Eindhoven wants to become a smart society. But how does that work? What’s going on in a society like that? Are there any good examples to learn from? DataStudio Eindhoven explores the transition a city has to go through to actually become such a smart society. Each week, we present a new contribution on E52. This week: Readability of data, part 2. Read all the articles in this series here.
Last week we discussed the examples of “misleading data practices”. This week: how it should be done.
How does it work?
The questions that need to be asked first are: how will data help you (as an individual or collective) in making a decision? Is data needed to make that decision? And how should that data be presented to make choices possible? Usman Haque develops design tactics to give these questions a meaning. A few examples are given:
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A direct answer to the demand for legibility – or readability – of data is provided by Thingful, an open data platform and search engine for data streams produced by so-called connected objects which the Internet of Things consists of. Thingful enables users to search, view, organize and respond to real-time data without the intervention of a central managing agent. Providers can determine how their sensor data is found. Thingful increases the legibility, accessibility, and usability of data streams that cannot be found or used otherwise.
The choices of users as starting point
Thingful democratizes data availability, but it is not yet an answer to the question of whether data helps in making decisions. Examples also include Cinder, a mixed-reality interface for a Building Management System, and Starling Crossing, a responsive intersection that adapts its configuration to the nature of the error participants present.
Cinder has the shape of a virtual cat, whose well-being runs parallel to climate control and the state of the sustainability systems at Trumpington Community College in Cambridge, UK. Cinder is a partly interactive mascot, partly avatar in front of the building and reacts in real time to sensors in the environment and to the people present. People can play with Cinder in the building’s atrium, where she appears on a large augmented reality mirror. If the solar cells emit more energy on the roof, she is more playful than when it is heavily cloudy. If too many doors and windows are left open, resulting in too much energy being lost, she behaves differently than if everything is OK. For the students of Trumpington College, Cinder leads to a great sense of shared ownership and responsibility. Cinder’s behaviour also directly shows the impact of their own actions.
Starling Crossing uses cameras to monitor the use of a crossroads and adjusts the familiar stripes and signs on the road using a large number of LEDs incorporated into the surface of the road, giving priority to pedestrians’ and cyclists’ safety. In the presence of a large number of pedestrians, a wide zebra crossing is projected at the safest point for crossing. If a pedestrian suddenly crosses in the blind spot of a car, the LEDs light up in a pattern warning the car; in wet weather or fog, buffer zones are projected around the pedestrian crossing. In the long term, the system learns the preferences of pedestrians, but always crosses them diagonally at the metro exit, and is becoming increasingly better at creating the optimum situation for pedestrians.
The system does not tell pedestrians what to do, it is the other way round: pedestrian decisions are used here as a starting point for data analysis, on the basis of which direct intervention in traffic situations is carried out.
The Urban Innovation Toolkit
In order to arrive at these designs, Umbrellium uses their Urban Innovation Toolkit – a method to develop urban technology projects with groups of users. The discussion that followed the story of Usman Haque was based on the questions raised by the toolkit.
The reason for developing the toolkit was a number of recurring issues, which Umbrellium recognized from the development of urban technology projects. Issues that followers of the DATA studio will also appear very well known:
Urban technology projects often start from the possibilities of technology, or from the conditions for a subsidy application for research, rather than from an urban question or problem. Many projects forget to involve certain key stakeholders in the project in the conversation; forget to focus on a specific, named impact, and do not evaluate. The toolkit was developed to learn to avoid these issues and in fact, consists of the structure for a number of conversations that should precede any data collection.
– What problem do you actually have to deal with?
– What impact would you like to have?
– How do you measure this impact?
– What decisions can be made and who are the ones who can make them?
Involve all stakeholders
Each question is discussed with as many stakeholders as possible, and each meeting ends with the question of which stakeholder is not yet represented, whereby people are invited to the next meeting.
Sufficient iteration, but not too often
Sufficient iteration turns out to be crucial. In practice, five rounds usually yield the best results. Each follow-up interview then takes the results of the previous conversation as the starting point. Every iteration comes down to a re-framing: what is the question behind the answer we formulated last time? In this way, other stakeholders can also come into play. The moment to stop iteration is determined by the coming together of the ambition around demand and the capacity to turn the ideas into action.
Collecting data is always political
The demand for impact comes down to a collective exercise in meaningfulness. What impact determines whether the project is effective and sufficiently successful? How do you measure this impact? Empirical data, derived from stakeholders’ experience, prove to be more decisive than objective data. Data can support stories about experiences, but it is essential that all those involved understand that stakeholders’ experience ultimately determines what the impact is – whatever the sensors may say.
In other words, the collection of data is inherently a political process. Politics precedes data collection and plays an emphatic role in the presentation of data. Collecting data is useful and increases the quality of a project, but only if it makes participants or stakeholders subject, observer, rather than object to be read out. If those involved have a better understanding of the possible relationships between data and politics, this gives them a better focus on what should be done in a project. Citizens must have an active role in collecting and presenting data, being aware that these are all political (and therefore not ‘purely scientific’) processes.
Modulation & the sweet spot
It is precisely this political dimension and the carefulness required to arrive at a concrete perspective of action with the toolkit that demands that the conversations are well moderated by someone who is not a stakeholder in the problem. An important task of the moderator is to keep an eye on the sweet spot, the best possible link between what the nature of the problem is and which action can actually be shaped in the project. The ‘sweet spot’ is always very well situated: it concerns this place and time, with these stakeholders, but the toolkit can work on all scales, from the scale of the city, with major stakeholders such as the municipality, construction and technology companies, and of course residents, to the scale of the intersection or the park, with mainly local stakeholders, and once again natural residents.
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