Dataset Information

Great cities look small.

ABSTRACT: Great cities connect people; failed cities isolate people. Despite the fundamental importance of physical, face-to-face social ties in the functioning of cities, these connectivity networks are not explicitly observed in their entirety. Attempts at estimating them often rely on unrealistic over-simplifications such as the assumption of spatial homogeneity. Here we propose a mathematical model of human interactions in terms of a local strategy of maximizing the number of beneficial connections attainable under the constraint of limited individual travelling-time budgets. By incorporating census and openly available online multi-modal transport data, we are able to characterize the connectivity of geometrically and topologically complex cities. Beyond providing a candidate measure of greatness, this model allows one to quantify and assess the impact of transport developments, population growth, and other infrastructure and demographic changes on a city. Supported by validations of gross domestic product and human immunodeficiency virus infection rates across US metropolitan areas, we illustrate the effect of changes in local and city-wide connectivities by considering the economic impact of two contemporary inter- and intra-city transport developments in the UK: High Speed 2 and London Crossrail. This derivation of the model suggests that the scaling of different urban indicators with population size has an explicitly mechanistic origin.

SUBMITTER: Sim A

PROVIDER: S-EPMC4535402 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Great cities look small.

Sim Aaron A Yaliraki Sophia N SN Barahona Mauricio M Stumpf Michael P H MP

Journal of the Royal Society, Interface 20150801 109

Great cities connect people; failed cities isolate people. Despite the fundamental importance of physical, face-to-face social ties in the functioning of cities, these connectivity networks are not explicitly observed in their entirety. Attempts at estimating them often rely on unrealistic over-simplifications such as the assumption of spatial homogeneity. Here we propose a mathematical model of human interactions in terms of a local strategy of maximizing the number of beneficial connections at ...[more]

PMID: 26179988

Similar Datasets

Project description:Rapidly increasing urbanisation is one of the most significant anthropogenic environmental changes which can affect demographic traits of animal populations, for example resulting in reduced reproductive success. The food limitation hypothesis suggests that the shortage of high-quality nestling food in cities is a major factor responsible for the reduced reproductive performance in insectivorous birds. To study this explanation, we collected data on the parental provisioning behaviour of urban and forest great tits (Parus major) in three years that varied both in caterpillar availability (the main food of great tit nestlings) and in reproductive success of the birds. In all years, urban parents provisioned caterpillars in a smaller proportion to their nestlings, but the total amount of food per nestling (estimated by the volumes of all prey items) did not differ between habitats. In the two years with much lower reproductive success in urban than forest habitats, urban parents had higher provisioning rates, but provided more non-arthropod food and brought smaller prey items than forest parents. In the year with reduced habitat difference in reproductive success, urban parents were able to compensate for the scarcity of caterpillars by provisioning other arthropods rather than non-arthropod food, and by delivering larger preys than in the other years. Specifically, in this latter year, caterpillars provisioned by urban pairs were cc. twice as large as in the other two years, and were similar in size to caterpillars provisioned in the forest broods. These results show that although urban great tit parents can provide the same quantity of food per nestling as forest parents by reducing their brood size and increasing the per capita feeding rates for nestlings, they cannot compensate fully for the scarcity of high-quality preys (caterpillars) in poor years. In some years, however, favourable conditions for urban caterpillar development can greatly reduce food limitation in cities, allowing urban birds to achieve higher reproductive success. We suggest that urban green areas designed and managed in a way to facilitate conditions for phytophagous arthropods could improve habitat quality for urban birds.

Project description:Urbanization and inequalities are two of the major policy themes of our time, intersecting in large cities where social and economic inequalities are particularly pronounced. Large scale street-level images are a source of city-wide visual information and allow for comparative analyses of multiple cities. Computer vision methods based on deep learning applied to street images have been shown to successfully measure inequalities in socioeconomic and environmental features, yet existing work has been within specific geographies and have not looked at how visual environments compare across different cities and countries. In this study, we aim to apply existing methods to understand whether, and to what extent, poor and wealthy groups live in visually similar neighborhoods across cities and countries. We present novel insights on similarity of neighborhoods using street-level images and deep learning methods. We analyzed 7.2 million images from 12 cities in five high-income countries, home to more than 85 million people: Auckland (New Zealand), Sydney (Australia), Toronto and Vancouver (Canada), Atlanta, Boston, Chicago, Los Angeles, New York, San Francisco, and Washington D.C. (United States of America), and London (United Kingdom). Visual features associated with neighborhood disadvantage are more distinct and unique to each city than those associated with affluence. For example, from what is visible from street images, high density poor neighborhoods located near the city center (e.g., in London) are visually distinct from poor suburban neighborhoods characterized by lower density and lower accessibility (e.g., in Atlanta). This suggests that differences between two cities is also driven by historical factors, policies, and local geography. Our results also have implications for image-based measures of inequality in cities especially when trained on data from cities that are visually distinct from target cities. We showed that these are more prone to errors for disadvantaged areas especially when transferring across cities, suggesting more attention needs to be paid to improving methods for capturing heterogeneity in poor environment across cities around the world.Supplementary informationThe online version contains supplementary material available at 10.1140/epjds/s13688-023-00394-6.

Dataset Information

Great cities look small.

Publications

Great cities look small.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets