Place is one of the imminent challenges in geographical information science. In distinction to space, the concept of place describes the way people think about the world and how they conceptualise their everyday geographies. However, people do this in a variety of often subjective and informal ways. For this reason, the concept of place in a human-geographical sense does not lend itself well to formalisation and quanitative treatment. We participate in the place discourse by working on basic aspects of place-related information, that is, on understanding how people reflect their meaningful places in information structures. We also investigate whether and how places can be studied statistically.
Geography has long been concerned with the subject of place. Geographers, in collaboration with philosophers, sociologists, psychologists and other related scholars, have acquired a rich understanding of how people feel attached to geographical locations. This encompasses a range of approaches, from humanistic perspectives to the application of critical theory and non-representational theories. Most of this research, however, is qualitative and narrative in nature. The formal sciences, including geographical information science and statistics, have contributed little to place-related research. At the Spatial Modelling Lab, we are interested in investigating in depth and from a formal perspective how people reflect their sense of place in information structures. We are looking at novel data sources such as social media, blogs, online discourses, and many others in order to obtain a systematic understanding of general, underlying structures that characterise place-based reflections in data and information. Furthermore, we are interested in modelling and processing place-based information. To this end, we explore possible data structures and formal constructs to capture places and their characteristics with sufficient accuracy and detail. Closely related to this, we investigate the methodological implications of the change from space to place with respect to clustering, visualisation and other methods. Based on our statistical expertise, we particularly want to contribute to possible statistical treatments of place. How can we extract generalisable knowledge about place making, place attachment and other topics from large amounts of possibly subjective, often even idiosyncratic information? By answering such questions, we contribute to cutting-edge research in this novel field of research.
Westerholt, R. (2021): Emphasising spatial structure in geosocial media data using spatial amplifier filtering. Environment and Planning B: Urban Analytics and City Science, volume and issue pending. DOI: 10.1177/2399808320987235.
Westerholt, R., Lorei, H. and Höfle, B. (2020): Behavioural effects of spatially structured scoring systems in location-based serious games – A case study in the context of OpenStreetMap. International Journal of Geo-Information, 9 (2), 129. DOI: 10.3390/ijgi9020129.
Lorei, H., Höfle, B., Westerholt, R. (2020): Spatial Structure as an Element of Motivation in Location- Based Games. Tagungsband der 40. Wissenschaftlich-Technischen Jahrestagung der DGPF, Stuttgart, Germany, Band 29, 290–298.
Wagner, D., Zipf, A., Westerholt, R. (2020): Place in the GIScience Community – an indicative and preliminary systematic literature review. In: Westerholt R., Mocnik, F.-B. (eds) Proceedings of the 2nd International Symposium on Platial Information Science (PLATIAL’19). Coventry, UK, 13–22. DOI: 10.5281/zenodo.3628855.
Westerholt, R. (2019): Statistische Räumliche Analyse in der Digitalen Transformation: das Beispiel Geosozialer Medien. Münchner GI-Runde 2019, Munich, Germany.
Westerholt, R. (2019): Methodological aspects of the spatial analysis of geosocial media feeds: from locations towards places. gis.Science, 31 (31), 65–76.
Westerholt, R., Gröbe, M., Zipf, A. and Burghardt, D. (2018): Towards the statistical analysis and visualization of places. 10th International Conference on Geographic Information Science, Melbourne, Australia, DOI: LIPIcs.GIScience.2018.63.