GeoTwin proposes EU first individual level demographic and dynamic location intelligence data
At GeoTwin, we have been working hard to develop a new approach to quantify mobility using passive data. Instead of actively collecting survey information from a small sample, we use existing data collected for a large proportion of the population during their normal activities to capture individual preferences and behavior. These datasets include population census, transportation and land use surveys and location data from mobile phones, for both household and individual levels.
We use this data to create a completely synthetic population for an area, using anonymous and aggregated data. This is a virtual population that is statistically representative of the real oneWe apply activity-based travel modeling to generate a sequence of activities and connecting trips for every person in the synthetic population on a specific day. Then we use computer simulation to model their interactions with the transportation supply and each other as they attempt to realise their activity schedules.
We can provide both the present and future (by 2024, 2030, 2050) population and its travel patterns for any city in the world.
What data is used to represent a population in a given region?
We use samples of census demographic data, travel survey, land use and longitudinal employer-household dynamics, service and facility location data to create a “synthetic population” that is statistically representative of the actual population in a region and then we also utilize cellular networks data as well as point of sale (POS) terminal data for ground truth activity data.
For which sector?
GeoTwin harnesses real-life data to understand and forecast people's movement and consumption patterns. The synthetic population data would be used to answer the different sector’s specific challenges such as market expansion, demand discovery and prediction and market share analysis. Please find how our population data and platform intelligence solve the most difficult sectoral challenges in the case study section.
- Shared & Electromobility
- City Planning & Land-use
- Health & Human Science
- Retail & Real Estate