Earth Science presents interesting issues of large, multi-dimensional datasets stored in a variety of idiosyncratic file formats. In this talk, we'll work through some specific workflows and explore how various tools - such as intake, dask, xarray, and datashader - can be used to effectively analyze and visualize these data. Working from within the notebook, we'll iteratively build a product that is interactive, scalable and deployable.
We'll be exploring the urban heat island effect by looking at the impact on surface temperature of roof color. We'll be replicating the process described here: http://urbanspatialanalysis.com/urban-heat-islands-street-trees-in-philadelphia/ but using Python tools rather than ESRI.
We'll also be adding interactivity and deploying the resulting application.
conda env create --file environment.yml
conda activate trees
jupyter notebookRecording: https://youtu.be/-XMXNmGRk5c
Static version: https://jsignell.github.io/heat_and_trees
I am a software developer at Anaconda Inc. currently working on developing best practices for Python-using earth scientists. I work on visualization tools within the PyViz ecosystem and data ingestion and analysis tools in the broader PyData world. I live in Philadelphia and previously did hydrology research at Princeton - studying lightning and rain patterns, water movement through the landscape, and streamflow.