Universities Scale Like Cities

Recent studies of urban scaling show that important socioeconomic city characteristics such as wealth and innovation capacity exhibit a nonlinear, particularly a power law scaling with population size. These nonlinear effects are common to all cities, with similar power law exponents. These findings mean that the larger the city, the more disproportionally they are places of wealth and innovation. Local properties of cities cause a deviation from the expected behavior as predicted by the power law scaling. In this paper we demonstrate that universities show a similar behavior as cities in the distribution of the ‘gross university income’ in terms of total number of citations over ‘size’ in terms of total number of publications. Moreover, the power law exponents for university scaling are comparable to those for urban scaling. We find that deviations from the expected behavior can indeed be explained by specific local properties of universities, particularly the field-specific composition of a university, and its quality in terms of field-normalized citation impact. By studying both the set of the 500 largest universities worldwide and a specific subset of these 500 universities -the top-100 European universities- we are also able to distinguish between properties of universities with as well as without selection of one specific local property, the quality of a university in terms of its average field-normalized citation impact. It also reveals an interesting observation concerning the working of a crucial property in networked systems, preferential attachment.     

Urban Scaling of Cities in the Netherlands

We investigated the socioeconomic scaling behavior of all cities with more than 50,000 inhabitants in the Netherlands and found significant superlinear scaling of the gross urban product with population size. Of these cities, 22 major cities have urban agglomerations and urban areas defined by the Netherlands Central Bureau of Statistics. For these major cities we investigated the superlinear scaling for three separate modalities: the cities defined as municipalities, their urban agglomerations and their urban areas. We find superlinearity with power-law exponents of around 1.15. But remarkably, both types of agglomerations underperform if we compare for the same size of population an agglomeration with a city as a municipality. In other words, an urban system as one formal municipality performs better as compared to an urban agglomeration with the same population size. This effect is larger for the second type of agglomerations, the urban areas. We think this finding has important implications for urban policy, in particular municipal reorganizations. A residual analysis suggests that cities with a municipal reorganization recently and in the past decades have a higher probability to perform better than cities without municipal restructuring.     

Impact of the Economic Structure of Cities on Urban Scaling Factors: Implications for Urban Material and Energy Flows in China

We explore the population‐scaling and gross domestic product (GDP)‐scaling relationships of material and energy flow (MEF) parameters in different city types based on economic structure. Using migration‐corrected population data, we classify 233 Chinese city propers (Shiqu) as “highly industrial” (share of secondary GDP exceeds 63.9%), “highly commercial” (share of tertiary GDP exceeds 52.6%), and “mixed‐economy” (the remaining cities). We find that, first, the GDP population‐scaling factors differ in the different city types. Highly commercial and mixed‐economy cities exhibit superlinear GDP population‐scaling factors greater than 1, whereas highly industrial cities are sublinear. Second, GDP scaling better correlates with city‐wide MEF parameters in Chinese cities; these scaling relationships also show differences by city typology. Third, highly commercial cities are significantly different from others in demonstrating greater average per capita household income creation relative to per capita GDP. Further, highly industrial cities show an apparent cap in population. This also translates to lower densities in highly industrial cities compared to other types, showing a size effect on urban population density. Finally, a multiple variable regression of total household electricity showed significant and positive correlation with population, income effect, and urban form effect. With such multivariate modeling, the apparent superlinearity of household electricity use with respect to population is no longer observed. Our study enhances understanding of MEFs associated with Chinese cities and provides new insights into the patterns of scaling observed in different city types by economic structure. Results recommend dual scaling by GDP and by population for MEF parameters and suggest caution in applying universal scaling factors to all cities in a country.     

Distance to the Scaling Law: A Useful Approach for Unveiling Relationships between Crime and Urban Metrics

We report on a quantitative analysis of relationships between the number of homicides, population size and ten other urban metrics. By using data from Brazilian cities, we show that well-defined average scaling laws with the population size emerge when investigating the relations between population and number of homicides as well as population and urban metrics. We also show that the fluctuations around the scaling laws are log-normally distributed, which enabled us to model these scaling laws by a stochastic-like equation driven by a multiplicative and log-normally distributed noise. Because of the scaling laws, we argue that it is better to employ logarithms in order to describe the number of homicides in function of the urban metrics via regression analysis. In addition to the regression analysis, we propose an approach to correlate crime and urban metrics via the evaluation of the distance between the actual value of the number of homicides (as well as the value of the urban metrics) and the value that is expected by the scaling law with the population size. This approach has proved to be robust and useful for unveiling relationships/behaviors that were not properly carried out by the regression analysis, such as the non-explanatory potential of the elderly population when the number of homicides is much above or much below the scaling law, the fact that unemployment has explanatory potential only when the number of homicides is considerably larger than the expected by the power law, and a gender difference in number of homicides, where cities with female population below the scaling law are characterized by a number of homicides above the power law.     

Scaling indicator and planning plane: An indicator and a visual tool for exploring the relationship between urban form,energy efficiency and carbon emissions

Ecosystems and other naturally resilient systems exhibit allometric scaling in the distribution of sizes of their elements. In this paper we define an allometry inspired scaling indicator for cities that is a first step toward quantifying the stability borne of a complex systems’ hierarchical structural composition. The scaling indicator is calculated using large census datasets and is analogous to fractal dimension in spatial analysis. Lack of numerical rigor and the resulting variation in scaling indicators – inherent in the use of box counting mechanism for fractal dimension calculation for cities – has been one of the hindrances in the adoption of fractal dimension as an urban indicator of note. The intra-urban indicator of scaling in population density distribution developed here is calculated for 58 US cities using a methodology that produces replicable results, employing large census-block wise population datasets from the 2010 US Census and the 2007 US Economic Census. We show that rising disparity – as measured by the proposed indicator of population density distribution in census blocks in Metropolitan Statistical Areas adversely affects energy consumption efficiency and carbon emissions in cities and leads to a higher urban carbon footprint. We then define a planning plane as a visual and analytic tool for incorporation of scaling indicator analysis into policy and decision-making.     

The Scientific Competitiveness of Nations

We use citation data of scientific articles produced by individual nations in different scientific domains to determine the structure and efficiency of national research systems. We characterize the scientific fitness of each nation—that is, the competitiveness of its research system—and the complexity of each scientific domain by means of a non-linear iterative algorithm able to assess quantitatively the advantage of scientific diversification. We find that technological leading nations, beyond having the largest production of scientific papers and the largest number of citations, do not specialize in a few scientific domains. Rather, they diversify as much as possible their research system. On the other side, less developed nations are competitive only in scientific domains where also many other nations are present. Diversification thus represents the key element that correlates with scientific and technological competitiveness. A remarkable implication of this structure of the scientific competition is that the scientific domains playing the role of “markers” of national scientific competitiveness are those not necessarily of high technological requirements, but rather addressing the most “sophisticated” needs of the society.     

Scaling the neural TSP algorithm

Recent attempts to find a procedure for scaling neural shortest path computations have failed, leading to speculation that the basic optimization method is unreliable and without justification. We report the first effective procedures for scaling such algorithms and demonstrate their validity. Independently of the scaling, an unc onventional approach to neural simulation is described which surprisingly has much the same effect as simulated annealing, but without the need for adjustable run-time parameters (such as temperature).     

The trade-off between a high and an equal biological standard of living—Evidence from Germany

Following German re-unification, East Germany moved from a state-socialist to a market-based economic system. Using West Germany as a “control group”, we examine how the change affected the level and the equality of the biological standard of living. We find that before unification, East Germany had a lower but somewhat more equally distributed biological standard of living than the West. After unification, East Germany rapidly caught up in terms of height but at the expense of equality. This suggests that a trade-off exists between a high and an equally distributed biological standard of living. Unlike previous research, we find that West Germany's pre-unification height advantage was smallest in towns with 5000-20,000 inhabitants and largest in cities with 20,000-100,000 inhabitants (females) or in cities with more than 100,000 inhabitants (males). Between regions, height converged both in East and West Germany, but particularly markedly among East-German males. Equality convergence, like height convergence, is significantly larger for East than for West-German males.     

Household crowding and reproductive behavior.

Ethological studies suggest that animal populations that live in crowded conditions display a number of behaviors that tend to limit the size of the population, such as aberrant forms of sexual behavior, small litter sizes, a higher incidence of spontaneous abortion, ineffectual maternal care, and even cannibalism of their young. Studies of household crowding in North America cities have produced only modest and selective evidence that crowding has similar effects among humans. In this paper, we examine the effect of household crowding on marital sexual relations, on desire for additional children, and on fetal and child loss in Bangkok, Thailand, a city with a much wider range of household crowding than is typically found in North America. In spite of the wider range, and higher mean level of crowding, we find that both the objective and subjective dimensions of crowding have only modest selective effects on sexual and reproductive behavior.     

Scale-Adjusted Metrics for Predicting the Evolution of Urban Indicators and Quantifying the Performance of Cities

More than a half of world population is now living in cities and this number is expected to be two-thirds by 2050. Fostered by the relevancy of a scientific characterization of cities and for the availability of an unprecedented amount of data, academics have recently immersed in this topic and one of the most striking and universal finding was the discovery of robust allometric scaling laws between several urban indicators and the population size. Despite that, most governmental reports and several academic works still ignore these nonlinearities by often analyzing the raw or the per capita value of urban indicators, a practice that actually makes the urban metrics biased towards small or large cities depending on whether we have super or sublinear allometries. By following the ideas of Bettencourt et al. [PLoS ONE 5 (2010) e13541], we account for this bias by evaluating the difference between the actual value of an urban indicator and the value expected by the allometry with the population size. We show that this scale-adjusted metric provides a more appropriate/informative summary of the evolution of urban indicators and reveals patterns that do not appear in the evolution of per capita values of indicators obtained from Brazilian cities. We also show that these scale-adjusted metrics are strongly correlated with their past values by a linear correspondence and that they also display crosscorrelations among themselves. Simple linear models account for 31%–97% of the observed variance in data and correctly reproduce the average of the scale-adjusted metric when grouping the cities in above and below the allometric laws. We further employ these models to forecast future values of urban indicators and, by visualizing the predicted changes, we verify the emergence of spatial clusters characterized by regions of the Brazilian territory where we expect an increase or a decrease in the values of urban indicators.     

Sampling for global epidemic models and the topology of an international airport network

Mathematical models that describe the global spread of infectious diseases such as influenza, severe acute respiratory syndrome (SARS), and tuberculosis (TB) often consider a sample of international airports as a network supporting disease spread. However, there is no consensus on how many cities should be selected or on how to select those cities. Using airport flight data that commercial airlines reported to the Official Airline Guide (OAG) in 2000, we have examined the network characteristics of network samples obtained under different selection rules. In addition, we have examined different size samples based on largest flight volume and largest metropolitan populations. We have shown that although the bias in network characteristics increases with the reduction of the sample size, a relatively small number of areas that includes the largest airports, the largest cities, the most-connected cities, and the most central cities is enough to describe the dynamics of the global spread of influenza. The analysis suggests that a relatively small number of cities (around 200 or 300 out of almost 3000) can capture enough network information to adequately describe the global spread of a disease such as influenza. Weak traffic flows between small airports can contribute to noise and mask other means of spread such as the ground transportation.     

What free‐ranging animals do at the zoo: a study of the behavior and habitat use of opossums (Didelphis virginiana) on the grounds of the St. Louis Zoo

Studies of urban wildlife are important because they can give us insight into both how animals adapt to novel environments and how some species survive and prosper in human‐dominated landscapes. Urban zoological institutions provide an ideal setting for research on such species. We report on a study of the behavior and ecology of opossums (Didelphis virginiana) on the grounds of the St. Louis Zoological Park. We used radio tracking to investigate the movement patterns and den site use of opossums on the grounds of the zoo and compared the results to data available for opossums elsewhere. We find that urban opossums in the St. Louis zoo have smaller home ranges and move shorter distances than their rural counterparts. We suggest that increased food availability and decreased risk of predation might explain such differences, and we suspect that conditions are even more favorable to opossums in a zoological setting than in cities in general. Our findings illustrate that there is much to be learned from scientific study of the free‐living animals found on zoological park grounds. Zoo Biol 00:1–17, 2005. © 2005 Wiley‐Liss, Inc.     

A Bayesian model averaging approach for estimating the relative risk of mortality associated with heat waves in 105 U.S. cities

Estimating the risks heat waves pose to human health is a critical part of assessing the future impact of climate change. In this article, we propose a flexible class of time series models to estimate the relative risk of mortality associated with heat waves and conduct Bayesian model averaging (BMA) to account for the multiplicity of potential models. Applying these methods to data from 105 U.S. cities for the period 1987-2005, we identify those cities having a high posterior probability of increased mortality risk during heat waves, examine the heterogeneity of the posterior distributions of mortality risk across cities, assess sensitivity of the results to the selection of prior distributions, and compare our BMA results to a model selection approach. Our results show that no single model best predicts risk across the majority of cities, and that for some cities heat-wave risk estimation is sensitive to model choice. Although model averaging leads to posterior distributions with increased variance as compared to statistical inference conditional on a model obtained through model selection, we find that the posterior mean of heat wave mortality risk is robust to accounting for model uncertainty over a broad class of models.     

Shifting and scaling patterns from gene expression data

MOTIVATION: During the last years, the discovering of biclusters in data is becoming more and more popular. Biclustering aims at extracting a set of clusters, each of which might use a different subset of attributes. Therefore, it is clear that the usefulness of biclustering techniques is beyond the traditional clustering techniques, especially when datasets present high or very high dimensionality. Also, biclustering considers overlapping, which is an interesting aspect, algorithmically and from the point of view of the result interpretation. Since the Cheng and Church's works, the mean squared residue has turned into one of the most popular measures to search for biclusters, which ideally should discover shifting and scaling patterns. RESULTS: In this work, we identify both types of patterns (shifting and scaling) and demonstrate that the mean squared residue is very useful to search for shifting patterns, but it is not appropriate to find scaling patterns because even when we find a perfect scaling pattern the mean squared residue is not zero. In addition, we provide an interesting result: the mean squared residue is highly dependent on the variance of the scaling factor, which makes possible that any algorithm based on this measure might not find these patterns in data when the variance of gene values is high. The main contribution of this paper is to prove that the mean squared residue is not precise enough from the mathematical point of view in order to discover shifting and scaling patterns at the same time. CONTACT: aguilar@lsi.us.es.     

The statistics of urban scaling and their connection to Zipf's law

Urban scaling relations characterizing how diverse properties of cities vary on average with their population size have recently been shown to be a general quantitative property of many urban systems around the world. However, in previous studies the statistics of urban indicators were not analyzed in detail, raising important questions about the full characterization of urban properties and how scaling relations may emerge in these larger contexts. Here, we build a self-consistent statistical framework that characterizes the joint probability distributions of urban indicators and city population sizes across an urban system. To develop this framework empirically we use one of the most granular and stochastic urban indicators available, specifically measuring homicides in cities of Brazil, Colombia and Mexico, three nations with high and fast changing rates of violent crime. We use these data to derive the conditional probability of the number of homicides per year given the population size of a city. To do this we use Bayes' rule together with the estimated conditional probability of city size given their number of homicides and the distribution of total homicides. We then show that scaling laws emerge as expectation values of these conditional statistics. Knowledge of these distributions implies, in turn, a relationship between scaling and population size distribution exponents that can be used to predict Zipf's exponent from urban indicator statistics. Our results also suggest how a general statistical theory of urban indicators may be constructed from the stochastic dynamics of social interaction processes in cities.     

Scaling effects and spatio-temporal multilevel dynamics in epileptic seizures

Epileptic seizures are one of the most well-known dysfunctions of the nervous system. During a seizure, a highly synchronized behavior of neural activity is observed that can cause symptoms ranging from mild sensual malfunctions to the complete loss of body control. In this paper, we aim to contribute towards a better understanding of the dynamical systems phenomena that cause seizures. Based on data analysis and modelling, seizure dynamics can be identified to possess multiple spatial scales and on each spatial scale also multiple time scales. At each scale, we reach several novel insights. On the smallest spatial scale we consider single model neurons and investigate early-warning signs of spiking. This introduces the theory of critical transitions to excitable systems. For clusters of neurons (or neuronal regions) we use patient data and find oscillatory behavior and new scaling laws near the seizure onset. These scalings lead to substantiate the conjecture obtained from mean-field models that a Hopf bifurcation could be involved near seizure onset. On the largest spatial scale we introduce a measure based on phase-locking intervals and wavelets into seizure modelling. It is used to resolve synchronization between different regions in the brain and identifies time-shifted scaling laws at different wavelet scales. We also compare our wavelet-based multiscale approach with maximum linear cross-correlation and mean-phase coherence measures.     

Urban scaling and its deviations: revealing the structure of wealth, innovation and crime across cities

With urban population increasing dramatically worldwide, cities are playing an increasingly critical role in human societies and the sustainability of the planet. An obstacle to effective policy is the lack of meaningful urban metrics based on a quantitative understanding of cities. Typically, linear per capita indicators are used to characterize and rank cities. However, these implicitly ignore the fundamental role of nonlinear agglomeration integral to the life history of cities. As such, per capita indicators conflate general nonlinear effects, common to all cities, with local dynamics, specific to each city, failing to provide direct measures of the impact of local events and policy. Agglomeration nonlinearities are explicitly manifested by the superlinear power law scaling of most urban socioeconomic indicators with population size, all with similar exponents (1.15). As a result larger cities are disproportionally the centers of innovation, wealth and crime, all to approximately the same degree. We use these general urban laws to develop new urban metrics that disentangle dynamics at different scales and provide true measures of local urban performance. New rankings of cities and a novel and simpler perspective on urban systems emerge. We find that local urban dynamics display long-term memory, so cities under or outperforming their size expectation maintain such (dis)advantage for decades. Spatiotemporal correlation analyses reveal a novel functional taxonomy of U.S. metropolitan areas that is generally not organized geographically but based instead on common local economic models, innovation strategies and patterns of crime.     

Dichotomy of eutherian reproduction and metabolism

How anatomical, physiological and ecological (life history) features scale with body mass is a fundamental question in biology. There is an ongoing debate in the scientific literature whether allometric scaling follows a universal pattern that can be described in a single model, or differs between groups. However, recently some analyses were published demonstrating a change in scaling across the body mass range: brain‐size allometry of mammals indicates that scaling follows a curvilinear pattern in double‐logarithmic space, and a quadratic pattern in double‐logarithmic space was found in one of the largest physiological datasets, on basal metabolic rate (MR) in mammals. Here, we analysed a variety of independent datasets on anatomical, physiological and ecological characteristics in mammals, birds and reptiles to answer the question whether the quadratic scaling is a universal biological law, or a pattern unique to mammals. The pattern was present in mammalian basal and field MR, brain size, and reproduction parameters, but neither in other organ allometries in mammals, nor in the scaling of MR in birds and reptiles. However, the curvature was better explained by separate allometric scaling of three different mammalian reproduction strategies: marsupials, and eutherian mammals with one and with many offspring. The two latter strategies are distributed unequally over the body mass range in eutherian mammals. Our findings show that a quadratic model, as well as a traditional allometric model with a universal scaling exponent (such as 0.67 or 0.75), may be inappropriate in mammals as they are a result of different scalings within these three reproductive groups. We propose that the observed distribution pattern is the result of the eutherian mammal clade's uniquely pronounced dichotomy of reproductive strategies.     

The metabolism of U.S. cities 2.0

In the fifty years since Abel Wolman first published an estimate of U.S. urban metabolism, the field of urban metabolism has begun to thrive, with cities outside the United States being much of the focus. As cities attempt to meet local and international sustainability goals, it is time to revisit the metabolism of cities within the United States. Using existing empirical databases for material flows (the Freight Analysis Framework) and a published database on urban water flux, we provide a revised estimate of urban metabolism for the typical U.S. city. We estimate median values of metabolism for a city of one million people, considering water resources, food, fuel, and construction materials. Food consumption and waste production increased substantially to 3,800 metric tons per day and 4,900 metric tons per day, respectively. To facilitate a second generation of urban metabolism, we extend traditional analyses to include the embedded energy required to facilitate material consumption with important implications in determining sustainable urban metabolism. We estimate that a city of one million people requires nearly 4,000 gigajoules of primary energy per day to facilitate its metabolism. Our results show high heterogeneity of urban metabolism across the United States. As a result of the study, we conclude that there is a distinct need to promote policies at the regional or city scale that collect data for urban metabolism studies. Urban metabolism is an important educational and decision‐making tool that, with an increase in data availability, can provide important information for cities and their sustainability goals.