Student housing localisation in Ghent
In the city of Ghent there are currently living 263.608 (2020) ‘de jure’ (meaning that they are registered as ‘Ghentians’) However, this does not include every person who is passing through, studying or commuting in Ghent. A large number of students stay in Ghent overnight but aren’t registered in Ghent.
In order to adapt the city services to the ever-changing “city user groups”, it is essential that the civil servants know how many citizens are living, working, studying, shopping, … in Ghent. With this knowledge, the city administration is able to make concrete decisions in the function of the “city users”, without ignoring the diversity within the city users.
For example, if the city wants to construct and build new student residencies, it is essential that the policymakers know what the specific needs of the civilians are. When planning decisions like this, policymakers don’t always know what the impact will be on the different ‘unknown’ city users, which can result in bad planning and decision making. The lack of data on some of the different ‘groups’ in the city makes it difficult to provide these urban services in a more effective way.
In Ghent there were several challenges that were faced for this case. The biggest challenge was related to the collection of the necessary data for this case. Many of the wanted/needed data is privately owned (by the educational institutions, energy or telecommunication companies, public transport company…) and is difficult (or even impossible) to obtain.
Another significant challenge that Ghent faces, was a quality-related problem. Most of the student-related datasets are incomplete or not very systematic. For example, the student addresses list of the functionary responsible for prevention and safety: This list/dataset only contains the cases that were treated by this servant.
- City of Ghent
- Housing department: problem owners. The housing department has great interest in knowing all (or at least more as they know now) the student housing locations.
- Data & Information unit: coördinating discussions between the departments about this topic and searching for possible data sources
- Telecommunication providers: the city of Ghent collaborates with Proximus, the local telecommunication provider. Together with Proximus, we organized two ‘data iterations’ and analyzed telecommunication data patterns in order to derive student housing locations.
- Educational institutions: some (but not all!) of the educational institutions share their list of student housing locations with the city.
- Negotiation internally with civil servants across the different departments
- Negotiations, brainstorms & whiteboard sessions with telecommunication providers to start testing with a sample of the dataset & redefine the questions.
- Investigation in scraping data from social media (twitter and facebook)
- Collecting internal feedback from city departments when they start testing the telecommunication data.
- When searching for different (big) data sources, try to think broadly! Explore different options and do brainstorm sessions about data findings.
- When working with a commercial partner (cf. Proximus) good agreements are key! When working with a commercial player, be very open and honest about the expectations that you as a city have and what the commercial player has towards the city.
- To implement data, collaboration with partners with specific expertise is essential. Not only internal expertise, but external (consultants) as well.
One of the main outcomes of this thorough exercise is the knowledge about the difficulties and challenges of finding the right (big) data sources. During the PoliVisu project, the city of Ghent learned to work with a rather difficult big data source, the telecommunication data.
Together with the commercial partner, Proximus, the city went through numerous discussions, meetings, and brainstorms in order to get the best possible data-insights.
This knowledge is not lost. The contrary, Ghent is already looking into other possible use-cases in which telecommunication data can help answering the policy questions. For example for measuring tourism in Ghent or controlling the crowdedness during events.
- Housing inspection list: data from civil servant who inspects student housing.
- Prevention and safety list of student addresses.
- Student housing list from some of the educational institutions
- Terrain survey student housing (2017)
- Telecommunication data proximus