Urban Science: Integrated Theory from the First Cities to Sustainable Metropolises

Lobo, José; Alberti, Marina; Allen‐Dumas, Melissa; Arcaute, Elsa; Barthélemy, Marc; Tapia, Luis Antonio Bojórquez; Brail, Shauna; Bettencourt, Luís M. A.; Beukes, Anni; Chen, Weiqiang; Florida, Richard; González, Marta C.; Grimm, Nancy B.; Hamilton, Marcus J.; Kempes, Chris; Kontokosta, Constantine E.; Mellander, Charlotta; Neal, Zachary P.; Ortman, Scott G.; Pfeiffer, Deirdre; Price, Michael Holton; Revi, Aromar; Rozenblat, Céline; Rybski, Diego; Siemiatycki, Matthew; Shutters, Shade T.; Smith, Michael E.; Stokes, Eleanor; Strumsky, Deborah; West, Geoffrey B.; White, Devin; Wu, Jingle; Yang, Vicky Chuqiao; York, Abigail M.; Youn, Hyejin

doi:10.2139/ssrn.3526940

Cited by 34 publications

(40 citation statements)

References 120 publications

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“…Fostered by a recent deluge of highly-detailed city data, researchers from diverse disciplines such as geography, economics, or physics have devoted ongoing efforts in identifying and understanding fundamental principles and regularities underlying urban systems [ 1 , 6 , 12 – 14 ]. While most of these works are either concerned with Zipf’s law [ 15 – 25 ] or urban scaling [ 26 – 34 ], very few have tackled the relationship between both.…”

Section: Introductionmentioning

confidence: 99%

Association between population distribution and urban GDP scaling

et al. 2021

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Urban scaling and Zipf’s law are two fundamental paradigms for the science of cities. These laws have mostly been investigated independently and are often perceived as disassociated matters. Here we present a large scale investigation about the connection between these two laws using population and GDP data from almost five thousand consistently-defined cities in 96 countries. We empirically demonstrate that both laws are tied to each other and derive an expression relating the urban scaling and Zipf exponents. This expression captures the average tendency of the empirical relation between both exponents, and simulations yield very similar results to the real data after accounting for random variations. We find that while the vast majority of countries exhibit increasing returns to scale of urban GDP, this effect is less pronounced in countries with fewer small cities and more metropolises (small Zipf exponent) than in countries with a more uneven number of small and large cities (large Zipf exponent). Our research puts forward the idea that urban scaling does not solely emerge from intra-city processes, as population distribution and scaling of urban GDP are correlated to each other.

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Section: Introductionmentioning

confidence: 99%

Association between population distribution and urban GDP scaling

et al. 2021

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“…We build on this previous study by using a larger dataset that includes several countries, and only rely on urban morphology predictors that are available at scale. The morphology of neighborhoods and road networks has been described quantitatively in the fields of urban science [25][26][27]. These studies developed a quantitative understanding of urban form, based, for example, on complex network analysis, and provide metrics from which machine learning algorithms can learn.…”

Section: Methodsmentioning

confidence: 99%

“…From 2D urban morphology data on buildings and street networks, we create 152 features, making use of domain knowledge from urban science, in particular urban morphology and spatial network studies e.g. [25,26,43]. Below, we provide rationales for different feature groups and their relation to building height.…”

Section: Feature Engineeringmentioning

confidence: 99%

Learning from urban form to predict building heights

et al. 2020

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Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks. The method allows to predict building heights for regions where no dedicated 3D models exist currently. We train our model using building data from four European countries (France, Italy, the Netherlands, and Germany) and find that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building. A test on the German state of Brandenburg shows that our model predicts building heights with an average error well below the typical floor height (about 2.5 m), without having access to training data from Germany. Furthermore, we show that even a small amount of local height data obtained by citizens substantially improves the prediction accuracy. Our results illustrate the possibility of predicting missing data on urban infrastructure; they also underline the value of open government data and volunteered geographic information for scientific applications, such as contextual but scalable strategies to mitigate climate change.

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“…Shocks often damage parts of a city’s structure that may require long periods of time and substantial resources to repair. Thus, it is imperative that we develop a deeper understanding of urban structures, how those structures respond to shocks, and how policy makers might alter those structures to enhance a city’s resilience [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Inferring Networks of Interdependent Labor Skills to Illuminate Urban Economic Structure

Shutters

Waters

2020

Entropy

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Cities are among the best examples of complex systems. The adaptive components of a city, such as its people, firms, institutions, and physical structures, form intricate and often non-intuitive interdependencies with one another. These interdependencies can be quantified and represented as links of a network that give visibility to otherwise cryptic structural elements of urban systems. Here, we use aspects of information theory to elucidate the interdependence network among labor skills, illuminating parts of the hidden economic structure of cities. Using pairwise interdependencies we compute an aggregate, skills-based measure of system “tightness” of a city’s labor force, capturing the degree of integration or internal connectedness of a city’s economy. We find that urban economies with higher tightness tend to be more productive in terms of higher GDP per capita. However, related work has shown that cities with higher system tightness are also more negatively affected by shocks. Thus, our skills-based metric may offer additional insights into a city’s resilience. Finally, we demonstrate how viewing the web of interdependent skills as a weighted network can lead to additional insights about cities and their economies.

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Urban Science: Integrated Theory from the First Cities to Sustainable Metropolises

Cited by 34 publications

References 120 publications

Association between population distribution and urban GDP scaling

Association between population distribution and urban GDP scaling

Learning from urban form to predict building heights

Inferring Networks of Interdependent Labor Skills to Illuminate Urban Economic Structure

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