Elizabeth Shope

Data Visualizations

Electricity Generation Sources Over Time

This stacked barplot visualization shows how the mix of sources with which the United States generates electricity has evolved over time. Built with: Python, Plotly.

Cancer Incidence and Mortality in the United States

This compound visualization shows the breakdown of cancer rates by year and gender from 1999-2014, 2014 cancer rates by gender and race, and 2014 cancer rates by gender and leading site. Built with: R, ggplot2.

Terrorism Mapping

Here, we look at where terrorism incidents occurred globally in 2016. We also take a deeper dive into Iraq and its terrorism history over time. Built with: R, Leaflet, Tableau.

Terrorism Sankey Network

Here, we use a Sankey Network to inspect the mapping between different regions of the world and different types of terrorism attacks. Built with: R, NetworkD3.

Capital Bikeshare Hourly Usage

This time series visualization shows hourly trip counts over the course of three different weeks. You can use a slider and other tools to zoom in on different portions of time. Built with: Python, Plotly.

Capital Bikeshare Resident vs. Tourist Riders

This set of visualizations shows how resident vs. tourist usage of Capital Bikeshare differs. The visualizations use data from September 2016 and consider the number of trips taken each day for the two groups, the average trip lenth each day, and the most frequent stations for starting trips for the two groups. Built with: R, ggplot2.

The Link Between Poverty and Education

This visualization shows the link between poverty and education in the United States. In the U.S. states, there is a strong positive correlation between rates of adults without a high school diploma and poverty rates. This visualization also shows the disparity between education and poverty levels in the North and South. Built with: Tableau.

World Happiness Exploration

This dashboard shows happiness levels around the world, lets you explore which countries are the happiest and least happy, and see which factors are most correlated with happiness levels. Built with: Tableau.

About Me

I have spent my career focused on creating positive change in the world. Currently, I am a data scientist at Analyst Institute. In the 2018 election cycle, I worked with progressive organizations to plan, execute, and analyze more than $10 million of experimental spending to influence more than 100 Congressional races. My work at Analyst Institute is focused on randomized controlled trials and causal inference as a means to understand what techniques and messages work best for mobilizing and persuading voters.

As a data scientist, I am particularly interested in issues relating to health, progressive politics, and the environment, and in techniques that involve neural networks, natural language processing, and signal processing.

I previously served as an Advocate at the Natural Resources Defense Council, where I worked with a small team to develop, run, and win the campaign against the Keystone XL tar sands oil pipeline.

I graduated from Harvard University with a Bachelor’s degree in Environmental Science and Public Policy, and from Georgetown University with an M.S. in Data Science.