Data@Urban’s Top 5 Posts of 2019
It’s that time of year again! As we approach silver white winters that melt into spring (and a new decade), the Data@Urban team is taking time to look back at some of our most engaging posts from 2019.
Last year, we were happily surprised by the amount of interest from our readers, and fortunately, this year proved to be no different. We continued publishing a post two times a month, bringing us to a total of 24 posts this year, and our readership has grown rapidly. The motivation to curate these posts wouldn’t exist if not for your continued support and valuable feedback. We thank you for championing on behalf of Data@Urban and continuing to share our work with your colleagues, networks, friends, and families. Cheers to the holidays and a new decade! Let’s jump in!
1. 4 Observations on Animating Your Data Visualizations
In our most popular post of the year, data visualization expert Jon Schwabish details what you need to know to create more engaging, detailed content through animations.
2. Announcing the Urban Institute Data Catalog
In September, chief data scientist Graham MacDonald and principal research associate Kathy Pettit announced the Urban Institute Data Catalog, a new way to discover, learn about, and download open data provided by Urban Institute researchers and data scientists. This resource allows anyone to find open data assets that reflect the breadth of Urban’s expertise through a central, searchable resource.
3. Community Mapping — Building Power and Agency with Data
There are many ways to engage communities around research findings, including online data tools, community meetings, and presentations. However, there are fewer examples for how to directly involve the community in generating and processing data. One model for engaging the community in data collection — community mapping — is a relatively low-barrier entry point. In this post, Metropolitan Housing and Communities Policy Center researcher Mychal Cohen walks readers through one example that typifies this strategy: the DC Preservation Network.
4. How to Categorize Large Amounts of Text Data
Urban has been working to identify best practices for social media use by law enforcement. We developed supervised machine learning models to read and automatically categorize millions of tweets containing “cop” or “police.” In this post, Justice Policy Center researchers Ashlin Oglesby-Neal and Emily Tiry explain how we developed a model to classify text data, along with some troubleshooting tips and lessons learned along the way.
5. Data @ Your Fingertips: Democratizing Data to Support Proactive Policymaking
Policy decisions affect our everyday lives. Here at Urban, we’re working to empower policymakers, researchers, and individuals to use data to drive change. Chief data scientist Graham MacDonald explains how, thanks to the Amazon Web Services Imagine Grant Program, our technology and data science team has had the opportunity to build a foundational system that enables anyone to build applications that provide access to data and analytics in seconds. Watch our impact story below.
The Data@Urban team:
