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.
The copyright on
this video is owned by the Regents of the University of California and is
licensed for reuse under the Creative Commons Attribution 4.0 International (CC
BY 4.0) License.
