SAMPLE Urban planning data| Europe coverage | workplace data on building level | AI-based ...

Understanding where people work within a region is an important basis for effective transport or infrastructure planning. Howver, such data can also be very useful for other use cases, such as location assessment.

The new AI tool is trained on several publicly available datasets. The main data sources include:

- OpenStreetMap (OSM): Provides detailed building footprints, land use and points-of-interest.
- Overture Maps: Offers land use and point-of-interest information.
- Corine Land Cover: Helps to classify land use and land cover.
- Census Data: Provides population information.
- 3D Building Models: Provides the height information for the buildings.

High-quality workforce estimates for buildings are a cornerstone of effective transport modeling, as they have direct implications for infrastructure and transportation planning. Conventional methods for estimating workforce distribution are often based on static assumptions, outdated data, or manual processing, which can lead to inefficiencies and inaccuracies. Learn how an AI-based approach can benefit you:

-Leveraging innovative data sources such as Overture Maps, OpenStreetMap, and the most recent census (in Germany, the 2022 census)

-Application of machine learning to determine the best model to explain the dependent variable

-Consistent quality – The quality of the input data used in the AI ​​tool for forecasting is consistent across the target country. Therefore, users can also expect consistent quality of staffing estimates for all regions of the country.

Solving the central location problem
The AI ​​tool's two-step approach helps overcome the central location problem of many official data sources, where all employees of one company are assigned to a single address. By predicting building types and estimating the number of employees based on individual building attributes, the tool distributes employees more precisely to the appropriate area, thus preventing unrealistic concentration in one location.