Tagurban data

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.

Developing a method to score Divvy station connectivity

A Divvy station at Halsted/Roscoe in Boystown, covered in snow after the system was shutdown for the first time to protect workers and members. Photo by Adam Herstein.

In researching for a new Streetsblog Chicago article I’m writing about Divvy, Chicago’s bike-share system, I wanted to know which stations (really, neighborhoods) had the best connectivity. They are nodes in a network and the bike-share network’s quality is based on how well (a measure of time) and how many ways one can move from node to node.

I read Institute for Transportation Development Policy’s (ITDP) report “The Bike-Share Planning Guide” [PDF] says that one station every 300 meters (984 feet) “should be the basis to ensure mostly uniform coverage”. They also say there should be 10 to 16 stations per square kilometer of the coverage area, which has a more qualitative definition. It’s really up to the system designer, but the report says “the coverage area must be large enough to contain a significant set of users’ origins and destinations”. If you make it too small it won’t meaningfully connect places and “the system will have a lower chance of success because its convenience will be compromised”. (I was inspired to research this after reading coverage of the report in Next City by Nancy Scola.)

Since I don’t yet know the coverage area – I lack the city’s planning guide and geodata – I’ll use two datasets to see if Chicago meets the 300 meters/984 feet standard.

Dataset 1

The first dataset I created was a distance matrix in QGIS that measured the straight-line distance between each station and its eight nearest stations. This means I would cover a station in all directions, N, S, E, W, and NW, NE, SE, and SW. Download first dataset, distance matrix.

Each dataset offers multiple ways to gauge connectivity. The first dataset, using a straight-line distance method, gives me mean, standard deviation, maximum value, and minimum value. I sorted the dataset by mean. A station with the lowest mean has the greatest number of nearby stations; in other words, most of its nearby stations are closer to it than the next station in the list.

Sorting the first dataset by lowest mean gives these top five best-connected stations:

  1. Canal St & Monroe St, a block north of Union Station (191), mean of 903.96 feet among nearest 8 stations
  2. Clinton St & Madison St, outside Presidential Towers and across from Northwestern Train Station (77), 964.19 feet
  3. Canal St & Madison St, outside Northwestern Train Station (174), 972.40
  4. Canal St & Adams St, north side of Union Station’s Great Hall (192), 982.02
  5. State St & Randolph St, outside Walgreens and across from Block 37 (44), 1,04.19

The least-connected stations are:

  1. Prairie Ave & Garfield Blvd (204), where the nearest station is 4,521 feet away (straight-line distance), or 8.8x greater than the best-connected station, and the mean of the nearest 8 stations is 6,366.82 feet (straight-line distance)
  2. California Ave & 21st St (348), 6,255.32
  3. Kedzie Ave & Milwaukee Ave (260), 5,575.30
  4. Ellis Ave & 58th St (328), 5,198.72
  5. Shore Drive & 55th St (247), 5,168.26

Dataset 2

The second dataset I manipulated is based on Alex Soble’s DivvyBrags Chrome extension that uses a distance matrix created by Nick Bennett (here’s the file) that estimates the bicycle route distance between each station and every other station. This means 88,341 rows! Download second dataset, distance by bike – I loaded it into MySQL to use its maths function, but you could probably use python or R.

The two datasets had some overlap (in bold), but only when finding the stations with the lowest connectivity. In the second dataset, using the estimated bicycle route distance, ranking by the number of stations within 2.5 miles, or the distance one can bike in 30 minutes (the fee-free period) at 12 MPH average, the following are the top five best-connected stations:

  1. Ogden Ave & Chicago Ave, 133 stations within 2.5 miles
  2. Green St & Milwaukee Ave, 131
  3. Desplaines St & Kinzie St, 129
  4. (tied) Larrabee St & Kingsbury St and Carpenter St & Huron St, 128
  5. (tied) Clinton St & Lake St and Green St & Randolph St, 125

Notice that none of these stations overlap with the best-connected stations and none are downtown. And the least-connected stations (these stations have the fewest nearby stations) are:

  1. Shore Drive & 55th St, 11 stations within 2.5 miles
  2. (tied) Ellis Ave & 58th St and Lake Park Ave & 56th St, 12
  3. (tied) Kimbark Ave & 53rd St and Blackstone Ave & Hyde Park Blvd and Woodlawn Ave & 55th St, 13
  4. Prairie Ave & Garfield Blvd, 14
  5. Cottage Grove Ave & 51st St, 15

This, the second dataset, gives you a lot more options on devising a complex or weighted scoring system. For example, you could weight certain factors slightly higher than the number of stations accessible within 2.5 miles. Or you could multiply or divide some factors to obtain a different score.

I tried another method on the second dataset – ranking by average instead of nearby station quantity – and came up with a completely different “highest connectivity” list. Stations that appeared in the least-connected stations list showed up as having the lowest average distance from that station to every other station that was 2.5 miles or closer. Here’s that list:

  1. Kimbark Ave & 53rd St – 13 stations within 2.5 miles, 1,961.46 meters average distance to those 13 stations
    Blackstone Ave & Hyde Park Blvd – 13 stations, 2,009.31 meters average
    Woodlawn Ave & 55th St – 13 stations, 2,027.54 meters average
  2. Cottage Grove Ave & 51st St – 15 stations, 2,087.73 meters average
  3. State St & Kinzie St – 101 stations, 2,181.64 meters average
  4. Clark St & Randolph St – 111 stations, 2,195.10 meters average
  5. State St & Wacker Dr – 97 stations, 2,207.10 meters average

Back to 300 meters

The original question was to see if there’s a Divvy station every 300 meters (or 500 meters in outlying areas and areas of lower demand). Nope. Only 34 of 300 stations, 11.3%, have a nearby station no more than 300 meters away. 183 stations have a nearby station no further than 500 meters – 61.0%. (You can duplicate these findings by looking at the second dataset.)

Concluding thoughts

ITDP’s bike-share planning guide says that “residential population density is often used as a proxy to identify those places where there will be greater demand”. Job density and the cluster of amenities should also be used, but for the purposes of my analysis, residential density is an easy datum to grab.

It appears that stations in Woodlawn, Washington Park, and Hyde Park west of the Metra Electric line fare the worst in station connectivity. The 60637 ZIP code (representing those neighborhoods) contains half of the least-connected stations and has a residential density of 10,468.9 people per square mile while 60642, containing 3 of the 7 best-connected stations, has a residential density of 11,025.3 people per square mile. There’s a small difference in density but an enormous difference in station connectivity.

However, I haven’t looked at the number of stations per square mile (again, I don’t know the originally planned coverage area), nor the rise or drop in residential density in adjacent ZIP codes.

There are myriad other factors to consider, as well, including – according to ITDP’s report – current bike mode share, transit and bikeway networks, and major attractions. It recommends using these to create a “demand profile”.

Station density is important for user convenience, “to ensure users can bike and park anywhere” in the coverage area, and to increase market penetration (the number of people who will use the bike-share system). When Divvy and the Chicago Department of Transportation add 175 stations this year – some for infill and others to expand the coverage area – they should explore the areas around and between the stations that were ranked with the lowest connectivity to decrease the average distance to its nearby stations and to increase the number of stations within 2.5 miles (the 12 MPH average, 30-minute riding distance).

N.B. I was going to make a map, but I didn’t feel like spending more time combining the datasets (I needed to get the geographic data from one dataset to the other in order to create a symbolized map). 

Frequency of Chicago women riding their bikes to work is down

UPDATE: I added data from years 2005-2007 to complement existing 2008-2009 data in Table 1 as well as a visual representation. I have also added data from the 3-year estimates to Table 2.

UPDATE 01/20/11: Added the most recent 3-year estimate that the Census Bureau released in January 2011 to Table 2.

In September 2009, I wrote about “what the Census tells us about bicycle commuting” and a couple of days ago I compared Chicago to Minneapolis and St. Paul.

I want to update readers on the changes between the 1-year estimate data reported in that article (from 2008) and the most recent 1-year estimate data (from 2009). Percentages represent workers in the City of Chicago aged 16 and older riding bicycles to work.

Table 1 – Bicycling to work, 16 and older, 1-year estimates

Year Total MOE Male MOE Female MOE
2005 0.7% +/-0.1 0.9% of 621,537 +/-0.2 0.4% of 541,013 +/-0.1
2006 0.9% +/-0.2 1.2% of 645,903 +/-0.3 0.7% of 563,219 +/-0.2
2007 1.1% +/-0.2 1.4% of 656,288 +/-0.3 0.7% of 574,645 +/-0.2
2008 1.0% +/-0.2 1.5% of 657,101 +/-0.3 0.5% of 603,640 +/-0.2
2009 1.1% +/-0.2 1.8% of 651,394 +/-0.3 0.4% of 620,350 +/-0.1

View graph of Table 1. MOE = margin of error, in percentage points.

We should be concerned about the possible decrease in the percentage of women riding bicycles to work, especially as the population size increased. The margin of error also decreased, thus suggesting an improvement in the accuracy of the data. There have already been many discussions (mine, others) as to why it is important to encourage women to ride bicycles and also what the woman cycling rate tells us about our cities and policies. If the decrease continues we must discover the causes.

But Table 1 doesn’t tell the full story.

As Matt points out in the comments below, the number of surveys returned for 1-year estimates is smaller than that from the Decennial Census. Therefore, I took a look at the two 3-year estimates available, each having a larger sample size than the 1-year estimates (see Table 2). The data below seem to show the opposite change than seen in Table 1: that the number of women bicycling to work has increased. The crux of our quandary is sample size. The sample size is the number of people who are asked, “How did this person usually get to work LAST WEEK?”

Table 2 – Bicycling to work, 16 and older, 3-year estimates

Click header for data source 2005-2007 2006-2008 2007-2009
Total workers 1,203,063 1,230,809 (+2.31%) 1,291,709 (+4.71%)
Males bicycling to work 7,549 9,014 (+19.41%) 11,014 (+18.16%)
Females bicycling to work 3,474 3,741 (+7.69%) 3,542 (-5.62%)

The number of discrete females who bike to work has decreased in the most recent survey (2007-2009) while the total number of workers 16 and older has increased, giving females bicycling to work a smaller share than the previous survey (2006-2008). We must be careful to also note the margin of error for females bicycling to work is ±499.

Matt suggested that sustainable transportation advocates “push for higher sampling” to reduce “data noise” and increase the accuracy of how this data represents actual conditions. I agree – I’d also like more data on all trips, and not just those made to go to work. Household travel surveys attempt to reveal more information about a region’s transportation.

One of the two overall goals of the Bike 2015 Plan is “to increase bicycle use, so that 5 percent of all trips less than five miles are by bicycle.” Unfortunately, the Plan doesn’t provide baseline data for this metric, but we can make some inferences (there will probably be no data for this in 2015, either). The CMAP Household Travel Survey summary from 2008 says that the mean trip distance (for all trips) for Cook County households is 4.38 miles (under five miles). The same survey says that for all trips, 1.3% were taken by bike. These can be our metrics. *See below for men/women breakdown. Note that no data for “all trips” exists for the City of Chicago.

We will not achieve the Bike 2015 Plan goal unless we do something about the conditions that promote and increase bicycling. Achieving the goals in the Bike 2015 Plan is not one group or agency’s responsibility. The Plan should be seen as a manifestation of what can and should be done for bicycling in Chicago and we all have a duty to promote its objectives.

Please leave a comment below for why you think the rate of women who bike to work has stayed flat and decreased, or what you think we can do to change this. Does it have to do with the urban environment, or are the reasons closer to home?

*The same survey also said: Cook County males used the bike for 1.9% of all trips. Cook County females used the bike for 0.8% of all trips.

Table 1 data comes from the 1-year estimates from the American Community survey, table S0801, Commuting Characteristics by Sex for the City of Chicago (permalink), which is a summary table of data in table B08006. Table 2 data directly from American Community Survey table B08006.

Google snaps up another open web advocate

I’ve been following the work of Chris Messina (also known by his handle, factoryjoe) for a couple years now. I can’t remember how I found him (maybe it was BarCamp, OpenID, of the Firefox ad), but I know why I follow him. Like me, he wants to keep the web open and data transferrable or transportable.

While browsing the New York Times Technology section Monday morning (my favorite tech news site, hands down), I saw the headline that he now works for Google (Monday was the first day). This kind of shocked me. I feel Google gets a little scarier every week: some of my friends have admitted that a lot of their online life exists on Google servers and feel queasy about what could happen (some call this “the cloud” and have pointed out the devastating possibilities for privacy and business).

Open web advocate, Chris Messina, presents at the Open Source Bridge conference in Portland, Oregon, in June 2009. Photo by Aaron Hockley.

The author pointed out Messina’s history in open web advocacy (he hijacked his high school’s website because of its refusal to allow an ad for a new gay/straight alliance). The article offers some speculative reasons why Messina made the move, but I want to discuss the inclusion of a quote from Eran Hammer-Lahav, who works for Yahoo!.

With Messina, Smarr, [inventor of OpenID and more Brad] Fitzpatrick and others all working for Google, focusing on the Social Web, there is less and less incentive for Google to reach out. Google has a strong coding culture which puts running code ahead of consensus and collaboration. Now with so many bright minds in house, they are even less likely to reach out. Quote continues…

In other words, with all of the open web advocates being Open Web Advocates (Messina’s new title), who will advocate for web users now? There’s me, for sure. And there are folks standing behind Open Government and Government 2.0. People like Barack Obama (he issued the Transparency and Open Government memo), Adriel Hampton (host of Gov 2.0 Radio podcast), Mark Abraham (urbandata on Twitter), and anyone in the government “black box” who’s willing to set government data free.

In addition, new websites are up and running that remix and mash up government data into useful applications that can promote, through the web, a different level of ownership of one’s community. Or websites that provide useful and relevant information for residents. Websites like SeeClickFix (identify problems in your neighborhood to get city politicians and staff to take notice), or the Center for Neighborhood Technology’s Housing and Transportation Affordability Index.

And don’t forget that the Chicago Bicycle Parking Program liberated its bike parking data into Excel, KML, and GIS-compatible formats in 2009.

Screenshot of the Advanced Search page in the Chicago Bike Parking Public Interface web application from which you can download bike rack installation data.

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