Autonomía digital y tecnológica

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Python geopandas choropleth map

There are different ways of creating choropleth maps in Python. In a previous notebook, I showed how you can use the Basemap library to accomplish this. More than 2 years have passed since publication and the available tools have evolved a lot. In this notebook I use the GeoPandas library to create a choropleth map. As you’ll see the code is more concise and easier to follow along.

Many datasets are intrinsically hierarchical. Consider geographic entities, such as census blocks, census tracts, counties and states; the command structure of businesses and governments; file systems and software packages. And even non-hierarchical data may be arranged empirically into a hierarchy, as with k-means clustering or phylogenetic trees.

This module implements several popular techniques for visualizing hierarchical data:

Node-link diagrams show topology using discrete marks for nodes and links, such as a circle for each node and a line connecting each parent and child. The “tidy” tree is delightfully compact, while the dendrogram places leaves at the same level. (These have both polar and Cartesian forms.) Indented trees are useful for interactive browsing.

Adjacency diagrams show topology through the relative placement of nodes. They may also encode a quantitative dimension in the area of each node, for example to show revenue or file size. The “icicle” diagram uses rectangles, while the “sunburst” uses annular segments.

Enclosure diagrams also use an area encoding, but show topology through containment. A treemap recursively subdivides area into rectangles. Circle-packing tightly nests circles; this is not as space-efficient as a treemap, but perhaps more readily shows topology.

In the last two decades, states have relied on private contractors to support military operations in conflict situations. Without the necessary democratic scrutiny and public debate, Private Military and Security Companies (PMSCs) have provided services that traditionally were performed by national armies and public authorities –such as interrogation of detainees, protection of military assets, training of local armed forces, collection of intelligence and the performance of defensive and even offensive military activities-.

In this paper, we begin to outline how feminist theory may be productively applied to information visualization research and practice. Other technology and design-oriented fields such as Science and Technology Studies, Human-Computer Interaction, Digital Humanities, and Geography/GIS have begun to incorporate feminist principles into their research. Feminism is not (just) about women,
but rather draws our attention to questions of epistemology – who is included in dominant ways of producing and communicating
knowledge and whose perspectives are marginalized. We describe potential applications of feminist theory to influence the information
design process as well as to shape the outputs from that process.

In this paper, we have outlined six principles for feminist data visualization: Rethink Binaries, Embrace Pluralism, Examine Power and Aspire to Empowerment, Consider Context, Legitimize Embodiment and Affect and Make Labor Visible. These are preliminary and offered for the purposes of beginning a dialogue about how the digital humanities and information visualization communities can productively exchange theories, concepts, and methods. Applying humanistic theories to design processes and artifacts may be new territory for many humanists, just as grappling with questions of subjectivity, power, and oppression may be new territory for many visualization researchers. As data visualization becomes a mainstream technique for making meaning and creating stories about the world, questions of inclusion, authorship,framing, reception, and social impact will become increasingly important. In this regard, the humanities and specifically feminist theory have much to offer.

Where Commuters Run Over Black Children on the Pointes-Downtown Track

the most important part of the Field Notes III for Gwendolyn Warren was the research on children’s deaths caused by automobile accidents. She described how a great deal of commuter traffic from the affluent white suburbs to the Downtown area passes through the Black community and poses significant threat to the children. On one single corner alone there were six children killed in six months. Just gathering the data that the community already knew to be true posed a difficult problem. No one was keeping detailed records of these deaths, nor making them publicly available. “Even in the information which the police keep, we couldn’t get that information. We had to use political people in order to use them as a means of getting information from the police department in order to find out exactly what time, where, how and who killed that child. (Warren, p. 12)”

This research culminated in the map entitled, provocatively, “Where Commuters Run Over Black Children on the Pointes-Downtown Track”.

As Warren points out in her analysis, the fact that the map establishes a pattern proves that the children’s deaths are not isolated incidents but rather indicative that the spatial and racial injustice of the city leads to the bodily harm of the most vulnerable members of its lower classes. Denis Wood, a geography scholar who has written about the map in various publications, is definitive, “Any Detroiter would have known that these commuters were white and on their way between work downtown and home in the exclusive Pointes communities to the east. That is, this is a map of where white people, as they rush to and from work, run over black children. That is, it is a map of where white adults kill black kids. It is a map of racist infanticide, a racial child-murder map. (Maps and Protest article)”

Designing captions

Information can often be divided into data and annotations. A web form needs a way to distinguish entry fields from labels; a graph needs not only labels for its x and y axes, but most crucially a verifiable reference for the source of its data. (#fakenews, I’m looking at you.) The most familiar and obvious way to establish this hierarchy is through type size, using palpably smaller type to distinguish the content from its notes. But at smaller-than-text sizes, even the most lucid typefaces can become difficult to read, their spacing overly tight, their counters congested, and their x-heights measly. Compare the tiny type in these two examples.

D’Ignazio says this issue is compounded by the fact that women and people of color are underrepresented in data science and technical fields in general, a trend that is worsening. She also highlights skewed quantity and quality of data that is collected about various groups of people. For instance, there are very detailed datasets on gross domestic product and prostate function, but very poor datasets on hate crimes and the composition breast milk.