Connecting intercity mobility with urban welfare

Abstract

While significant effort has been devoted to understand the role of intraurban characteristics on sustainability and growth, much remains to be understood about the effect of interurban interactions and the role cities have in determining each other's urban welfare. Here we consider a global mobility network of population flows between cities as a proxy for the communication between these regions, and analyze how it correlates with socioeconomic indicators. We use several measures of centrality to rank cities according to their importance in the mobility network, finding PageRank to be the most effective measure for reflecting these prosperity indicators. Our analysis reveals that the characterization of the welfare of cities based on mobility information hinges on their corresponding development stage. Namely, while network-based predictions of welfare correlate well with economic indicators in mature cities, for developing urban areas additional information about the prosperity of their mobility neighborhood is needed. We develop a simple generative model for the allocation of population flows out of a city that balances the costs and benefits of interaction with other cities that are successful, finding that it provides a strong fit to the flows observed in the global mobility network and highlights the differences in flow patterns between developed and developing urban regions. Our results hint towards the importance of leveraging interurban connections in service of urban development and welfare.

Fig. 1.

Connecting network position and welfare. Weights W_ij, of the global mobility network for (a) the raw mobility flows (β = 0 in Eq. 1) and (c) when incorporating the effect of distance in the weights (β = 2.19), shown for North American cities. Node sizes are proportional to their PageRank in both maps. By including distance in the flows, we give stronger weight to long-range trips. (b) BHI vs PageRank for β = 0, where the horizontal axis indicates the ordered ranking based on their PageRank values, with a Spearman correlation coefficient ρ_s = 0.53. (d) The same for BHI vs PageRank for β = 2.19, yielding ρ_s = 0.77.

Fig. 2.

Disaggregating cities by development level PageRank vs BHI when cities are grouped with respect to their development level. Spearman’s correlation in each panel shows association level of the socioeconomic indicator and the network centrality measure. Colors represent the strength of connectivity for each city with dark red indicating higher levels of weighted-degree.

Fig. 3.

Correlation of PageRank with socioeconomic indicators BHI, TREI, GDP, and CBREI. Correlation strength is measured by Spearman’s coefficient ρ_s.

Fig. 4.

Rich-club organization and core–periphery structure of the mobility network. The richness parameter ρ(r) (Eq. 3) (a) for the connectivity r = k^out and (b) weighted out-degree r = s^out. The monotonically increasing trend of ρ(r) with r indicates subsets of high-connectivity cities that connect to each other more than would be expected as merely a consequence of the distribution of links and edge-weights. (c) The number of neighbors of each node, with a higher out-degree than the node itself, as a function of the node’s out-degree. The scatter plot has a peak separating cities into two clusters of core (in red) and peripheral (in blue) nodes. (d) The distribution of BHI in the core and peripheral cities, showing that cities in the core typically have much higher values of BHI than those in the periphery.

Fig. 5.

Interplay between benefit (success) and cost (distance). (a) and coefficient of determination r² for all city subgroups considered, using the model in Eq. (6) to fit the outflows of all cities in the subgroup. Black circles indicate development subgroups, and red triangles indicate geographical subgroups, while the size of the marker is proportional to the number of cities in the subgroup. Cities with r² > 0.8 and are highlighted as having high relative flow association with BHI due to high model correlations and contributions from . All fits demonstrated significant improvement through the inclusion of the term, as evidenced by the corresponding AIC and BIC values (Table S3 in Supplementary Material). (b) Model schematic, showing the variables involved in Eq. (6) around a central node i for which outflows are being predicted. (c) Example predicted and true outflows from cities in the Australasia subgroup, showing significant improvement from including the BHI in the model fit. Only flows with T_ij > 100 are displayed, for clearer visualization, and the line of equality is shown for reference.