Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by Simon Brewer, Ph.D.

The spatial context of health data is increasingly recognized as a critical factor in disease surveillance, resource allocation, and planning. Indexing health outcomes by location allows the exploration of patterns in space and the correlation of those patterns to social and environmental factors. This allows us to gain insights into exposure to environmental hazards, healthcare accessibility, and inequities in health outcomes. However, the spatial nature of these data can lead to biases when using standard inferential approaches (e.g. OLS models), necessitating specialized approaches.

The goal of this seminar is to provide you with an understanding of the issues that can arise when working with geospatial health data, and give you experience with the tools necessary to successfully address these issues. Key topics include accessing and working with spatial data, visualizing spatial patterns through static and interactive maps, exploring spatial dependency, and building spatial regression models. We will also extend these methods to temporal datasets with multiple time points.

Placing public health data in a spatial context allows for the exploration of environmental and social factors that may be linked to health outcomes. Spatial data are characterized by spatial dependency, where similar values cluster, and spatial heterogeneity, where correlations among variables differ across locations. Standard inferential approaches risk ignoring these effects and can lead to inefficient and/or biased model results. This seminar will introduce a structured workflow for managing, visualizing, and modeling spatial health data, with a particular focus on areal data (data aggregated within regions).

Throughout the seminar, you will gain experience using R/RStudio as the computational environment, which will support the entire workflow. Beginning with an introduction to spatial data concepts, you will learn how to manipulate, filter, and organize these data. We will then explore techniques for visualizing spatial data, from simple base plots to dynamic interactive maps.

Concurrently, you will be introduced to spatial regression modeling, learning how to incorporate spatial dependency into your models. Worked examples will demonstrate the setup, execution, and interpretation of spatial regression models, and we will conclude by extending these techniques to spatiotemporal data. Hands-on exercises throughout the course will enhance your understanding of these methods, including advanced techniques such as Integrated Nested Laplacian Approximation (INLA) for fitting complex spatial models.