Geographic Data Science using the example of urban sprawl.

Data Science refers to the extraction of knowledge from data. In times of Spatial Big Data (including Volume, Velocity, Variety, Veracity) and related new challenges, Geographic Data Science is gaining importance as a new multidisciplinary research field. Data science methods and approaches address all phases of the transition from data to knowledge, including data acquisition, information extraction, aggregation and representation, data analysis and explanation, and knowledge discovery. Urban sprawl represents a typical multi-dimensional, spatial science phenomenon and serves as a canonical example in the GeoDS project for exploring and applying methods of geographic data science.

Sprawl will be multi-scale measured, described, and explained using modern geospatial data as well as methods of Geographic Data Science for multiple points in time at global and national scales. A discussion forum places the elaborated national and global findings on the characteristics of urban sprawl into the planning, legal, and economic context.

The Leibniz Institute of Ecological Urban and Regional Development is jointly funded by the federal government and the federal states.

FS Sachsen

This measure is co-financed by tax funds on the basis of the budget approved by the Saxon State Parliament.