For many of us, social media is a familiar feature in our lives and relationships, but it can also function as an important tool for researchers. At SPIN Unit, the social media platforms that we utilize in our research provide us with an abundance of accessible, timely, quantitatively, and qualitatively rich data that we can use to analyse the ways in which populations interact with their cities’ urban spaces. The geolocated, publically shared information offered by platforms like Twitter, Instagram, and Foursquare provide us with the potential for both quantitative and qualitative analysis. This data, which can be collected and studied by third parties, can be used to provide a granular and detailed description of social processes through space and time, showing us not only where, but when people use their city spaces. We demonstrate the value that social media analysis has for researchers and third parties, such as SPIN. As social media engagement continues to grow (especially in remote parts of the world), even more data becomes available for us to use.
We are especially interested in the analytic use of Location Based Social Network Data (LBSNd), in combination with other data sources, which is used for urban and traffic planning and—in this particular study—for remote mapping and configurational analysis. This hybrid approach reveals a city’s rhythm (on a weekly, or even daily, scale), it shows us how urban space is used, and it gives insights into the meanings that citizens attach to certain spaces. We can study activity patterns by mapping spaces that are likely to attract certain kinds of human activities, and we are able to focus on the different or varied uses of a particular space during specific periods of time. For these types of analyses, LBSNd works as an invisible treasure-trove of data and metadata supplied directly by the urban population (including visitors and tourists). In this explorative study on Turku, Finland, we will present a few important potential uses of this data.
We created a dataset using all possible sources to describe and assess accessibility in Turku, and to study where citizens and visitors are most likely to pass by and travel through. In this case, we gathered data from Instagram, Twitter, and Foursquare; but this could easily be expanded to include platforms like Facebook, LinkedIn, car-sharing services, sport trackers, trip planners, and other networks. We complemented this data with other open sources: we used multivariate and multi-temporal datasets to reveal the active and idle spots in central Turku, which we visualised on ‘gravity maps.’ Perceived and spatial accessibility were analysed using Space Syntax analysis, while car traffic and light traffic data (provided by the City of Turku) is included alongside OpenStreetMap users’ GPS traces and Turku’s public transportation network.
Our objective was to study how LBSNd can be used for urban and transportation planning. In this case, findings can be used to discuss the current spatio-temporal dynamics in Turku’s city centre, to demonstrate the potential for future development, and to assess how a new tramline would impact accessibility. We explored which spaces are most likely to attract people today, and how the current situation would change with the addition of a new tramline. The study was assigned by the City of Turku and funded from Turku Urban Research Programme.