Identification and measurement of intensive economic growth in a Roman imperial province.

A key question in economic history is the degree to which preindustrial economies could generate sustained increases in per capita productivity. Previous studies suggest that, in many preindustrial contexts, growth was primarily a consequence of agglomeration. Here, we examine evidence for three different socioeconomic rates that are available from the archaeological record for Roman Britain. We find that all three measures show increasing returns to scale with settlement population, with a common elasticity that is consistent with the expectation from settlement scaling theory. We also identify a pattern of increase in baseline rates, similar to that observed in contemporary societies, suggesting that this economy did generate modest levels of per capita productivity growth over a four-century period. Last, we suggest that the observed growth is attributable to changes in transportation costs and to institutions and technologies related to socioeconomic interchange. These findings reinforce the view that differences between ancient and contemporary economies are more a matter of degree than kind.

Information synergy maximizes the growth rate of heterogeneous groups.

Collective action and group formation are fundamental behaviors among both organisms cooperating to maximize their fitness and people forming socioeconomic organizations. Researchers have extensively explored social interaction structures via game theory and homophilic linkages, such as kin selection and scalar stress, to understand emergent cooperation in complex systems. However, we still lack a general theory capable of predicting how agents benefit from heterogeneous preferences, joint information, or skill complementarities in statistical environments. Here, we derive general statistical dynamics for the origin of cooperation based on the management of resources and pooled information. Specifically, we show how groups that optimally combine complementary agent knowledge about resources in statistical environments maximize their growth rate. We show that these advantages are quantified by the information synergy embedded in the conditional probability of environmental states given agents' signals, such that groups with a greater diversity of signals maximize their collective information. It follows that, when constraints are placed on group formation, agents must intelligently select with whom they cooperate to maximize the synergy available to their own signal. Our results show how the general properties of information underlie the optimal collective formation and dynamics of groups of heterogeneous agents across social and biological phenomena.

Evidence of COVID-19 fatalities in Swedish neighborhoods from a full population study.

The COVID-19 pandemic has highlighted a debate about whether marginalized communities suffered the disproportionate brunt of the pandemic's mortality. Empirical studies addressing this question typically suffer from statistical uncertainties and potential biases associated with uneven and incomplete reporting. We use geo-coded micro-level data for the entire population of Sweden to analyze how local neighborhood characteristics affect the likelihood of dying with COVID-19 at individual level, given the individual's overall risk of death. We control for several individual and regional characteristics to compare the results in specific communities to overall death patterns in Sweden during 2020. When accounting for the probability to die of any cause, we find that individuals residing in socioeconomically disadvantaged neighborhoods were not more likely to die with COVID-19 than individuals residing elsewhere. Importantly, we do find that individuals show a generally higher probability of death in these neighborhoods. Nevertheless, ethnicity is an important explanatory factor for COVID-19 deaths for foreign-born individuals, especially from East Africa, who are more likely to pass away regardless of residential neighborhood.

Implicit racial biases are lower in more populous more diverse and less segregated US cities.

Implicit biases - differential attitudes towards members of distinct groups - are pervasive in human societies and create inequities across many aspects of life. Recent research has revealed that implicit biases are generally driven by social contexts, but not whether they are systematically influenced by the ways that humans self-organize in cities. We leverage complex system modeling in the framework of urban scaling theory to predict differences in these biases between cities. Our model links spatial scales from city-wide infrastructure to individual psychology to predict that cities that are more populous, more diverse, and less segregated are less biased. We find empirical support for these predictions in U.S. cities with Implicit Association Test data spanning a decade from 2.7 million individuals and U.S. Census demographic data. Additionally, we find that changes in cities' social environments precede changes in implicit biases at short time-scales, but this relationship is bi-directional at longer time-scales. We conclude that the social organization of cities may influence the strength of these biases.

Integration of urban science and urban climate adaptation research: opportunities to advance climate action.

There is a growing recognition that responding to climate change necessitates urban adaptation. We sketch a transdisciplinary research effort, arguing that actionable research on urban adaptation needs to recognize the nature of cities as social networks embedded in physical space. Given the pace, scale and socioeconomic outcomes of urbanization in the Global South, the specificities and history of its cities must be central to the study of how well-known agglomeration effects can facilitate adaptation. The proposed effort calls for the co-creation of knowledge involving scientists and stakeholders, especially those historically excluded from the design and implementation of urban development policies.

Learning increases growth and reduces inequality in shared noisy environments.

Stochastic multiplicative dynamics characterize many complex natural phenomena such as selection and mutation in evolving populations, and the generation and distribution of wealth within social systems. Population heterogeneity in stochastic growth rates has been shown to be the critical driver of wealth inequality over long time scales. However, we still lack a general statistical theory that systematically explains the origins of these heterogeneities resulting from the dynamical adaptation of agents to their environment. In this paper, we derive population growth parameters resulting from the general interaction between agents and their environment, conditional on subjective signals each agent perceives. We show that average wealth-growth rates converge, under specific conditions, to their maximal value as the mutual information between the agent's signal and the environment, and that sequential Bayesian inference is the optimal strategy for reaching this maximum. It follows that when all agents access the same statistical environment, the learning process attenuates growth rate disparities, reducing the long-term effects of heterogeneity on inequality. Our approach shows how the formal properties of information underlie general growth dynamics across social and biological phenomena, including cooperation and the effects of education and learning on life history choices.

Digital Microfluidics-Powered Real-Time Monitoring of Isothermal DNA Amplification of Cancer Biomarker.

We introduce a digital microfluidics (DMF) platform specifically designed to perform a loop-mediated isothermal amplification (LAMP) of DNA and applied it to a real-time amplification to monitor a cancer biomarker, c-Myc (associated to 40% of all human tumors), using fluorescence microscopy. We demonstrate the full manipulation of the sample and reagents on the DMF platform, resulting in the successful amplification of 90 pg of the target DNA (0.5 ng/µL) in less than one hour. Furthermore, we test the efficiency of an innovative mixing strategy in DMF by employing two mixing methodologies onto the DMF droplets-low frequency AC (alternating current) actuation as well as back-and-forth droplet motion-which allows for improved fluorescence readouts. Fluorophore bleaching effects are minimized through on-chip sample partitioning by DMF processes and sequential droplet irradiation. Finally, LAMP reactions require only 2 µL volume droplets, which represents a 10-fold volume reduction in comparison to benchtop LAMP.

Evidence and theory for lower rates of depression in larger US urban areas.

It is commonly assumed that cities are detrimental to mental health. However, the evidence remains inconsistent and at most, makes the case for differences between rural and urban environments as a whole. Here, we propose a model of depression driven by an individual's accumulated experience mediated by social networks. The connection between observed systematic variations in socioeconomic networks and built environments with city size provides a link between urbanization and mental health. Surprisingly, this model predicts lower depression rates in larger cities. We confirm this prediction for US cities using four independent datasets. These results are consistent with other behaviors associated with denser socioeconomic networks and suggest that larger cities provide a buffer against depression. This approach introduces a systematic framework for conceptualizing and modeling mental health in complex physical and social networks, producing testable predictions for environmental and social determinants of mental health also applicable to other psychopathologies.

Creatures of the state? Metropolitan counties compensated for state inaction in initial U.S. response to COVID-19 pandemic.

Societal responses to crises require coordination at multiple levels of organization. Exploring early efforts to contain COVID-19 in the U.S., we argue that local governments can act to ensure systemic resilience and recovery when higher-level governments fail to do so. Event history analyses show that large, more urban areas experience COVID-19 more intensely due to high population density and denser socioeconomic networks. But metropolitan counties were also among the first to adopt shelter-in-place orders. Analyzing the statistical predictors of when counties moved before their states, we find that the hierarchy of counties by size and economic integration matters for the timing of orders, where both factors predict earlier shelter-in-place orders. In line with sociological theories of urban governance, we also find evidence of an important governance dimension to the timing of orders. Liberal counties in conservative states were more than twice as likely to adopt a policy and implement one earlier in the pandemic, suggesting that tensions about how to resolve collective governance problems are important in the socio-temporal dynamic of responses to COVID-19. We explain this behavior as a substitution effect in which more urban local governments, driven by risk and necessity, step up into the action vacuum left by higher levels of government and become national policy leaders and innovators.

ChicagO Multiethnic Prevention and Surveillance Study (COMPASS): Increased Response Rates Among African American Residents in Low Socioeconomic Status Neighborhoods.

African American (AA) populations experience persistent health disparities in the USA. Low representation in bio-specimen research precludes stratified analyses and creates challenges in studying health outcomes among AA populations. Previous studies examining determinants of bio-specimen research participation among minority participants have focused on individual-level barriers and facilitators. Neighborhood-level contextual factors may also inform bio-specimen research participation, possibly through social norms and the influence of social views and behaviors on neighbor's perspectives. We conducted an epidemiological study of residents in 5108 Chicago addresses to examine determinants of bio-specimen research participation among predominantly AA participants solicited for participation in the first 6 years of ChicagO Multiethnic Prevention and Surveillance Study (COMPASS). We used a door-to-door recruitment strategy by interviewers of predominantly minority race and ethnicity. Participants were compensated with a $50 gift card. We achieved response rates of 30.4% for non-AA addresses and 58.0% for AA addresses, with as high as 80.3% response among AA addresses in low socioeconomic status (SES) neighborhoods. After multivariable adjustment, we found approximately 3 times the odds of study participation among predominantly AA addresses in low vs. average SES neighborhoods (odds ratio (OR) = 3.06; 95% confidence interval (CI) = 2.20-4.24). Conversely, for non-AA addresses, we observed no difference in the odds of study participation in low vs. average SES neighborhoods (OR = 0.89; 95% CI = 0.69-1.14) after multivariable adjustment. Our findings suggest that AA participants in low SES neighborhoods may be recruited for bio-specimen research through door-to-door approaches with compensation. Future studies may elucidate best practices to improve bio-specimen research participation among minority populations.

Tobacco and marijuana use and their association with serum prostate-specific antigen levels among African American men in Chicago.

African American (AA) men experience more than twice the prostate cancer mortality as White men yet are under-represented in academic research involving prostate-specific antigen (PSA), a biomarker of prostate cancer aggressiveness. We examined the impact of self-reported tobacco (cigarette pack-years and current tobacco use including e-cigarettes) and current regular marijuana use on serum PSA level based on clinical laboratory testing among 928 AA men interviewed 2013-2018 in Chicago. We defined outcome of elevated PSA ≥ 4.0 ng/mL for logistic regression models and continuous PSA increases for general linear models. All models were adjusted for age, sociodemographic characteristics, healthcare utilization, body mass index, and self-reported health. Among 431 AA men age ≥ 55 years, we observed ∼ 5 times the odds of elevated PSA among those with > 1 pack-years of cigarette smoking vs. never-smokers (odds ratio [OR] = 5.09; 95% confidence interval [CI] = 1.57-16.6) and a quarter the odds of elevated PSA among current marijuana users vs. non-users (OR = 0.27; 95% CI = 0.08-0.96). PSA increased on average 1.20 ng/mL among other current tobacco users vs. non-users. Among older AA men, cigarette smoking history and current tobacco use were positively associated with an increase in PSA levels and current marijuana use were inversely associated with PSA levels. Future work with studies of diverse patient populations with cancer outcomes are needed to assess whether these behavioral characteristics contribute to racial/ ethnic disparities in prostate cancer outcomes. Our study provides novel evidence regarding potential differences in PSA levels among older AA men according to behavioral characteristics.

Urban growth and the emergent statistics of cities.

Urban theory models cities as spatial equilibria to derive their aggregate properties as functions of extensive variables, such as population size. However, this assumption seems at odds with cities' most interesting properties as engines of fast and variable processes of growth and change. Here, we build a general statistical dynamics of cities across scales, from single agents to entire urban systems. We include agents' strategic behavior to produce predictable growth rates, which requires balancing relative incomes and costs over time. We implement these dynamics using stochastic differential equations and control theory to demonstrate a number of general emergent properties of cities deriving from limit theorems applied to growth rates. This framework establishes necessary conditions for scaling to be conserved by urban dynamics and shows how exponent corrections can be calculated. These ideas are tested using stochastic simulations and a long timeseries for 382 US Metropolitan Areas over nearly five decades.

Demography and the emergence of universal patterns in urban systems.

Urban areas exist in a wide variety of population sizes, from small towns to huge megacities. No proposed form for the statistical distribution of city sizes has received more attention than Zipf's law, a Pareto distribution with power law exponent equal to one. However, this distribution is typically violated by empirical evidence for small and large cities. Moreover, no theory presently exists to derive city size distributions from fundamental demographic choices while also explaining consistent variations. Here we develop a comprehensive framework based on demography to show how the structure of migration flows between cities, together with the differential magnitude of their vital rates, determine a variety of city size distributions. This approach provides a powerful mathematical methodology for deriving Zipf's law as well as other size distributions under specific conditions, and to resolve puzzles associated with their deviations in terms of concepts of choice, symmetry, information, and selection.

The interpretation of urban scaling analysis in time.

Scaling is a general analytical framework used by many disciplines-from physics to biology and the social sciences-to characterize how population-averaged properties of a collective vary with its size. The observation of scale invariance over some range identifies general system types, be they ideal gases, ecosystems or cities. The use of scaling in the analysis of cities quantifies many of their arguably fundamental general characteristics, especially their capacity to create interrelated economies of scale in infrastructure and increasing returns to scale in socio-economic activities. However, the measurement of these effects, and the relationship of observable parameters to theory, hinge on how scaling analysis is used empirically. Here, we show how two equivalent approaches to urban scaling-cross-sectional and temporal-lead to the measurement of different mixtures of the same fundamental parameters describing pure scale and pure temporal phenomena. Specifically, temporal exponents are sensitive to the intensive growth of urban quantities and to circumstances when population growth vanishes, leading to instabilities and infinite divergences. These spurious effects are avoided in cross-sectional scaling, which is more common and closer to theory in terms of quantitative testable expectations for its parameters.

Growth and development in prefecture-level cities in China.

Nowhere has the scale and scope of urbanization been larger than in China over the last few decades. We analyze Chinese city development between the years 1996 and 2014 using data for the urbanized components of prefecture-level cities. We show that, despite much variability and fast economic and demographic change, China is undergoing transformations similar to the historical trajectory of other urban systems. We also show that the distinguishing signs of urban economies-superlinear scaling of agglomeration effects in economic productivity and economies of scale in land use-also characterize Chinese cities. We then analyze the structure of economic change in Chinese cities using a variety of metrics, characterizing employment, firms and households. Population size estimates remain a major challenge for Chinese cities, as official numbers are often reported based on the Hukou registration system. We use the information in the residuals to scaling relations for economic quantities to predict actual resident population and show that these estimates agree well with data for a subset of cities for which counts of total resident population exist. We conclude with a list of issues that must be better understood and measured to make sense of present urban development trajectories in China.

Urban geography and scaling of contemporary Indian cities.

This paper attempts to create a first comprehensive analysis of the integrated characteristics of contemporary Indian cities, using scaling and geographical analysis over a set of diverse indicators. We use data of urban agglomerations in India from the Census 2011 and from a few other sources to characterize patterns of urban population density, infrastructure, urban services, crime and technological innovation. Many of the results are in line with expectations from urban theory and with the behaviour of analogous quantities in other urban systems in both high and middle-income nations. India is a continental scale, fast developing urban system, and consequently there are also a number of interesting exceptions and surprises related to both particular quantities and strong regional patterns of variation. Specifically, these relate to the potential salience of gender and caste in driving sub-linear scaling of crime and to the geography of technological innovation. We characterize these patterns in detail for crime and invention, and connect them to the existing literature on their determinants in a specifically Indian context. The paucity of data at the urban level and the absence of official definitions for functional cities in India create a number of limitations and caveats to any present analysis. We discuss these shortcomings and spell out the challenge for a systematic statistical data collection relevant to cities and urban development in India.

Toward cities without slums: Topology and the spatial evolution of neighborhoods.

The world is urbanizing quickly with nearly 4 billion people presently living in urban areas, about 1 billion of them in slums. Achieving sustainable development from rapid urbanization relies critically on creating cities without slums. We show that it is possible to diagnose systematically the central physical problem of slums-the lack of spatial accesses and related services-using a topological analysis of neighborhood maps and resolved by finding solutions to a sequence of constrained optimization problems. We set up the problem by showing that the built environment of any city can be decomposed into two types of networked spaces-accesses and places-and prove that these spaces display universal topological characteristics. We then show that while the neighborhoods of developed cities express the same common topology, urban slums fall into a different topological class. We demonstrate that it is always possible to find solutions that grow a street network in existing slums, providing universal accesses at minimal disruption and cost. We then show how elaborations of this procedure that include local preferences and reduce travel distances between places result from additional access construction. These methods are presently taking effect in neighborhoods in Cape Town (South Africa) and Mumbai (India), demonstrating their practical feasibility and emphasizing their role as a platform to enable communities and local governments to combine technical knowledge with local aspirations into contextually appropriate urban sustainable development solutions.