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The COVID-19 pandemic has impacted the national systems of population movement around the world. Existing work has focused on countries of the Global North and restricted to the immediate effects of COVID-19 data during 2020. Data have represented a major limitation to monitor change in mobility patterns in countries in the Global South. Drawing on aggregate anonymised mobile phone location data from Meta-Facebook users, we aim to analyse the extent and persistence of changes in the levels (or intensity) and spatial patterns of internal population movement across the rural-urban continuum in Argentina, Chile and Mexico over a 26-month period from March 2020 to May 2022. We reveal an overall systematic decline in the level of short- and long-distance movement during the enactment of nonpharmaceutical interventions in 2020, with the largest reductions occurred in the most dense areas. We also show that these levels bounced back closer to pre-pandemic levels in 2022 following the relaxation of COVID-19 stringency measures. However, the intensity of these movements has remained below pre-pandemic levels in many areas in 2022. Additionally our findings lend some support to the idea of an urban exodus. They reveal a continuing negative net balances of short-distance movements in the most dense areas of capital cities in Argentina and Mexico, reflecting a pattern of suburbanisation. Chile displays limited changes in the net balance of short-distance movements but reports a net loss of long-distance movements. These losses were, however, temporary, moving to neutral and positive balances in 2021 and 2022., Comment: 29 pages, 5 figures

Suppose $A$ and $B$ are two stations within the mass rapid transit network of a city. Both stations see approximately the same average daily number of passengers entering and exiting their gates. However, passengers are evenly distributed at $A$, whereas activity is concentrated mainly during peak hours at $B$. Although the daily travel demand is the same for both stations, $B$ requires more resources since the number of vehicles, station dimensions and staffing level must be tailored to meet the demands of peak hours. This hypothetical scenario underscores the need to quantify the concentration of travel demand for optimising resource allocation and planning efficiency in an urban transport network. To this end, we introduce a novel metric for assessing the temporal concentration of travel demand at different locations in a generic transport network. Our approach is validated using granular data sourced from smart travel cards, encompassing 272 London Underground (LU) stations. Additionally, we present a methodological framework based on Random Forests to identify attributes of the locations of interest within the transport network that contribute to varying levels of temporal concentration of travel demand. Our case study unveils that LU stations located in areas characterised by low residential, retail, and employment density, predominantly situated in outer London, exhibit the most pronounced temporal concentration of travel demand. Conversely, within inner London, stations servicing high-density employment zones, especially around the City of London, experience a greater temporal concentration of travel demand compared to those catering to commercial and residential districts, typically situated in West London.

Transport systems are vulnerable to disruption. This is particularly true in Africa, where there are large areas with few highways and heightened risk of violence. Here we attempt to estimate the costs of violent events on African transport in order to understand the way that it may be limiting integration between regions. In the absence of detailed data on trade or migration, we quantify the cost of violence by relating observed incidents to imputed spatial interaction between cities. We produce indices representing the expected intensity of violent events $\mu$ and the expected strength of interaction $\nu$ between cities in the African interurban network. We estimate the intensity of conflict in a city and, considering the network of all highways on the continent, use a gravity model to generate flows between pairs of cities. We systematically compare $\mu$ to $\nu$ and classify areas according to their combined impact and intensity. Results show that certain cities and roads in the network contain outsize risk to Africa's transportation infrastructure. These cities have a high propensity for subsequent violence against civilians, and given their role in the network, they also substantially affect regional connectivity and thus economic integration. According to our model, removing just ten edges due to conflict would require rerouting 32$\%$ of trips. The top 100 edges where violence is likely to happen account for 17$\%$ of all trips. We find that cities with the highest $\mu-\nu$ risk are typically small and medium size with large degree, meaning they act as hubs. Vulnerable areas tend to be characterised by the presence of terrorist groups like Boko Haram in Nigeria or Al Shabaab in Somalia.

The polycentric city model has gained popularity in spatial planning policy, since it is believed to overcome some of the problems often present in monocentric metropolises, ranging from congestion to difficult accessibility to jobs and services. However, the concept 'polycentric city' has a fuzzy definition and as a result, the extent to which a city is polycentric cannot be easily determined. Here, we leverage the fine spatio-temporal resolution of smart travel card data to infer urban polycentricity by examining how a city departs from a well-defined monocentric model. In particular, we analyse the human movements that arise as a result of sophisticated forms of urban structure by introducing a novel probabilistic approach which captures the complexity of these human movements. We focus on London (UK) and Seoul (South Korea) as our two case studies, and we specifically find evidence that London displays a higher degree of monocentricity than Seoul, suggesting that Seoul is likely to be more polycentric than London., Comment: 15 pages, 5 figures