The PyMC library offers a solid foundation for probabilistic programming and Bayesian inference in Python, but it has some drawbacks. Although the API is robust, it has changed frequently along with the shifting momentum of the entire PyMC project (formerly "PyMC3"). This is most evident in the abandoned "PyMC4" project …
Satellite remote sensing products often represent quality control (QC) information as bit-flags, a sequence of binary digits that individually (single digit) or collectively (multiple digits) convey additional information about a remote sensing measurement. This information may be related to cloud cover, atmospheric conditions in general, the status of the space-borne …
In Earth system models that run on daily or weekly time steps, there are many quantities that are we may wish to calculate only when the sun is in the sky. Vapor pressure deficit (VPD), as one important example, has strong diurnal variation, tending to decrease in the evening as …
Note: This post builds and improves upon an earlier one, where I introduce the Gapminder dataset and use it to explore how diagnostics for fixed effects panel models can be implemented.
Note (July 2019): I have since updated this article to add material on making partial effects plots and to …
I've been using Google Earth Engine recently to scale up my remote sensing analyses, particularly by leveraging the full Landsat archive available on Google's servers. I've followed Earth Engine's development for years, and published results from the platform, but, before now, never had a compelling reason to use it. Now …
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 …
Here, I present a simple graphical tool in R for visual detection and statistical testing of a trend among group means, where the groups are (usually) quantiles. I argue this is a good tool for exploratory data analysis as it allows non-linear trends to be detected.
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.
In working with linear fixed-effects panel models, I discovered that I had to develop goodness-of-fit tests and diagnostics on my own, as the libraries for working with these kinds of models haven't progressed that far yet.
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.