15 05 2017
Address mapping: how we process spatial data from a survey
With a host of easy-to-use online tools available, spatial data visualisation has become a helpful research tool. Having accurate data, we can conveniently generate clear and engaging statistical maps. The situation becomes more complicated when we work with faulty or erratic content downloaded from social media or received from survey participants. To get any use out of it, the data needs to undergo a laborious verification process.
Karol Piekarski, Paweł Jaworski
25 04 2017
Visualisation of Facebook data. What you will not learn about your fan page from admin stats. Part 2.
We typically expect the analyses of large data sets to provide us with simple clues that will increase the number and engagement of visitors to our page. Detailed research on interactions with Facebook posts shows that trends tend to vary from month to month, making it difficult to find universal rules governing social media. Our visualisations, therefore, do not provide ready-made solutions, but rather help users embark on data exploration for a broader look at fan page statistics.
Karol Piekarski, Waldemar Węgrzyn
21 03 2017
Where’s the Culture? A Cultural Event Geography by Facebook
Is it possible to map cultural activity across the city based on social data? Well, we gave it a go! Using the information available on Facebook, we set out to trace the dynamics of the local cultural life to find out whether the city’s geographical centre is also where its cultural heart beats.
Hanna Kostrzewska, Paweł Jaworski
09 03 2017
Visualisation of Facebook data. What you will not learn about your fan page from admin insights
Administrators of Facebook pages have at their disposal a set of advanced statistical tools to track and study the engagement of its users. However, if they want to compare their own performance in relation to similar institutions or competitors, they are forced to seek the paid services of analyst firms. Alternatively, they may refer to the data from another source, i.e. Facebook’s API. This is exactly the way we decided to check whether researchers or cultural event organisers can gain a basic understanding of any fan page, while performing their own studies and visualisations.
Karol Piekarski, Waldemar Węgrzyn
20 02 2017
Studio NAND: There is nothing worse than a visualisation that tries to convey too much information at once
On January 9–10, Medialab once again had the pleasure of hosting Steffen Fiedler and Stephan Thiel of Berlin’s design studio NAND, who gave an open lecture, and conducted a two-day workshop for representatives of institutions working jointly on the Shared Cities project. In an interview given to Medialab, the data visualisation experts talked about their work, best visualisation practices and the future of the industry.
Łukasz Mirocha
30 01 2017
Cultural data analysis is consistent with the positive smart city vision
Working under guidance from data science expert Piotr Migdał as part of our Shared Cities project, the workgroups are developing a range of tools for the analysis and visualisation of social media data. Network analysis conducted with these tools allows us to study the declared participation in Katowice’s cultural activities and present it in different forms.
Łukasz Mirocha
12 01 2017
From response to visualisation: how to streamline survey data processing
Before a response given in a survey makes it into a report or spectacular visualisation, it must go through a multistage data handling process. We provide a step-by-step guide to improving data acquisition and cleaning in order to accelerate the presentation of research outcomes and produce better quality responses.
Karol Piekarski
10 01 2017
What does data tell us about culture? Data-driven research
Thanks to data-driven research we are able to explore the relationship between knowledge about the behaviours of social media users with the research on consumers of culture conducted by traditional methods. This way, we combine the accuracy of surveys and their capacity for revealing the motivations of respondents with the advantages of automated analysis of large data sets, which makes it easy to discover the so-far unnoticed trends and relationships.
Karol Piekarski