The Jane Jacobs Index Part II: Testing

Brian Parker
Aug 19, 2020 · 5 min read

In Part I, I took you through the data gathering and compilation required to rank Census tracts by the four features identified by Jane Jacobs as the foundation of a great neighborhood:

  • Density
  • A mix of uses
  • A mix of building ages, types and conditions
  • A street network of short, connected blocks

Now that we have our data, we’re going to test it against some other metrics of urban quality of life and see if it is a strong predictor. I am going to test the Jane Jacobs Index and it’s component pieces against the following data:

  • Crime statistics from the King County Sheriff’s Department
  • Immunization rates at King County schools
  • Housing affordability
  • Commute mode share

I’m going to focus on King County in this post because my Ada County dataset, while interesting, turned out to be too small to provide meaningful results.

Crime

I was very curious to see the results of this one. A lot of Death and Life is focused on the right urban design to naturally prevent crime. Jacobs’ recommendations made a lot of sense in the context of the 1950s as it focused on making the streets an interesting place to be and to simply watch from apartment windows. City streets are cool and all, but more interesting than Netflix?

I’m going to compare the JJI to the Seattle Police Department’s publicly available crime database. I took this dataset, performed a spatial join with a Census tract shapefile, counted the incidents within each tract, then normalized it by area to get a per mile crime density. Plotting this against the Jane Jacobs Index and it’s components yields the following:

I’m going to avoid over-statistics-ing my analysis in this post and just look at the pictures. Remember that the components of the JJI are rankings of census tracts based on their scores in the four qualities identified above, and the JJI is an average of those four rankings, so a low score is the Jacobs-y-est. Under the DaLoGAC hypothesis, we would see the lowest crime density in the tracts with the lowest JJI, and the dots moving upward from left to right. Interestingly, we see the opposite pattern emerging in all except the measure of housing age homogeneity. My theory on why: homelessness is the primary driver of crime in Seattle. Places that have been allowed to organically evolve and replace older, smaller buildings with newer, denser places are better able to naturally meet the housing demand, thus enabling people to meet their needs without resorting to criminal activity.

Immunization Rates

This one was more out of curiosity and because the data was there than any real hunt for urban design solutions. I am curious because trust in governments, scientific research, and similar institutions has been shown to correlate with immunization rates, and I wanted to see if there was a correlation between this trust and urban design. King County publishes immunization coverage rates by school, so I again joined that to census tract, then took an average. Here are the results:

No correlation. Crazy theory set aside for now…

Housing Affordability

For this, I took the Census Bureau’s data on median household income and median housing cost to get a percent of income spent on housing for each census tract.

Some correlation, but not much. Seattle is pretty uniformly expensive, so it might not be the best place for this particular research. I suspect some Midwestern cities like Chicago where parts of the city have very high housing costs while other neighborhoods are still cheaper would yield different results.

Commute Mode Share

Getting people out of their cars has been a challenge for planners for as long as there have been cars. The results of this one are cool, so I’m going to take a deeper dive into them. The Census surveys how people are getting to work in the following high-level categories:

  • Drove Alone
  • Carpooled
  • Public Transit
  • Taxicab
  • Motorcycle
  • Bicycle
  • Walking
  • Other

I’m going to compare these percent of respondents who use the various modes against the JJI, as well as the pre-ranking versions of the components. First off, here’s the population density per square mile:

A lot of stuff starts happening when you get past 10,000 people per square mile, specifically people stop driving and start walking and using transit. This really isn’t new information, but it’s still interesting to see how strong that relationship is.

Next, the relationship between age of construction homogeneity and transportation:

I’m not surprised that this one is scattershot.

Next, street network design:

There’s a lot of hockey-stick going on here. Remember that the street score is the average length of each block divided by the average number of streets at each intersection, so a low score is more Jacobs-y. Clearly people in neighborhoods with a score under 50 are much more likely to use active transportation methods. This relationship is especially strong with walking and biking.

Next, the percent of daily needs within one kilometer:

If your daily needs can be met by walking, you tend to walk more. Shocking!

Finally, putting them all together, the Jane Jacobs Index:

The strong correlation continues here between Jacobs-y places and active transportation.

Conclusion

  • If you want less car dependence, build dense, mixed use neighborhoods with a short interconnected street network.
  • If you want lower crime, support incremental development.
  • Listening to Jane Jacobs is unlikely to have negative impacts on your community.

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Brian Parker

Written by

Urban planner and data nerd. Feel free to hire me: https://brianparkerresume.herokuapp.com/

Analytics Vidhya

Analytics Vidhya is a community of Analytics and Data Science professionals. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com

Brian Parker

Written by

Urban planner and data nerd. Feel free to hire me: https://brianparkerresume.herokuapp.com/

Analytics Vidhya

Analytics Vidhya is a community of Analytics and Data Science professionals. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com

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