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GeoTwin’s platform is designed not to track people but to help cities gain insights about movement patterns.
At GeoTwin, Data privacy is extremely important to us
Our platform is designed to gain insight from people’s movement patterns in specific areas but cannot be used to track individuals.
This is because we generate a completely synthetic, digital twin population from input data sources.
A digital twin city is similar to a human identical twin in that it looks the same and behaves like its copy because it has the same DNA structure.
However, like a human twin it is not composed of the same particles, and we recognize twins as separate individuals.
In the case of a city, the composing particles include the vehicles, infrastructures, buildings, goods and, of course, individuals. For this reason, the maps, statistics and simulations shown to our customers, cannot reveal individual identities because the individuals that our results are derived from are from the synthetic twin and do not exist in reality.
GeoTwin uses a variety of techniques, policies and tools to ensure its systems are safe and secure
Our digital twin population behaves the same due to sharing the ‘DNA’ of its real-world counterpart - which means you can draw reliable insights from experiments, observation and analysis of the synthetic city and its population.
On the back-end, we only work with anonymized and aggregated data and never attempt to re-identify individuals. Where customers upload their own privately sourced data, we are committed to maintaining the security and integrity of such data on our servers (as set out in our terms of service).
Is GeoTwin GDPR compliant?
Our partnering agencies place a high priority on privacy and confidentiality. It is important to note that GeoTwin’s outputs do not release raw data. Instead, GeoTwin turns the raw data into synthesized “replicas.” That way, no actual raw geolocation data of individuals is accessible, making tracking individual movements impossible. Only the synthesized outputs are shared.
More specifically, the GeoTwin platform uses EU and American Census and Survey data and other sources about who lives in a given area as well as regional housing and employment availability. In addition, GeoTwin uses GSM data mostly in the EU and they are all by default anonymized and GDPR compliant.
GeoTwin then applies modeling and optimization algorithms to generate a representative population that is statistically equivalent to the census population, and then matches the population to “personas,”which are an amalgam of synthetic travel and activity patterns based on raw device data. These representative people and households are assigned housing units and locations of workplaces and schools. Personas extract behavioral patterns from de-identified mobile location data collected from mobile devices of real people. In this way, the source data is aggregated and de-personalized so the resulting insights are anonymized.