Illustration by Rhiannon Newman for the Urban Institute

Over the past year, journalists and researchers have found creative ways to analyze and visualize COVID-19 data, including trends on infections, deaths, and vaccinations around the world. But getting, cleaning, contextualizing, and visualizing COVID-19 data can be challenging because it’s hard to know where to start.

Our small team at the Urban Institute partnered with the data visualization consulting firm HealthDataViz to collect COVID-19 vaccination data and create a daily updated public Tableau dashboard that allows users to explore vaccination trends across the United States. But we didn’t want to simply re-create others’ interactive dashboards and visualizations, especially the excellent…

Illustration by Anna Minkina/Shutterstock

Data management and quality assurance is key to delivering rigorous and reliable policy analysis. The Urban Institute’s Housing Finance Policy Center uses multiple property records data sources, including Agency Mortgage-Backed Security data and Home Mortgage Disclosure Act data, to study structural barriers homeowners of color face. Our data science team aims to support the Housing Finance Policy team, and all Urban researchers, in building a robust data quality assurance system, which makes quality checks automated, iterative, and fast.

Before statistical analysis or building machine learning models, cleaning data can take up a lot of time in the project cycle. Without…

Illustration by Rhiannon Newman for the Urban Institute

The State and Local Finance Initiative (SLFI) at the Urban Institute works with state policymakers across the country on tax and budget issues. However, understanding how a state brings in revenue and funds expenditures is impossible without considering a state’s demographics, its economy, and its leaders’ priorities. This context comprises countless data points spanning numerous sources, as well as qualitative analysis on a state’s politics and history, repeated for all 50 states (and the District of Columbia).

To meet this challenge, SLFI used R Markdown to publish its State Fiscal Briefs in January 2020. We built upon best practices established…

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The COVID-19 pandemic has presented extreme challenges to families and children facing elevated levels of food insecurity. Understanding nutritional need levels is important for local and federal policymakers to determine where, when, and how to provide support to families. But getting a handle on these needs has been made difficult because of a lack of data to compare to the prepandemic period and to examine changes during the pandemic. …

Illustration by Irina Strelnikova/Shutterstock

Last year I, along with a few colleagues from the Urban Institute’s Technology and Data Science team, participated in a Data@Urban digital discussion about women in tech. Though the topic was obviously broad, we felt that our perspective — as women working in technology at a nonprofit institution — was unique and could help continue an important dialogue. We discussed the inclusionary and exclusionary aspects of tech as they relate to women, unequal pay, gender bias, and more.

A few months before this event, on International Women’s Day, I was part of a similar conversation with women leaders in tech…

Illustration by On Lollipops/Shutterstock

Last year, we announced our collaboration with the Cloudera Foundation to accelerate our progress toward a vision of providing free and open access to high-quality education data for a diverse set of stakeholders pushing for change. Our long-term goal is to make it easier for people to use data to inform decisionmaking and hold decisionmakers accountable.

Thanks to the financial, technical, and software support of the foundation and its top-tier engineering experts, and generous product donations from Cloudera, Inc., …

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The Urban Institute Education Data Portal offers open, free access to education data from a number of data sources in one place, with the goal of making it easier for researchers, practitioners, and policymakers to generate rigorous, accurate, and actionable insights to improve outcomes for students.

As more and more people have begun to rely on the portal for their work — we now have well over 1,000 unique active users every month — we decided to take a closer look at our public data policy to ensure that it aligned with best practices from leading data providers in the…

Illustration by Urban Institute
  1. Applying racial equity awareness in data visualization

Applying an equity lens to data visualization requires critically examining default thinking around language, ordering, color choice, and who is excluded and why. As we update our data visualization style guide, we’re centering the experiences of those we study, our audiences, and those we hope to reach.

2. Pivoting community engagement during COVID-19

COVID-19 has changed how organizations interact with the communities they work with, and innovative community engagement approaches should accurately reflect these residents’ priorities, needs, and preferences. Three key strategies can guide organizations working with those needing support during the pandemic.

Illustration by wan wei/Shutterstock

Following the presidential election, Rob Santos, Urban Institute vice president, chief methodologist, and president-elect of the American Statistical Association, sat down with Graham MacDonald, Urban’s chief data scientist, to discuss the state of polling and related issues in light of the unfolding election results. The following is a lightly edited transcript of their conversation.

Graham: Early indicators from last week’s 2020 general election show polling has been somewhat off in some places and relatively good in other areas. Some are asking the question, “Why do we even need polls?” …

Illustration by Allison Feldman for the Urban Institute

Data-driven decisionmaking in city government has expanded rapidly in recent years, driven by advances in technology and the digitization of many city services. While we at the Urban Institute applaud the growth of data-driven decisionmaking, we also recognize that there are real concerns about the potential for bias in data used to guide public decisions. Left unchecked, unrepresentative data can directly lead to inequitable policy outcomes that harm vulnerable groups.

For example, many public works departments have started using citizen complaint data, like 311 requests, to allocate scarce city resources to perform sidewalk repairs and fix potholes. On the surface…


Data@Urban is a place to explore the code, data, products, and processes that bring Urban Institute research to life.

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