Autonomía digital y tecnológica

Código e ideas para una internet distribuida

Linkoteca. Data commons


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.

Meridian 2 vs OSM data bases

In March 2008, I started comparing OpenStreetMap in England to the Ordnance Survey Meridian 2, as a way to evaluate the completeness of OpenStreetMap coverage. The rational behind the comparison is that Meridian 2 represents a generalised geographic dataset that is widely use in national scale spatial analysis. At the time that the study started, it was not clear that OpenStreetMap volunteers can create highly detailed maps

Cartel de la iniciativa Un barrio feliz

El proyecto del colectivo Un Barrio Feliz desarrollado dentro del taller Open Up de Medialab Prado hasta el pasado 23 de febrero no ha hecho más que generar polémica. Y no es una expresión, es que no ha querido ser nada más. En su presentación, el proyecto consistía enhackear el sistema técnico que registra las imágenes de videovigilancia para poder mostrarlas en la pantalla de la nueva fachada digital. Luego el hackeo técnico se torno sistémico y la idea era colocar otras cámaras debajo de las instaladas por el ayuntamiento para registrar lo mismo.

El responsable del proyecto, David Rodríguez, recibió un aviso de demanda por vulnerar la Ley Española de Protección de Datos. Como se explica en Madridiario, además de que el hackeo es falso, ni siquiera se han grabado las imágenes en calles con cámaras:

Las calles que grabamos no tienen cámaras de videovigilancia, salíamos en las imágenes y hasta montamos la instalación delante de las cámaras del Ayuntamiento para que se viese que era mentira. Si el sistema controlara algo, deberían haber actuado al respecto ¿no?

Sidewalk Labs says the sensor information would also support long-term planning. The data would fuel a virtual model of Quayside, which urban planners could use to test infrastructure changes quickly, at low cost, and without bothering residents. It could also be stored in a shared repository that entrepreneurs and companies could draw on to make their own products and services for Quayside.

Unsurprisingly for a company spawned, in part, by technologists, Sidewalk thinks of smart cities as being rather like smartphones. It sees itself as a platform provider responsible for offering basic tools (from software that identifies available parking spots to location-based services monitoring the exact position of delivery robots), much as Google does with its smartphone operating system, Android. Details are still under discussion, but Sidewalk plans to let third parties access the data and technologies, just as developers can use Google’s and Apple’s software tools to craft apps.

Though Sidewalk Labs says the data would be used for a community purpose, such as giving transit discounts to low-income residents, regulating building temperatures, and keeping trash cans from overflowing, not everyone is convinced. “There are definitely questions about whether Sidewalk Labs will try to make money by tracking people’s daily interactions,” says David Roberts, who studies cities at the University of Toronto. “What data will be collected, how personal will it be, how will it be used, and who will have access to it?”

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)”

La revolución digital es también la revolución de los datos: La ciudad se ha convertido en un espacio de producción masiva de datos en tiempo real. Y las nuevas tecnologías como el 5G y el internet de las cosas aumentarán esta tendencia: por ejemplo, el 90 % de los datos que generamos hoy día, como ciudad no existían hace tres años. Los datos se consideran como el petróleo del siglo XXI.

El gran reto es entender el valor de los datos como bien común y devolver el control a los ciudadanos. Para responder a este reto, fortalecemos el liderazgo público en la gobernanza de los datos de la ciudad:

entendiendo los datos como una infraestructura urbana, como lo son la red de suministro de agua o de energía;
tratando los datos como un bien común y poniéndolos a disposición de los procesos de innovación social y económica;
protegiendo la privacidad y la soberanía de datos de los ciudadanos.

Data Culture Project. The Data Process

Data is everywhere right now. But many organizations like your’s are struggling to figure out how to build capacity to work with data. You don’t need a data scientist; you need a data culture.

Use our self-service learning program to facilitate fun, creative introductions for the non-technical folks in your organization. These are not boring spreadsheet trainings. The free tools and activites below are hands-on and designed to meet people where they are across your organization and build their capacity to work with data.

The Open Data Inventory (ODIN) assesses the coverage and openness of official statistics to help identify gaps, promote open data policies, improve access, and encourage dialogue between national statistical offices (NSOs) and data users. ODIN 2017 includes 180 countries, including most all OECD countries. Two-year comparisons are for all countries with two years of data between 2015-2017. Scores can be compared across topics and countries.

The Open Data Institute works with companies and governments to build an open, trustworthy data ecosystem, where people can make better decisions using data and manage its harmful impacts.

Our toolbox is a collection of free tools that help with data publishing. Many of them work together to create an integrated ecosystem for open data.

La Oficina [Open Data Barcelona], que es parte del Plan de Transformación Digital del Ayuntamiento que dirige la Comisionada de Tecnología e Innovación Digital, Francesca Bria, pretende el gobierno público de los datos en un trabajo en tres líneas: captación y almacenamiento, analítica y predicción, y comunicación y difusión. Es decir, el organismo captará información por sus propios medios y sensores pero también los pedirá a compañías que operan en el entorno urbano (telefónicas, energéticas y otras), los analizará y empleará para hacer con mejor tino sus políticas y los podrá a disposición de la ciudadanía, la universidad o quien los requiera.

Una oficina para “remunicipalizar la información” y convertir los datos en lo que son, un bien común.

[Sidewalk Labs] …el modelo urbanístico de Google no está tan lejos del de Blackstone (recuerdo: uno de los grandes imperios inmobiliarios del mundo) pero suma a éste la apropiación de la información, su gestión y su uso. Es decir, ya no sólo se trata de privatizar el espacio público, sino los datos que se generan en él (y en los espacios privados de cada familia y empresa que habite el barrio).

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.