Moran’s I Spatial Autocorrelation The data For this exercise, we will use the Pennsylvania county-level lung cancer counts for 2002. The data are provided by the SpatialEpi package, which we will install for this exercise. The dataset comes a tibble with a the following components: Do you see a degree Read more…
In today’s lab, we will be looking at some of the more fundamental exploratory analysis tools. Cluster Analysis (using K-Means), and Quadrat Analysis. K-Means Clustering Clustering algorithms are useful for separating data into groups with similar qualities, clustering is often not the end goal in and of itself, but it Read more…
Principal Component Analysis Eigenvectors, Eigenvalues, Identity Matrices It is helpful to have some background in linear algebra. For our purposes, though, we’ll start with a remote sensing example. A visual example To start our exploration of PCA we will first look at the principal components of an image as it Read more…
Working with spatial data sf & sp packages The tools for handling spatial data in R were built around the SP package. However, recently, the sf (simple features) package has been developed to conform with formal geospatial standards, which has mostly replaced the sp tools. The sf and sp data Read more…
Basic Analytics in R data.frames and tibble When working with tabular data, generally each row represents a record of the phenomena of interest (such as a spatial object, a person…) and each columns represents an attribute of that feature. In R, data can be in a matrix, but matrices can only Read more…
R is a programming language that is widely used in areas of scientific activity and quantitative research. The extensive functionally is through R packages, which are referred to as Libraries when installed. These libraries extend the core R packages. The Basics You can type commands directly in the R console Read more…
Review the project map guidelines from lab 09. In designing your project output, review the ‘Tips for Success’ posted near the bottom of Lab 4: