaicfill`や`mosaic_explode`関数の動作とは異なります。
:::

```sql:SQL
%sql -- drop table if exists airports_h3;
create table if not exists airports_h3 as (
  select
    locationid,
    explode(h3_polyfillash3(the_geom, 12)) as cell
  from
    airports
)
```

それぞれの空港で使用するセルの数をカウントします。

```sql:SQL
%sql
select
  locationid,
  count(*)
from
  airports_h3
group by
  locationid
order by
  locationid
```
![Screen Shot 2022-09-18 at 13.20.41.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/21bed683-5072-1b02-a508-4341ccab28e1.png)

**2つの空港間の移動の数、移動した乗客の数、発生した料金(チップを除く)を見つけ出します。**

> 15K の移動, 27K の乗客, 料金は $1.38M

```sql:SQL
%sql
select
  count(*) as num_trips,
  sum(passenger_count) as num_passengers,
  ('$' || cast(sum(fare_amount) as string)) as earnings
from
  trips_h3 t,
  airports_h3 src,
  airports_h3 trg
where
  t.pickup_cell = src.cell
  and t.dropoff_cell = trg.cell
  and src.locationid <> trg.locationid
```
![Screen Shot 2022-09-18 at 13.21.20.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/56e78cb5-67ea-7861-e007-833cc2bdf2de.png)

`airports_h3_c`を作成するために`airport`をコンパクトにします

> `explode(h3_compact(h3_polyfillash3(the_geom, 12))) as cell`

```sql:SQL
%sql -- drop table if exists airports_h3_c;
create table if not exists airports_h3_c as (
  select
    locationid,
    explode(h3_compact(h3_polyfillash3(the_geom, 12))) as cell
  from
    airports
)
```

それぞれの空港で使用するセルの数をカウントします。

```sql:SQL
%sql
select
  locationid,
  count(*)
from
  airports_h3_c
group by
  locationid
order by
  locationid
```
![Screen Shot 2022-09-18 at 13.22.22.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/f11a94a5-3f91-9299-9ea4-adc3b5651c98.png)

最後に作成したテーブルを確認します。

```sql:SQL
%sql show tables
```
![Screen Shot 2022-09-18 at 13.25.30.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/ce4394e4-0b58-3c8b-6105-497041f19886.png)

# データ分析

バインディングをインポートします。

```py:Python
# Import Databricks functions to get H3
from pyspark.databricks.sql.functions import *
```

```sql:SQL
%sql -- DATABASE FROM NB-01
use h3_prod_api_demo;
show tables;
```
![Screen Shot 2022-09-18 at 13.26.44.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/72120dac-82a5-aee9-7c6b-41f7ca8cbb63.png)

## 空港の参照

> サニティチェックとして2つの空港(LaGuardiaとNewark)のジオメトリクスをレンダリングします。

```py:Python
as_kepler("airports", "the_geom", "geometry")
```

ノートブック上に地図と空港の領域がレンダリングされます。
![Screen Shot 2022-09-18 at 13.28.20.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/67c68b0a-c0e8-d3b0-e1f1-19ff2111382c.png)

`airports_h3` をレンダリングします。

> このテーブルには3万3千のH3インデックスが存在しており、結果を参照するためにリミットを若干高めに設定します。

```py:Python
as_kepler("airports_h3", "cell", "h3", limit_cnt=100000)
```

セルがレンダリングされます。
![Screen Shot 2022-09-18 at 13.29.34.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/d209d50d-a9df-9271-09cb-6e333bddf9bf.png)

`airports_h3_c`をレンダリングします。

> 異なる解像度が使われているので、このコンパクト化されたテーブルには2K程度のH3インデックスのみが含まれています。

:::note
**注意**
ミックスされた解像度でのレンダリングの説明に役立つ [h3 indexing compaction](https://h3geo.org/docs/highlights/indexing) も参照ください。
:::

```py:Python
as_kepler("airports_h3_c", "cell", "h3", limit_cnt=5000)
```

解像度の異なるセルがレンダリングされます。
![Screen Shot 2022-09-18 at 13.30.43.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/d607d826-f896-eb4d-cf80-5db336593569.png)

trips_h3` と `trips_h3_15` にはそれぞれ16億行が含まれています。

> レンダリングする際には何かしらの方法でフィルタリングしたいと考えており、スタート地点として2つの空港の移動のみを表示します！

空港の最小の解像度を見つけます。

> `min(h3_resolution(cell))` = 8 の結果を `h3_toparent` 呼び出しで使用します。

```sql:SQL
%sql
select
  min(h3_resolution(cell))
from
  airports_h3_c
```
![Screen Shot 2022-09-18 at 13.31.56.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/594220d4-78dd-697b-b3db-caf8187ebfb1.png)

:::note
代わりにコンパクト化されたセルを使用し、フィルターとして親IDと`h3_ischildof`エクスプレッションを使用します。
:::

> `h3_toparent(t.pickup_cell, 8) = h3_toparent(src.cell, 8)`  
> `and h3_toparent(t.dropoff_cell, 8) = h3_toparent(trg.cell, 8)`  
> `and h3_ischildof(t.pickup_cell, src.cell)`  
> `and h3_ischildof(t.dropoff_cell, trg.cell)`

```sql:SQL
%sql create
or replace view airport_trips_h3_c as (
  select
    t.passenger_count,
    t.fare_amount,
    src.locationid as src_locationid,
    trg.locationid as trg_locationid,
    src.cell as src_cell,
    trg.cell as trg_cell
  from
    trips_h3_15 t,
    airports_h3_c src,
    airports_h3_c trg
  where
    h3_toparent(t.pickup_cell, 8) = h3_toparent(src.cell, 8)
    and h3_toparent(t.dropoff_cell, 8) = h3_toparent(trg.cell, 8)
    and h3_ischildof(t.pickup_cell, src.cell)
    and h3_ischildof(t.dropoff_cell, trg.cell)
    and src.locationid <> trg.locationid
);
-- HOW MANY TRIPS?
select
  format_number(count(*), 0) as count
from
  airport_trips_h3_c
```
![Screen Shot 2022-09-18 at 13.32.49.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/c2c95261-ca35-f14f-a598-eab9323fbf90.png)

移動データに関する一連の質問に答えるために、H3のセルIDを用いてairportとtripsテーブルを容易に組み合わせることができます。例えば、料金による収益はどれだけだったのでしょうか？

```sql:SQL
%sql
select
  format_number(count(*),0) as num_trips,
  format_number(sum(passenger_count),0) as num_passengers,
  (
    '$' || cast(format_number(sum(fare_amount), 2) as string)
  ) as earnings
from
  airport_trips_h3_c
```
![Screen Shot 2022-09-18 at 13.33.26.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/82ecbbe5-16a8-cb0f-ee3a-996e818bfb5c.png)

```sql:SQL
%sql create
or replace view src_airport_trips_h3_c as (
  select
    src_cell as cell,
    src_locationid as locationid,
    format_number(count(*), 0) as trip_cnt_disp,
    format_number(sum(passenger_count), 0) as passenger_sum_disp,
    (
      '$' || cast(format_number(sum(fare_amount), 2) as string)
    ) as fare_sum_disp,
    count(*) as trip_cnt,
    sum(passenger_count) as passenger_sum,
    sum(fare_amount) as fare_sum
  from
    airport_trips_h3_c
  group by
    cell,
    src_locationid
);
```

### LaGuardiaとNewarkのピックアップの割合は？

```sql:SQL
%sql create
or replace temp view src_full_airport_trips_h3_c as (
  select
    cell,
    zone,
    borough,
    trip_cnt_disp,
    passenger_sum_disp,
    fare_sum_disp,
    airports.locationid,
    trip_cnt,
    passenger_sum,
    fare_sum
  from
    src_airport_trips_h3_c
    inner join airports on src_airport_trips_h3_c.locationid = airports.locationid
);
```

```sql:SQL
%sql select locationid, sum(trip_cnt) as num_trips, zone, borough from src_full_airport_trips_h3_c group by locationid, zone, borough
```
![Screen Shot 2022-09-18 at 13.34.35.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/2a4edaf5-750b-3331-eeab-7b06efd19840.png)

### コンパクト化された乗車位置の活用

> 空港における乗車です (大部分はNewarkではなくLaGuardiaからの乗車であることがわかります)

:::note
**注意**
このレンダリングはカスタマイズすることもできます。例えば、以下の様に、セルごとの`trip_cnt`に色を関連づけ、高さに`passenger_sum`を関連づけた3D表示が可能です。
:::

```py:Python
as_kepler("src_airport_trips_h3_c", "cell", "h3")
```
![Screen Shot 2022-09-18 at 13.36.00.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/f8afd467-5846-db9b-7122-530bb57403e9.png)

### お客様がLaGuardiaで乗車した際、他に降車した場所はどこでしょうか？

```sql:SQL
%sql create
or replace temp view lga_dropoffs as (
  select
    pickup_cell,
    dropoff_cell,
    passenger_count,
    fare_amount,
    air.locationid as pickup_locationid
  from
    trips_h3
    INNER JOIN (
      select
        *
      from
        airports_h3
      where
        locationid = 132
    ) air ON trips_h3.pickup_cell = air.cell
);
-- HOW MANY TOTAL DROP-OFFS from LGA?
select
  format_number(count(*), 0) as count
from
  lga_dropoffs;
```

降車したH3セルの位置ごとにカウントを集計しましょう。

```sql:SQL
%sql create
or replace temp view lga_agg_dropoffs as (
  select
    count(*) as dropoff_cnt,
    dropoff_cell
  from
    lga_dropoffs
  group by
    dropoff_cell
);
select
  format_number(count(*), 0) as count
from
  lga_agg_dropoffs;
```
![Screen Shot 2022-09-18 at 13.37.18.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/4480bd8b-87d9-a7fa-c28c-175f7bdaf347.png)

#### (LGAからの)降車ゾーンに対するビューを作成

> 以前作成したNYCタクシーゾーンテーブル(geojsonから作り出した) `taxi_zone_explode_h3` を使用します

```sql:SQL
%sql create
or replace temp view lga_agg_zone_dropoffs_h3 as (
  select
    dropoff_cnt,
    dropoff_cell,
    locationid,
    borough,
    zone
  from
    lga_agg_dropoffs
    INNER JOIN taxi_zone_explode_h3 ON lga_agg_dropoffs.dropoff_cell = taxi_zone_explode_h3.cell
);
```

```sql:SQL
%sql select *
from
  lga_agg_zone_dropoffs_h3;
```
![Screen Shot 2022-09-18 at 13.38.11.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/1bab20e1-a9cd-2360-8b3a-7bd2ea73dcdc.png)

```sql:SQL
%sql create
or replace temp view lga_agg_zone_dropoffs as (
  select
    sum(dropoff_cnt) as dropoff_sum,
    locationid,
    borough,
    zone
  from
    lga_agg_zone_dropoffs_h3
  group by
   zone, borough, locationid
);
```

```sql:SQL
%sql select * from lga_agg_zone_dropoffs
```
![Screen Shot 2022-09-18 at 13.38.54.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/1332c8c3-e23e-ee6d-7a25-305d60523b52.png)

```sql:SQL
%sql create
or replace temp view lga_agg_zone_dropoffs_geom as (
select
    dropoff_sum,
    locationid,
    borough,
    zone,
    taxi_zone_explode.geometry
from lga_agg_zone_dropoffs
inner join taxi_zone_explode on lga_agg_zone_dropoffs.zone = taxi_zone_explode.properties.zone);
```

```sql:SQL
%sql select * from lga_agg_zone_dropoffs_geom
```
![Screen Shot 2022-09-18 at 13.39.43.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/38851017-c8bd-a479-22c9-f5ba1a0815ee.png)

タクシー降車ゾーンをレンダリングします。

:::note
**注意** このレンダリングもカスタマイズすることができます。例えば、以下に示す様にタクシーゾーンごとの`dropoff_sum`に色を関連づけることができます。
:::

```py:Python
as_kepler("lga_agg_zone_dropoffs_geom", "geometry", "geometry")
```
![Screen Shot 2022-09-18 at 13.43.30.png](https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/1168882/fe36a099-f87c-0960-f673-ff9bb32777b1.png)

このように、SQLやScala/Pythonを介してH3を活用することで、Databricks上で様々な地理空間情報の解析が行える様になります。

### Databricks 無料トライアル

[Databricks 無料トライアル](https://databricks.com/jp/try-databricks)