Högskolan i Skövde

The current data visualization provided by European Institute of Gender Equality (EIGE) for their Gender Equality Index (GEI) has been, in literature, proven to be inadequate; among other spatiotemporal visualizations, heatmaps were proven to be an effective alternative. Furthermore, interactivity and responsiveness are both pertinent for ensuring the effectiveness of any visualization. The problem this study aims to solve is the lack of effective and fast performing scalable spatiotemporal data visualizations of the EIGE’s GEI data. The study created four virtually identical heatmaps, using the JavaScript frameworks D3.js and Plotly.js, based on both the original GEI data and an algorithmically scaled GEI data, subsequently measuring the heatmaps’ performance in regards to the load time as well as the responsiveness via user applied filters. D3 was statistically significantly faster than Plotly in all regards. However, Plotly had over 50% less code required to create a virtually identical heatmap compared to D3.