jsignell / Heat_and_trees

Visualizing and Analyzing Earth Science Data Using HoloViz and PyData

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.

Case Study: Heat and Street Trees

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.

Run on binder

Run locally

conda env create --file environment.yml
conda activate trees
jupyter notebook

Relevant materials

Recording: https://youtu.be/-XMXNmGRk5c

Static version: https://jsignell.github.io/heat_and_trees

About me

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 HoloViz 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.