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. 2015 Aug 6;12(109):20150473.
doi: 10.1098/rsif.2015.0473.

Human diffusion and city influence

Maxime Lenormand  1 , Bruno Gonçalves  2 , Antònia Tugores  3 , José J Ramasco  3
Affiliations

Human diffusion and city influence

Maxime Lenormand et al. J R Soc Interface. .

Abstract

Cities are characterized by concentrating population, economic activity and services. However, not all cities are equal and a natural hierarchy at local, regional or global scales spontaneously emerges. In this work, we introduce a method to quantify city influence using geolocated tweets to characterize human mobility. Rome and Paris appear consistently as the cities attracting most diverse visitors. The ratio between locals and non-local visitors turns out to be fundamental for a city to truly be global. Focusing only on urban residents' mobility flows, a city-to-city network can be constructed. This network allows us to analyse centrality measures at different scales. New York and London play a central role on the global scale, while urban rankings suffer substantial changes if the focus is set at a regional level.

Keywords: city–city interaction; epidemiology; geolocated data; human mobility; networks.

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Figures

Figure 1.
Figure 1.
Positions of the geolocated tweets. Each tweet is represented as a point on the map from where it was posted. (Online version in colour.)
Figure 2.
Figure 2.
Geolocated tweets of users who have been at least once in Paris (a) and New York (b). The colour changes according to the number of days Δt since the first passage in the city. In red, 1 day; in yellow, between 1 and 10 days; in green, between 10 and 100 days; and in blue, more than 100 days.
Figure 3.
Figure 3.
Evolution of the average radius. Each curve represents the evolution of the average radius R averaged over 100 independent extractions of a set of u = 300 users as a function of the number of days Δt since the first passage in the city. In order to show the general trend, each grey curve corresponds to a city. The evolution of the radius for several cities is highlighted, such as the top and bottom rankers or representatives of the two main detected behaviours. Curves with a linear and square root growth are also shown as a guide for the eye. The dashed lines represent the standard deviation.
Figure 4.
Figure 4.
Rankings of the cities according to the average radius and the coverage. (a) Top 10 cities ranked by the average radius R. (b) Top 10 cities ranked by the normalized average radius [Image: see text]. (c) Top 10 cities ranked by the coverage (number of visited cells). All the metrics are averaged over 100 independent extractions of a set of u = 300 users. (Online version in colour.)
Figure 5.
Figure 5.
Relation between local and non-local users. (a) Scatter-plot of [Image: see text] as a function of the coverage for locals (blue triangles) and non-locals (red squares). (b) Coverage as a function of the proportion of non-local Twitter users. (c) Top 10 ranking cities based only on local users according to the coverage. (d) The same ranking but based only on the movements of non-local users. In all the cases, the number of local and non-local users extracted is u = 100 for every city and all the metrics are averaged over 100 independent extractions. (Online version in colour.)
Figure 6.
Figure 6.
City attractiveness. Top 10 cities ranked by the number of distinct cells of residence for u = 1000 Twitter users drawn at random. The metric is averaged over 100 independent extractions. (Online version in colour.)
Figure 7.
Figure 7.
Mobility network. Local Twitter users’ mobility network between the 58 cities. Only the flows representing the top 95% of the total flow have been plotted. The flows are drawn from the least to the greatest. The inset shows the top eight cities ranked by weighted betweenness and weighted degree.
See this image and copyright information in PMC

References

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