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DTSTART;TZID=Europe/Amsterdam:20221103T160000
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SUMMARY:The anatomy of a population-scale social network
DESCRIPTION:Lecture by Eszter Bokányi for the Network Seminar series of the Learning Planet Institute \n\n\n\n Common large-scale approaches to inferring social structure make use of digital traces such as online social networks or mobile communication data. However\, these networks are often agnostic of node and edge representativity and type. This talk investigates the structure of a social network sourced from administrative registers for an entire population based on family\, household\, work\, school\, and next-door neighbor relations\, alongside rich demographic node attributes. We revisit three of the most common concepts in social network analysis: degree\, closure and distance. We find that observed degrees are the result of a combination of degree distributions in various layers\, disqualifying common explanatory mechanisms such as preferential attachment. Low node-to-node distances are realized through particular edge types that shortcut paths in already clustered areas. Measuring closure across layers shows how we can realistically capture the extent to which people have closed or open network opportunity structures. Finally\, we highlight how people’s network structure varies greatly along demographic axes such as age\, income and level of education. This shows that understanding of both the type of edge and the part of the population that is considered is of great importance. Therefore\, leveraging register data to capture the social structure of a complete population is one of the most fruitful ways forward to obtain actionable insights and ultimately evidence-based policies.
URL:https://www.popnet.io/events/the-anatomy-of-a-population-scale-social-network-4/
CATEGORIES:Lecture
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DTSTART;TZID=Europe/Amsterdam:20221103T161500
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SUMMARY:Capturing the social fabric: Population-scale socio-economic segregation patterns
DESCRIPTION:Conference talk by Yuliia Kazmina at the Odissei Conference for Social Science in the Netherlands 2022 in session 4.1.  \n\n\n\nSegregation is a widely studied issue traditionally explored from the point of the spatial distribution of different groups as defined by any individual attribute such as race\, religion\, social class\, etc. Nevertheless\, we argue that the issues of persistent segregation\, specifically socio-economic segregation\, are networked phenomena and should be studied as such. In this paper\, we make a methodological contribution that would allow the scholarship and policymakers to move away from a traditional spatial understanding of segregation that ignores interactions beyond neighborhoods and shift the focus of segregation measurement to the social network aspect applied to a diverse set of previously unexplored distinct social contexts. \n\n\n\nThe study is based on the Dutch population register data sourced from multiple existing sub-registers that contain information on formal ties and affiliations of ~17 million legal residents in multiple social contexts such as kinship\, household\, neighborhood\, school\, and work. With the multiplex network of geospatially embedded formal ties in hand\, we aim to observe to what extent areas of social segregation are clustered in geospatially embedded social networks\, and how each network layer contributes to the issue. More specifically\, we measure to what extent Dutch residents in different municipalities are exposed to individuals of different socio-economic statuses in diverse social contexts and what social contexts provide diverse social contact opportunities with respect to the socio-economic status and\, on the contrary\, what social contexts play a role of socio-economic bubbles. Our findings suggest great heterogeneity in socio-economic assortativity between different social contexts (the layers of the analysed network) as well as different municipalities.
URL:https://www.popnet.io/events/capturing-the-social-fabric-population-scale-socio-economic-segregation-patterns-3/
CATEGORIES:Conference talk
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