Diagnosing the performance of human mobility models at small spatial scales using volunteered geographical information

This is the final version. Available from The Royal Society via the DOI in this record. Data are available from Zenodo at https://zenodo.org/record/3383443.Accurate modelling of local population movement patterns is a core, contemporary concern for urban policymakers, affecting both the short-term deployment of public transport resources and the longer-term planning of transport infrastructure. Yet, while macro-level population movement models (such as the gravity and radiation models) are well developed, micro-level alternatives are in much shorter supply, with most macro-models known to perform poorly at smaller geographical scales. In this paper, we take a first step to remedy this deficit, by leveraging two novel datasets to analyse where and why macro-level models of human mobility break down. We show how freely available data from OpenStreetMap concerning land use composition of different areas around the county of Oxfordshire in the UK can be used to diagnose mobility models and understand the types of trips they over- and underestimate when compared with empirical volumes derived from aggregated, anonymous smartphone location data. We argue for new modelling strategies that move beyond rough heuristics such as distance and population towards a detailed, granular understanding of the opportunities presented in different regions.Innovate UKNatural Environment Research Council (NERC)Engineering and Physical Sciences Research Council (EPSRC

Debye–Hückel theory for refugees’ migration

Abstract In this paper, we follow the short-ranged Syrian refugees’ migration to Lebanon as documented by the UNHCR. We propose a model inspired by the Debye–Hückel theory and show that it properly predicts the refugees’ mobility while the gravity model fails. We claim that the interaction between origin cities attenuates and/or extenuates the flux to destinations, and consequently, in analogy with the effective charges of interacting particles in a plasma, these source cities are characterized by effective populations determined by their pairwise remoteness/closeness and defined by areas of control between the fighting parties

The importance of social networks amongst refugees resettled through the Community Sponsorship scheme and The Vulnerable Persons Resettlement Scheme

The dramatic increase in the number of displaced people in the last decade, predominantly hosted by low and middle-income countries, has prompted a reassessment of solutions to ensure international protection and shared responsibilities for refugees. In response, resettlement programmes have been developed and expanded worldwide, allowing the transfer of refugees from first asylum countries to third countries. In 2014, the UK launched the Vulnerable Persons Resettlement Scheme (VPRS), aiming to resettle 20,000 refugees from the Syrian conflict. Through the programmes, refugees are supported by local authorities and an assigned caseworker to aid their integration processes. Simultaneously, sponsorship programmes, allowing individuals to sponsor refugee families and support their integration processes, have emerged in nearly twenty countries. In Europe, the UK developed the first Community Sponsorship (CS) scheme in 2016. Behind the enthusiasm for sponsorship programmes often lies the assumption that sponsored refugees receive better assistance than those resettled by government-led programmes, due to the support provided by volunteers' networks facilitating their integration processes. However, there is a paucity of comparative studies providing evidence supporting this assumption, especially outside Canada, where sponsorship programmes differ significantly from those developed in Europe. Additionally, although the importance of social networks in integration processes is widely recognised, several knowledge gaps exist regarding the formation and development of refugees' social networks and their role in supporting integration processes. Drawing on migration literature and social network analysis, this study addresses these knowledge gaps by comparing the social networks of refugees resettled through VPRS and CS with a view to understanding how different types of social connections and resources they provide shape integration processes. In addition to distinguishing between types of relationships and resources, this study's conceptual framework also considers individual-level factors, such as refugees' socio-demographic and migration characteristics, as well as contextual-level factors in shaping social networks and integration outcomes and processes. The findings reveal significant depth and breadth differences between the social networks developed by CS and VPRS refugees, suggesting that through CS, refugees are more likely to develop broader and more diversified social networks compared to VPRS refugees. Differences in the types of resources available through social networks further highlight that sponsored refugees can access more tailored practical and emotional support than VPRS refugees. Utilising the Indicators of Integration framework, the study indicates that these variations in social networks and resources do not always lead to substantial disparities in integration outcomes, particularly in functional aspects, as the presence of a social network is insufficient to overcome structural barriers hindering integration processes. However, the presence of caring relationships providing emotional support among sponsored refugees' social networks underscores the significance of these relationships in integration processes. Specifically, sponsored refugees reported how these relationships enable them to feel more confident and comfortable, develop relationships with the wider community and increase their willingness and ability to reciprocate, positively impacting their integration processes

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Distance. The Distance.csv contains the pairwise distance matrix between Origin and destination cities retrieved through the google API. (CSV 3 kB

MOESM3 of Debye–Hückel theory for refugees’ migration

Syria–Syria-distance. The Syria-Syria-Distance.csv contains the pairwise distance matrix between Syrian cities retrieved through the google API. (CSV 2 kB

MOESM4 of Debye–Hückel theory for refugees’ migration

Density. The DensityMatrixSij.csv contains the population density between every Syrian city i and Lebanese city j. (CSV 7 kB