Spatial factors play a significant role in many economic issues. For example, how intensively two regions trade with each other will depend, among other things, on how far they are from each other, as this has an effect on transport costs. Geographical factors such as topography can also play a role in the level of transport costs. But spatial factors do not only play a role in foreign trade. For example, the distance to the destination often plays a role in migration decisions, as greater distances are often associated with greater cultural differences. Companies not only evaluate locations based on the conditions at the location itself, but also take into account the conditions in the surrounding area, for example in the context of recruiting suitable personnel who may have to commute. And the effects of research institutions often radiate into the surrounding area.

HWWI researchers are specialists in the representation and analysis of spatial data. On the one hand, the classic methods of spatial econometrics are used, which typically use data from official statistics at the regional authority level. As digitalization grows, very small-scale geo-referenced data is now often available, which mostly comes from remote sensing (e.g. aerial photographs, satellite images, radar) or from mobile devices with a GPS signal. Today, survey data is often geo-referenced. HWWI scientists use all of these data sources to make spatial analyzes possible using modern methods of econometrics and machine learning.