The diaspora model for human migration
- URL: http://arxiv.org/abs/2309.03070v1
- Date: Wed, 6 Sep 2023 15:17:53 GMT
- Title: The diaspora model for human migration
- Authors: Rafael Prieto-Curiel and Ola Ali and Elma Dervic and Fariba Karimi and
Elisa Omodei and Rainer St\"utz and Georg Heiler and Yurij Holovatch
- Abstract summary: Existing models primarily rely on population size and travel distance to explain flow fluctuations.
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.
- Score: 0.07852714805965527
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 flow fluctuations, 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 due to recent natural and societal crises, this study
enlightens our understanding of migration flow heterogeneities, helping design
more inclusive, integrated cities.
Related papers
- Labor Migration Modeling through Large-scale Job Query Data [36.87413768190629]
We propose a deep learning-based spatial-temporal labor migration analysis framework, DHG-SIL, by leveraging large-scale job query data.
Specifically, we first acquire labor migration intention as a proxy of labor migration via job queries from one of the world's largest search engines.
We introduce four interpretable variables to quantify city migration properties, which are co-optimized with city representations.
arXiv Detail & Related papers (2024-10-03T16:24:14Z) - Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold [83.18058549195855]
We argue that multiple processes in natural sciences have to be represented as vector fields on the Wasserstein manifold of probability densities.
In particular, this is crucial for personalized medicine where the development of diseases and their respective treatment response depends on the microenvironment of cells specific to each patient.
We propose Meta Flow Matching (MFM), a practical approach to integrating along these vector fields on the Wasserstein manifold by amortizing the flow model over the initial populations.
arXiv Detail & Related papers (2024-08-26T20:05:31Z) - The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented Intervention [61.80236015147771]
We quantify the trade-off between using diversity interventions and preserving demographic factuality in T2I models.
Experiments on DoFaiR reveal that diversity-oriented instructions increase the number of different gender and racial groups.
We propose Fact-Augmented Intervention (FAI) to reflect on verbalized or retrieved factual information about gender and racial compositions of generation subjects in history.
arXiv Detail & Related papers (2024-06-29T09:09:42Z) - Combining Twitter and Mobile Phone Data to Observe Border-Rush: The Turkish-European Border Opening [2.5693085674985117]
Following Turkey's 2020 decision to revoke border controls, many individuals journeyed towards the Greek, Bulgarian, and Turkish borders.
However, the lack of verifiable statistics on irregular migration and discrepancies between media reports and actual migration patterns require further exploration.
This study is to bridge this knowledge gap by harnessing novel data sources, specifically mobile phone and Twitter data.
arXiv Detail & Related papers (2024-05-21T09:51:15Z) - On the steerability of large language models toward data-driven personas [98.9138902560793]
Large language models (LLMs) are known to generate biased responses where the opinions of certain groups and populations are underrepresented.
Here, we present a novel approach to achieve controllable generation of specific viewpoints using LLMs.
arXiv Detail & Related papers (2023-11-08T19:01:13Z) - Estimating Latent Population Flows from Aggregated Data via Inversing
Multi-Marginal Optimal Transport [57.16851632525864]
We study the problem of estimating latent population flows from aggregated count data.
This problem arises when individual trajectories are not available due to privacy issues or measurement fidelity.
We propose to estimate the transition flows from aggregated data by learning the cost functions of the MOT framework.
arXiv Detail & Related papers (2022-12-30T03:03:23Z) - Investigating internal migration with network analysis and latent space
representations: An application to Turkey [0.0]
We provide an in-depth investigation into the structure and dynamics of the internal migration in Turkey from 2008 to 2020.
We identify a set of classical migration laws and examine them via various methods for signed network analysis, ego network analysis, representation learning, temporal stability analysis, and network visualization.
The findings show that, in line with the classical migration laws, most migration links are geographically bounded with several exceptions involving cities with large economic activity.
arXiv Detail & Related papers (2022-01-10T18:58:02Z) - JKOnet: Proximal Optimal Transport Modeling of Population Dynamics [69.89192135800143]
We propose a neural architecture that combines an energy model on measures, with (small) optimal displacements solved with input convex neural networks (ICNN)
We demonstrate the applicability of our model to explain and predict population dynamics.
arXiv Detail & Related papers (2021-06-11T12:30:43Z) - Unsupervised embedding of trajectories captures the latent structure of
scientific migration [4.028844692958469]
We show the ability of the model word2vec to encode nuanced relationships between discrete locations from migration trajectories.
We show that the power of word2vec to encode migration patterns stems from its mathematical equivalence with the gravity model of mobility.
Using techniques that leverage its semantic structure, we demonstrate that embeddings can learn the rich structure that underpins scientific migration.
arXiv Detail & Related papers (2020-12-04T18:58:41Z) - Forecasting asylum-related migration flows with machine learning and
data at scale [0.0]
We show that adaptive machine learning algorithms can effectively forecast asylum-related migration flows.
We exploit three tiers of data - geolocated events and internet searches in countries of origin, detections of irregular crossings at the EU border, and asylum recognition rates in countries of destination.
arXiv Detail & Related papers (2020-11-09T11:31:17Z) - Magnify Your Population: Statistical Downscaling to Augment the Spatial
Resolution of Socioeconomic Census Data [48.7576911714538]
We present a new statistical downscaling approach to derive fine-scale estimates of key socioeconomic attributes.
For each selected socioeconomic variable, a Random Forest model is trained on the source Census units and then used to generate fine-scale gridded predictions.
As a case study, we apply this method to Census data in the United States, downscaling the selected socioeconomic variables available at the block group level, to a grid of 300 spatial resolution.
arXiv Detail & Related papers (2020-06-23T16:52:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.