Data Cartographies, Dynamic Maps and Remote Sensing



The first session of the DATA Dialogues focused on the digital technologies that enable designers to better represent the environment and understand the complex relationships in the built and natural environments. Some questions and concerns were raised and discussion ensued after the PechaKucha presentations.

Data Literacy

  • As we prepared for today, we saw that with the explosion of data and digital mapping, the opportunity for the manipulation of data also has increased substantially. Evidence of this manipulation is particularly evident in social media, where for example, Facebook linked people to right-wing sites through an algorithm that included friend groups and spatial proximity. However, have we always been subject to the manipulation of information? Is this something that has occurred at an increasing rate?
  • Data is always being “translated” before it is deployed in society: maps, analysis, etc. Is there a way to make these data more directly active in people lives and in space, to have a direct effect?
  • In some ways, we have an oversupply of data and technology, and we don’t do a good job of sharing data, coordinating platforms, and making the data usable.
  • We can use these data to optimize how we understand both cause and effect, but we need to understand better what data we really need.
  • As designers, we should also be able to imagine things that don’t exist – either making use of the data or creating new data.
  • We need more clearly defined performance indicators in order to use these data effectively.

Tool Making

  • What tools do we have to analyze and design with these data?
  • Citizen science is an idea that has existed for a long time. Citizen science and tool hacking have the potential to “invent” new tools for design professions, which are often considered outside of the mainstream of technological investment.

Data Ethics and Governance

  • Should we really be sharing all of the data that we, as individuals and households, generate? Is it ethical to use those data?  Are personal data, if in the cloud, public?
  • Is it ethical to shame or guilt people using their data?
  • There may be a boundary crossed with the “bigness” of data today and our reliance on it for major social and personal choices.
  • These technologies are helping “us” – people, practitioners, governments – make better decisions. Precision agriculture is a perfect example of better spatial data leading to better outcomes.
  • There’s a tension between top-down and bottom-up uses of spatial data that touches on many of the cases presented today. The government in some places doesn’t really care about key social outcomes, and it’s up to people to collect and analyze the data themselves to effect change.
  • It’s critical to understand how people in the city and other governments are actually going to use the data and analysis generated. People in the community have a justified concern around whether, if provided with data and analysis, the city will act on it.

Re-conceptualizing problems

  • Can we rethink standards that have existed since the 19th century?
  • Working on microcontrollers and code is an early 21st-century version of working on a car.
  • Are we using these new data for old conceptualizations of the problems? For example, should we use data to keep making vehicle traffic flow more efficient, or can we use data to rethink whether traffic flows should be a priority at all?  These same data can be used to privilege other forms of movement over vehicles if we make that choice and develop the right methods to do it.
  • Causality is not always an essential aspect of the analysis done with spatial data. The NYC Bloomberg Administration data team was more focused on seeing connections through big data and analysis and then diving in with multiple methods to understand causality and other aspects of a spatial issue.

A Shift in Pedagogy

  • Should we be introducing coding, computer skills, and data analysis to design and planning students?
  • Students should also be able to produce their own data, rather than rely on existing sources.
  • What if our students actually acted “as the network” in order to experience these systems rather than simply learn GIS and data analysis.
  • Digital technology education starts at an ever earlier age through STEM programs, and, in 5-10 years, an increasing number of students with these skills will be coming to design school. Are we ready for them?

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