Week 6 – City Dashboards & Open Data

City dashboards are popping up all over the world, as illustrated below in the example from London.

“Big data” like the one shown above is being used by governments to define and justify strategies, as well as course-correct when necessary. “Open data” is now available to the general public, redefining the relationship between citizenry and government. Organizations like 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.” Residents of London can feel empowered to make decision of their everyday lives by accessing the information from these indicators. City managers and policy-makers use evidence-based tools like these dashboards that visualize how the city is performing.
The straightforward approach seems to be that data is objective, data is a reflection of reality. Kitchin however warns us that even though indicators enable informed decision-making, we must acknowledge their limitations. Facts and figures are not enough to fully understand a city, and by no means, do the above indicators provide a comprehensive understanding of the city of London. Indicators like Tube updates, traffic, pollution and weather updates address issues within their scope. And to compare a city like London through its dashboard with another city becomes, as Kitchin states, a zero-sum game as “cities are rated and ranked, with only one city being able to occupy each place, so that despite the fact that they may have improved their performance they are still lowly ranked vis-à-vis other locales.” (Kitchin et al, 2015, page 19). Every city is different, has different goals and is in different points of maturity or historical existence. Similar city dashboards are available across the United Kingdom. However, benchmarking Edinburgh or Glasgow against the indicators appearing in the London dashboard would be like comparing apples and oranges, and it would be a zero-sum game, with the gains of one city being taken from the other in the ranking.
In the image below, we see updated information coming in from different points of London. Weather, bicycles, stocks, traffic, tube, air pollution. These are the slices and perspectives chosen to understand the current status of the city. Kitchin also acknowledges that data does not only reflect what a city is, but also produces and frames these cities by highlighting certain issues while hiding others. “A dashboard seeks to act as a translator, not simply a mirror, setting the forms and parameters for how data are communicated and thus what the user can see and engage with.” (Kitchin et al, 2015, page 20).
But according to Leszczynski, big data like the one dashboarded below by the Mayor of London provide inputs for future-ing, or speculating about what the city could look like in the future: “(Big) data about/from cities likewise feed a speculative security calculus that projects urban derivatives onto ‘an array of uncertain futures’ in the interests of securitizing against that very uncertainty by rendering it actionable in the present through various kinds of preemptive urban interventions” (Amoore in Leszczynski, 2016, page 1693). The gang crime indicators do not provide a solution, but an interpretation of reality. They can shape decisions moving forward with regards to crime prevention and public policy. They shape the future of the city by defining for example what investments should be allocated for high crime areas. Is it more schools and hospitals, or is it more policing and security cameras? Is this information enough for business to decide their next office location or their next investment?
Dashboards, indicators and open data provide transparency through big data. They can make citizens’ lives easier by giving them real-time updates, as well as provide city planners access to evidence-based decisions. This is extremely valuable. However, they cannot be viewed an impartial mirror of society. Indicators are not developed in a vacuum, devoid of ideological intent. The decisions made from these dashboards shape cities and define their future. Through preemptive urban interventions, both governments and citizens set out to address uncertain futures by interpreting data visualizations that showcase slices of a city’s identity. No matter how many slices are monitored and tracked, it can never add up to the whole pie.

2 thoughts on “Week 6 – City Dashboards & Open Data

  1. Hi Mercedes, reading your blog reminded me of some of the popular benchmarking projects which reveal every so often the ‘world’s most livable cities’ or most ‘expensive’ or ‘most polluted’. As Kitchin et al (2015) argue and as you highlight, we shouldn’t take these declarations at face value but rather be aware of the shortcomings such as those that “decontextualise a city from it’s history, political economy, the wider set of social , economic and environmental relations that frame its development…interconnections and interdependencies that stretch out over space and time”. (P19). I think though that despite this, some of these studies and benchmarking data have taken pride of place in many of society’s fora and international media to the extent that it would take a lot of public education and awareness and maybe requiring firms which release such data to publish prominent disclaimers to reinforce understanding of such data’s limitations. Cities’ reputations, economies and industries like tourism are affected by such ‘branding’ and I believe open data and public education can help promote more unbiased understanding.

  2. As I read your post, particularly the part about the limitations of using open dashboards to rank and rate cities, I recalled my own experience of working for local government. I can certainly envisage a scenario whereby one council is using data from another council as a comparison (while not realising the malady of the ‘zero-sum-game’ they are playing), and trying to ‘beat’ other cities / towns on all fronts (while constantly asking why they are unable to). I agree with your opinion on publishing disclaimers to reinforce the understanding of data’s limitations but, again, given my own experience of how (at least North East English) councils operate, I imagine the reaction to such knowledge would be then something akin to: ‘then what’s the point in using them?’. I too believe that until the level of understanding you highlighted is achieved, that such data will continue to be used in different ways in different places, and that the understanding will develop through significant trial and (for the third time, in my experience) error.

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