Wherobots open data

Wherobots provides datasets from various sources in two catalogs that Wherobots manages, wherobots_open_data and wherobots_pro_data. The datasets are cleaned and stored as Apache Iceberg tables for analytics with WherobotsDB, and you can browse them in Browse and manage catalogs. All of these datasets are available to every Organization Edition, including Professional Edition trials.

Dataset Categories

This table is a non-exhaustive list of datasets available in Wherobots Data Catalogs, along with their types, counts, and descriptions. For the most up-to-date list of datasets, refer to Data Catalogs in Wherobots Cloud.

Dataset Name	Type	Count	Description
Overture Maps buildings/building	Polygon	785 million	Any human-made structures with roofs or interior spaces
Overture Maps places/place	Point	59 million	Any business or point of interest within the world
Overture Maps admins/administrativeBoundary	LineString	96 thousand	Any officially defined border between two Administrative Localities
Overture Maps admins/locality	Point	2948	Countries and hierarchical subdivisions of countries
Overture Maps transportation/connector	Point	330 million	Points of physical connection between two or more segments
Overture Maps transportation/segment	LineString	294 million	Center-line of a path which may be traveled
Copernicus GLO-30 digital elevation model (DEM)	Raster (GeoTiff)	3.96 million tiles	Global surface elevation at 1 arc-second (about 30 m), including vegetation and buildings. Useful for regional elevation context and surface hillshade; bare-earth hydrology requires a terrain model or additional processing.
ASTER Global Digital Elevation Model (GDEM) v3	Raster (GeoTiff)	5.16 million tiles	Global elevation derived primarily from optical stereo imagery at the same nominal resolution. Compare with GLO-30 to flag differences after grid and vertical-datum alignment; some GLO-30 gaps use ASTER data.
Meta Canopy Height v2	Raster (GeoTiff)	13.6 million tiles	Global tree height at about 1 m, fine enough to resolve individual trees. Wildfire fuel, vegetation encroachment, and urban canopy analysis.
US Census American Community Survey (ACS) 5-year estimates	Non-spatial	3.2 billion estimates	Income, age, housing, tenure, commute, and language estimates down to block group level, for site selection, market sizing, and population exposure counts. 2020-2024 vintage, 1,143 ACS tables.
US Census TIGER/Line boundaries	Polygon	365 thousand	The geometry for the ACS estimates: state, county, tract, block group, and ZIP Code Tabulation Area (ZCTA) boundaries, joined to the estimates on `geoid`, which matches the `GEOID` column on each boundary table except `tiger_zcta`, where it is `GEOID20`. TIGER/Line is the Census Bureau’s Topologically Integrated Geographic Encoding and Referencing database.
National Weather Service (NWS) watches and warnings	Polygon	940 thousand+	Historical watch, warning, advisory, and statement areas dating back to 2005. Issuance times and hazard types support exposure analysis; hail, wind, and damage-threat tags vary by event and year.
Overture Places with Isochrones	Point	53.2 million precomputed travel isochrones	Wherobots added precomputed 5, 10, 15, and 20-min outbound driving isochrones for all 13.3M US locations in the Overture Places dataset.
Google & Microsoft Open Buildings	Polygon	2.5 billion	Google & Microsoft Open Buildings, combined by VIDA
Landsat surface temperature	Raster (GeoTiff)	166K images, 10 TB size	The temperature of the Earth’s surface in Kelvin, from Aug 2023 to Oct 2023
US Census ZCTA codes	Polygon	33144	ZIP Code Tabulation Areas defined in 2018. For newer boundaries, `tiger_zcta` in `wherobots_open_data` holds the 33,791 ZCTAs defined in 2020 and is available to all Organization Editions.
NYC TLC taxi trip records	Point	200 million	NYC TLC taxi trip pickup and dropoff records per trip
Open Street Maps all nodes	Point	8 billion	All the nodes of the OpenStreetMap Planet dataset
Open Street Maps postal codes	Polygon	154 thousand	Boundaries of postal code areas as defined in OpenStreetMap
Weather events	Point	8.6 million	Events such as rain, snow, storm, from 2016 - 2022
Wildfires	Point	1.8 million	Wildfires that occurred in the United States from 1992 to 2015

Overture Places with Isochrones LicensingThe Overture Places with Isochrones dataset is provided under the Community Data License Agreement - Permissive, Version 2.0, governing its use if obtained or redistributed. This dataset also carries the licenses inherited from its source data.

Example notebooks

Explore our collection of example notebooks to see how you can link your data to the physical world and drive new insights. To get started, simply start a runtime and open the notebook that best fits your use case. If you are new to the platform, learn how to Start Your First Notebook.

Query datasets in Wherobots

You can query datasets in Wherobots Cloud with an interactive notebook or within a Job Run or SQL Session. For more information on querying data, see Query tables with WherobotsDB.

Query Overture Maps tables

The Overture Maps tables are in the overture_maps_foundation database of the wherobots_open_data catalog. Use the full name, such as wherobots_open_data.overture_maps_foundation.places_place. A shortened name such as wherobots_open_data.overture.places_place fails with TABLE_OR_VIEW_NOT_FOUND. The examples in this section run in a sedona session. In a new notebook or job script, create the session first:

from sedona.spark import *

config = SedonaContext.builder().getOrCreate()
sedona = SedonaContext.create(config)

To list a table’s columns and their types, run DESCRIBE:

sedona.sql("DESCRIBE wherobots_open_data.overture_maps_foundation.places_place").show()

Filter on nested fields

Several Overture columns are STRUCT or ARRAY types. In places_place, taxonomy is a STRUCT with a primary string, a hierarchy array ordered from the most general category to the most specific, and an alternates array. Access its fields with dot notation, and search the arrays with ARRAY_CONTAINS. Comparing taxonomy itself to a string, as in taxonomy LIKE '%ballpark%', fails with a DATATYPE_MISMATCH error.

WHERE taxonomy.primary = 'baseball_stadium'
   OR ARRAY_CONTAINS(taxonomy.alternates, 'baseball_stadium')

Category values are fixed strings, so check the exact value before you filter on it. For example, Overture has both baseball_stadium and baseball_field and no ballpark category. This query lists the categories that mention baseball or stadiums:

sedona.sql("""
SELECT DISTINCT taxonomy.primary
FROM wherobots_open_data.overture_maps_foundation.places_place
WHERE taxonomy.primary LIKE '%baseball%' OR taxonomy.primary LIKE '%stadium%'
""").show()

Filter by a country or region boundary

places_place has no country or region column. To limit results to a place, filter with a spatial predicate such as ST_Intersects against that place’s boundary. wkls (Well-Known Locations), an open-source Python library maintained by Wherobots, returns the boundary of a country, region, county, or city from Overture Maps data, using ISO codes or English names such as wkls.us.ca.sanfrancisco.

wkls comes pre-installed in WherobotsDB, so you do not need to install it separately if you’re using Wherobots Cloud. Just add import wkls to your notebook.

This example counts the baseball stadiums in the United States:

import wkls

# Boundary of the United States as WKT
usa_wkt = wkls.us.wkt()

result = sedona.sql(f'''
SELECT COUNT(*) AS stadium_count
FROM wherobots_open_data.overture_maps_foundation.places_place
WHERE (
  taxonomy.primary = 'baseball_stadium'
  OR ARRAY_CONTAINS(taxonomy.alternates, 'baseball_stadium')
)
AND ST_Intersects(ST_GeomFromWKT('{usa_wkt}'), geometry)
''')
print(result.collect()[0][0])

wkls also returns WKB (.wkb()) and GeoJSON (.geojson()), lists the regions, counties, and cities at each level, searches by name, and can pin an Overture Maps release. Outside Wherobots Cloud, install it with pip install wkls. For the full API, see the wkls repository.

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Havasu data catalogs

Vector data

Raster data

Iceberg tables

Maps and visualization

Wherobots open data

Dataset Categories

Example notebooks

Query datasets in Wherobots

Query Overture Maps tables

Filter on nested fields

Filter by a country or region boundary

​Dataset Categories

​Example notebooks

​Query datasets in Wherobots

​Query Overture Maps tables

​Filter on nested fields

​Filter by a country or region boundary

Dataset Categories

Example notebooks

Query datasets in Wherobots

Query Overture Maps tables

Filter on nested fields

Filter by a country or region boundary