Region In Motion


The times where the majority of employees (e.g. tailors and blacksmiths) worked on their own propety have passed. The industrialization brought up manufacturing and factories. The Athens Charta segragated the different urban fucntions. Also, the quality and accessibility of means of transport increased. Furthermore, the general lifestyle became more and more specialized, including the professional sphere. Consequently, the distance between the place of residence and the workplace increased as well [Destatis 2009: 2; Holz-Rau 2011: 8]. The increase in the length of the commutes in Germany had its peak between 1970 and 1999 [Holz-Rau et. al. 2011: 8]. It has remained at the same level ever since [Destatis 2009: 2; Destatis 2014: 1]. The average German takes 3,4 ways a day. Of this, 14% are the ways to work and from work back home (the educational traffic amounts to 6%, the bussiness traffic amounts to 7%) [Mobilität in Deutschland (MiD) 2008: 116]. Regarding the average length of these ways, the business ways are the most lengthy ones (20,4km, 17,7km) [MiD 2008 in: ADAC 2010: 13]. Residents of economically underdeveloped regions have to take especially long commutes. The travel time is especially short in metropolitan regions [Eckey et al. 2007: 5-12; Destatis 2014: 1-3; Winkelmann 2010: 42]. Commutes bring motion to a region. Mobility and space are interdependent of each other: mobility supplies create mobility demands, mobility demands bring up new mobility supplies. Long commutes optimize the attraction of specialized and extensive pension institutions along the commute route. Simultaneously, small local suppliers lose their attraction.

The regional mobility has a huge importance for space, not only for mid-sized towns and big cities. Big quantities of traffic and space for manufacturing companies and office parks for have to be managed. Economically underdeveloped places turn into sleeping cities or villages, as can be examined by the example of Extertal in Lippe. More than 30% of the Extertal population has to leave Extertal on weekdays [Website IT.NRW]. This has a huge importance for the supply. Also, it makes other questions arise, such as: how can volunteer fire departments be maintained when the parallel developments of a general aging process and commuting rule the majority of potential personnel? What does this say about the identification of the citizens with their village/ city in which the political life takes place and should be influence by their voice when the majority of the citizens leave the community boarders during the majority of their day? How should communities be designed in order to optimize their supply to the citizens? The project Region in Motion by the research focus nextPlace wants to make regional mobility visible. In order to do so, we refer to the commutes between the different districts of the Ostwestfalen-Lippe region. Based on a twodimensional map, the districts are being displaced into the third dimension according to their population. During a typical day, the communities lose the people that are commuting to another place und win the people that commute to the community. Simultaneously, the detailed commute connections and statistical data regarding the commutes can be retrieved for every community. Technical groundwork, rating and modification are the same as in previous projects (Accidents in 4D, GeoTwitterStrea,…). We also used the open-source JavaScript library Cesium again. This showed that the raising of the 70 district polygons of the Ostwestfalen-Lippe region is too much for regular hardware to handle. This could be improved slightly by visualizing the communities and their population as columns. Within a day, the columns show: (1) the population (as transparent blue container), (2) the remaining population (residents minus the commuters) in blue and (3) the persons that commute into the community in yellow (that partially refill the container). By clicking on a designated community or a designated column, the commuters that travel in and out of the region can be viewed. An infobox shows the relevant statistics for every community (population, commuters that travel in and out the community)/ for every commuter flow. However, performance problems remain when using the codes that are being used here. We also dealt with that again in our Project HolzmindenHealthMap.

Prototype rating as analysis tool and content rating tool

It shows that the use of columns (instead of extruded community areas) does not enable us to compare the commuter flows to the extend that we expected. Comparisons between communities are only  slightly possible from certain points of views. Furthermore, it is clear that the visualization of the commuter flows are not contributing to the rating of the region. Therefore, in our project „To drive like a commuter“, we deal with that question again. Nevertheless, the prototype does show the important commuter flows of the region. By doing so, the distinct communities empty themselves, especially in the rural areas. However, the bigger centers like Bielefeld, Paderborn, Gütersloh and Detmold win daily population on a large scale (relatively and absolutely).


In the framework of developing the prototype, we have had experiences with Cesium. While conducting our project HolzmindenHealthMap we applied these experiences one more time. In a more narrow sense, the prototype can be used for example to visualize regional population developments for time rows.

The data used in the prototype are mainly based on the commuter flows of insurable employees. For all the employees, we used a classic 9 to 5 job on the weekdays (Monday to Friday). There is still potential for a optimization oft he prototype (pupils, students, apprentices, other employees; weekdays, consideration oft he weekly commuters, …). However, the focus of the work was on the experimental application of the relevant software libraries.

Leave a Reply