SedonaDB Overture Examples

Note: Before running this notebook, ensure that you have installed SedonaDB: pip install "apache-sedona[db]"

This notebook demonstrates how to query and analyze the Overture Maps dataset using SedonaDB.

The notebook explains how to: * Load Overture data for the buildings and divisions themes directly from S3. * Perform spatial queries to find features within a specific geographic area. * Optimize subsequent query performance by caching a subset of data in memory.

import sedona.db
import os

os.environ["AWS_SKIP_SIGNATURE"] = "true"
os.environ["AWS_DEFAULT_REGION"] = "us-west-2"

sd = sedona.db.connect()

Overture buildings table

df = sd.read_parquet(
    "s3://overturemaps-us-west-2/release/2025-08-20.0/theme=buildings/type=building/"
)

df.limit(10).show()

┌──────────────────────────────────────┬─────────────────────────────────────────┬───┬─────────────┐
│                  id                  ┆                 geometry                ┆ … ┆ roof_height │
│                 utf8                 ┆                 geometry                ┆   ┆   float64   │
╞══════════════════════════════════════╪═════════════════════════════════════════╪═══╪═════════════╡
│ 97a89436-5295-417e-bde1-2dbf8fe10700 ┆ POLYGON((-107.0377042 28.8102782,-107.… ┆ … ┆             │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ e09fcdeb-65ab-443c-bc59-4f9acf32af17 ┆ POLYGON((-107.0372022 28.8103812,-107.… ┆ … ┆             │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ cb06e75f-66a0-40ca-9029-e1a24dff1434 ┆ POLYGON((-107.0372848 28.8104923,-107.… ┆ … ┆             │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 5b8eaaca-5628-41d2-9e74-8c4c1205ed54 ┆ POLYGON((-107.0371986 28.8104859,-107.… ┆ … ┆             │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1a4fba29-6160-4a98-b8f7-03395fd6466a ┆ POLYGON((-107.0373331 28.8105281,-107.… ┆ … ┆             │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 9de2d803-ab58-46e1-9a35-7423f8d0d121 ┆ POLYGON((-107.0378928 28.8112268,-107.… ┆ … ┆             │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1704c72b-6f2b-445b-b9a3-36fdd0c74233 ┆ POLYGON((-107.0376099 28.8112085,-107.… ┆ … ┆             │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ b30bf41d-479f-4ee5-bd45-d4e17ffd8875 ┆ POLYGON((-107.0377094 28.8115554,-107.… ┆ … ┆             │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ c88928d4-b165-4335-9bd8-ccc5c9f79cee ┆ POLYGON((-107.0374986 28.8111847,-107.… ┆ … ┆             │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ ca1000b0-6af3-47fc-bdf2-61e8b1b716fc ┆ POLYGON((-107.0370972 28.8115247,-107.… ┆ … ┆             │
└──────────────────────────────────────┴─────────────────────────────────────────┴───┴─────────────┘

df.to_view("buildings")

# the buildings table is large and contains billions of rows
sd.sql("""
SELECT
    COUNT(*)
FROM
    buildings
""").show()

┌────────────┐
│  count(*)  │
│    int64   │
╞════════════╡
│ 2539170484 │
└────────────┘

# check out the schema of the buildings table to see what it contains
df.schema

SedonaSchema with 24 fields:
  id: utf8<Utf8View>
  geometry: geometry<WkbView(ogc:crs84)>
  bbox: struct<Struct(xmin Float32, xmax Float32, ymin Float32, ymax Float32)>
  version: int32<Int32>
  sources: list<List(Field { name: "element", data_type: Struct([Field { name: "property", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "dataset", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "record_id", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "update_time", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "confidence", data_type: Float64, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "between", data_type: List(Field { name: "element", data_type: Float64, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }), nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }]), nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} })>
  level: int32<Int32>
  subtype: utf8<Utf8View>
  class: utf8<Utf8View>
  height: float64<Float64>
  names: struct<Struct(primary Utf8, common Map(Field { name: "key_value", data_type: Struct([Field { name: "key", data_type: Utf8, nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "value", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }]), nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }, false), rules List(Field { name: "element", data_type: Struct([Field { name: "variant", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "language", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "perspectives", data_type: Struct([Field { name: "mode", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "countries", data_type: List(Field { name: "element", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }), nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }]), nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "value", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "between", data_type: List(Field { name: "element", data_type: Float64, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }), nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "side", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }]), nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }))>
  has_parts: boolean<Boolean>
  is_underground: boolean<Boolean>
  num_floors: int32<Int32>
  num_floors_underground: int32<Int32>
  min_height: float64<Float64>
  min_floor: int32<Int32>
  facade_color: utf8<Utf8View>
  facade_material: utf8<Utf8View>
  roof_material: utf8<Utf8View>
  roof_shape: utf8<Utf8View>
  roof_direction: float64<Float64>
  roof_orientation: utf8<Utf8View>
  roof_color: utf8<Utf8View>
  roof_height: float64<Float64>

# find all the buildings in New York City that are taller than 20 meters
nyc_bbox_wkt = (
    "POLYGON((-74.2591 40.4774, -74.2591 40.9176, -73.7004 40.9176, "
    "-73.7004 40.4774, -74.2591 40.4774))"
)
sd.sql(f"""
SELECT
    id,
    height,
    num_floors,
    roof_shape,
    ST_Centroid(geometry) as centroid
FROM
    buildings
WHERE
    is_underground = FALSE
    AND height IS NOT NULL
    AND height > 20
    AND ST_Intersects(
        geometry,
        ST_SetSRID(ST_GeomFromText('{nyc_bbox_wkt}'), 4326)
    )
LIMIT 5;
""").show()

┌─────────────────────────┬────────────────────┬────────────┬────────────┬─────────────────────────┐
│            id           ┆       height       ┆ num_floors ┆ roof_shape ┆         centroid        │
│           utf8          ┆       float64      ┆    int32   ┆    utf8    ┆         geometry        │
╞═════════════════════════╪════════════════════╪════════════╪════════════╪═════════════════════════╡
│ 1b9040c2-2e79-4f56-aba… ┆               22.4 ┆            ┆            ┆ POINT(-74.230407502993… │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1b5e1cd2-d697-489e-892… ┆               21.5 ┆            ┆            ┆ POINT(-74.231451103592… │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ c1afdf78-bf84-4b8f-ae1… ┆               20.9 ┆            ┆            ┆ POINT(-74.232593032240… │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 88f36399-b09f-491b-bb6… ┆               24.5 ┆            ┆            ┆ POINT(-74.231878209597… │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ df37a283-f5bd-4822-a05… ┆ 24.154542922973633 ┆            ┆            ┆ POINT(-74.241910239840… │
└─────────────────────────┴────────────────────┴────────────┴────────────┴─────────────────────────┘

Overture divisions table

df = sd.read_parquet(
    "s3://overturemaps-us-west-2/release/2025-08-20.0/theme=divisions/type=division_area/"
)

# inspect a few rows of the data
df.show(10)

┌─────────────────┬────────────────┬────────────────┬───┬────────────────┬────────┬────────────────┐
│        id       ┆    geometry    ┆      bbox      ┆ … ┆ is_territorial ┆ region ┆   division_id  │
│       utf8      ┆    geometry    ┆     struct     ┆   ┆     boolean    ┆  utf8  ┆      utf8      │
╞═════════════════╪════════════════╪════════════════╪═══╪════════════════╪════════╪════════════════╡
│ cd743a89-9507-… ┆ POLYGON((111.… ┆ {xmin: 111.40… ┆ … ┆ true           ┆        ┆ e08d9394-cbc8… │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ eb9f314e-1184-… ┆ POLYGON((112.… ┆ {xmin: 112.15… ┆ … ┆ true           ┆        ┆ aa2561c6-a578… │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 89fbee90-bca0-… ┆ MULTIPOLYGON(… ┆ {xmin: 112.15… ┆ … ┆ true           ┆        ┆ 1cf6b19e-756f… │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 758c0e1e-67aa-… ┆ POLYGON((112.… ┆ {xmin: 112.27… ┆ … ┆ true           ┆        ┆ 505f563f-7aa3… │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ e5d4c707-66d7-… ┆ POLYGON((109.… ┆ {xmin: 109.64… ┆ … ┆ true           ┆ CN-HI  ┆ 86939aee-1fbd… │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 26015e14-62ae-… ┆ POLYGON((109.… ┆ {xmin: 109.66… ┆ … ┆ true           ┆ CN-HI  ┆ ed052bae-e26f… │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ eb42fd17-ad1e-… ┆ POLYGON((109.… ┆ {xmin: 109.83… ┆ … ┆ true           ┆ CN-HI  ┆ 201eeb03-4f5a… │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ d63e1220-a308-… ┆ POLYGON((109.… ┆ {xmin: 109.90… ┆ … ┆ true           ┆ CN-HI  ┆ 71e30c32-2bc1… │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ e62cef5e-6768-… ┆ POLYGON((109.… ┆ {xmin: 109.74… ┆ … ┆ true           ┆ CN-HI  ┆ 24478621-6802… │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ c6fcbed1-ad8d-… ┆ POLYGON((110.… ┆ {xmin: 110.00… ┆ … ┆ true           ┆ CN-HI  ┆ ac0f434d-1e10… │
└─────────────────┴────────────────┴────────────────┴───┴────────────────┴────────┴────────────────┘

df.to_view("division_area")

sd.sql("""
SELECT
    COUNT(*)
FROM division_area
""").show()

┌──────────┐
│ count(*) │
│   int64  │
╞══════════╡
│  1035749 │
└──────────┘

df.schema

SedonaSchema with 13 fields:
  id: utf8<Utf8View>
  geometry: geometry<WkbView(ogc:crs84)>
  bbox: struct<Struct(xmin Float32, xmax Float32, ymin Float32, ymax Float32)>
  country: utf8<Utf8View>
  version: int32<Int32>
  sources: list<List(Field { name: "element", data_type: Struct([Field { name: "property", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "dataset", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "record_id", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "update_time", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "confidence", data_type: Float64, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "between", data_type: List(Field { name: "element", data_type: Float64, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }), nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }]), nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} })>
  subtype: utf8<Utf8View>
  class: utf8<Utf8View>
  names: struct<Struct(primary Utf8, common Map(Field { name: "key_value", data_type: Struct([Field { name: "key", data_type: Utf8, nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "value", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }]), nullable: false, dict_id: 0, dict_is_ordered: false, metadata: {} }, false), rules List(Field { name: "element", data_type: Struct([Field { name: "variant", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "language", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "perspectives", data_type: Struct([Field { name: "mode", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "countries", data_type: List(Field { name: "element", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }), nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }]), nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "value", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "between", data_type: List(Field { name: "element", data_type: Float64, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }), nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }, Field { name: "side", data_type: Utf8, nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }]), nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }))>
  is_land: boolean<Boolean>
  is_territorial: boolean<Boolean>
  region: utf8<Utf8View>
  division_id: utf8<Utf8View>

# get all the divisions in Nova Scotia and save them in memory with to_memtable()
nova_scotia_bbox_wkt = (
    "POLYGON((-66.5 43.4, -66.5 47.1, -59.8 47.1, -59.8 43.4, -66.5 43.4))"
)
ns = sd.sql(f"""
SELECT
    country, region, names, geometry
FROM division_area
WHERE
    ST_Intersects(
        geometry,
        ST_SetSRID(ST_GeomFromText('{nova_scotia_bbox_wkt}'), 4326)
    )
""").to_memtable()

ns.to_view("ns_divisions")

df = sd.sql("""
SELECT UNNEST(names), geometry
FROM ns_divisions
WHERE region = 'CA-NS'
""")

%%time
# this executes quickly because the Nova Scotia data was persisted in memory with `to_memtable()`
df.show(2)

┌────────────────────────┬────────────────────────┬────────────────────────┬───────────────────────┐
│ __unnest_placeholder(n ┆ __unnest_placeholder(n ┆ __unnest_placeholder(n ┆        geometry       │
│ s_divisions.names).pr… ┆ s_divisions.names).co… ┆ s_divisions.names).ru… ┆        geometry       │
╞════════════════════════╪════════════════════════╪════════════════════════╪═══════════════════════╡
│ Seal Island            ┆                        ┆                        ┆ POLYGON((-66.0528452… │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ Mud Island             ┆                        ┆                        ┆ POLYGON((-66.0222822… │
└────────────────────────┴────────────────────────┴────────────────────────┴───────────────────────┘
CPU times: user 1.31 ms, sys: 1.02 ms, total: 2.33 ms
Wall time: 2.54 ms

Visualize the results with lonboard

import lonboard

lonboard.viz(df)

Map(basemap_style=<CartoBasemap.DarkMatter: 'https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json'…