Research Examples

The Center for Spatial Data Science (CSDS) publishes research on new methods and open-source software tools for spatial econometric modeling, clustering, exploratory analysis, and geovisual analytics.

CSDS also addresses research questions where location represents an important dimension of the research problem, with applications in areas such as economic geography, real estate, sociology, psychology, or urban planning. 

Here are some examples of our recent spatial research:

Spatial Methods & Tools
specification search
Luc Anselin, Renan Serenini, and Pedro Amaral. (2024). Spatial Econometric Model Specification Search: Another Look. Preprint. DOI: 10.13140/RG.2.2.10650.86721.
Research
Anselin, L., Li, X., & Koschinsky, J. (2022). GeoDa, from the desktop to an ecosystem for exploring spatial data. Geographical Analysis (GA), 54(3), 439-466. Most downloaded GA paper in 2021.
pysal
Rey, S. J., Anselin, L., Amaral, P., Arribas‐Bel, D., Cortes, R. X., Gaboardi, J. D., ... & Wolf, L. J. (2022). The PySAL ecosystem: Philosophy and implementation. Geographical Analysis, 54(3), 467-487.
Research
Anselin, L., & Amaral, P. (2023). Endogenous spatial regimes. Journal of Geographical Systems, 1-26.
Research
Anselin, L. (2019). A local indicator of multivariate spatial association: extending Geary's C. Geographical Analysis, 51(2), 133-150.
Research
Anselin, L. and X. Li. “Tobler’s Law in a Multivariate World.” Geographical Analysis 52 (4), 2020: 494-510 (DOI:10.1111/GEAN.12237, April 2020).
Research
Anselin, L. “Quantile Local Spatial Autocorrelation.” Letters in Spatial and Resource Science 12 (2), 2019: 155-166 (DOI: 10.1007/s12076-019-00234-0, July 2019).
Research
Anselin, L. and X. Li. “Operational Local Join Count Statistics for Cluster Detection.” Journal of Geographical Systems 21 (2), 2019: 189-210 (DOI: 10.1007/s10109-019-00300-7, April 2019).
Research
Saxon, J., Koschinsky, J., Acosta, K., Anguiano, V., Anselin, L., & Rey, S. (2021). An open software environment to make spatial access metrics more accessible. Journal of Computational Social Science, 1-20. See also the PySAL access package: https://pysal.org/access/
Research
Lin, Y., Xu, C., & Wang, J. (2023). sandwichr: Spatial prediction in R based on spatial stratified heterogeneity. Transactions in GIS, 27(5), 1579–1598.
Research
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.
Research
Dodge, S., Toka, M., & Bae, C. J. (2021). DynamoVis 1.0: an exploratory data visualization software for mapping movement in relation to internal and external factors. Movement Ecology, 9:55: 218–240.
Research
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.
Research
Kolak, M. and L. Anselin. “A Spatial Perspective on the Econometrics of Program Evaluation.” International Regional Science Review 43 (1-2), 2020: 128-153.
Research
Pereira, R. H., Braga, C. K. V., Servo, L. M., Serra, B., Amaral, P., Gouveia, N., & Paez, A. (2021). Geographic access to COVID-19 healthcare in Brazil using a balanced float catchment area approach. Social Science & Medicine, 273, 113773.
Research
Medeiros, V., Ribeiro, R. S. M., & do Amaral, P. V. M. (2021). Infrastructure and household poverty in Brazil: A regional approach using multilevel models. World Development, 137, 105118.
Research
Algeri, C., L. Anselin, A. Fabio Forgione, C. Migliardo. “Spatial Dependence in the Technical Efficiency of Local Banks.” Papers in Regional Science 101 (3), 2022: 685-716 (DOI:10.1111/pirs.12669)
Protecting Privacy in Mapping
Research
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.
Research
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.
Research
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.
Research
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.
Spatial Cognition and Wayfinding
Research
Bae, C. J. & Montello, D. R. (2019). Dyadic Route Planning and Navigation in Collaborative Wayfinding. 14th International Conference on Spatial Information Theory (COSIT 2019).
Research
Montello, D. R., Curtin, K. M., Bae, C., & Mitchell, P. (2024). With a Little Help From My Friends: An Overview of Collective Spatial Cognition. Collective Spatial Cognition, 3-33.
Research
Bae, C. J., & Dodge, S. (2023). Assessing the cognition of movement trajectory visualizations: interpreting speed and direction. Cartography and Geographic Information Science, 50(2), 143-161.
Research
Bae, C. J. H., & Montello, D. R. (2018). Representations of an urban ethnic neighbourhood: Residents' cognitive boundaries of Koreatown, Los Angeles. Built Environment, 44(2), 218-240.