#maptimeDavis: Introduction to Spatial Statistics in R - 2021-05-04

From Glenda Gaines April 22, 2024

23 plays comments You unliked the media.

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.

Tags: #maptimedavisspatial statisticsr languagedatalabdatalab eventdatalab workshop

Appears In: UC Davis DataLab: Data Science and Informatics Information and Educational Technology Library Research Graduate Studies Computer Science

Related Media

#maptimeDavis: Introduction to Spatial Statistics in R - 2021-05-04

Related Media