It is relatively easy to find data and visualizations for residential population density. Here is a map of Seattle census tract densities via the City of Seattle, for example. But everyone who commutes to a job knows (sometimes painfully) that a static view of residential density is just a slice of a larger, dynamic landscape. The geographic distribution of people in the city on an average Thursday afternoon is significantly different than it is at midnight on a Sunday. This is especially true for areas with strong, single primary uses like Paine Field (Boeing Factory), Overlake (Microsoft), and Downtown Seattle.
In an effort to understand these daily shifts in our region’s population density, I built a (very) simple model of the home-to-employer population shift on an average weekday for the Seattle metro area. I used two data sources: American Community Survey population estimates and Longitudinal Employer-Household Dynamics data with origin-destination employment statistics. Both sources are published by the Census Bureau and contain data down to the census block group level. Assuming that most* people with outside-the-home employment leave for work in the morning and return in the late afternoon, I produced the population density animation in the embedded video.
A few areas really stand out in the animation: the downtowns of Seattle and Bellevue, UW, the Microsoft headquarters in Overlake, and the Boeing Factory in Everett. If you know your Puget Sound geography, you can also spot the Boeing Factory in Renton, Factoria office parks, SeaTac Airport, and the warehouse district in Kent. There are also subtle decreases in density for heavily residential areas in suburban districts. Of course, these observations are not qualitatively surprising to anyone who knows the Seattle area. More interesting are the estimated population densities in these areas. Parts of downtown Seattle seem to achieve
5200,000+ ppl/sq-mile during some weekday afternoons – literally off of my scale! Good luck serving that kind of density with single occupant vehicles.
I should probably mention some obvious shortcoming of this model. It is built on a simple set of assumptions and cannot account for non-standard commutes, like night-shifts, and non-commute trips which are a certainly a significant portion of trips made**. The model also doesn’t know about the paths that people take between home and work. Still, it is quite striking to see how the region’s population concentrates into half-a-dozen CBDs during the course of a weekday. And I have a renewed appreciation for the economic importance of downtown Seattle to the region.
[*] I assumed that 80% of people with employment outside the home will need to commute on a given weekday. This is not a scientific estimate; it is a guess and nothing more.
[**] Non-commute trips are likely more spread throughout day and night, however, which would dilute their aggregate contribution to shifting population density