Unsupervised learning for time series data: Singular spectrum versus principal components analysis

Recently, I was working with a colleague on a project involving time series observations of neighborhoods in Los Angeles. We wanted to see if there were patterns in the time series data that described how similar neighborhoods evolved in time. For multivariate data, this is a great application for unsupervised …

Teaching the Q Method in a class on urban sustainability

The Q Method is a mixed method that combines a survey of individuals with factor analysis to determine what distinct perspectives are embedded in a population. In a class on urban sustainability, I demonstrated how this method can be used to reveal students' diverse perspectives on issues about which we assume they mostly agree.

The Tasseled Cap transformation and band ratios: Applications for urban studies

Urban environments are heterogeneous at relatively small scales and composed of multiple land cover types but are dominated by vegetation, impervious surface, and soil (V-I-S). Land cover indices such as the Biophysical Composition Index (BCI), based on the tasseled cap transformation, attempt to capture the spatial pattern of these three broad classes of urban land cover. Here I present Python examples for applying the tasseled cap transformation and for calculating the BCI.

Clipping rasters in Python

Clipping rasters can be trivial with a desktop GIS like QGIS or with command line tools like GDAL. However, I recently ran into a situation where I needed to clip large rasters in an automated, online Python process. It simply wouldn't do to interrupt the procedure and clip them myself …