Workshop: Mapping and Spatial Analysis with the 2020 Decennial Census

In this 2.5 hour workshop, you'll learn how use mapping and spatial analysis to work with and visualize 2020 Decennial Census data. The Demographic and Housing Characteristics (DHC) file, released on May 25, includes our first look at detailed age, sex, and household characteristics from the 2020 Census all the way down to the Census block. You'll use R along with tools like tidycensus, sf, and mapview to explore and communicate trends in the new data.

In the first hour, you'll learn how to use tidycensus to access linked demographic and geographic decennial Census data within R. You'll get up to speed with geospatial Census data management in R, and you'll learn how to make a variety of Census maps with tools like ggplot2 and mapview.

The second hour covers spatial data analysis with the 2020 DHC data. Given that new privacy protections in the data increase uncertainty for small areas, the Census Bureau recommends aggregating small counts to improve reliability. You'll learn how to use spatial analysis tools to perform this aggregation in your Census data work, and you'll gain experience with R's powerful GIS toolkit to generate custom insights.

The last 30 minutes are a live question-and-answer session where we addressed how to connect your own data resources with spatial Census data in R.

You get:

• Unlimited access to the workshop video, so you can learn at your own pace
• An annotated tutorial on mapping and spatial analysis with Census data in R, with insights not found anywhere else
• The Quarto and R source code behind the workshop to help you learn these skills yourselves

About the instructor:

Kyle Walker is an internationally-recognized researcher, consultant, and software developer in the field of spatial data science, and was the 2022 recipient of the Spatial Data Scientist of the Year award from the software company CARTO. He is the author of the book Analyzing US Census Data: Methods, Maps, and Models in R, and has published several popular software packages for spatial data science in R and Python including tidycensus that have been collectively downloaded over 1 million times.