The statistical measurement of cities data lauded by proponents do the smart city such as smartplanet in this interview by Melanie D.G. Kaplan is yet to be accepted by designers and architects although the commercial and government sectors have been quick to leap on the bandwagon , seeking objective proof that their planing policies and implementations are “going to work”, my concern is the causal relationships porposed by these statistics is seldom able to hold up under the scrutiny of post implementation analysisi – mcuh of this “what works” inone place has no relationship to what owrksk intanother, as has been discovered by scinece in its ever expandintg goal and failure to map diversity and complexity in natural or large scale man-made systems.
Urban scaling shows that as cities expand, the trajectories of measures such as wages, crime and carbon dioxide emissions are somewhat surprising.
Hyejin Youn is a postdoctoral fellow working with theSanta Fe Institute’s Cities and Urbanization team, which includes Luis Bettencourt and Geoffrey West. Her expertise is in human behavior and statistical analysis on population distribution. She said the best way to create sustainable growth in our cities is to understand the mathematics behind their growth. Excerpts of our recent conversation are below.
Why is it important to study urban areas?
More than half of all people now live in urban areas, which
increased up to 80 percent in about 40 years, as reported by the United Nations. The rapid urbanization implies that understanding urban dynamics is a key to our sustainability.
Studying cities reveals the advantages and disadvantages of urban
life. We know cities usually have more educated, wealthy people but also have more crime. However we only recently started looking quantitatively at by how much the cities are educated and dangerous.
In the past we haven’t been able to compare cities’ performances over time because data were not available across cities. Rather, data were gathered over time for a particular city or groups of cities and this data was inconsistent with the data gathered for other cities. Cross-sectional data for many cities allows us to compare many cities, systematically and consistently, to get a better idea of trends and patterns. This allows us to create a generic baseline for cities for, say, carbon dioxide emissions. From that we can then see the significant or unique features of a particular city, what urban scaling reveals. We can also think in a more macroscopic way than those conventional studies have allowed.
The relationship from these cross-sectional data analyses we now know as urban scaling — a power-law that describes how quantities increase with city size. My colleagues Luis Bettencourt and Geoffrey West of Santa Fe Institute are pioneers of these beautiful urban scaling forms. The power-law is the simplest yet most powerful, as the name indicates, mathematical form. This power-law fascinates lots of researchers because it reveals mathematical relationships at many scales of cities.
How are you using urban scaling in your work?
According to urban scaling, cities whose population doubles in size will have 15 percent more than twice socio-economic quantities such as wages, GDP, number of patents produced and number of educational and research institutions. The significance of this work comes from this systematic increase. The growth of cities also have negative aspects like more crime and more epidemic disease.