Alongside custom datasets provided by our clients, we leverage open datasets that are comprehensive, reliable, and up to date. Our primary data sources are integral to our geospatial analysis solutions and are available to Platform users out of the box for further analysis.
Please check the Contacts page and reach out to us if you have any questions about our data and how we can use it for your benefit.
geoBoundaries
geoBoundaries is a global, openly available database of standardized administrative boundaries, from countries (ADM0 level) down to districts where available. We use geoBoundaries to harmonize polygons across regions, perform reliable spatial joins and aggregations, and power clear map labeling and thematic analysis. It’s ideal for defining service areas, aligning external statistics to administrative units, and building cross-border dashboards.
See more information about this dataset at www.geoboundaries.org.
OpenStreetMap (OSM)
OpenStreetMap is a collaborative project that provides detailed, openly licensed map data. We use OSM to extract geographic information such as roads, buildings, points of interest (POIs), administrative boundaries, and public infrastructure. This data forms the backbone of our mapping and routing solutions, enabling accurate spatial analysis and visualization for a wide range of applications.
GeoForm Platform offers datasets with OSM points of interest grouped into the following categories: entertainment, gastronomy, hotels, recreation, tourism, basics, financial, healthcare, shops, temples, and transportation.
See more information about the OSM datasets at www.openstreetmap.org.
WorldPop
WorldPop offers high-resolution population data at a primary resolution of 100 meters, including demographic breakdowns by age and sex, on a global scale. We use WorldPop data to analyze population density, demographic distribution, and community profiling, helping businesses understand the characteristics of specific locations. This data is invaluable when identifying target markets, evaluating customer reach, or planning service coverage.
See more information about the WorldPop datasets at www.worldpop.org.
NoiseModelling (Noise Pollution Modeling Framework)
NoiseModelling is an open-source scientific framework used to simulate environmental noise, particularly from road traffic. In GeoForm, we use this framework together with OpenStreetMap road network data to model long-term road traffic noise exposure. The resulting datasets represent estimated sound levels (such as LDEN – day-evening-night noise level) based primarily on road geometry, traffic assumptions, and propagation models. These layers do not include all real-world noise sources (such as rail, industry, or aircraft unless explicitly modeled) and should be understood as modeled estimates rather than direct measurements.
This data supports assessments of environmental quality, residential exposure, and location-based comparisons of noise burden.
See more information about NoiseModelling at noise-planet.org/noisemodelling.html
VIIRS Nighttime Lights (VNL)
VIIRS Nighttime Lights data is derived from satellite observations of the Earth at night, capturing the brightness of artificial lighting. We use annual composites of VIIRS Day/Night Band (DNB) data, which represent average radiance values over a year at approximately 500-meter resolution. These datasets provide a proxy for human activity, urbanization intensity, and infrastructure presence. While not a direct measure of population or economic output, nighttime lights are widely used as an indicator of development patterns and settlement structure.
In GeoForm, VNL data helps characterize urban intensity, built environment presence, and relative brightness differences between locations.
See more information about VIIRS Nighttime Lights at eogdata.mines.edu/products/vnl/.
Global High PM2.5 (GHAP) – Air Pollution Data
GlobalHighPM2.5 is a machine-learning-based global dataset estimating annual average concentrations of fine particulate matter (PM2.5). The data integrates satellite observations, atmospheric models, and ground monitoring data to produce high-resolution global air pollution surfaces (data reference year: 2022). These values represent modeled long-term exposure levels rather than measurements from a specific local sensor.
We use this dataset to assess relative air quality conditions, compare locations within cities or regions, and support environmental exposure analysis in property and urban studies.
This data is part of the Global High Air Pollutants (GHAP) family of data products used in GeoForm for environmental assessments.
See more information about Global High Air Pollutants datasets at weijing-rs.github.io/product.html
Copernicus River Flood Hazard Maps (CEMS)
The Copernicus River Flood Hazard Maps for Europe and the Mediterranean Basin provide harmonized, pan-European modeling of river flood hazard under multiple return-period scenarios. The dataset represents simulated flood inundation depth (in meters) for nine different return periods, ranging from 1-in-10-year to 1-in-500-year events, at approximately 90-meter spatial resolution.
Flood simulations are based on river discharge modeling using the LISFLOOD hydrological model and inundation modeling using LISFLOOD-FP, a hydrodynamic flood propagation model. The data is produced under the Copernicus Emergency Management Service (CEMS) and provides consistent flood hazard information across Europe and surrounding basins.
In GeoForm, this dataset supports property-level flood risk assessment, including floodplain classification, modeled flood depth extraction, probability estimation based on return periods, and regional flood exposure comparison. The maps represent modeled river flood hazard and should be interpreted as probabilistic simulations rather than records of observed flood events.
See more information about the Copernicus River Flood Hazard Maps at emergency.copernicus.eu.