Browsing by Subject "human mobility"

The Greater Region of Luxembourg is the largest cross-border labor market in the European Union with the greatest number of cross-border workers in the area. European integration, the Schengen Area, and socio-economical divergences have been the main factors facilitating human cross-border movements in the area and thus the birth and expansion of the borderland community. Despite the freedom of movement, country borders have not been erased and socio-economic divergences have not been levelled. In addition, the spatial extent of the daily movements is not well known. Thus, it is important to study cross-border dynamics and try to separate daily movements from infrequent mobility patterns. Thus far, cross-border mobility studies have mainly leaned on national registers and census data. These datasets have mostly been too scarce in trying to understand the complexities of cross-border mobility. Many studies have only focused on aggregate-level movement patterns, and the viewpoint of individuals has been missing. Hence, there has been a growing need for individual-level data to be applied in cross-border mobility research. In this study, a person-based approach is employed using geotagged Twitter Big Data to study spatio-temporal cross-border mobility patterns in the Greater Region of Luxembourg. The aim is to examine how to implement social media in cross-border research as well as how to separate daily cross-border movers from infrequent border crossers and consequently move beyond aggregate-level inspections. Being one of the first studies of its kind, a heuristic programmatic approach is utilized. To the writer’s knowledge, social media data sources have not been applied previously to distinguish different cross-border mobility types. All developed scripts in this study are openly available on Digital Geography Lab’s GitHub -pages (https://github.com/DigitalGeographyLab/cross-border-mobilitytwitter) to promote open science and to introduce new quantitative method tools for cross-border mobility research. The results show that social media can be implemented in cross-border mobility research, and social media Big Data can provide a relatively good proxy for daily cross-border mobility of people on a regional level. Aggregate-level cross-border mobility patterns and activity location densities correspond closely with previous studies, and outcomes from temporal variation inspections indicate a valid cross-border mover type identification; Twitter users classified as daily cross-border movers seem to be more mobile on weekdays whereas infrequent border crossers on weekends. Daily cross-border mobility patterns also provided new information about the spatial extent of the movements. In addition, heuristic approach resulted in high accuracy in home detection; the “unique weeks” algorithm introduced in this study produced an accuracy of 88.6 % with respect to the ground truth. Although the results are promising on a regional level, they should be considered in relation to population densities and Twitter use activity; attributes that both vary spatio-temporally and thus can cause bias. Further studies and method development are also needed to draw global conclusions about cross-border mobility; other geographical areas and study settings could result in varied outcomes. In addition, some solutions with data and methods should be considered with a critical stance due to scarcity of valid references. Yet, this study has identified that the coverage of geotagged Twitter data is dependent on data acquisition processes and that Twitter can provide valuable information for cross-border mobility research. In future studies, multi-level data acquisition processes are recommended jointly with person-based approach combining spatio-temporal and content analysis methodologies.