Doctoral student in Rachel Morello-Frosch's laboratory in the Department of Environmental Science, Policy, and Management working at the intersection of environmental epidemiology, environmental justice, and causal inference. Particularly interested in developing quantitative methods to investigate the operation of social power in environmental monitoring regimes in the United States.
Have you ever found yourself in the midst of an analysis when suddenly, out of nowhere, it happens. That tiny, dreaded pinwheel appears indicating an error has occurred. Yes, that's right, they call it the spinning wheel of death. Your application freezes. Everything fades. Did it save?! You clutch your stress ball, watching helplessly as your computer approaches molten temperatures and begins to sputter uncanny, otherworldly sounds. WHIRRRRRRR. Your fate seems to rest on that...
If you work with geospatial data in Python, you most likely are familiar with the fantastic GeoPandas library. GeoPandas leverages the power of Maplotlib to enable users to make maps of their data. However, until recently, it has not been easy to add basemaps to these maps. Basemaps are the contextual map data, like Google Maps, on top of which geospatial data are often displayed.
Since the early days of the COVID-19 crisis (the past few months!), the spatial and temporal enormity of the situation has been tellingly conveyed in mapped data visualizations. The most compelling maps, in my opinion, have been those created by the New York Times ….
