Events Calendar

The Department of Statistics and Actuarial Science Spring Colloquium Series presents:

Abhi Datta, Associate Professor, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health

"Machine learning for geospatial data with explicit modeling of spatial correlations"

Abstract: Traditionally geospatial analysis has relied on statistical models that explicitly model spatial correlations in the data. Recently, machine learning algorithms, such as neural networks and random forests, are increasingly used in geospatial analysis. However, most machine learning algorithms do not possess the functionality to directly encode spatial correlations. There is limited understanding of the consequences of ignoring spatial correlations in machine learning algorithms applied to geospatial data, despite this practice becoming increasingly common. We show empirically and theoretically that ignoring spatial correlations reduces accuracy of machine learning algorithms for geospatial data. We then propose well-principled machine learning algorithms for geospatial data that explicitly model the spatial correlation as in traditional geostatistics. The basic principle is guided by how ordinary least squares (OLS) extends to generalized least squares (GLS) for linear models to explicitly account for data covariance. We demonstrate how the same extensions can be done for random forests and neural networks, presenting the RF-GLS and NN-GLS algorithms. We provide extensive theoretical and empirical support for the methods and show how they fare better than naïve or brute-force approaches to use machine learning algorithms for spatially correlated data. We present the software packages RandomForestsGLS and geospaNN implementing these methods.

This virtual presentation begins at 3:15 p.m.

Topic: UIowa Department of Statistics & Actuarial Science Colloquium

Time: Feb. 20, 2025, 3:15 p.m. Central Time (US and Canada)

Zoom link

Meeting ID: 939 3303 8709

Passcode: iowa