The following content is a read-only preview of an executable Jupyter notebook.To run this notebook interactively:
- Go to Wherobots Cloud.
- Start a runtime.
- Open the notebook.
- In the Jupyter Launcher:
- Click File > Open Path.
- Paste the following path to access this notebook:
examples/Open_Data_Connections/Overture_Maps.ipynb - Click Enter.
- buildings
- places (points of interest)
- transportation, including segments and connectors
Configuring WherobotsDB
First, we’ll import the necessary Python dependencies and configure WherobotsDB to access the Wherobots Open Data Catalog. Read more about configuring WherobotsDB file access in the documentation.from sedona.spark import *
import os
import time
config = SedonaContext.builder().getOrCreate()
sedona = SedonaContext.create(config)
State Boundary
Pick a state or other boundary
Bellevue city boundary is selected
Click here for boundaries of other states# Washington state boundary
# spatial_filter = "POLYGON((-123.3208 49.0023,-123.0338 49.0027,-122.0650 49.0018,-121.7491 48.9973,-121.5912 48.9991,-119.6082 49.0009,-118.0378 49.0005,-117.0319 48.9996,-117.0415 47.9614,-117.0394 46.5060,-117.0394 46.4274,-117.0621 46.3498,-117.0277 46.3384,-116.9879 46.2848,-116.9577 46.2388,-116.9659 46.2022,-116.9254 46.1722,-116.9357 46.1432,-116.9584 46.1009,-116.9762 46.0785,-116.9433 46.0537,-116.9165 45.9960,-118.0330 46.0008,-118.9867 45.9998,-119.1302 45.9320,-119.1708 45.9278,-119.2559 45.9402,-119.3047 45.9354,-119.3644 45.9220,-119.4386 45.9172,-119.4894 45.9067,-119.5724 45.9249,-119.6013 45.9196,-119.6700 45.8565,-119.8052 45.8479,-119.9096 45.8278,-119.9652 45.8245,-120.0710 45.7852,-120.1705 45.7623,-120.2110 45.7258,-120.3628 45.7057,-120.4829 45.6951,-120.5942 45.7469,-120.6340 45.7460,-120.6924 45.7143,-120.8558 45.6721,-120.9142 45.6409,-120.9471 45.6572,-120.9787 45.6419,-121.0645 45.6529,-121.1469 45.6078,-121.1847 45.6083,-121.2177 45.6721,-121.3392 45.7057,-121.4010 45.6932,-121.5328 45.7263,-121.6145 45.7091,-121.7361 45.6947,-121.8095 45.7067,-121.9338 45.6452,-122.0451 45.6088,-122.1089 45.5833,-122.1426 45.5838,-122.2009 45.5660,-122.2641 45.5439,-122.3321 45.5482,-122.3795 45.5756,-122.4392 45.5636,-122.5676 45.6006,-122.6891 45.6236,-122.7647 45.6582,-122.7750 45.6817,-122.7619 45.7613,-122.7962 45.8106,-122.7839 45.8642,-122.8114 45.9120,-122.8148 45.9612,-122.8587 46.0160,-122.8848 46.0604,-122.9034 46.0832,-122.9597 46.1028,-123.0579 46.1556,-123.1210 46.1865,-123.1664 46.1893,-123.2810 46.1446,-123.3703 46.1470,-123.4314 46.1822,-123.4287 46.2293,-123.4946 46.2691,-123.5557 46.2582,-123.6209 46.2573,-123.6875 46.2497,-123.7404 46.2691,-123.8729 46.2350,-123.9292 46.2383,-123.9711 46.2677,-124.0212 46.2924,-124.0329 46.2653,-124.2444 46.2596,-124.2691 46.4312,-124.3529 46.8386,-124.4380 47.1832,-124.5616 47.4689,-124.7566 47.8012,-124.8679 48.0423,-124.8679 48.2457,-124.8486 48.3727,-124.7539 48.4984,-124.4174 48.4096,-124.2389 48.3599,-124.0116 48.2964,-123.9141 48.2795,-123.5413 48.2247,-123.3998 48.2539,-123.2501 48.2841,-123.1169 48.4233,-123.1609 48.4533,-123.2220 48.5548,-123.2336 48.5902,-123.2721 48.6901,-123.0084 48.7675,-123.0084 48.8313,-123.3215 49.0023,-123.3208 49.0023))"
# Colorado state boundary
#spatial_filter = "POLYGON((-109.0448 37.0004,-102.0424 36.9949,-102.0534 41.0006,-109.0489 40.9996,-109.0448 37.0004,-109.0448 37.0004))"
# Wyoming state boundary
# spatial_filter = "POLYGON((-104.0556 41.0037,-104.0584 44.9949,-111.0539 44.9998,-111.0457 40.9986,-104.0556 41.0006,-104.0556 41.0037))"
# Bellevue city boundary
spatial_filter = "POLYGON ((-122.235128 47.650163, -122.233796 47.65162, -122.231581 47.653287, -122.228514 47.65482, -122.227526 47.655204, -122.226175 47.655729, -122.222039 47.656743999999996, -122.218428 47.657464, -122.217026 47.657506, -122.21437399999999 47.657588, -122.212091 47.657464, -122.212135 47.657320999999996, -122.21092999999999 47.653552, -122.209834 47.650121, -122.209559 47.648976, -122.209642 47.648886, -122.21042 47.648658999999995, -122.210897 47.64864, -122.211005 47.648373, -122.21103099999999 47.648320999999996, -122.211992 47.64644, -122.212457 47.646426, -122.212469 47.646392, -122.212469 47.646088999999996, -122.212471 47.645213, -122.213115 47.645212, -122.213123 47.644576, -122.21352999999999 47.644576, -122.213768 47.644560999999996, -122.21382 47.644560999999996, -122.21382 47.644456999999996, -122.21373299999999 47.644455, -122.213748 47.643102999999996, -122.213751 47.642790999999995, -122.213753 47.642716, -122.213702 47.642697999999996, -122.213679 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47.635534, -122.227918 47.635565, -122.228953 47.635624, -122.22895199999999 47.635571999999996, -122.231018 47.635574999999996, -122.233276 47.635588999999996, -122.233287 47.63617, -122.233273 47.63639, -122.233272 47.636469999999996, -122.23327 47.636578, -122.233266 47.636827, -122.233263 47.636851, -122.233262 47.637014, -122.23322999999999 47.638110999999995, -122.233239 47.638219, -122.233262 47.638279, -122.233313 47.638324999999995, -122.233255 47.638359, -122.233218 47.638380999999995, -122.233153 47.638450999999996, -122.233136 47.638552999999995, -122.233137 47.638692, -122.232715 47.639348999999996, -122.232659 47.640093, -122.232704 47.641375, -122.233821 47.645111, -122.234906 47.648874, -122.234924 47.648938, -122.235128 47.650163))"
Visualizing Overture Maps
Click here to learn more about Overture Maps. For each dataset we follow the same steps:-
df = sedona.read.format("geoparquet").load(DATA_LINK+"theme=XX/type=YY")It reads the dataset mentioned by theme and type, that’s stored in GeoParquet format. -
df = df.filter("ST_Contains(ST_GeomFromWKT('"+state_boundary+"'), geometry) = true")This filters out all the data that is not in the mentionedstate_boundarystring. Please select a state as you wish. ST_GeomFromWKT() - constructs a geometry from WKT (Well Known Text) ST_Contains(A, B) - checks if A fully contains B and returns True -
XX_geom = df.selectExpr("geometry")Storing geometry column for SedonaKepler. -
map = SedonaKepler.create_map(XX_geom, 'XX')Creating a map object using SedonaKepler with inputs geometry column and the name of dataset.
Building Dataset
##%%time
df_building = sedona.table("wherobots_open_data.overture_maps_foundation.buildings_building")
df_building = df_building.filter("ST_Contains(ST_GeomFromWKT('"+spatial_filter+"'), geometry) = true")
df_building = df_building.limit(200_000)
##%%time
map_building = SedonaKepler.create_map(df_building.select("geometry"), 'Building')
map_building
Place Dataset
##%%time
df_place = sedona.table("wherobots_open_data.overture_maps_foundation.places_place")
df_place = df_place.filter("ST_Contains(ST_GeomFromWKT('"+spatial_filter+"'), geometry) = true")
##%%time
map_place = SedonaKepler.create_map(df_place, "Place")
map_place
Transportation Theme Datasets
Connector Dataset
##%%time
df_connector = sedona.table("wherobots_open_data.overture_maps_foundation.transportation_connector")
df_connector = df_connector.filter("ST_Contains(ST_GeomFromWKT('"+spatial_filter+"'), geometry) = true")
##%%time
map_connector = SedonaKepler.create_map(df_connector, "Connector")
map_connector
Segment Dataset
##%%time
df_segment = sedona.table("wherobots_open_data.overture_maps_foundation.transportation_segment")
df_segment = df_segment.filter("ST_Contains(ST_GeomFromWKT('"+spatial_filter+"'), geometry) = true")
df_segment = df_segment.limit(200000)
##%%time
map_segment = SedonaKepler.create_map(df_segment, "Segment")
map_segment