> ## Documentation Index > Fetch the complete documentation index at: https://docs.wherobots.com/llms.txt > Use this file to discover all available pages before exploring further. # Using Overture Maps The following content is a read-only preview of an executable Jupyter notebook. To run this notebook interactively: 1. Go to [**Wherobots Cloud**](https://cloud.wherobots.com). 2. Start a runtime. 3. Open the notebook. 4. In the Jupyter Launcher: 1. Click **File > Open Path**. 2. Paste the following path to access this notebook: `examples/Open_Data_Connections/Overture_Maps.ipynb` 3. Click **Enter**. In this notebook we will explore each of the [Overture Maps](https://overturemaps.org/) dataset themes, including: * buildings * places (points of interest) * transportation, including segments and connectors You can find more information about these datasets in the [Overture Maps documentation.](https://docs.overturemaps.org/) ## Configuring WherobotsDB First, we'll import the necessary Python dependencies and configure WherobotsDB to access the [Wherobots Open Data Catalog](https://docs.wherobots.com/latest/tutorials/opendata/introduction/). Read more about configuring WherobotsDB file access in [the documentation.](https://docs.wherobots.com/latest/references/havasu/configuration/cross-account/) ```python theme={"system"} 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](https://gist.github.com/JoshuaCarroll/49630cbeeb254a49986e939a26672e9c) ```python theme={"system"} # 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 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-122.235128 47.650163))" ``` ### Visualizing Overture Maps [Click here to learn more about Overture Maps.](https://docs.overturemaps.org) For each dataset we follow the same steps: 1. `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. 2. `df = df.filter("ST_Contains(ST_GeomFromWKT('"+state_boundary+"'), geometry) = true")` This filters out all the data that is not in the mentioned `state_boundary` string. 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 3. `XX_geom = df.selectExpr("geometry")` Storing geometry column for SedonaKepler. 4. `map = SedonaKepler.create_map(XX_geom, 'XX')` Creating a map object using SedonaKepler with inputs geometry column and the name of dataset. # Building Dataset ```python theme={"system"} ##%%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) ``` ```python theme={"system"} ##%%time map_building = SedonaKepler.create_map(df_building.select("geometry"), 'Building') map_building ``` # Place Dataset ```python theme={"system"} ##%%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") ``` ```python theme={"system"} ##%%time map_place = SedonaKepler.create_map(df_place, "Place") map_place ``` # Transportation Theme Datasets ## Connector Dataset ```python theme={"system"} ##%%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") ``` ```python theme={"system"} ##%%time map_connector = SedonaKepler.create_map(df_connector, "Connector") map_connector ``` ## Segment Dataset ```python theme={"system"} ##%%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) ``` ```python theme={"system"} ##%%time map_segment = SedonaKepler.create_map(df_segment, "Segment") map_segment ```