Revealing mobilities of people to understand cross-border regions: insights from Luxembourg using social media data

References

• Aagesen, H., O. Järv, and P. Gerber. 2022. “The Effect of COVID-19 on Cross-Border Mobilities of People and Functional Border Regions: The Nordic Case Study from Twitter Data.” Geografiska Annaler: Series B. doi:10.1080/04353684.2022.2101135.
   Web of Science ®Google Scholar
• Ahas, R., A. Aasa, A. Roose, Ü Mark, and S. Silm. 2008. “Evaluating Passive Mobile Positioning Data for Tourism Surveys: An Estonian Case Study.” Tourism Management 29: 469–486. doi:10.1016/j.tourman.2007.05.014.
   Web of Science ®Google Scholar
• Ahas, R., S. Silm, O. Järv, E. Saluveer, and M. Tiru. 2010. “Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones.” Journal of Urban Technology 17: 3–27. doi:10.1080/10630731003597306.
   Web of Science ®Google Scholar
• Andrienko, N., G. Andrienko, G. Fuchs, and P. Jankowski. 2016. “Scalable and Privacy-Respectful Interactive Discovery of Place Semantics from Human Mobility Traces.” Information Visualization 15: 117–153. doi:10.1177/1473871615581216.
   Web of Science ®Google Scholar
• Beduschi, A. 2020. “International Migration Management in the Age of Artificial Intelligence.” Migration Studies 9: 576–596. doi:10.1093/migration/mnaa003.
   Web of Science ®Google Scholar
• Blanford, J., Z. Huang, A. Savelyev, and A. Maceachren. 2015. “Geo-Located Tweets. Enhancing Mobility Maps and Capturing Cross-Border Movement R. Lambiotte, ed.” PLOS ONE 10: e0129202. doi:10.1371/journal.pone.0129202.
   PubMed Web of Science ®Google Scholar
• Burrows, R., and M. Savage. 2014. “After the Crisis? Big Data and the Methodological Challenges of Empirical Sociology.” Big Data & Society, 1: 1–6. doi: 10.1177/2053951714540280
   Google Scholar
• Carling, J., M. Erdal, and C. Talleraas. 2021. “Living in Two Countries: Transnational Living as an Alternative to Migration.” Population, Space and Place 27. doi:10.1002/psp.2471.
   Web of Science ®Google Scholar
• Castells, M. 1996. The Rise of the Network Society. The Information Age: Economy, Society and Culture. Malden, MA: Blackwell.
   Google Scholar
• Chua, A., L. Servillo, E. Marcheggiani, and A. Moere. 2016. “Mapping Cilento: Using Geotagged Social Media Data to Characterize Tourist Flows in Southern Italy.” Tourism Management 57: 295–310. doi:10.1016/j.tourman.2016.06.013.
   Web of Science ®Google Scholar
• Collins, F. 2009. “Transnationalism Unbound: Detailing New Subjects, Registers and Spatialities of Cross-Border Lives.” Geography Compass 3: 434–458. doi:10.1111/j.1749-8198.2008.00187.x.
   Google Scholar
• Conradson, D., and A. Latham. 2005. “Transnational Urbanism: Attending to Everyday Practices and Mobilities.” Journal of Ethnic and Migration Studies 31: 227–233. doi:10.1080/1369183042000339891.
   Web of Science ®Google Scholar
• Decoville, A., and F. Durand. 2018. “Exploring Cross-border Integration in Europe: How Do Populations Cross Borders and Perceive Their Neighbours?” European Urban and Regional Studies, 26: 134–157. doi: 10.1177/0969776418756934
   PubMed Web of Science ®Google Scholar
• Docquier, F., N. Golenvaux, S. Nijssen, P. Schaus, and F. Stips. 2021. “Cross-Border Mobility Responses to Covid-19 in Europe: New Evidence from Facebook Data”. Globalization and Health, 18. doi: 10.1186/s12992-022-00832-6
   Google Scholar
• Dörry, S., and A. Decoville. 2016. “Governance and Transportation Policy Networks in the Cross-Border Metropolitan Region of Luxembourg: A Social Network Analysis.” European Urban and Regional Studies 23: 69–85. doi:10.1177/0969776413490528.
   Web of Science ®Google Scholar
• Drevon, G., P. Gerber, O. Klein, and C. Enaux. 2018. “Measuring Functional Integration by Identifying the Trip Chains and the Profiles of Cross-Border Workers: Empirical Evidences from Luxembourg.” Journal of Borderlands Studies 33: 549–568. doi:10.1080/08865655.2016.1257362.
   Web of Science ®Google Scholar
• Durand, F. 2015. “Theoretical Framework of the Cross-border Space Production – The Case of the Eurometropolis Lille–Kortrijk–Tournai.” Journal of Borderlands Studies 30: 309–328. doi:10.1080/08865655.2015.1066701.
   Web of Science ®Google Scholar
• European Commission. 2017. Boosting growth and cohesion in EU border regions. Communication from the Commission to the Council and the European parliament. Brussels: European Commission.
   Google Scholar
• Gendronneau, C., A. Wisniowski, D. Yildiz, E. Zagheni, L. Florio, Y. Hsiao, M. Stepanek, I. Weber, G. Abel, and S. Hoorens. 2019. “Measuring Labour Mobility and Migration Using Big Data: Exploring the Potential of Social-Media Data for Measuring EU Mobility Flows and Stocks of EU Movers.”Brussels: Publications Office of the European Union.
   Google Scholar
• Gerber, P. 2012. “Advancement in Conceptualizing Cross-Border Daily Mobility: The Benelux Context in the European Union.” European Journal of Transport and Infrastructure Research 12: 178–197. doi: 10.18757/ejtir.2012.12.3.2980
   Web of Science ®Google Scholar
• Gerber, P., M. Thériault, C. Enaux, and S. Carpentier-Postel. 2020. “Links Between Attitudes, Mode Choice, and Travel Satisfaction: A Cross-Border Long-Commute Case Study.” Sustainability 12: 9203. doi:10.3390/su12219203.
   Web of Science ®Google Scholar
• González, M., C. Hidalgo, and A. Barabási. 2008. “Understanding Individual Human Mobility Patterns.” Nature 453: 779–782. doi:10.1038/nature06958.
   PubMed Web of Science ®Google Scholar
• Gutiérrez, M., A. Sánchez-Atondo, L. García, A. Mungaray-Moctezuma, and J. Calderón. 2021. “Understanding Cross-border Mobility in Medium-Small Mexico-U.S. Binational Regions. Mexicali-Imperial Valley Case Study.” Transportation Research Interdisciplinary Perspectives 9: 100324. doi:10.1016/j.trip.2021.100324.
   Google Scholar
• Hall, C. 2005. “Reconsidering the Geography of Tourism and Contemporary Mobility.” Geographical Research 43: 125–139. doi:10.1111/j.1745-5871.2005.00308.x.
   Web of Science ®Google Scholar
• Harrison, J., and A. Growe. 2014. “From Places to Flows? Planning for the New ‘Regional World’ in Germany.” European Urban and Regional Studies 21: 21–41. doi:10.1177/0969776412441191.
   Web of Science ®Google Scholar
• Hasnat, M., and S. Hasan. 2018. “Identifying Tourists and Analyzing Spatial Patterns of Their Destinations from Location-Based Social Media Data.” Transportation Research Part C: Emerging Technologies 96: 38–54. doi:10.1016/j.trc.2018.09.006.
   Web of Science ®Google Scholar
• Hawelka, B., I. Sitko, E. Beinat, S. Sobolevsky, P. Kazakopoulos, and C. Ratti. 2014. “Geo-Located Twitter as Proxy for Global Mobility Patterns.” Cartography and Geographic Information Science 41: 260–271. doi:10.1080/15230406.2014.890072.
   PubMed Web of Science ®Google Scholar
• Hägerstrand, T. 1970. “What About People in Regional Science?” Papers of the Regional Science Association 24: 6–21. doi:10.1007/BF01936872.
   Google Scholar
• Heikinheimo, V., O. Järv, H. Tenkanen, T. Hiippala, and T. Toivonen. 2022. “Detecting Country of Residence from Social Media Data: A Comparison of Methods.” International Journal of Geographical Information Science, doi:10.1080/13658816.2022.2044484.
   Web of Science ®Google Scholar
• Hiippala, T., T. Väisänen, T. Toivonen, and O. Järv. 2020. “Mapping the Languages of Twitter in Finland: Richness and Diversity in Space and Time.” Neuphilologische Mitteilungen 121: 12–44. doi:10.51814/nm.99996.
   Web of Science ®Google Scholar
• Jackson, P., P. Crang, and C. Dwyer. 2004. Transnational Spaces. London: Routledge.
   Google Scholar
• Järv, O., R. Ahas, and F. Witlox. 2014. “Understanding Monthly Variability in Human Activity Spaces: A Twelve-Month Study Using Mobile Phone Call Detail Records.” Transportation Research Part C: Emerging Technologies 38: 122–135. doi:10.1016/j.trc.2013.11.003.
   Web of Science ®Google Scholar
• Järv, O., A. Masso, S. Silm, and R. Ahas. 2020. “The Link Between Ethnic Segregation and Socio-Economic Status: An Activity Space Approach.” Tijdschrift Voor Economische en Sociale Geografie 112: 319–335. doi:10.1111/tesg.12465.
   Web of Science ®Google Scholar
• Järv, O., A. Tominga, K. Müürisepp, and S. Silm. 2021. “The Impact of COVID-19 on Daily Lives of Transnational People Based on Smartphone Data: Estonians in Finland.” Journal of Location Based Services, 15: 169–197. doi:10.1080/17489725.2021.1887526
   Web of Science ®Google Scholar
• Justarrived.lu. 2022. “Shopping Close to Luxembourg Borders.” Accessed 2 February 2022. https://www.justarrived.lu/en/practical-information/shopping-belgium-germany-france/.
   Google Scholar
• Kaufmann, V. 2014. “Mobility as a Tool for Sociology.” Sociologica, 1: 0–0. doi: 10.2383/77046
   Google Scholar
• Kemp, S. 2019. Digital 2019: Global Digital Report. Kepios.
   Google Scholar
• Kitchin, R. 2014. The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences. Los Angeles, CA: SAGE.
   Google Scholar
• Kwan, M. 2013. “Beyond Space (As We Knew It): Toward Temporally Integrated Geographies of Segregation, Health, and Accessibility.” Annals of the Association of American Geographers 103: 1078–1086. doi:10.1080/00045608.2013.792177
   Web of Science ®Google Scholar
• Lansley, G., and P. Longley. 2016. “The Geography of Twitter Topics in London.” Computers, Environment and Urban Systems 58: 85–96. doi:10.1016/j.compenvurbsys.2016.04.002.
   Web of Science ®Google Scholar
• Liu, X., Q. Huang, S. Gao, and J. Xia. 2021. “Activity Knowledge Discovery: Detecting Collective and Individual Activities with Digital Footprints and Open Source Geographic Data.” Computers, Environment and Urban Systems 85: 101551. doi:10.1016/j.compenvurbsys.2020.101551.
   Web of Science ®Google Scholar
• Löfgren, O. 2008. “Regionauts: The Transformation of Cross-Border Regions in Scandinavia.” European Urban and Regional Studies 15: 195–209. doi:10.1177/0969776408090418.
   Web of Science ®Google Scholar
• Martí, P., L. Serrano-Estrada, and A. Nolasco-Cirugeda. 2019. “Social Media Data: Challenges, Opportunities and Limitations in Urban Studies.” Computers, Environment and Urban Systems 74: 161–174. doi:10.1016/j.compenvurbsys.2018.11.001.
   Web of Science ®Google Scholar
• Mathä, T., A. Porpiglia, and M. Ziegelmeyer. 2017. “Cross-Border Commuting and Consuming: An Empirical Investigation.” Applied Economics 49: 2011–2026. doi:10.1080/00036846.2016.1231902.
   Web of Science ®Google Scholar
• Mazzoli, M., B. Diechtiareff, A. Tugores, W. Wives, N. Adler, P. Colet, and J. Ramasco. 2020. “Migrant Mobility Flows Characterized with Digital Data J. Paniagua, ed.” PLOS ONE 15: e0230264. doi:10.1371/journal.pone.0230264.
   PubMed Web of Science ®Google Scholar
• Medeiros, E. 2020. “Delimiting Cross-Border Areas for Policy Implementation: A Multi-Factor Proposal.” European Planning Studies 28: 125–145. doi:10.1080/09654313.2019.1687654.
   Web of Science ®Google Scholar
• Moisio, S. 2011. “Geographies of Europeanization: The EU's Spatial Planning as a Politics of Scale”, in Europe in the World: EU Geopolitics and the Making of European Space, edited by L. Bialasiewicz L., pp. 19-39. Farnham: Ashgate.
   Google Scholar
• Möller, C., E. Alfredsson-Olsson, B. Ericsson, and K. Overvåg. 2018. “The Border as an Engine for Mobility and Spatial Integration: A Study of Commuting in a Swedish–Norwegian Context.” Norsk Geografisk Tidsskrift - Norwegian Journal of Geography 72: 217–233. doi:10.1080/00291951.2018.1497698.
   Web of Science ®Google Scholar
• Ortúzar, J., J. Armoogum, J. Madre, and F. Potier. 2011. “Continuous Mobility Surveys: The State of Practice.” Transport Reviews 31: 293–312. doi:10.1080/01441647.2010.510224.
   Web of Science ®Google Scholar
• Pierrard, O. 2008. “Commuters, Residents and job Competition.” Regional Science and Urban Economics 38: 565–577. doi:10.1016/j.regsciurbeco.2008.04.003.
   Web of Science ®Google Scholar
• Poom, A., O. Järv, M. Zook, and T. Toivonen. 2020. “COVID-19 is Spatial: Ensuring That Mobile Big Data is Used for Social Good.” Big Data & Society 7. doi:10.1177/2053951720952088.
   PubMed Web of Science ®Google Scholar
• Poorthuis, A., and M. Zook. 2017. “Making Big Data Small: Strategies to Expand Urban and Geographical Research Using Social Media.” Journal of Urban Technology 24: 115–135. doi:10.1080/10630732.2017.1335153.
   Web of Science ®Google Scholar
• Pupier, P. 2020. “Spatial Evolution of Cross-Border Regions. Contrasted Case Studies in North-West Europe.” European Planning Studies 28: 81–104. doi:10.1080/09654313.2019.1623975.
   Web of Science ®Google Scholar
• Ramirez, M., and A. Winkler. 2012. Information Services for Cross-Border Workers in European Border Regions. Gronau: Association of European Border Regions.
   Google Scholar
• Rashidi, T., A. Abbasi, M. Maghrebi, S. Hasan, and T. S. Waller. 2017. “Exploring the Capacity of Social Media Data for Modelling Travel Behaviour: Opportunities and Challenges.” Transportation Research Part C: Emerging Technologies 75: 197–211. doi:10.1016/j.trc.2016.12.008.
   Web of Science ®Google Scholar
• Raun, J., R. Ahas, and M. Tiru. 2016. “Measuring Tourism Destinations Using Mobile Tracking Data.” Tourism Management 57: 202–212. doi:10.1016/j.tourman.2016.06.006.
   Web of Science ®Google Scholar
• Rodríguez, M., J. Molina, and J. Camacho. 2022. “Mapping Functional Areas in Spain Using Mobile Positioning Data.” European Urban and Regional Studies, 29: 145–151. 096977642110662.
   Web of Science ®Google Scholar
• Ruktanonchai, N., C. Ruktanonchai, J. Floyd, and A. Tatem. 2018. “Using Google Location History Data to Quantify Fine-Scale Human Mobility.” International Journal of Health Geographics 17: 28. doi:10.1186/s12942-018-0150-z.
   PubMed Web of Science ®Google Scholar
• Saluveer, E., J. Raun, M. Tiru, L. Altin, J. Kroon, T. Snitsarenko, A. Aasa, and S. Silm. 2020. “Methodological Framework for Producing National Tourism Statistics from Mobile Positioning Data.” Annals of Tourism Research 81: 102895. doi:10.1016/j.annals.2020.102895.
   Web of Science ®Google Scholar
• Schmitz, F., G. Drevon, and P. Gerber. 2012. La mobilité des frontaliers du Luxembourg : dynamiques et perspectives.
   Google Scholar
• Schönfelder, S., and K. Axhausen. 2010. Urban Rhythms and Travel Behaviour. Urban Rhythms and Travel Behaviour. Surrey: Ashgate.
   Google Scholar
• Sheller, M., and J. Urry. 2006. “The New Mobilities Paradigm.” Environment and Planning A 38: 207–226. doi:10.1068/a37268.
   Web of Science ®Google Scholar
• Silm, S., J. Jauhiainen, J. Raun, and M. Tiru. 2020b. “Temporary Population Mobilities Between Estonia and Finland based on Mobile Phone Data and the Emergence of a Cross-Border Region.” European Planning Studies, 29: 1–21. doi: 10.1080/09654313.2020.1774514
   Web of Science ®Google Scholar
• Silm, S., O. Järv, and A. Masso. 2020a. “Tracing Human Mobilities Through Mobile Phones.” In Handbook of Research Methods and Applications for Mobilities, edited by M. Büscher, M. Freudendal-Pedersen, S. Kesselring, and N. G. Kristensen, 182–192. Cheltenham: Edward Elgar Publishing.
   Google Scholar
• Sloan, L., J. Morgan, P. Burnap, and M. Williams. 2015. “Who Tweets? Deriving the Demographic Characteristics of Age, Occupation and Social Class from Twitter User Meta-Data T. Preis, ed.” PLOS ONE 10: e0115545. doi: 10.1371/journal.pone.0115545
   PubMed Web of Science ®Google Scholar
• Sohn, C., and B. Reitel. 2016. “The Role of National States in the Construction of Cross-Border Metropolitan Regions in Europe: A Scalar Approach.” European Urban and Regional Studies 23: 306–321. doi:10.1177/0969776413512138.
   Web of Science ®Google Scholar
• Spyratos, S., M. Vespe, F. Natale, I. Weber, E. Zagheni, and M. Rango. 2019. “Quantifying International Human Mobility Patterns Using Facebook Network Data I. Benenson, ed.” PLOS ONE 14: e0224134. doi:10.1371/journal.pone.0224134.
   PubMed Web of Science ®Google Scholar
• Statec. 2021. Labour market statistics.
   Google Scholar
• Steinmetz, C., H. Rahmat, N. Marshall, K. Bishop, S. Thompson, M. Park, L. Corkery, and C. Tietz. 2021. “Liking, Tweeting and Posting: An Analysis of Community Engagement Through Social Media Platforms.” Urban Policy and Research 39: 85–105. doi:10.1080/08111146.2020.1792283.
   Web of Science ®Google Scholar
• Takhteyev, Y., A. Gruzd, and B. Wellman. 2012. “Geography of Twitter Networks.” Social Networks 34: 73–81. doi:10.1016/j.socnet.2011.05.006.
   Web of Science ®Google Scholar
• Toivonen, T., V. Heikinheimo, C. Fink, A. Hausmann, T. Hiippala, O. Järv, H. Tenkanen, and E. Di Minin. 2019. “Social Media Data for Conservation Science: A Methodological Overview.” Biological Conservation 233: 298–315. doi:10.1016/j.biocon.2019.01.023.
   Web of Science ®Google Scholar
• Twitter Inc. 2022. “Twitter API: Academic Research access.” Accessed 6 January 2022. https://developer.twitter.com/en/products/twitter-api/academic-research.
   Google Scholar
• United Nations. 2017. International Migration Report 2017: Highlights. United Nations, Department of Economic and Social Affairs, Population Division.
   Google Scholar
• Väisänen, T., V. Heikinheimo, T. Hiippala, and T. Toivonen. 2021a. “Exploring Human–Nature Interactions in National Parks with Social Media Photographs and Computer Vision.” Conservation Biology 35: 424–436. doi:10.1111/cobi.13704.
   PubMed Web of Science ®Google Scholar
• Väisänen, T., S. Sirkiä, T. Hiippala, O. Järv, and T. Toivonen. 2021b. tweetsearcher: A Python Tool to Download Tweets Using Academic Research Credentials. Zenodo.
   Google Scholar
• Wang, Z., S. He, and Y. Leung. 2018b. “Applying Mobile Phone Data to Travel Behaviour Research: A Literature Review.” Travel Behaviour and Society 11: 141–155. doi:10.1016/j.tbs.2017.02.005.
   Web of Science ®Google Scholar
• Wang, Q., N. Phillips, M. Small, and R. Sampson. 2018a. “Urban Mobility and Neighborhood Isolation in America’s 50 Largest Cities.” Proceedings of the National Academy of Sciences 115: 7735–7740. doi:10.1073/pnas.1802537115.
   PubMed Web of Science ®Google Scholar
• World Tourism Organization, ed. 2018. UNWTO Tourism Highlights: 2018 Edition. UNWTO.
   Google Scholar
• Yue, Y., T. Lan, A. Yeh, and Q. Li. 2014. “Zooming Into Individuals to Understand the Collective: A Review of Trajectory-Based Travel Behaviour Studies.” Travel Behaviour and Society 1: 69–78. doi:10.1016/j.tbs.2013.12.002.
   Web of Science ®Google Scholar
• Zagheni, E., V. Garimella, I. Weber, and B. State. 2014. “Inferring International and Internal Migration Patterns from Twitter Data.” In: Proceedings of the 23rd International Conference on World Wide Web – WWW ‘14 Companion, 439–444. ACM Press: Seoul, Korea.
   Google Scholar
• Zhang, G., and A. Zhu. 2018. “The Representativeness and Spatial Bias of Volunteered Geographic Information: A Review.” Annals of GIS 24: 151–162. doi:10.1080/19475683.2018.1501607
   Web of Science ®Google Scholar