Is urban theory changing big data?
The event began with the GSD’s Antoine Picon offering a historical perspective on data (big and small) amid the many networks and flows of cities, then MIT Media Lab’s Cesar Hidalgo offered examples of his work in urban science to first crowdsource perception of the visual appeal of the “safety” of public space using Google Streetview, work that he has now automated through machine learning. Carlo Ratti of MIT’s Sensable City Lab then argued, using examples from his lab’s work, that big data can offer a means to design cities in new ways. I presented last; my comments are posted below. The debate proved lively, to say the least. I want to thank Andres Sevtsuk and his researcher Kevin Chong of the GSD’s City Form Lab for inviting me to participate, and Rob Kitchin, Taylor Shelton, and Neil Brenner’s thoughts on approaching the topic.
To answer Is big data changing urban theory?, I conducted a small counting exercise of “big data” in Google Scholar, searching publications from the first popular use of the term in Wired Magazine in 2008, which also offers about a decade to look at.
So from 2008–2018 the terms:
-“big data” = 145,000 results
-“urban theory” = 14,000 results
-“big data”+”urban theory” = 406 results
3% of theory-driven urban scholarship engages with the term “big data” in any capacity
This measurement is certainly imprecise, since much urban theory scholarship doesn’t necessarily include the keywords “urban theory”, so I looked at the top ten Urban Studies journals in the Scimago Journal Ranking, where I found 75 total articles that included the keywords “big data”. So I’d argue that no, big data is not changing urban theory as yet. Will it do so in the near future? Certainly, but if the best judge of tomorrow is yesterday, not to a great extent.
If we consider two of the primary urban theory debates in recent years, first to consider cities and urbanization in and beyond the majority world as ‘ordinary’, ‘everyday’ and worthy of comparison without necessarily linking back to the North Atlantic colonial metropoles, and, second, the imperative to consider the urbanization of the planet, what then does big data offer to advance understandings of these theories? Big data can expand our ability to map resource flows, population concentrations, economic agglomeration and social polarization. And this certainly has scholarly value. However, the usefullness of big data in and between cities can be hindered by the lack of uniformity of cities, the informality of its neighborhoods, and even the general absence of quantifiable data itself. Which then raises the question of what big data can offer for better understandings of cities across the global North and South that is not already known. That resources are unequally distributed, cities are heavily polluted and bound by traffic, that the urbanized planet is composed of flows of resources, goods, information, and bodies that stretch further and further. Big data can absolutely help us quantify these metrics, and in so doing perhaps affect policy decisions, governance, and politics in a productive fashion.
And yet, critical urban theory emerged out of the social upheavals of the 1960s with the calls for the right to the city, for social justice, and to directly confront the longstanding inequities and unevenness of development in cities around the world.
To that end, What is the urban theory that big data will advance? Where is it? Both figuratively, in that What cities can best be studied with big data? The global cities like New York or London that are already over-studied? And even within those cities, where will studies utilizing big data take place? In the central business districts and in wealthier neighborhoods, or in the poor and marginalized neighborhoods most in need of change?
And lastly, how will big data advance social justice? Can big data better articulate the right to the city? I would argue, No. ‘Analog’ and ‘small’ data has shown us for at least fifty years what, where, and how urban theory can contribute to more liveable, more just cities. The research opportunities that big data offers today do not offer enough to urban theory. That said, the increasing importance of automation such as self-driving cars and drones, as well as the increasing importance of algorithms and artificial intelligence in the workforce, medical fields, and higher education, layered on top of the ongoing interest in smart city digital platforms, speaks to the imperatives of urbanists to engage with and use big data.
In conclusion I will flip the question to ask, “How is urban theory changing big data?” and offer a short example from my own research.**
Camden, New Jersey is one of the poorest cities in the United States. In recent years the city has installed a automated, smart city surveillance and policing network. This system relies on numerous ‘big data’ algorithmic technologies such as Shotspotter gunshot triangulation microphones, automated license plate readers, a citywide surveillance camera network, and body cameras for police officers. This ‘smart’ surveillance was implemented in anticipation of economic redevelopment, seen here with the US headquarters of American Water, who manages Camden’s municipal water supply in a public private partnership with the city. These big data policing technologies facilitated, in part, the decision of the corporation to relocate from a nearby suburb into a new building at the heart of a very poor city (massive tax breaks also helped their decision). While this move and other corporations’ moves brings much-needed revitalization to Camden’s downtown, the city still relies on a 19th century sewer system that struggles with widespread flooding during rainstorms, where raw sewage pools on the streets.
This sign warning of sewage overflow into the Delaware River sits directly outside American Water’s headquarters, highlighting the disconnect between economic development and widespread change in the city. This juxtaposition between the usefulness of big data to understand urban issues like crime or flooding and bring about transformation like in Camden, and the imperative of critical urban theory to shine light on inequalities such as those found in this city leads me to question what big data offers to urban theory.
Which then leads to a reworking of the core social justice question that critical urban theory asks in Camden’s case, of: “Who benefits from urban transformation?” to then ask: “For whom does big data change cities?”. In Camden’s case, data-driven digital infrastructures may have facilitated some new jobs and a transformed urban core, but the sewers have not yet been fixed, nor are there plans to do so.
**Wiig, A. (2017, early release). Secure the city, revitalize the zone: Smart urbanization in Camden, New Jersey. Environment and Planning C: Politics and Space. p. 1–20 https://doi-org/10.1177/2399654417743767