Big Data for Good Cities

This essay considers Big Data and its research potential from a normative point of view; specifically, from the perspective of urban planning and design.

First, some background.

There is a kind of urbanism that is now firmly planted in the urban planners’ consciousness. It replaces the twin calamities of suburban sprawl and urban decay by advancing unambiguous normative ideals. This follows decades of research and debate, whereby, drawing from the writings of Kevin Lynch, Jane Jacobs, Christopher Alexander and others, relativism has been replaced with more durable standards about what good cities are supposed to be.

The normative proposal can be summarized by two meta-principles: human scale, which means pedestrianism rather than car dependence; and spatial equity, which means access to the good stuff and distance from the bad stuff—for everyone. As good stuff and bad stuff are never spatially regular, diversity at the neighborhood or at least district level is necessary. While there is strong consensus that achievement of these two principles will help sustain the planet, application is not formulaic. Human scale and spatial equity apply differently in Detroit versus New York, in Peoria versus Miami, in center city versus outlying suburb.

The need for human scale and spatial equity are, in other words, settled subjects. One hopes that Christakis’ admonition that social science lacks the ability to “declare victory and move on” does not apply to urban planning.

A whole cadre of urban design proposals has been enlisted to advance these ideals. There are the first tier strategies of connectivity, geographic access, legibility, diversity, and smallness, alongside a second tier that seeks to balance such things as coherence and order with authenticity and informality (note to social scientists: mixed housing is but one strategy in the toolbox). Urban planners engage citizens to find consensus on physical parameters, i.e., to see how far human scale and spatial equity can be pushed politically. And yet, the application of normative principles does not mean that democratic processes are supplanted. On the contrary, normative ideals increase accountability and democratic participation by bringing crucial debates out into the open. This is superior to a system in which values about what cities ought to be are dealt with in a confusing, ad hoc, incremental manner, providing multiple openings for the public interest to be sabotaged by private power.

Further, it is generally agreed that implementation requires two methods: self-determination (meaningful engagement leading to empowerment) and public intervention (laws, codes, investments, and other policies). These are seemingly contradictory, but both top-down and bottom-up are needed. The problem is that often top-down investment/control fails to promote human scale and spatial equity, while self-determination—without which human scale and spatial equity are ineffectual—may either be powerless, or may be dominated by NIMBYism.

Recapping: we have two normative ends (human scale, spatial equity), and two means of effectuation (bottom-up, top-down). All are in constant tension and misalignment, not only ends battling means, but human scale battling spatial equity, and bottom-up battling top-down. For example:

- Human scale may compromise spatial equity (e.g., walkable neighborhoods are increasingly the least affordable)
- State investment may compromise human scale (e.g., funding for road “improvements” often undermines pedestrianism)
- Spatial equity may compromise self-determination (e.g., affordable housing in well-serviced neighborhoods may raise strong objections from residents)

And so on.

Could big data help procure better methods of self-determination, or give us stronger insight about the effects of intervention? Could it go even further and offer some enlightenment regarding the battle between, and within, means and ends?

Enlisting big data to improve methods of self-determination is further along. There is now a global movement afoot to leverage social media toward urban planning goals—less than an Arab Spring, but more than fixing potholes. There is a research angle, too. For example, Justin Hollander’s Urban Attitudes Lab uses Twitter data to categorize “sentiments” that can be used to help understand what people want in their communities. It is a significant innovation for garnering community input, well beyond static surveys or neighborhood meetings at which there are more planners than residents. If orchestrated outside the bounds of official planning-dom, it may even be labeled a tool of empowerment, or at least pro-technology work that is not anti-community. More research is needed to understand exactly how big data translates to better capacity-building, and how it could lead to the proliferation of more humanly scaled, inclusive places.

Untapped as of yet is the use of big data to provide a window on the complex relationship among public intervention, neighborhood change, and the goals of human scale and spatial equity. What laws, codes, investments, and other interventions move neighborhoods toward increased human scale and spatial equity, and what interventions move them in the opposite direction? And further, why do some neighborhoods have the qualities of human scale and spatial equity while others are in the process of losing them? As neighborhoods transition in and out of their normative position, what is happening “on the ground” in terms of flows and exchanges of human and other capital? It seems big data might have a lot to tell us about these dynamics. To put a face on it: in the graphic below (Figure 1), can big data reveal something about the “operating system” of the places in red versus the places in green?

All the questions big data might answer—how people move around, whom they network with, how the housing market changes, where people spend money and on what—could be directed toward deeper understanding of neighborhood change and policy effects vis-à-vis the dominant normative position of urban planning and design. Would this understanding motivate the political will that seems to be lacking to grow more places that are human-scaled and spatially equitable?

 

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References: 

1. Alexander, Christopher. 1979. The Timeless Way of Building. New York: Oxford University Press; Jacobs, Jane. 1961. The Death and Life of Great American Cities. New York: Random House; Lynch, Kevin. 1981. A Theory of Good Urban Form. Cambridge: MIT Press. A discussion of the normative turn is presented in Talen, E. and C. Ellis. “Beyond Relativism: Reclaiming the Search for Good City Form.” Journal of Planning Education and Research 22, no. 1 (September 1, 2002): 36–49.

2. Christakis, Nicholas. “Let’s Shake Up the Social Sciences - NYTimes.com,” July 19, 2013. http://www.nytimes.com/2013/07/21/opinion/sunday/lets-shake-up-the-social-sciences.html.

3. “Urban Attitudes Lab | Department of Urban and Environmental Policy and Planning, Tufts University.” Accessed December 11, 2014. http://sites.tufts.edu/ualab/.

4. The argument that the “smart city” is pro-technology and anti-community is made in “My Thoughts on the Smart City - by Rem Koolhaas.” Neelie KROES. Accessed December 11, 2014. https://ec.europa.eu/commission_2010-2014/kroes/en/content/my-thoughts-smart-city-rem-koolhaas.

About the Author
Emily Talen is professor in the School of Geographical Sciences and School of Sustainability at Arizona State University.
Posted on September 21st, 2015.