When I moved to New York, besides becoming a student again, a newbie in town, a foreigner and so on… I became a “Latina”. An the truth is that I’ve never thought of myself as a latina. I am Brazilian. My first language is portuguese. I do not have any hispanic origin in my family. But yes, I was born in Latin America.
Accordingly, at the same time that it is uncomfortable to fill census reports and forms here in the United States, it is still interesting to understand how my identity as a human being is redefined once moving to a foreign country.
My goal is to play with that idea of how culture, bias and identity are created and defined in American society and somehow transform that into a map. One reference that I really like is Alfredo Jaar’s A Logo for America, a piece that doesn’t explicitly talk about those concepts but have them in its statement.
I’m not sure how I would develop that but I’ll look into more references and update it soon.
For this week’s readings we went over the article What would Feminist Data Visualization look like? and the chapter on Representation and the Necessity of Interpretation form Laura Kurgan. Both readings invite us to rethink about the way data is shown in maps, in order to understand that it is presented for a reason and purpose and therefore there will always be a bias and therefore a relationship of power involved.
The first article touches in the concept of how feminist standpoint theory would approach data visualization, mentioning that all knowledge is socially situated and that the perspectives of oppressed groups including women, minorities and others are systematically excluded from “general” knowledge. Despite that, it suggests interesting approaches that creators should think when trying to develop “unbiased” maps according to feminist data viz, such as developing new ways to represent uncertainty, invent new ways to reference the material economy behind the data and create ways to make dissent possible so we can find ways to go back to the material that originated that visualization.
The second reading starts by breaking down the perception that satellite images analysis are somehow neutral and can be deliberately taken as statements. It mentions about the use of satellite images to justify the invasion on Iraq, proving this point. Accordingly, it states that there is no such thing as raw-data and suggests that working with data is a para-empiricism.
I believe that both readings prove their points. I really liked the suggestions on the Feminist article about new ways to present data that would clarify the choices and make the “bias” explicit. What if we visually problematized the provenance of the data? The interests behind the data? The stakeholders in the data? I believe that it is part of the visualization experience to highlight some aspects over others, according to the maps functionality. Thus is impossible not to create somehow biased maps. Accordingly, as creators, it is our responsibility to be aware of those choices and keep them explicit to the public.
So for this assignment we were asked to play with our own .csv files to create a data visualization inside a map using Mapbox. Thus, Hadar and I decided to compare two datasets: the gini index of each country with its deaths by firearms.
Gini index or Gini coefficient is is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation’s residents, and is the most commonly used measurement of inequality. Death by firearms is identified as the number of deaths caused by the use of firearms during an year in that specific country. By collecting these data I aimed to analyze how does income inequality relates to violence rate.
The blue circles are the gini index (the bigger the gini index, the more social inequality), and the red circles represent the deaths by firearms.
According to this map it doesn’t become clear if there is a connection between these two datasets. I believe we have to map the values better and to add the actual number of the datasets when the user hove some of the circles. Also, there are some circles that are too big and are covering some of the countries, which makes it hard to identify.
Besides, the datasets we used to collect this data are not very reliable. First, they didn’t include all country’s in in and second, the data is not correspondent from the same year.
Still, even though this map needs iteration, this was a very interesting experiment.