Category Archives: Data Explanations

Kickoff Event Data Dig Response

We’ve been taking some time to go over all of your responses from various community engagement activities. While we’re still processing and researching a lot of the points brought up in the various activities, there’s a handful of questions and data points from the data dig that we can talk about now. For your reference, here is the data snapshot that was provided for the activity.

A common question revolved around education attainment data, which showed that 27.7% of residents have a less than high school diploma, and 32.3% have a high school diploma. Together, that’s 60% of people who live in the planning area who have attained at most, a high school diploma or GED.

This number comes from the census data for educational attainment for individuals 25 years of age or older. The fact that close to 22% of the population is youth aged 5-17 does not have any bearing on this statistic, and is designed to give an idea of the adult education attainment after you have been given ample time as an adult to attain this.

This statistic, along with all of the statistics in our data dig activity that didn’t have to do with parcels, housing tenure, and vacancy, come from the US census bureau’s numbers for census tracts 1164,1241 and 1242. While this is not an exact overlay of the planning area, the public is unable to draw our own shapes and come up with numbers for a limited area and have to work with what the census gives us: tracts, block groups, municipalities, zip codes, counties, etc.

We also got a lot of questions regarding where school aged children go to school, we’re in the process of trying to see if we can get ahold of any of that, however none of it is readily available. The census does not ask these questions, they’re simply interested in attainment and enrollment: not where. Schools are often very quiet on how these enrollment numbers look outside of actual enrollment, especially for charter schools who look to craft an image based on their style of education, not where their students come from. We do not anticipate that this will be easy to find unless already compiled into a report.

In terms of healthcare and health rates for things like obesity, diabetes, etc. we have to refer to the data that is a few years old and is likely not only the most recent data available, but is very unlikely to have changed. Health studies last a few years at least, and health data is typically only available at the zip code level to remain HIPPA compliant. So if you’ve seen a study that talks about health or disease info for St. Louis that is dated 2014, 2012, and even 2010, we have to assume that this data is still accurate enough within the margin of error that we can

There were also a few questions about the owner occupancy rate. The owner occupancy rate is somewhat lower than the city as a whole; for all occupied housing units, St. Louis as a city is 55% renter, while the planning area is around 67% renter. Considering the price of real estate in St. Louis is relatively low, we have to assume that these are people who are not able to afford homes or have the necessary credit / down payment, and desire to be homeowners as opposed to folks who choose to rent (of course, many renters will fall into this category). Not everyone desires to be a homeowner, and there is no universal formula for what is a “good” mix of renter and owner. In the San Francisco metropolitan area, home ownership is 53%. In New York City, it’s 31%. Nationwide, homeownership is at about 64%, which is low nationwide since the housing boom following WWII. For further reading, the census bureau releases a quarterly report on home ownership and vacancy that you can read here

While we can’t answer everything in a simple blog post, we are working on digging further into your data dig to find out what we can about the other points that were brought up, these are the ones that are some of the most recurring themes that we have answers for.

See you all at the first working meeting on the 29th!