Tagweb mapping

Is it possible for us to “greenline” neighborhoods?

(I don’t mean extending the Green Line to its original terminal, to provide more transportation options in Woodlawn.)

Maps have been used to devalue neighborhoods and to excuse disinvestment. There should be maps, and narratives, to “greenline” – raise up – Chicago neighborhoods.

The Home Owners’ Loan Corporation “residential lending security” maps marked areas based on prejudicial characteristics and some objective traits of neighborhoods to assess the home mortgage lending risk. (View the Cook County maps.) The red and yellow areas have suffered almost continuously since the 1930s, and it could be based on the marking of these neighborhoods as red or yellow (there is some debate about the maps’ real effects).

The Home Owners’ Loan Corporation and its local consultants (brokers and appraisers, mostly) outlined areas and labeled them according to objective and subjective & prejudicial criteria in the 1930s. Each area is accompanied by a data sheet and narrative description. The image is a screenshot of the maps as hosted and presented on Chicago Cityscape.

The idea of “greenlining”

I might be thinking myopically, but what would happen if we marked *every* neighborhood in green, and talked about their strengths, and any historical and current disinvestment – actions that contribute to people’s distressed conditions today?

One aspect of this is a form of affirmative marketing – advertising yourself, telling your own story, in a more positive way than others have heard about you in the past.

In 1940, one area on the Far West Side of Chicago, in the Austin community area, was described as “Definitely Declining”, a “C” grade, like this:

This area is bounded on the north by Lake St., on the south by Columbus Park, and on the west by the neighboring village of Oak Park. The terrain is flat and the area is about 100% built up. There is heavy traffic along Lake St., Washington Blvd. Madison St., Austin Ave. (the western boundary) and Central Ave. (the eastern boundary).

High schools, grammar schools, and churches are convenient. Residents shop at fine shopping center in Oak Park. There are also numerouss small stores along Lake St., and along Madison St. There are many large apartment buildings along the boulevards above mentioned, and these are largely occupied by Hebrew tenants. As a whole the area would probably be 20-25% Jewish.

Some of this migration is coming from Lawndale and from the southwest side of Chicago. Land values are quite high due to the fact that the area is zoned for apartment buildings. This penalizes single family occupancy because of high taxes based on exclusive land values, which are from $60-80 a front foot, altho one authority estimates them at $100 a front foot. An example of this is shown where HOLC had a house on Mason St. exposed for sale over a (over) period of two years at prices beginning at $6,000 and going down to $4,500. it was finally sold for $3,800. The land alone is taxed based on a valuation exceeding that amount. This area is favored by good transportation and by proximity to a good Catholic Church and parochial school.

There are a few scattered two flats in which units rent for about $55. Columbus Park on the south affords exceptional recreational advantages. The Hawthorne Building & Loan, Bell Savings Building & Loan, and Prairie State Bank have loaned in this area, without the FHA insurance provision. The amounts are stated to be up to 50% and in some cases 60%, of current appraisals.

Age, slow infiltration, and rather indifferent maintenance have been considered in grading this area “C”.

Infiltration is a coded reference to people of color, and Jews.

My questions about how to “greenline” a neighborhood

  1. How would you describe this part of Austin today to stand up for the neighborhood and its residents, the actions taken against them over decades, and work to repair these?
  2. How do you change the mindset of investors (both small and large, local and far) to see the advantages in every neighborhood rather than rely on money metrics?
  3. What other kinds of data can investors use in their pro formas to find the positive outlook?
  4. What would these areas look like today if they received the same level of investment (per square mile, per student, per resident, per road mile) as green and blue areas? How great was the level of disinvestment from 1940-2018?

In the midst of writing this, Paola Aguirre pointed me to another kind of greenlining that’s been proposed in St. Louis. A new anti-segregation report from For the Sake of All recommended a “Greenlining Fund” that would pay to cover the gap between what the bank is appraising a house for and what the sales price is for a house, so that more renters and Black families can buy a house in their neighborhoods.

That “greenlining” is a more direct response to the outcome of redlining: It was harder to get a mortgage in a red area. My idea of greenlining is to come up with ways to say to convince people who have a hard time believing there are qualities worth investing in that there they are people and places worth investing in.


The Digital Scholarship Lab at the University of Richmond digitized the HOLC maps and published them on their Mapping Inequality website as well as provided the GIS data under a Creative Commons license.

Use Turf to perform GIS functions in a web browser

Turf's merge function joins invisible buffers around each Divvy station into a single, super buffer.

Turf’s merge function joins invisible buffers around each Divvy station into a single, super buffer –all client-side, in your web browser.

I’m leading the development of a website for Slow Roll Chicago that shows the distribution of bike lane infrastructure in Chicago relative to key and specific demographics to demonstrate if the investment has been equitable.

We’re using GitHub to store code, publish meeting notes, and host discussions with the issues tracker. Communication is done almost entirely in GitHub issues. I chose GitHub over Slack and Google Groups because:

  1. All of our research and code should be public and open source so it’s clear how we made our assumptions and came to our conclusions (“show your work”).
  2. Using git, GitHub, and version control is a desirable skill and more people should learn it; this project will help people apply that skill.
  3. There are no emails involved. I deplore using email for group communication.*

The website focuses on using empirical research, maps, geographic analysis to tell the story of bike lane distribution and requires processing this data using GIS functions. Normally the data would be transformed in a desktop GIS software like QGIS and then converted to a format that can be used in Leaflet, an open source web mapping library.

Relying on desktop software, though, slows down development of new ways to slice and dice geographic data, which, in our map, includes bike lanes, wards, Census tracts, Divvy stations, and grocery stores (so far). One would have to generate a new dataset if our goals or needs changed .

I’ve built maps for images and the web that way enough in the past and I wanted to move away from that method for this project and we’re using Turf.js to replicate many GIS functions – but in the browser.

Yep, Turf makes it possible to merge, buffer, contain, calculate distance, transform, dissolve, and perform dozens of other functions all within the browser, “on the fly”, without any software.

After dilly-dallying in Turf for several weeks, our group started making progress this month. We have now pushed to our in-progress website a map with three features made possible by Turf:

  1. Buffer and dissolving buffers to show the Divvy station walk shed, the distance a reasonable person would walk from their home or office to check out a Divvy station. A buffer of 0.25 miles (two Chicago blocks) is created around each of the 300 Divvy stations, hidden from display, and then merged (dissolved in traditional GIS parlance) into a single buffer. The single buffer –called a “super buffer” in our source code – is used for another feature. Currently the projection is messed up and you see ellipsoid shapes instead of circles.
  2. Counting grocery stores in the Divvy station walk shed. We use the “feature collection” function to convert the super buffer into an object that the “within” function can use to compare to a GeoJSON object of grocery stores. This process is similar to the “select by location” function in GIS software. Right now this number is printed only to the console as we look for the best way to display stats like this to the user. A future version of the map could allow the user to change the 0.25 miles distance to an arbitrary distance they prefer.
  3. Find the nearest Divvy station from any place on the map. Using Turf’s “nearest” function and the Context Menu plugin for Leaflet, the user can right-click anywhere on the map and choose “Find nearby Divvy stations”. The “nearest” function compares the place where the user clicked against the GeoJSON object of Divvy stations to select the nearest one. The problem of locating 2+ nearby Divvy stations remains. The original issue asked to find the number of Divvy stations near the point; we’ll likely accomplish this by drawing an invisible, temporary buffer around the point and then using “within” to count the number of stations inside that buffer and then destroy the buffer.
Right-click the map and select "Find nearby Divvy stations" and Turf will locate the nearest Divvy station.

Right-click the map and select “Find nearby Divvy stations” and Turf will locate the nearest Divvy station.

* I send one email to new people who join us at Open Gov Hack Night on Tuesdays at the Mart to send them a link to our GitHub repository, and to invite them to a Dropbox folder to share large files for those who don’t learn to use git for file management.

© 2018 Steven Can Plan

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