Posted May 14th 2014 at 1:32 pm by
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Cities are Unique and so are Brains: Lessons from City Science

Image Courtesy of Flickr.


In the previous installment of CoLab Radio’s City Science series we examined the City Science approach to energy and the Urban Metabolism. This week’s post concludes our series with a few final insights.

I began this series expecting to deliver an answer to what kind of city would emerge from an ideal collaboration between scientists and designers. If quantitative and qualitative thinking could harmonize into a streamlined city-design process, all our needs would be met; we could have our cake and eat it too.

Throughout the series I’ve catalogued the emergent and historic trends of City Science and uncovered the acute complexities of such a dialogue. In the process I’ve examined planning attitudes towards quantitative analysis of cultural constructs, ethics and actors in terms of data accessibility and power, and applications of City Science initiatives in spatial planning and energy.

Consider the file unzipped! Here are my conclusions:

1. We shouldn’t turn a blind eye to big data. Planning academics and professionals need to change their attitudes towards quantitative methods in order to reap the latent benefits of big data in the 21st century and beyond. In our roles as mediators, this means being able to translate the technical language of our collaborators into good urban design and planning policy. In the near future, I believe these collaborators will include scientists from a variety of disciplines – ranging from programming to physics – who have consistently demonstrated interest in urban issues. Let’s harness their capacity and get comfortable with computation.

2. Analysis is only as good as the data it uses. Urban experts are in a unique position to specify what constitutes as good, reliable urban data and what doesn’t. Planners should consider establishing these values within their local contexts in collaboration with their communities. Establishing best practices for data collection and processing is a key role for planners in the 21st century technological discourse.

3. We need to identify relationships to understand how cities work. As we saw in our spatial planning segment, dynamic complex systems are understood in terms of interdependencies, not causality. Urban phenomena like gentrification can’t be mapped to any one causal factor; instead they are emergent properties of dynamic, multi-scalar interactions between countless factors in real estate, economics, and culture. If we accept that cities meet the criteria of complexity, as scholarly intuitions throughout history suggest, then we must explore how to process them as such. Modeling techniques used in the life sciences to identify and model relationships between components of organic systems is a powerful means to this end and a largely unexplored application.

4. Cities are unique, and so are brains. Establishing general principles, theories or even laws about how cities function doesn’t imply that local context isn’t important. Take the human brain as an analogy. Each individual brain is unique, hosting a distinctive set of experiences, intelligence, memories, feelings, personality constructs, and so on. But on the operating table, a brain is a bit like a car: a skilled mechanic can repair problems with a standard knowledge of working parts. This standard knowledge was developed over years of systematic experimentation, and scales to our technological capacity at any given point in history. Accepted frameworks of knowledge are required to develop treatments for each new patient, but individual nuances are key to their refinement. Likewise, we shouldn’t shy away from analyses that establish generalized laws for how cities operate. We can use global insights while keeping in mind the limits of the model.

5. Knowledge is never fixed. The peer-review process for scientific publishing is based on this premise. The body of scientific knowledge is a feedback loop: it evolves as information is reintroduced or discarded from a working model. City Science can contribute to healthy urbanism by establishing a system for evaluating and standardizing our body of knowledge about cities. Citizen participation in this process is invaluable. Open government practices and crowdsourcing platforms, while admittedly undeveloped, are ripe with potential for improving public contribution to the body of City Science knowledge.

So what kind of city emerges from City Science? The answer to this question is gleaned from designing a robust and mindful City Science process. Using tools and methodology from the life sciences we can make powerful inquiries into the nature of city function, but only with a compassionate editing eye can we use the results to make cities better for future communities.

Emily Royall is an Master of City Planning candidate in City Design and Development at MIT. 

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