THE UNIVERSITY OF CHICAGO
Yue Lin is an Assistant Instructional Professor of Geographic Information Science in the Division of the Social Sciences and the College. Her research is focused on geocomputation, geospatial data science, and digital privacy and justice. Her current work involves developing computational methods to ensure privacy, fidelity, and equity in the dissemination and mining of geospatial data. Yue received her doctorate in Geography from the Ohio State University in 2023.
Recent Research / Recent Publications
Lin, Y. (2023). Geo-indistinguishable masking: Enhancing privacy protection in spatial point mapping. Cartography and Geographic Information Science. In Press. doi: 10.1080/15230406.2023.2267967.
Lin, Y. & Xiao, N. (2023). Generating small areal synthetic microdata from public aggregated data using an optimization method. The Professional Geographer. In Press. doi:10.1080/00330124.2023.2207640.
Lin, Y. & Xiao, N. (2023). Assessing the impact of differential privacy on population uniques in geographically aggregated data: The case of the 2020 U.S. Census. Population Research and Policy Review, 42(5), 81.
Lin, Y., Xu, C., & Wang, J. (2023). sandwichr: Spatial prediction in R based on spatial stratified heterogeneity. Transactions in GIS, 27(5), 1579–1598.
Lin, Y., Li, J., Porr, A., Logan, G., Xiao, N. & Miller, H. (2023). Creating building-level, three-dimensional digital models of historic urban neighborhoods from Sanborn Fire Insurance maps using machine learning. PLoS ONE, 18(6), e0286340.
Lin, Y. & Xiao, N. (2023). A computational framework for preserving privacy and maintaining utility of geographically aggregated data: A stochastic spatial optimization approach. Annals of the American Association of Geographers, 113(5), 1035–1056.
Lin, Y. & Xiao, N. (2022). Identifying high accuracy regions in traffic camera images to enhance the estimation of road traffic metrics: A quadtree-based method. Transportation Research Record, 2676(12), 522–534.
Lin, Y. & Xiao, N. (2023). Investigating MAUP effects on census data using approximately equal-population aggregations. 12th International Conference on Geographic Information Science (GIScience 2023), September 12–15, Leeds, UK.
Lin, Y. & Xiao, N. (2022). Developing synthetic individual-level population datasets: The case of contextualizing maps of privacy-preserving census data. AutoCarto 2022, November 2–4, Redlands, CA.
Lin, Y., Xu, C., & Wang, J. sandwichr: Spatial prediction based on spatial stratified heterogeneity. R package version 1.0.4.