Is this scaling nonlinear?

Leitão, Jorge C.; Miotto, José M.; Gerlach, Martin; Altmann, Eduardo G.

doi:10.1098/rsos.150649

Cited by 114 publications

(204 citation statements)

References 35 publications

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“…Empirical data from recent works [35,36] suggest that the residuals surrounding the scaling laws are log-normally distributed. The present model can explain such properties as a consequence of fluctuation of the interaction range of the citizen, given by particularities of the cities.…”

Section: Discussionmentioning

confidence: 99%

A model of urban scaling laws based on distance dependent interactions

et al. 2017

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Socio-economic related properties of a city grow faster than a linear relationship with the population, in a log–log plot, the so-called superlinear scaling. Conversely, the larger a city, the more efficient it is in the use of its infrastructure, leading to a sublinear scaling on these variables. In this work, we addressed a simple explanation for those scaling laws in cities based on the interaction range between the citizens and on the fractal properties of the cities. To this purpose, we introduced a measure of social potential which captured the influence of social interaction on the economic performance and the benefits of amenities in the case of infrastructure offered by the city. We assumed that the population density depends on the fractal dimension and on the distance-dependent interactions between individuals. The model suggests that when the city interacts as a whole, and not just as a set of isolated parts, there is improvement of the socio-economic indicators. Moreover, the bigger the interaction range between citizens and amenities, the bigger the improvement of the socio-economic indicators and the lower the infrastructure costs of the city. We addressed how public policies could take advantage of these properties to improve cities development, minimizing negative effects. Furthermore, the model predicts that the sum of the scaling exponents of social-economic and infrastructure variables are 2, as observed in the literature. Simulations with an agent-based model are confronted with the theoretical approach and they are compatible with the empirical evidences.

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Section: Discussionmentioning

confidence: 99%

A model of urban scaling laws based on distance dependent interactions

et al. 2017

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“…, Leitao et al. ). The use of the power scaling relationship in this study was based on the following considerations: (1) our observations fit the power scaling relationship well (Appendix ); (2) as described earlier, the power scaling has been successfully adopted to scale many urban properties (Bettencourt et al.…”

Section: Methodsmentioning

confidence: 98%

“…We realized that the relationship between urban attributes can be analyzed using other methods such as direct comparison or statistic regression (Bloom et al 2008, Leitao et al 2016. The use of the power scaling relationship in this study was based on the following considerations:…”

Section: Horizontal Organization and Scaling Of Urban Attributes Acromentioning

confidence: 99%

Contemporary evolution and scaling of 32 major cities in China

Zhao

Liu

et al. 2018

Ecological Applications

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Most of the planet's population currently lives in urban areas, and urban land expansion is one of the most dramatic forms of land conversion. Understanding how cities evolve temporally, spatially, and organizationally in a rapidly urbanizing world is critical for sustainable development. However, few studies have examined the coevolution of urban attributes in time and space simultaneously and the adequacy of power law scaling across cities and through time, particularly in countries that have experienced abrupt, widespread, political and economic changes. Here, we show the temporal coevolution of multiple physical, demographic, socioeconomic, and environmental attributes in individual cities, and the cross-city scaling of urban attributes at six time points (i.e., 1978, 1990, 1995, 2000, 2005, and 2010) in 32 major Chinese cities. We found that power law scaling could adequately characterize both the cross-city scaling of urban attributes across cities and the longitudinal scaling describing the temporal coevolution of urban attributes within individual cities. The cross-city scaling properties demonstrated substantial changes over time signifying evolved social and economic forces. A key finding was that the cross-city linear or superlinear scaling of urban area with population contradicts the theoretical sublinear power law scaling proposed between infrastructure and population. Furthermore, the cross-city scaling between area and population transitioned from linear to superlinear over time, and the superlinear scaling in recent times suggests decreased infrastructure efficiency. Our results demonstrate a diseconomy of scale in urban areal expansion that indicates a significant waste of land resources in the urbanization process. Future planning efforts should focus on policies that increase urban land use efficiency before continuing expansion.

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“…where N s is the state population and β = 1.1397 is a scaling exponent independently estimated on the total volume of pageviews in each state by adopting the probabilistic framework of Leitão et al [59] (details are reported in the S2 Appendix of the Supporting Information). By K-fold (k = 10) and leave-one-out cross validation, we test the performance of the model considering different linear combinations of features X i .…”

Section: Equal-time Regression Modelmentioning

confidence: 99%

The impact of news exposure on collective attention in the United States during the 2016 Zika epidemic

et al. 2020

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In recent years, many studies have drawn attention to the important role of collective awareness and human behaviour during epidemic outbreaks. A number of modelling efforts have investigated the interaction between the disease transmission dynamics and human behaviour change mediated by news coverage and by information spreading in the population. Yet, given the scarcity of data on public awareness during an epidemic, few studies have relied on empirical data. Here, we use fine-grained, geo-referenced data from three online sources-Wikipedia, the GDELT Project and the Internet Archive-to quantify populationscale information seeking about the 2016 Zika virus epidemic in the U.S., explicitly linking such behavioural signal to epidemiological data. Geo-localized Wikipedia pageview data reveal that visiting patterns of Zika-related pages in Wikipedia were highly synchronized across the United States and largely explained by exposure to national television broadcast. Contrary to the assumption of some theoretical epidemic models, news volume and Wikipedia visiting patterns were not significantly correlated with the magnitude or the extent of the epidemic. Attention to Zika, in terms of Zika-related Wikipedia pageviews, was high at the beginning of the outbreak, when public health agencies raised an international alert and triggered media coverage, but subsequently exhibited an activity profile that suggests nonlinear dependencies and memory effects in the relation between information seeking, media pressure, and disease dynamics. This calls for a new and more general modelling framework to describe the interaction between media exposure, public awareness and disease dynamics during epidemic outbreaks. Data Availability Statement: Data are available from the Zenodo repository (https://doi.org/10. 5281/zenodo.3603916).data, and we compare it with mentions of Zika in US news outlets and TV shows. We also compare the collective attention received by the outbreak with the incidence of Zika reported by the US Centers for Disease Control and Prevention in each state. We find that the attention dynamics was highly synchronized across states, irrespective of the local risk of transmission of the virus. By building a linear regression model, we show that the dynamics of collective attention is highly predictable, even at state level, only based on the national media coverage received by the outbreak. PLOS COMPUTATIONAL BIOLOGYNews exposure and collective attention during epidemics PLOS Computational Biology | https://doi.org/10.

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Is this scaling nonlinear?

Cited by 114 publications

References 35 publications

A model of urban scaling laws based on distance dependent interactions

A model of urban scaling laws based on distance dependent interactions

Contemporary evolution and scaling of 32 major cities in China

The impact of news exposure on collective attention in the United States during the 2016 Zika epidemic

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