Subdivisions and Crossroads: Identifying Communities in Data Citation Networks

Sara Lafia, PhD

November 1, 2022

Archives that provide access to high quality research data offer an ideal setting to study the community impact of data sharing. In this talk, I will describe the data citation network at ICPSR – a large social science data archive at the University of Michigan – which reflects patterns of data reuse in academic literature. I have detected 41 research communities that use the same scientific datasets, which I characterize as disciplinary "subdivisions" or as integrative "crossroads" connecting research areas. I show how spatial perspectives on data use offer insights into scholarly communication processes, such as the organization of scientific communities.


Spatial modelling of the COVID-19 epidemic in China

Gang Xu, PhD

November 8, 2022

Although socioeconomic disparities in cognitive ability emerge early in the life course, most research on the consequences of living in a disadvantaged neighborhood focuses on school-age children or adolescents. In this study, we outline and test a theoretical model of neighborhood effects on cognitive development during early childhood that highlights the mediating role of environmental health hazards, and in particular, exposure to neurotoxic lead. To evaluate this model, we follow 1,266 children in Chicago from birth through the time of school entry, tracking their areal risk of lead exposure and the socioeconomic composition of their neighborhoods over time. We then estimate the joint effects of neighborhood poverty and environmental lead contamination on receptive vocabulary ability. We find that sustained exposure to disadvantaged neighborhoods reduces vocabulary skills during early childhood and that this effect operates through a causal mechanism involving lead contamination.


Evaluation of Spatial Clustering Algorithms for Analysis and Division of Hydrogen Ecosystems

Friedrich Weise

November 29, 2022

Geospatial data is of high relevance in modeling of hydrogen ecosystems, for instance to evaluate available areas, transport distances or existing infrastructure. Spatial clustering can play a key role to group this data in spatial entities depending on geo-technical conditions rather than administrative boundaries. Therefore, I evaluated multiple clustering algorithms with various variables and parameters to find the best spatial resolution fitting to our techno economic modeling done at Fraunhofer ISE. During the talk I will give insights to our applications, the selected data and methodology, my results, and indicators I developed to compare the clustering algorithms beyond the classic sum of squares.