06 June 2019 - 07 June 2019
Seville, Spain
Digital demography in the era of big data
Demography has been a data-driven discipline since its birth. Data collection and the development of formal methods have sustained most of the major advances in our understanding of population processes. The global spread of Internet, social media, cellphones and, more broadly, digital technologies, have generated new opportunities for demographic research. At the same time, the use of social media, Internet and smartphones is affecting people's daily activities as well as life planning, with implications for demographic behavior.
In this seminar of the IUSSP participants will discuss the the implications of digital technologies for demographic behavior as well as the applications of new data from digital sources to understand population processes.
IIASA researcher Dilek Yildiz will give a presentation entitled "Probabilistic methods for combining traditional and social media bilateral migration data" during the seminar. Yildiz joined the World Population (POP) Program as a research scholar in July 2017. Her current research interests are in statistical demography with a focus on Bayesian projections/reconstruction of multistate populations, population count estimates, and investigating on the use of big data sources.
For more information please visit the event website.
Abstract
Probabilistic methods for combining traditional and social media bilateral migration data
Official migration statistics are developed and published by national offices of statistics and collated by international organizations. These statistics are based on rigorous internationally harmonized principles, but they come with a considerable time lag. New data sources offer opportunities to complement traditional sources for migration statistics. In particular, the availability of high quantities of individual geo-located data from social media has opened new opportunities. In this research, we develop probabilistic methods to combine provide traditional and social media bilateral migration data to estimate timelier and potentially more accurate migration statistics.
Bayesian methods offer a powerful mechanism to combine data sources. Previously models have been developed for solely combining traditional migration data sources using the prior models for measurement parameters. We adapt the basic methodologies of these former models to combine migration data from both traditional and new data sources derived from social media.
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