Publications

Refereed Journal Articles

• Lin, Y. & Zhao, B. (2024). Posthuman cartography? Rethinking artificial intelligence, cartographic practices, and reflexivity. Annals of the American Association of Geographers. In Press.

• Xiao, N., Kim, M.J., & Lin, Y. (2024). A multistart and recombination algorithm for finding many unique solutions to spatial aggregation problems. Geoinformatica. In Press. doi: 10.1007/s10707-024-00520-0.

• Lin, Y. & Xiao, N. (2024). Exploring the tradeoff between privacy and utility of complete-count census data using a multiobjective optimization approach. Geographical Analysis, 56(3), 427–450.

• Lin, Y. (2024). Moving beyond anonymity: Embracing a collective approach to location privacy in data-intensive geospatial analytics. Environment and Planning F, 3(1-2), 45–63.

• Lin, Y. (2024). You are where you live? Evaluating the racial and ethnic (mis)representation in geodemographic classification. Applied Geography, 165, 103244.

• Lin, Y. (2024). Synthetic population data for small area estimation in the United States. Environment and Planning B: Urban Analytics and City Science, 51(2), 553–562.

• Lin, Y. & Xiao, N. (2023). Generating small areal synthetic microdata from public aggregated data using an optimization method. The Professional Geographer, 75(6), 905–915.

• Lin, Y. (2023). Geo-indistinguishable masking: Enhancing privacy protection in spatial point mapping. Cartography and Geographic Information Science, 50(6), 608–623.

• 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.

• Zhang, X., Lin, Y., Cheng, C., & Li, J. (2021). Determinant powers of socioeconomic factors and their interactive impacts on particulate matter pollution in North China. International Journal of Environmental Research and Public Health, 18(12), 6261.

• Lin, Y., Kang, M., & He, B. (2021). Spatial pattern analysis of address quality: A study on the impact of rapid urban expansion in China. Environment and Planning B: Urban Analytics and City Science, 48(4), 724–740.

• Lin, Y., Wang, J., & Xu, C. (2020). Theoretical and empirical comparative evaluations on measures of map association. Journal of Geographical Systems, 22, 361–390.

• Lin, Y., Kang, M., Wu, Y., Du, Q., & Liu, T. (2020). A deep learning architecture for semantic address matching. International Journal of Geographical Information Science, 34(3), 559–576.

• Lin, Y., Cai, Y., Gong, Y., Kang, M., & Li, L. (2019). Extracting urban landmarks from geographical datasets using a random forests classifier. International Journal of Geographical Information Science, 33(12), 2406–2423.

Refereed Conference Proceedings

• Lin, Y. (2024). A multi-objective optimization approach to balancing utility and equity in location-allocation problems. CaGIS-UCGIS 2024 Symposium, June 3–6, Columbus, OH.

• 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.

Software

• Lin, Y., Xu, C., & Wang, J. sandwichr: Spatial prediction based on spatial stratified heterogeneity. R package version 1.0.4.