#maptimeDavis: Introduction to Spatial Statistics in R

Description

We often see that values observed in closer spatial proximity are more alike than those from distant locations, and thus the data may not be independent. This can cause problems, and opportunities, for our analyses. In this workshop, we will discuss how spatial data can break the assumptions of common statistical methods, and work towards identifying and implementing appropriate methods in R. Specifically, this workshop will focus on the uncertainty of spatial interpolation and regression.

Learning Objectives

By the end of this workshop, participants will be able to:

– Identify primary spatial data types (lattice, geostatistical, and point data)
– Describe some popular R packages for spatial data analysis
– Run code to execute common tasks in interpolation and regression.

Prerequisites

Participants should have a basic understanding of R (for example, understand how to create variables and load common data formats like a CSV) and a basic understanding of GIS data formats (e.g., raster and vector data).

Software

All participants will need a computer on which they have administrative rights and are able to install software, and have the latest versions of Zoom, R and RStudio installed.

Instructor

Wesley Brooks

Instructor Bio

Dr. Brooks is a data scientist at DataLab. He has a Ph.D. in statistics, where his research was on methods in spatial statistics.