For the data lovers out there…
Minneapolis is a relatively dense residential city with a fairly dense central business district (5th highest job density in the country). Minneapolis is also consistently rated one of the best cities for cycling in the country (as if that really means much). Yet as a city we probably under-perform on transit ridership. I wanted to understand commuter behavior in Minneapolis a bit better than high-level data sometimes allows us to, and even ACS surveys tend to gloss over in the easy-to-access information. Specifically, I became interested in where the roughly 165,000 Minneapolitans work, where that is relative to their home, and how those distances line up with real-world commuting patterns.
Example of home zip (55408) vs top work destination zip codes
Census On The Map is a fairly powerful tool for those without GIS software or data access, but enough MS Excel gumption to do some heavy lifting. I used OTM’s home vs work location on a zip code basis to perform my analysis. OTM does go down to Census Tract level, but that’s much more challenging to tie to a geographic area (address, latitude/longitude, etc), even if it would be slightly more accurate. I ran reports for the main Minneapolis zip codes (55401-55419 & 55455), understanding there’d be a few left out.
You used to be cool, Google.
While the geographic centroid of a zip code area doesn’t necessarily match its residential or employment centroid, I figured it would be close enough. Obviously the margin of error increases as the destination zip code becomes more suburban. Most Minneapolis zip codes have areas between 1 and 5 square miles (roughly a 1-3 mile diameter circle of land to help visualize), while suburban zip codes start at 7 square miles and go up from there. I intended to use Google’s geocoding capability to find driving directions from each zip code to one another, but they heavily restrict queries per day (and waiting over the weekend didn’t seem to help…).
Instead, I decided to go the ol’ mathematical route, using latitude/longitude data by zip code to approximate distance from one center to the next “as the crow flies.” Of course, most people can’t fly, so I wanted to model what many commuters experience: a grid of some sort. See image below for an example and the math used. Yes, there are some diagonals in our cities, also freeways. Yes, some people travel in just one direction or at a distance that requires longer mileage. Yes, barriers, both natural (rivers, lakes) and man-made (freeways), muddy this simple one-turn model a bit. I was just looking for a “close enough” number here. (In hindsight, I could have just calculated E-W and N-S distances separately from the lat/long. For another time…). For people who work in the same zip code as their home address, I approximated the distance to 0.5 miles – a good chunk work from home (5% according to ACS) and the rest are at most 3 miles. Also, to keep data clean, I excluded zip code work destinations with 5 or fewer total workers (which was only about 2% of the data set, very negligible).
OK. Enough boring you with my methodology. What you really care about is the breakdown of commute length. Well, here you go:
Some quick facts: 165,000 workers live in the 20 zip codes I analyzed, and over 72,000 (44%) work within the city borders. Over 16% of Minneapolis residents commute a mile or less to work. A whopping 55% commute 5 or fewer miles. The weighted average trip distance for all commuters is 8.3 miles
You’ll notice I injected a representative “potential 30 minute commute” at the 5 mile maximum. 5 miles could be covered in 30 minutes or less by these modes if 1) most of our buses operated under the arterial bus line proposal (5-8 minute wait time, ~17 mph operating speeds, average 1/4 mile walk to a station), and 2) we built out protected bikways and gave cyclists better priorities at intersections (allowing an average 10 mph speed). Neither of those are particularly expensive (at least compared to other transit/road investments we’re making). However, our current transit and bike infrastructure probably allows a 4 mile commute in about 30 minutes, which is still 47.7% of all Minneapolis workers.
So why is that number drastically different than reality? The latest 3-year averages from the American Community Survey put mode shares at [Walk: 6.3%, Bike: 3.6%, Transit: 14.2%] for a grand total of 24.1% – a far cry from the potential. Why is this? A few thoughts:
- Parking is “free” (deducted from your salary) at many job sites, even in the city
- Parking can be cheaper than 2-way transit fare in parts of downtown (thanks to city-subsidized lots/on-street spaces and low property tax rates on private garages)
- Buses are slow relative to cars (ie not given the dedicated space they deserve, particularly at rush hours)
- Buses are slow (ie not given proper station design, ticketing, and bus boarding/alighting methods)
- Buses are confusing and are sometimes inflexible for users, a barrier to entry
- Daily needs (day cares, groceries, etc) aren’t near transit or easy to handle by bus or bike as designed (example: no strollers on the bus, which makes bringing even one infant to daycare difficult)
- Short-distance trips to suburbs from Mpls are not well-served by transit or quality bike infrastructure
- Many parts of town (especially downtown) are at best highly unpleasant to walk or bike by
- Certain modes are cheaper than they might otherwise be if they paid external costs
- It gets both cold/snowy and hot/humid in Minnesota
- Jobs sprawled away after population and highway investments. Barring complete abandonment of low-intensity land-uses in the suburbs, transit/bike/walk-accessible jobs will always have a capped (even with reverse-commuting possibilities)
I put these in a deliberate order – the city can’t easily control or change items toward the bottom. But for the most part, the speed, comfort, and ease of using non-auto modes rests directly on transportation planners, while land-uses (jobs, goods, services) should be more integrated to ensure accessibility by these modes. Instead, land use regulations and transportation investments have done the opposite and sprawled people and jobs away from walkable areas:
This is despite the fact that about 75% of our region’s jobs are office, retail, education, or government-oriented – in other words not manufacturing or warehousing that tend to require low land costs, large spaces, and freight-supportive transportation investments. We could do better. None of these points are major revelations to readers of this site, I just wanted to bring forward some data that surprised me a bit.
Finally, apologies to St Paul and its residents. I don’t mean to leave you out of the picture, it was just easier to analyze only one city.