Modeling international mobility using roaming cell phone traces during COVID-19 pandemic

Abstract

Most of the studies related to human mobility are focused on intra-country mobility. However, there are many scenarios (e.g., spreading diseases, migration) in which timely data on international commuters are vital. Mobile phones represent a unique opportunity to monitor international mobility flows in a timely manner and with proper spatial aggregation. This work proposes using roaming data generated by mobile phones to model incoming and outgoing international mobility. We use the gravity and radiation models to capture mobility flows before and during the introduction of non-pharmaceutical interventions. However, traditional models have some limitations: for instance, mobility restrictions are not explicitly captured and may play a crucial role. To overtake such limitations, we propose the COVID Gravity Model (CGM), namely an extension of the traditional gravity model that is tailored for the pandemic scenario. This proposed approach overtakes, in terms of accuracy, the traditional models by 126.9% for incoming mobility and by 63.9% when modeling outgoing mobility flows.

Mobility constraints in segregation models

“…An unhappy agent located at a cell A moves to a new cell B based on a probability function, p ( B ), which depends on two factors: the distance d ( A , B ) between A and B , and the relevance r ( B ) of destination B . This probability captures the gravity law of human mobility 43 , 47 , 48 , 52 , 55 , 60 – 64 , positing that people tend to travel to nearby and relevant locations, a concept that has been supported by extensive research in fields ranging from transport planning 65 and spatial economics 62 , 66 , 67 to epidemic spreading 57 , 68 – 71 . The distance between points A and B , represented by coordinates and , is computed as their Euclidean distance on the grid, .…”

“…However, recent empirical studies have shown that human movement, far from being random, follows specific statistical patterns across various spatial scales, including daily movements and migrations 40 – 53 . These individual mobility patterns are characterized by a preference for short distances and relevant places over longer distances and sparse ones 40 – 45 , 48 , 54 – 57 . Despite considerable interest in modelling and predicting human mobility 43 , 52 , it remains unclear how mobility patterns relate to segregation patterns.…”

Crime, inequality and public health: a survey of emerging trends in urban data science

“…A single event is a privacy-enhanced record (u, t, A, k ) where t is the timestamp of the event, A is the RBS that managed the connection, and k the amount of uploaded/downloaded information. Given the higher frequency of mobile internet connections, XDRs reduce the problem of sparsity characterizing CDRs (Chen et al, 2019 ; Luca et al, 2022 ).…”

Human mobility prediction with causal and spatial-constrained multi-task network

“…Human mobility modeling aims to explore the regularities and patterns of human behavior [1,2] and plays a significant role in numerous applications, such as urban planning [3], travel demand management [4,5], health risk assessment [6], epidemic spreading modeling and control [7][8][9], and so on. In the big data era, the accessibility to GPS, mobile phone records, and location-based social networks (LBSNs) provides an unprecedented chance to understand and model human mobility [2,10].…”