The shift of mainstream International Relations after the collapse of the Soviet Union as well as societal changes challenged the way we see the world. This research is concerned with the, for the researcher most pressing issue of our social coexistence, (In-)equality. To further narrow down, the author engages with identity formation and the issue of gender in Estonia. After assessing the International Relations relevance of Feminist IR and pointing out Constructivist similarities ontological and epistemological assumptions are presented, where the author shares the Feminist claims of seeing states as the analytical unit in the international system but as a representational conglomeration of a population. Estonia´s transformation process, from a oppressed socialist society to economic liberalization will show a deeper connection to the population´s national identity. The methodological endeavour takes two indexes the Gender Equality Index and the World Value Survey and cross-analyses opinion with reality and finds correlation in the Socialist past influencing the opinion in Gender Equality. The comparative study with Slovenia shows ineffectiveness of Gender quotas and a general slow progress in terms of Gender Equality. Concluding, that the past and the constructed identity against the former Soviet ideology and the constructed national identity has an influence on Gender Equality.

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. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p><p>There are other digital material (eg film, image or audio files) or models/artifacts that belongs to the thesis and need to be archived.</p>