»[Place means] the ways in which people feel and think about space, how they form attachments to home, neighborhood, and nation, and how feelings about space and place are affected by the sense of time.« (Yi-Fu Tuan, 1977)
Place is one of the most mundane, but also one of the most complex topics in the social sciences. The concept reflects the geographical dimension of our human existence. It means how we maintain emotional attachments to geographical places such as our home. It also describes the way we act in certain designated areas, prescribed by explicit or implicit norms. Some aspects of places are even shaped by superstructures such as political systems, economic models and social value systems. Being one of the main objects of study in human geography, place-related research in the formal sciences, including information science and statistics, is still in its infancy. The sheer complexity and informality of the subject makes it difficult to formally grasp the concept of place. As a result, we have not yet come close to what we might call place-based GIS or place-based statistics, or even a good understanding of how places are reflected in information communicated by people. These are the areas to which the Spatial Modelling Lab contributes.
We are interested in the key dimensions of how people reflect their place concepts in information structures. A large part of the novel Big Data sources that have become available in recent years are not only spatial in nature, but reflect to a large extent how people perceive, live and use space. As a result, these data sets are place-based and open up ample opportunities for in-depth study of the geographical aspects of everyday life. For this, however, it is crucial to understand how places defined in this way are reflected in information structures. Otherwise, we will hardly be able to formalise place in analogy to the well-known spatial counterparts from the field of GIS. It will also be impossible for us to develop methods for the analysis of place-based information beyond the qualitative domain. Therefore, one of the main interests of our lab is to understand the nature of place representations not only in social media or other specific types of data sets, but in a general sense.
Besides understanding place-based information, we are also interested in exploring the interfaces between place and statistics. Statistics is based on finding regularities by analysing large amounts of data of similar phenomena. Places, however, are often described as idiosyncratic and unique and are therefore not ideally suited for statistical investigations. Starting from the assumption that there are fundamental characteristics common to all specific instances of place that characterise the nature of specific places, we will look at how we can nonetheless model and analyse places in a statistical sense, and where the bounds of this approach are. We contribute to fundamental topics related to place in statistics, such as philosophical questions, novel formal representations of places, and possible statistical routines for revealing patterns. Place is a fairly new topic in the information sciences. However, the combination of place and statistics is even more innovative and thus a unique selling point of our lab.
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Location & approach
The campus of TU Dortmund University is located close to interstate junction Dortmund West, where the Sauerlandlinie A 45 (Frankfurt-Dortmund) crosses the Ruhrschnellweg B 1 / A 40. The best interstate exit to take from A 45 is "Dortmund-Eichlinghofen" (closer to Campus Süd), and from B 1 / A 40 "Dortmund-Dorstfeld" (closer to Campus Nord). Signs for the university are located at both exits. Also, there is a new exit before you pass over the B 1-bridge leading into Dortmund.
To get from Campus Nord to Campus Süd by car, there is the connection via Vogelpothsweg/Baroper Straße. We recommend you leave your car on one of the parking lots at Campus Nord and use the H-Bahn (suspended monorail system), which conveniently connects the two campuses.
TU Dortmund University has its own train station ("Dortmund Universität"). From there, suburban trains (S-Bahn) leave for Dortmund main station ("Dortmund Hauptbahnhof") and Düsseldorf main station via the "Düsseldorf Airport Train Station" (take S-Bahn number 1, which leaves every 20 or 30 minutes). The university is easily reached from Bochum, Essen, Mülheim an der Ruhr and Duisburg.
You can also take the bus or subway train from Dortmund city to the university: From Dortmund main station, you can take any train bound for the Station "Stadtgarten", usually lines U41, U45, U 47 and U49. At "Stadtgarten" you switch trains and get on line U42 towards "Hombruch". Look out for the Station "An der Palmweide". From the bus stop just across the road, busses bound for TU Dortmund University leave every ten minutes (445, 447 and 462). Another option is to take the subway routes U41, U45, U47 and U49 from Dortmund main station to the stop "Dortmund Kampstraße". From there, take U43 or U44 to the stop "Dortmund Wittener Straße". Switch to bus line 447 and get off at "Dortmund Universität S".
The AirportExpress is a fast and convenient means of transport from Dortmund Airport (DTM) to Dortmund Central Station, taking you there in little more than 20 minutes. From Dortmund Central Station, you can continue to the university campus by interurban railway (S-Bahn). A larger range of international flight connections is offered at Düsseldorf Airport (DUS), which is about 60 kilometres away and can be directly reached by S-Bahn from the university station.
The H-Bahn is one of the hallmarks of TU Dortmund University. There are two stations on Campus Nord. One ("Dortmund Universität S") is directly located at the suburban train stop, which connects the university directly with the city of Dortmund and the rest of the Ruhr Area. Also from this station, there are connections to the "Technologiepark" and (via Campus Süd) Eichlinghofen. The other station is located at the dining hall at Campus Nord and offers a direct connection to Campus Süd every five minutes.