Spatial Statistics

Spatial Statistics is the study of spatially correlated data. Spatial data arises from a wide range of fields including mining/geology, geography, agriculture, meteorology, air quality, mapping, epidemiology, and imaging, such as satellite or biomedical images.  Analysis of spatial data accounts for statistical dependencies, accuracy and uncertainty. Statistical methods applied to spatial data will analyze distributions, patterns, processes, and predictions. Analytic methods can be frequentist and Bayesian. Future directions have incorporated a temporal component where spatial data is also measured over time. This produces high dimensional data that requires increased computing power and specialized models.

Our Research:

• Smith, L. M., Stroup, W. W., & Marx, D. B. (2020). Poisson cokriging as a generalized linear mixed model. Spatial statistics. 35: 100399.
• Kolovos, A., Smith, L. M., Schwab-McCoy, A., Gengler, S., & Yu, H. L. (2016). Emerging patterns in multi-sourced data modeling uncertainty. Spatial Statistics. 18: 300-317.
• Samson, K. K., Haynatzki, G., Soliman, A. S., & Valerianova, Z. (2016). Temporal changes in the cervical cancer burden in Bulgaria: Implications for eastern european countries going through transition., Cancer epidemiology. 44:154-160.
• Schmid K.K., Marx D., and Samal A. (2011). Weighted Bidimensional Regression. Geographical Analysis. 43(1): 1-13.
• Lahiri, P., & Meza, J. L. (2006). Small area estimation. Encyclopedia of Environmetrics. 4.
• Buskirk, T. D., & Meza, J. L. (2003). A Post-stratified Raking-ratio Estimator Linking National and State Survey Data for Estimating Drug Use. Journal of Official Statistics. 19(3): 237.
• Meza, J. L. (2003). Empirical Bayes estimation smoothing of relative risks in disease mapping. Journal of Statistical Planning and Inference. 112(1-2): 43-62.