Geospin Identifies Geographical Success Factors
The future is now. Electric cars are more and more common, air taxis are on the brink of take-off and we are entering the new era of space flight. But how can cities project such changes in infrastructure? That’s when geodata comes in play.
The startup Geospin analyzes freely available data on networks, such as location data from the open source map service “Open Street Map”. As well as, they collect opening hours from shops and Google’s popular visiting times, weather and location data that users attach to their posts in social media.
By analyzing this data, Geospin recognizes how customers move through cities, how busy stores and cafes are etc. Combined with in-house data and machine learning, the result is a forecasting system that allows companies to predict the best location for a store, usage rates for charging stations and much more.
Their key areas of focus are: Data Analytics for urban mobility, Predictive Analytics for E-Mobility and Geo-Big-Data for the financial sector. The company claims that: “In the future, company decisions will no longer be possible without the analysis of dynamic and fine-grained geographic data.”
Spatial Predictions Tailored to Specific Needs
Geospin uses advanced statistical methods and machine learning to predict when and where companies’ products and services will be in demand. They aim to tackle the spatial challenges companies usually face in the mobility sector, logistics, construction, automotive industry and much more.
They started as a spin-off of the University of Freiburg, Germany. The team has been working extensively on their award-winning research in the area of mobility, energy and crime, which later served as a basis for the company’s portfolio. They raised a seven-figure sum in the seed round, providing them funds to initiate a widespread market entry.
This article is also available in: Deutsch (German)