dekart-xyz · delfrrr · Aug 28, 2024 · Aug 28, 2024
@@ -42,11 +42,16 @@ h4:hover a {
 }
 
 .view-on-map {
-  margin-top: -2rem;
+  margin-top: -1.5rem;
   text-align: center;
   font-size: 0.8em;
 }
 
+.view-on-map-cloud {
+  margin-top: -1.5rem;
+  // font-size: 0.8em;
+}
+
 p.playground-link {
   text-align: center;
   margin-top: -1.5rem;

@@ -25,9 +25,10 @@ Visualize population density anywhere in the world and at any level of detail
 
 
 ## Overture Maps
-Examples creating kepler.gl maps from Overture Data in BigQuery public dataset
 
-* [Overture Maps: All Nevada roads](https://cloud.dekart.xyz/reports/15540f2b-2411-44a4-92b5-206a9bee5753)
+Examples of Kepler.gl maps created using Overture Data in BigQuery, focusing on geospatial visualizations from the segment, division_area, land_use, and place tables.
+
+👉 [Overture Map Example](/docs/about/overture-maps-examples/)
 
 ## OpenStreetMap
 

@@ -0,0 +1,338 @@
+---
+title: "Overture Maps Examples"
+description: "Collection of kepler.gl maps created from Overture Data in BigQuery public dataset using SQL and Dekart."
+lead: ""
+date: 2024-08-28T07:26:19+02:00
+lastmod: 2024-08-28T07:26:19+02:00
+draft: false
+images: []
+menu:
+  docs:
+    parent: "about"
+weight: 999
+toc: true
+canonical: ""
+---
+
+
+Collection of kepler.gl maps created from Overture Data in BigQuery public dataset using BigQuery SQL and Dekart. Each example includes a SQL query and a visualized map.
+
+## Segment
+
+The Overture Maps `segment` table represents paths, roads, and transportation segments, storing their geospatial data as LineStrings along with attributes like class, surface, speed limits, and access restrictions.
+
+
+### Nevada Roads by Speed and Class
+{{< img src="77dc6f7f-c91c-4099-8dc3-8f043d46cdfb.png" cloud="77dc6f7f-c91c-4099-8dc3-8f043d46cdfb" >}}
+
+```sql
+-- Step 1: Get the geometry of Nevada
+WITH nevada_geometry AS (
+  SELECT
+    geometry
+  FROM
+    `bigquery-public-data.overture_maps.division_area`
+  WHERE
+    country = 'US'
+    AND region = 'US-NV'
+    AND subtype = 'region'
+)
+
+-- Step 2: Select roads within Nevada
+SELECT
+  s.geometry,
+  s.class,
+  s.road,
+  SAFE_CAST(JSON_EXTRACT_SCALAR(s.road, '$.restrictions.speed_limits[0].max_speed.value') AS INT64) AS speed_limit
+FROM
+  `bigquery-public-data.overture_maps.segment` AS s,
+  nevada_geometry AS ng
+WHERE
+  s.subtype = 'road'
+  and class not in ('track', 'driveway', 'path', 'footway', 'sidewalk', 'pedestrian', 'cycleway', 'steps', 'crosswalk', 'bridleway', 'alley')
+  AND ST_WITHIN(s.geometry, ng.geometry)
+
+```
+{{< try-query-cloud report="77dc6f7f-c91c-4099-8dc3-8f043d46cdfb" >}}
+
+### Berlin Roads
+{{< img src="410b857a-aad1-4f05-8ddd-551d0f0fe650.png" cloud="410b857a-aad1-4f05-8ddd-551d0f0fe650" >}}
+
+```sql
+WITH berlin_boundary AS (
+    SELECT geometry
+    FROM `bigquery-public-data.overture_maps.division_area`
+    WHERE LOWER(names.primary) = "berlin"
+    AND country = "DE"
+)
+SELECT s.id, s.geometry, s.class, s.subtype
+FROM `bigquery-public-data.overture_maps.segment` s
+JOIN berlin_boundary b
+ON ST_CONTAINS(b.geometry, s.geometry)  -- Spatial filter for roads inside Berlin boundary
+WHERE s.subtype = 'road'
+AND s.class IN ('primary', 'secondary', 'tertiary');
+
+```
+{{< try-query-cloud report="410b857a-aad1-4f05-8ddd-551d0f0fe650" >}}
+
+### Nevada highways and main roads
+{{< img src="db0e26c2-00b0-4f6b-8f21-a26ab312f9e1.png" cloud="db0e26c2-00b0-4f6b-8f21-a26ab312f9e1" >}}
+
+```sql
+-- Step 1: Get the simplified geometry of Nevada
+WITH nevada_geometry AS (
+  SELECT
+    ST_SIMPLIFY(geometry, 0.01) AS geometry
+  FROM
+    OVERTURE_MAPS__DIVISIONS.CARTO.DIVISION_AREA
+  WHERE
+    country = 'US'
+    AND region = 'US-NV'
+    AND subtype = 'region'
+)
+
+-- Step 2: Select main roads and highways within simplified Nevada geometry and convert geometry to WKT
+SELECT
+  ST_ASWKT(s.geometry) AS geo,
+  s.class
+FROM
+  OVERTURE_MAPS__TRANSPORTATION.CARTO.SEGMENT AS s
+  JOIN nevada_geometry AS ng ON ST_WITHIN(s.geometry, ng.geometry)
+WHERE
+  s.subtype = 'road'
+  AND s.class IN ('primary', 'secondary', 'tertiary', 'trunk', 'motorway')
+
+```
+{{< try-query-cloud report="db0e26c2-00b0-4f6b-8f21-a26ab312f9e1" >}}
+
+### Germany & France Road Networks
+{{< img src="a4e308a3-b2e8-4183-bfd6-b68866209f50.png" cloud="a4e308a3-b2e8-4183-bfd6-b68866209f50" >}}
+
+```sql
+WITH country_boundaries AS (
+    -- Define the boundaries for Germany and France
+    SELECT geometry, country
+    FROM `bigquery-public-data.overture_maps.division_area`
+    WHERE country IN ("DE", "FR")
+    AND subtype = "country"  -- Ensure we're selecting the entire country boundaries
+),
+filtered_roads AS (
+    -- Filter the roads inside the Germany and France boundaries that are accessible to cars, including main roads but excluding highways
+    SELECT s.id, s.geometry, s.class, s.subtype, b.country,
+           ST_LENGTH(s.geometry) / 1000 AS road_length_km  -- Convert road length to kilometers
+    FROM `bigquery-public-data.overture_maps.segment` s
+    JOIN country_boundaries b
+    ON ST_CONTAINS(b.geometry, s.geometry)  -- Spatial filter for roads inside Germany and France boundaries
+    WHERE s.subtype = 'road'
+    AND s.class IN ('primary', 'secondary', 'tertiary')  -- Main roads for traffic, excluding highways
+),
+hexagonized_roads AS (
+    -- Assign each road segment to an H3 hexagon at level 7
+    SELECT
+        id,
+        country,
+        `bqcarto.h3.ST_ASH3`(ST_CENTROID(s.geometry), 7) AS h3_hexagon,  -- H3 hexagon for each road segment at level 5
+        road_length_km  -- Use the length in kilometers
+    FROM filtered_roads s
+)
+-- Aggregate the total length of roads for each hexagon and country
+SELECT country, h3_hexagon, SUM(road_length_km) AS total_road_length_km
+FROM hexagonized_roads
+GROUP BY country, h3_hexagon
+ORDER BY total_road_length_km DESC;
+
+  ```
+{{< try-query-cloud report="a4e308a3-b2e8-4183-bfd6-b68866209f50" >}}
+
+### Road density US
+{{< img src="eb5b25bf-4c62-44bc-9e69-f0257134e3f8.png" cloud="eb5b25bf-4c62-44bc-9e69-f0257134e3f8" >}}
+
+```sql
+WITH country_boundaries AS (
+    -- Define the boundaries for the US
+    SELECT geometry, country
+    FROM `bigquery-public-data.overture_maps.division_area`
+    WHERE country = "US"
+    AND subtype = "country"  -- Ensure we're selecting the entire country boundaries
+),
+filtered_roads AS (
+    -- Filter the roads inside the US boundaries that are accessible to cars, including main roads but excluding highways
+    SELECT s.id, s.geometry, s.class, s.subtype, b.country,
+           ST_LENGTH(s.geometry) / 1000 AS road_length_km  -- Convert road length to kilometers
+    FROM `bigquery-public-data.overture_maps.segment` s
+    JOIN country_boundaries b
+    ON ST_CONTAINS(b.geometry, s.geometry)  -- Spatial filter for roads inside US boundaries
+    WHERE s.subtype = 'road'
+    AND s.class IN ('primary', 'secondary', 'tertiary')  -- Main roads for traffic, excluding highways
+),
+hexagonized_roads AS (
+    -- Assign each road segment to an H3 hexagon at level 6
+    SELECT
+        id,
+        country,
+        `bqcarto.h3.ST_ASH3`(ST_CENTROID(s.geometry), 6) AS h3_hexagon,  -- H3 hexagon for each road segment at level 5
+        road_length_km  -- Use the length in kilometers
+    FROM filtered_roads s
+)
+-- Aggregate the total length of roads for each hexagon and country
+SELECT country, h3_hexagon, SUM(road_length_km) AS total_road_length_km
+FROM hexagonized_roads
+GROUP BY country, h3_hexagon
+ORDER BY total_road_length_km DESC;
+```
+
+{{< try-query-cloud report="eb5b25bf-4c62-44bc-9e69-f0257134e3f8" >}}
+
+## Division Area
+
+The Overture Maps division_area table contains boundary polygons for administrative areas, such as cities, countries, and neighborhoods, along with related attributes like subtype, population, and country codes.
+
+### Berlin Boundary
+
+{{< img src="5f7144cd-24f0-4698-ba7e-a63e872a4659.png" cloud="5f7144cd-24f0-4698-ba7e-a63e872a4659" >}}
+
+```sql
+  SELECT id, geometry, names, subtype, country, region
+FROM `bigquery-public-data.overture_maps.division_area`
+WHERE names.primary = 'Berlin'
+AND country = 'DE'
+```
+{{< try-query-cloud report="5f7144cd-24f0-4698-ba7e-a63e872a4659" >}}
+
+<!-- ffbe0a05-7794-465c-ab5b-de54d69cdb38 -->
+### Regions and Cities in France
+
+{{< img src="ffbe0a05-7794-465c-ab5b-de54d69cdb38.png" cloud="ffbe0a05-7794-465c-ab5b-de54d69cdb38" >}}
+
+```sql
+SELECT
+  division_id,
+  names.primary AS division_name,
+  subtype,
+  geometry
+FROM
+  `bigquery-public-data.overture_maps.division_area`
+WHERE
+  country = 'FR' -- ISO code for France
+  AND subtype IN ('region', 'city') -- Filtering for regions and cities
+ORDER BY
+  subtype;
+```
+{{< try-query-cloud report="ffbe0a05-7794-465c-ab5b-de54d69cdb38" >}}
+
+## Land Use
+
+The Overture Maps `land_use` table represents different types of land use, such as residential, agricultural, industrial, and others, by storing their spatial data as polygons or multipolygons, along with attributes like subtype, class, surface, and names.
+
+<!-- 34d0ba2c-0fd5-4323-a677-d5b05b65d86d -->
+
+### Berlin Playgrounds
+
+{{< img src="34d0ba2c-0fd5-4323-a677-d5b05b65d86d.png" cloud="34d0ba2c-0fd5-4323-a677-d5b05b65d86d" >}}
+
+```sql
+SELECT id, subtype, class, geometry, surface, level
+FROM `bigquery-public-data.overture_maps.land_use`
+WHERE ST_WITHIN(geometry, ST_GEOGFROMTEXT('POLYGON((13.08835 52.33826, 13.76116 52.33826, 13.76116 52.67551, 13.08835 52.67551, 13.08835 52.33826))'))
+AND LOWER(class) = 'playground'
+```
+{{< try-query-cloud report="34d0ba2c-0fd5-4323-a677-d5b05b65d86d" >}}
+
+### All parks in London
+
+{{< img src="8cb1566f-0237-4d99-9cc4-bdd70859763a.png" cloud="8cb1566f-0237-4d99-9cc4-bdd70859763a" >}}
+
+```sql
+SELECT
+    id,
+    geometry,
+    names.primary AS primary_name,
+    subtype,
+    country,
+    region
+FROM
+    `bigquery-public-data.overture_maps.division_area`
+WHERE
+    names.primary = "London"
+    AND country = 'GB'  -- ISO code for the United Kingdom
+    AND subtype = 'locality';  -- Ensure we are selecting a city or locality
+```
+{{< try-query-cloud report="8cb1566f-0237-4d99-9cc4-bdd70859763a" >}}
+
+### Ukraine Schools
+
+{{< img src="f0941a67-350f-4a80-a9d0-27594f2f853d.png" cloud="f0941a67-350f-4a80-a9d0-27594f2f853d" >}}
+
+```sql
+WITH ukraine_boundary AS (
+  SELECT geometry
+  FROM `bigquery-public-data.overture_maps.division_area`
+  WHERE LOWER(names.primary) = 'ukraine'
+  AND subtype = 'country'
+)
+
+SELECT l.id, l.names.primary, l.geometry, l.subtype, l.class
+FROM `bigquery-public-data.overture_maps.land_use` l, ukraine_boundary u
+WHERE ST_WITHIN(l.geometry, u.geometry)
+AND LOWER(l.subtype) = 'education'
+AND LOWER(l.class) = 'school';
+```
+{{< try-query-cloud report="f0941a67-350f-4a80-a9d0-27594f2f853d" >}}
+
+## Places
+
+The `place` table in the Overture Maps dataset contains points of interest (POIs) such as businesses, amenities, and public facilities.
+
+
+### London EV Charging Density
+
+{{< img src="af836766-9ec4-40fc-afbe-fc6b32d6593b.png" cloud="af836766-9ec4-40fc-afbe-fc6b32d6593b" >}}
+
+```sql
+WITH london_boundary AS (
+  SELECT geometry
+  FROM `bigquery-public-data.overture_maps.division_area`
+  WHERE LOWER(names.primary) = 'london'
+  AND country = 'GB'
+),
+ev_charging_stations AS (
+  SELECT
+    p.geometry
+  FROM
+    `bigquery-public-data.overture_maps.place` AS p,
+    london_boundary AS lb
+  WHERE
+    ST_WITHIN(p.geometry, lb.geometry)
+    AND LOWER(p.categories.primary) LIKE '%charging%'
+)
+SELECT
+  bqcarto.h3.ST_ASH3(ev.geometry, 6) AS h3_cell,
+  COUNT(*) AS station_count
+FROM
+  ev_charging_stations ev
+GROUP BY
+  h3_cell;
+
+```
+{{< try-query-cloud report="af836766-9ec4-40fc-afbe-fc6b32d6593b" >}}
+
+### Las Vegas EV Charging
+
+
+{{< img src="72781fb6-8bc5-4c41-839f-66f5bcf7c122.png" cloud="72781fb6-8bc5-4c41-839f-66f5bcf7c122" >}}
+
+```sql
+  WITH las_vegas_boundary AS (
+  SELECT geometry
+  FROM `bigquery-public-data.overture_maps.division_area`
+  WHERE names.primary = "Las Vegas"
+  AND region = "US-NV"
+  AND subtype = "locality"
+)
+
+SELECT p.id, p.geometry, p.names.primary AS station_name, p.addresses, p.websites, p.phones
+FROM `bigquery-public-data.overture_maps.place` AS p, las_vegas_boundary AS lv
+WHERE (p.categories.primary LIKE "%charging%" OR p.categories.primary LIKE "%ev%")
+AND ST_WITHIN(p.geometry, lv.geometry)
+```
+{{< try-query-cloud report="72781fb6-8bc5-4c41-839f-66f5bcf7c122" >}}
@@ -32,3 +32,11 @@
 {{ with .Get "report" }}
 <p class="view-on-map"><a href="https://play.dekart.xyz/reports/{{ . }}" target="_blank">view on a map</a></p>
 {{ end }}
+{{ with .Get "cloud" }}
+<!-- <p class="view-on-map"><a href="https://cloud.dekart.xyz/reports/{{ . }}" target="_blank">View on Dekart</a></p> -->
+<p class="view-on-map">
+<a href="https://cloud.dekart.xyz/reports/{{ . }}" target="_blank" class="btn btn-outline-primary btn-sm">
+  View interactive map
+</a>
+</p>
+{{ end }}
@@ -0,0 +1,8 @@
+<p class="playground-link">
+    <!-- <a class="btn btn-secondary btn-sm" href='https://cloud.dekart.xyz/reports/{{ .Get "report" }}/source'
+        target='_blank'>Run on Dekart</a> -->
+        <a href="https://cloud.dekart.xyz/reports/{{ . }}" target="_blank" class="btn btn-outline-primary btn-sm">
+            Open query in editor
+          </a>
+
+</p>