FOCUS ON THE OPEN DATA ENGAGEMENT FUND: SOCIAL PATTERN DECODED – A DATA ART PROJECT

In this series, we are taking a closer look at some of our recent Engagement Fund 2023/2024 winners. This month, Burcin Yazgi Walsh tells us about her project, which created a digital data art exhibition using open data.

This digital exhibition presents data art pieces created using open data from Ireland. The project uses socio-economic characteristics like education and age to develop abstract representations of Dublin neighbourhoods. The exhibition takes the audience on a journey of Dublin’s social patterns through a designed visual language.

As a data art project, the aim is to create aesthetically pleasing pieces to look at while telling the story of the data through these visual outputs. One of the many interesting aspects of this project is that each visual piece’s uniqueness is due to the unique combinations of people living in the place represented in that piece.

Technical aspect

Two different open datasets, non-spatial and spatial, were merged for this project. These two datasets are social characteristics from the latest 2022 Census Population (non-spatial) and Small Area boundaries (spatial).

Small Areas are statistical units/boundaries organised to include an average of 65-90 households but would vary in size. This project aims to counteract the effect of large areas visually dominating the smaller ones in visual representations since the Small Area concept was developed to represent the areas based on their average household numbers, not their areas. Therefore, instead of their irregular spatial forms, the Small Areas are visualised as a grid of squares in the output visuals.

The transformation from spatial to grid form is achieved using the Kuhn-Munkres algorithm, which optimises the assignment of small areas to the grid cells so that the total distance of all movements is minimal. As a result of the algorithm, irregular-shaped Small Areas are transformed into a same-sized regular grid of squares, and the grid layout maintains the spatial relationship between the areas as closely as possible so that spatial patterns are still visible.

Design aspect

This exhibition exhibits 12 different visual outputs from different parts of the Dublin greater area. Each piece consists of 165 squares, and each square represents a single Small Area from the Dublin region.

The audience is expected to explore the social dynamics of these Dublin neighbourhoods through age and education aspects. For the age component, 40 is taken as the barrier (closest to the median age in Ireland), and the number of people below and over this age is calculated for each Small Area. For the education component, the ‘low’ category includes (no education, primary, secondary, technical qualification) and ‘high’ (higher certificate, bachelor, postgraduate, PhD). Again, the total population for each category is calculated before the visualisations. Then, the proportions of each of these categories are used as input for the visual representations.

Social characteristics from different neighbourhoods of Dublin are represented through a visual language designed by the components of colour and shape. Changing colours reflect the changing education categories (low vs. high education, equal or almost equal), and changing shapes reflect the age categories (young vs. mature, equal or almost equal). You can see a set of these visuals in the image below.

This way of data representation makes it possible to discover the similarities and variations in different parts of Dublin at a glance and opens up many possibilities in the fields of community and neighbourhood studies, spatial and social policy development, data visualisation and data art.

This exhibition brings a new data art concept to Ireland’s agenda and does it using open data. This project, in which art, science, and technology work hand in hand, also introduces the possibility of reproducibility, which can be expanded to other neighbourhoods in Ireland as well as to other datasets.

As a data enthusiast, whether it is varying characteristics or existing similarities within the social structure, it is an exciting output to share. I hope you, as an audience, find it interesting, too. Hopefully, this work will motivate other potential visualisation projects and encourage data art studies. 

For more information, visit the exhibition at https://datapatterns.art and explore other visual outputs.

If you would like to share your comments, please contact hello@datapatterns.art .