Research Area (5)
As we witness the growing attention to the scientific and business opportunities of Big Data and Data Science, we realize that the array of methods and models developed in the GI domain are central to derive value in the growing availability of open data, sensor data, transaction or social media data. There is a widespread expectation that data analytics can be applied in many diverse sectors. We are in a perfect position to support the development of sound scientific methods to support these developments as well as to train and coach professionals and scientists.
The research area tackles the role of spatial realms in the light of the recent adaptation of spatial concepts in conventional practices and for mass user applications. The overarching research perspective is to address the “science behind the systems” rather than the hype caused by virtual globes such as Google Earth and related fast technology-driven developments. In particular, we want to investigate how behaviour in real worlds is reflected in virtual worlds and vice versa. We work interdisciplinary in a way that we will identify those domains of inquiry that share objects of study and we will investigate values, terms, concepts and assumptions governed by a certain set of rules and categories guiding the pursuit of knowledge.
GIS data will become more and more granular and utilizable for place-based or person-centred information on the Quality of Life (QoL). In co-operations with the Research Studio iSPACE and with the GIScience group, University of Heidelberg, we explore a range of technologies which are able to sense, directly or indirectly, a variety of environmental, human and social phenomena. Such sensing technologies generate vast and rapidly increasing volumes of digital sensor data. It is claimed that this data may at least partially reflect the dynamics of both environmental and social phenomena in remarkable spatial and temporal detail, thus open novel research opportunities also for the GIScience domain. Several empirical studies shall be carried out for urban areas. Conceptually, the methods described would work everywhere where the information content is ‘dense enough’ to characterize people and their environment at a micro-scale.