Inferring Migrations: Traditional Methods and New Approaches based on Mobile Phone, Social Media, and other Big Data: Feasibility study on Inferring (labour) mobility and migration in the European Union from big data and social media data

Methods for selecting loglinear models were among Steve Fienberg’s research interests since the start of his long and fruitful career. After we dwell upon the string of papers focusing on loglinear models that can be partly attributed to Steve’s contributions and influential ideas, we develop a new algorithm for selecting graphical loglinear models that is suitable for analyzing hyper-sparse contingency tables. We show how multi-way contingency tables can be used to represent patterns of human mobility. We analyze a dataset of geolocated tweets from South Africa that comprises $46$ million latitude/longitude locations of $476\mbox{,}601$ Twitter users that is summarized as a contingency table with $214$ variables.

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Loglinear model selection and human mobility

Exploring the Potential of Social-Media Data for Measuring EU Mobility Flows and Stocks of EU Movers

Measuring Labour Mobility and Migration Using Big Data

Statistics on migration flows are often derived from census data, which suffer from intrinsic limitations, including costs and infrequent sampling. When censuses are used, there is typically a time gap - up to a few years - between the data collection process and the computation and publication of relevant statistics. This gap is a significant drawback for the analysis of a phenomenon that is continuously and rapidly changing. Alternative data sources, such as surveys and field observations, also suffer from reliability, costs, and scale limitations. The ubiquity of mobile phones enables an accurate and efficient collection of up-to-date data related to migration. Indeed, passively collected data by the mobile network infrastructure via aggregated, pseudonymized Call Detail Records (CDRs) is of great value to understand human migrations. Through the analysis of mobile phone data, we can shed light on the mobility patterns of migrants, detect spontaneous settlements and understand the daily habits, levels of integration, and human connections of such vulnerable social groups. This Chapter discusses the importance of leveraging mobile phone data as an alternative data source to gather precious and previously unavailable insights on various aspects of migration. Also, we highlight pending challenges that would need to be addressed before we can effectively benefit from the availability of mobile phone data to help make better decisions that would ultimately improve millions of people's lives.

Leveraging Mobile Phone Data for Migration Flows.

Methods for selecting loglinear models were among Steve Fienberg's research interests since the start of his long and fruitful career. After we dwell upon the string of papers focusing on loglinear models that can be partly attributed to Steve's contributions and influential ideas, we develop a new algorithm for selecting graphical loglinear models that is suitable for analyzing hyper-sparse contingency tables. We show how multi-way contingency tables can be used to represent patterns of human mobility. We analyze a dataset of geolocated tweets from South Africa that comprises 46 million latitude/longitude locations of 476,601 Twitter users that is summarized as a contingency table with 214 variables. 
KEYWORDS: contingency tables, model selection, human mobility, graphical models, Bayesian structural learning, birth-death processes, pseudo-likelihood

Migration is a major challenge for the European Union, resulting in early preparedness being an imperative for target states and their stakeholders such as border police forces. This preparedness is necessary for multiple reasons, including the provision of adequate search and rescue measures. To support preparedness, there is a need for early indicators for detection of developing migratory push-factors related to imminent migration flows. To address this need, we have investigated the daily number of geo-referenced Tweets in three regions of Ukraine and the whole of Japan from August 2014 until October 2014. This analysis was done by using the data handling tool Ubicity. Additionally, we have identified days when relevant natural, civil or political events took place in order to identify possible event triggered changes of the daily number of Tweets. In all the examined Ukrainian regions a considerable increase in the number of daily Tweets was observed for the election day of a new parliament. Furthermore, we identified a significant decrease in the number of daily Tweets for the Crimea for the whole examined period which could be related to the political changes that took place. The natural disasters identified in Japan do not show a clear relationship with the changes in the degree of use of the social media tool Twitter. The results are a good basis to use communication patterns as future key indicator for migration analysis.

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Stefan Schirnhofer

Papers

On the Volume of Geo-referenced Tweets and Their Relationship to Events Relevant for Migration Tracking

Validation of the Management of Past Crisis and Disasters