Generalized radiation model for human migration

The diaspora model for human migration

Migration's impact spans various social dimensions, including demography, sustainability, politics, economy, and gender disparities. Yet, the decision-making process behind migrants choosing their destination remains elusive. Existing models primarily rely on population size and travel distance to explain the spatial patterns of migration flows, overlooking significant population heterogeneities. Paradoxically, migrants often travel long distances and to smaller destinations if their diaspora is present in those locations. To address this gap, we propose the diaspora model of migration, incorporating intensity (the number of people moving to a country), and assortativity (the destination within the country). Our model considers only the existing diaspora sizes in the destination country, influencing the probability of migrants selecting a specific residence. Despite its simplicity, our model accurately reproduces the observed stable flow and distribution of migration in Austria (postal code level) and US metropolitan areas, yielding precise estimates of migrant inflow at various geographic scales. Given the increase in international migrations, this study enlightens our understanding of migration flow heterogeneities, helping design more inclusive, integrated cities.

An agent-based framework to study forced migration: A case study of Ukraine

The ongoing Russian aggression against Ukraine has forced over eight million people to migrate out of Ukraine. Understanding the dynamics of forced migration is essential for policy-making and for delivering humanitarian assistance. Existing work is hindered by a reliance on observational data which is only available well after the fact. In this work, we study the efficacy of a data-driven agent-based framework motivated by social and behavioral theory in predicting outflow of migrants as a result of conflict events during the initial phase of the Ukraine war. We discuss policy use cases for the proposed framework by demonstrating how it can leverage refugee demographic details to answer pressing policy questions. We also show how to incorporate conflict forecast scenarios to predict future conflict-induced migration flows. Detailed future migration estimates across various conflict scenarios can both help to reduce policymaker uncertainty and improve allocation and staging of limited humanitarian resources in crisis settings.

Measuring human mobility in times of trouble: an investigation of the mobility of European populations during COVID-19 using big data

Spatial mobility is a distinctive feature of human history and has important repercussions in many aspects of societies. Spatial mobility has always been a subject of interest in many disciplines, even if only mobility observable from traditional sources, namely migration (internal and international) and more recently commuting, is generally studied. However, it is the other forms of mobility, that is, the temporary forms of mobility, that most interest today's societies and, thanks to new data sources, can now be observed and measured. This contribution provides an empirical and data-driven reflection on human mobility during the COVID pandemic crisis. The paper has two main aims: (a) to develop a new index for measuring the attrition in mobility due to the restrictions adopted by governments in order to contain the spread of COVID-19. The robustness of the proposed index is checked by comparing it with the Oxford Stringency Index. The second goal is (b) to test if and how digital footprints (Google data in our case) can be used to measure human mobility. The study considers Italy and all the other European countries. The results show, on the one hand, that the Mobility Restriction Index (MRI) works quite well and, on the other, the sensitivity, in the short term, of human mobility to exogenous shocks and intervention policies; however, the results also show an inner tendency, in the middle term, to return to previous behaviours.

Spatial Patterns and Determinants of Inter-county Migration in California: A Multilevel Gravity Model Approach

Understanding migration patterns and their determinants is crucial for population estimation and resource allocation for policymakers. Utilizing residential mobility data collected by the Department of Motor Vehicles, this present study provides a spatiotemporal analysis of inter-county migration in California for the period 2014-2021. We use multilevel gravity models to address the hierarchical nature of migration data and the effects of migration flows sharing common origins, destinations, and regions, providing a substantively complete examination of push and pull forces affecting migration. Our findings show that populous counties in Southern California and the San Francisco Bay Area represent the largest origins and destinations, despite a systemic decline in intra-state migration. Migration is strongly associated with population size, geographic proximity (i.e., distance and contiguity), job availability, and industrial composition similarity between origins and destinations. Our findings also highlight the contribution of shared origins, destinations, and regions in explaining the systematic variation of migration flows. Counties vary more in the number of migrants they attract than the number they send. The purposed multilevel modeling approach is useful in identifying place-specific influences on migration and in improving estimation accuracy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11113-023-09782-2.

Amenity counts significantly improve water consumption predictions

Anticipating the increase in water demand in an urban area requires us to properly understand daily human movement driven by population size, land use, and amenity types among others. Mobility data from phones can capture human movement, but not only is this hard to obtain, but it also does not tell where the population is going. Previous studies have shown that amenity types can be used to predict people’s movement patterns; thus, we propose using crowd-sourced amenity data and other open data sources as reasonable proxies for human mobility. Here we present a framework for predicting water consumption in areas with established service water connections and generalize it to underserved areas. Our work used features such as geography, population, and domestic consumption ratio and compared the prediction performance of various machine learning algorithms. We used 44 months of monthly water consumption data from January 2018 to July 2021, aggregated across 1790 district metering areas (DMAs) in the east service zone of Metro Manila. Results show that amenity counts reduce the mean absolute error (MAE) of predictions by 1,440 m³/month or as much as 5.73% compared to just using population and topology features. Predicted consumption during the pandemic also improved by as much as 1,447 m³/month or nearly 16% compared to just using population and topology features. We find that Gradient Boosting Trees are the best models to handle the data and feature set used in this work. Finally, the developed model is robust to disruptions in human mobility, such as lockdowns, indicating that amenities are sufficient to predict water consumption.