Charlotte, NC

Charlotte is one of the fastest-growing cities in the country; its population rose almost 50% from 2000-2014, from a combination of migration and city expansion. It has become the second-largest banking center in the country after New York City. According to the American Community Survey, it has one of the lowest commute biking rates in the country (0.2%).

Thoughts from Charlotte

MLK Way part 9: Summary data

As I’ve worked through this series I’ve had to improve my code to deal with more edge cases. I buffered the street by about 100 meters to smooth out inconsistencies in the census tract boundaries. I wound up splitting out the three New York City boroughs which have MLK Ways, because their stories are very different. I special-cased a few places where the tract selections were not comparable. I had to code for non-reporting Census tracts.

There are probably still some data issues, especially in cities I didn’t build a map for. But overall I’m fairly comfortable with the data set, given the caveats I’ve already raised. The biggest problem I see remaining is that the cost-of-living index is using national numbers, which understate the cost of living changes in expensive metros like Oakland. Some of the cities also have very small sample sizes, especially in the ACS data.

That being said, here are some summaries.


I find two things interesting about the real median income numbers. One is that they are entirely split down the middle: 29 cities are increasing and 29 are decreasing. The average decrease is 0.3%, a mere $72/year. So it’s more or less a wash; the developing cities are evenly balancing out the decaying cities.

The other thing is that the top two cities are Charlotte, NC and Columbus, OH, and the bottom is St. Paul, MN, all of which were part of my field work. That’s not entirely coincidental; the fact that Charlotte and Columbus are expanding is part of why they were selected. But it’s mostly just a cute data point; in all three cities the MLK Way is only a few blocks long, so they show up as outliers partly because the sample size is small.


The racial dynamics, on the other hand, aren’t balanced at all; the proportion of Black people in the tracts near MLK Way fell in 55 of 59 cities, with a median decrease of 11.9%. In aggregate, these areas lost 122K Black people.

St. Paul and Columbus show up again; this time as the top and bottom in proportional change. Black population grew by over 30% in St. Paul’s neighborhoods, more than three times the amount of the #2 city (Toledo, OH). Columbus’ proportion of Black people fell by a similar amount (28%). This is a much more common outcome; Black representation fell by more than 10 percentage points in 22 of the study cities.

Chart of changes in Black population, showing that many more cities lost than gained Black population

The change in proportion of Whites is not quite so dramatic, though the overall trend is the same. White representation increased by over 10% in nine of the study cities, which gained 101K White persons in aggregate.

Graph showing increase or decrease in White population in 58 cities, with 35 cities increasing.

The gap between the increase in white people and the decrease in Black people mostly is due to an increase in Hispanic people. Hispanic representation increased by over 10 percentage points in 11 of the study cities, and the total population increase was 98K persons.

Income and population

As a whole, income increased where the White population increased. Every city where real income increased by more than 20% also increased in White representation (top right quadrant). Every city where White representation declined by over 10% saw a decrease in income (lower left quadrant). The correlation between the two is regrettably strong (r=0.61).
Note: This chart omits St. Paul to improve the visual display.
Scatter plot, displaying a correlation between increase in White population and increase in income


Returning to the premise of the Bike Lab: Did an increase in White population lead to more cycling? Only a bit. Whiteness is correlated with cycling, but not nearly as strongly as with income (r=0.40). Cycling rose overall (by 0.5 percentage points), but there are cities like Columbus where White representation rose by over 20 percentage points while cycling rates went down.

Note: This chart omits Portland to improve the visual display.

Scatter plot showing a weak relationship of White population to cycling rates.

When I visited Columbus I stayed not far from MLK Blvd. That eastern area of town does face some real barriers to cycling; getting to downtown requires a harrowing freeway crossing and a jaunt on a one-way high speed three-lane road. So that might explain why cycling didn’t increase, but it doesn’t really explain why it decreased. My hypothesis would be that white people view poor infrastructure as more of a barrier to cycling than Black people do.

MLK Way part 6: Development without displacement

The Holy Grail of community economic development is “development without displacement”: reinvestment in decayed neighborhoods which can provide long-time residents with new opportunities, without forcing them from their homes. It’s hard to achieve in a market-driven society, especially one where home ownership rates and household wealth are as wildly disparate as they are here in America. But it’s not impossible. Here are six U.S. cities where the neighborhoods around MLK Way are increasing both in income and in Black population.

Total population change, 2000-2017 Population change, Blacks, 2000-2017 Real median income change (2018 $), 2000-2017
Charlotte, NC 8,096 1,270 $52,915.51
Louisville, KY 1,387 228 $344.69
Memphis, TN 2,318 725 $5,785.05
Phoenix, AZ 19,657 2,335 $595.78
St. Petersburg, FL 11,015 2,254 $688.64
Washington, DC 7,176 1,561 $11,132.69

Perhaps the most promising of the stories is in southeastern Washington, DC. These tracts were 94.1% Black in 2000, and in the middle of the pack in median income in the study cities. Given the high cost of living in the nation’s capital, this neighborhood was poor.

In the ensuing years, many Whites have moved in, primarily on the north side of the river. Capitol Hill is just north of those tracts. But while the proportion of Blacks in these tracts has fallen to 81%, the absolute number of Blacks has actually risen.

Map of southeastern Washington DC showing changes in demographic composition from 2000-2017

More analysis is needed to determine what’s going on for the people living south of the river. It could be that more affluent Blacks are displacing poorer Blacks. It may be that all of the increase in median income is happening north of the river, and the southeast neighborhoods are still poor. So it would be premature to say that this a policy success. But it’s safe to say that this is what policy success looks like; neighborhoods where people are able to stay in their homes (if they want to) while experiencing less crime and getting better access to job opportunities and education.

That airport on the other side of the river (Reagan National) is in Arlington, right next to where Amazon is building its HQ2 campus. How will the influx of tech workers affect these nearby neighborhoods? Presumably the median incomes will rise further, and White and Asian populations will grow relative to Black populations. Are there protections in place (home ownership, rent control, community land trusts) to allow long-time lower-income residents to stay if they want to? Or will they be re-segregated out into the exurbs?

Equity concerns still exist in places developing without displacement. Even when residents are not being displaced spatially, they may be displaced culturally. New, more affluent residents, from different cultural backgrounds will bring with them new institutions which change the feel of the neighborhood. The new residents may also object to long-standing cultural practices, as BBQ Becky (a Stanford-educated chemical engineering PhD) famously did at Lake Merritt in Oakland. Lake Merritt also saw cultural conflict over the Samba Funk drummers who regularly play there on weekends, and a complaint was filed about loud gospel music at a church in West Oakland.

I think Oakland has done a pretty good job responding to these complaints. Many people came to Pleasant Grove Church’s defense (the city declined to fine them), Libby Schaaf invited Samba Funk to play her out of the chambers at her State of the City address, and the BBQ’n While Black event at Oakland celebrated the long-standing BBQ culture of the city. But in the long term, culture will change, and a city that cares has to grapple with figuring out how to honor long-time communities without sealing them in amber.

One more map from Charlotte, which was also part of my field work. Charlotte is one of the fastest-growing cities in the country, and the amount of construction going on when I was there in 2016 was astounding. Martin Luther King Jr. Boulevard happens to be right in the middle of the financial district downtown, which explains the huge increase in median income; Charlotte over the past 20 years has become the biggest banking center in the U.S. outside of New York. The presence of Johnson C. Smith University, a HBCU in the northern tract here, probably explains the stable (but still small) Black population.

Only two of the six cities here (Washington, D.C. and Memphis, TN) really qualify as representing significant development without displacement. Memphis, of course, is where Reverend King was shot, and Dr. Martin Luther King, Jr. Avenue is only a few blocks away from the location, which is now the National Civil Rights Museum memorializing his life and struggle.

It is ironic that Memphis is one of the few places where blacks who live near the street named for Dr. King seem to be doing well today. What can we learn from Washington and Memphis that we can use elsewhere to counteract the forces of racism and the logics of capitalism?

One thing is clear: the struggle never ends.

Map output

I wanted to get as much data analysis done on my home computer as I could before I head out for study abroad in Berlin. It’s not entirely perfect yet, but I have a workflow which generates these maps right out of Python for all of my cities, with some provision for longitudinal comparisons (limited by data availability). Code has improved enough that on that setup (Mac Pro 2013, 6-core, 12 GB) it takes about a day to generate these for 100 cities.

To do still are to deal with projection issues (they’re all in “web Mercator” because that’s what Leaflet uses), and centering issues caused by inconsistencies between folium, selenium, and PhantomJS. And to improve legends and captions. But it’s pretty cool, if you ask me.


[flickr_set id=”72157683818724493″ max_num_photos=”21″]

See all maps from 2015


[flickr_set id=”72157686472156785″ max_num_photos=”21″]

See all maps from 2010

Intersection density

OSMnx makes it easy to generate statistics on street networks by city extent, or by polygon shape. Here are the stats for the study cities, clipping the bike network (including bike paths, excluding freeways) to the 5-mile circle:

Austin Charlotte Columbus Minneapolis
Nodes (intersections) 10470 5417 12208 17892
Streets per node (avg) 2.98 2.82 3.16 3.13
Segment length (ft., avg) 323 394 321 299
Street segments, total 17733 9291 20976 30909
Bicycle mode share 3.6% 0.7% 2.3% 4.7%

This covers the entire circle, not just the census tracts, so the area is identical for the four cities. Once again these aggregated stats show very strong correlations, especially between bike mode share and intersection density (r=0.91). The fact that there are three times as many intersections in Minneapolis’ central city than in Charlotte’s is a somewhat remarkable finding, though it’s consistent with the experience of riding around those cities. Higher intersection density means that there are more choices for people getting around the city, which in turn means that the arterials have less traffic on average, and there are more alternate routes for bikes.

There’s an analogy to the geophysical concept of drainage density. A river network with fine drainage density (relatively small space between river channels) will drain very quickly during storm events because there are so many different ways for the water to reach a river channel. If you think about the networks below as representing the morning commute, the “coarse” network will require much larger, higher-speed roads than the “fine” network. And it’ll suck for bicycles.

Image depicting river systems with three different drainage densities (fine, medium, and coarse)

One of the problems of cycling advocacy is that there’s often not much that can be done to change this indigenous condition. Charlotte is already built out with high-volume streets and long blocks, and at this point you can’t make its street network look like Minneapolis’ (or Copenhagen’s).

Density = destiny?

One striking result from the target area analysis is the correlation between residential density and cycling rates in the target area.

Austin Charlotte Columbus Minneapolis
Target area (mi^2) 4.86 4.11 5.11 5.25
Population (persons) 147676 71312 129290 214592
Cyclists (work commute) 5380 464 3006 10040
Density (persons/mi^2) 30396 17342 25311 40850
Bicycle mode share 3.6% 0.7% 2.3% 4.7%
Source: ACS 2015 5-year estimates

Note that the square mileage in the above table is the square mileage of the census tracts fully contained within the target circle. Obviously the circles themselves have the same area, but because of differences in tract shapes, and non-land areas (lakes and rivers) within the circles, the census tract areas differ. This table reflects only the census tract areas, because it’s using census data.

For these four data points, the correlation between density and bicycle mode share is dramatic (r=0.97), which seems to speak to the importance of indigenous conditions in people’s mode choices. And there’s probably a real effect there. Unfortunately, the effect disappears when examined at the census tract level, becoming slightly negative in all four cities. My hypothesis is that there are threshold effects, where the most dense census tracts actually have less cycling because their walk mode share is higher. I didn’t have time to dig into that question, but it would be consistent with some of the work done by urban design superstar Jan Gehl.

Two things I would like to do are to automate the analysis to the point where I could examine this same question across many more cities, and, to combine the city data to see if the correlation flips back to the positive side when looking at census tracts across the four cities. I expect that both of those methods will show a strong positive correlation between density and bicycle mode share, but not near r=0.97.

Maps of network connectivity to jobs

One of the factors I’m trying to measure in valuing facilities is their usefulness; do they actually go where utility cyclists want to ride? I was unable to get BikeScore data (which looks pretty questionable anyway), so I had to build my own metric around it. One of the factors I used was access to jobs along the cycling network (with cycling network data obtained from OpenStreetMaps via Geoff Boeing’s awesome OSMnx tool). These maps show access to jobs within 1.5 miles from each intersection in the city; cycling routes through and near the dense areas here were scored higher.

One of the interesting things about these maps is how they visualize the density of the street network. The difference between Minneapolis’ densely connected street grid, and Charlotte’s broken-up and sparse network visualize really substantial differences in the indigenous conditions for cycling in the two cities.

These maps are using the same extents as the target area maps (the circle extenst rather than the census tract extents).

Maps of commute biking and value-added facilities

Combining my concepts of target area and value-added facilities, I developed maps for each city which select the circle of census tracts near the central city which have the great number of commute cyclists (by the 2015 ACS 5-year estimates). Each of these maps is equal area and uses the same scale.

One thing that’s visibly notable is the relatively weak connection of value-added facilities to cycling rates, by census tract; only in Columbus are they well-connected. Part of what I conclude is that cultural factors and the indigenous qualities of the street network are more important than value-added facilities in cycling mode choice.

One of my summer projects is to try to re-generate these in Python. I was trying to do all the GIS work in Python, but when it came to final output, I ran out of time and wound up having to export shapefiles and do the maps manually in QGIS. That’s why they’re not perfectly identical. Once it’s working in Python, I should be able to auto-generate the circles and the census tract data for any city in the U.S.

The methodology is potentially interesting for other kinds of analysis; I’m using bike mode share data, but you could just as easily optimize for any other census data, like median income, non-white population, educational attainment, etc. It could be a useful way to make urban areas more comparable for data analysis.

Target area data analysis

During the fall semester I took two relevant classes, one an introduction to GIS, and one on Active Transportation (with Professor Daniel Rodríguez, formerly of UNC-Chapel Hill, who will act as my thesis advisor). For my final project in the Active Transportation class, I used GIS tools to analyze bike mode share, bikeway mileage, and crash data for the four cities.

One problem I noticed while doing field research is that the city extents vary greatly; Austin, for example, has over six times Minneapolis’ land area, which means that Austin includes some sprawling, low-density areas with low cycling rates that are excluded from Minneapolis’ mode share numbers. Nathan Wilkes (from Austin’s Active Transportation Department) had done some work comparing mode share in the downtown area of Austin to San Francisco, to point out that the city has actually made more progress than would be evident from looking at the city-wide data.

Map of Austin bike mode share by census tract, showing much higher rates in the central city

Bike mode share by census tracts in Austin city extent (1:250,000)

Map of Minneapolis bike mode share by census tract, showing much higher rates in the central city

Bike mode share by census tracts in Minneapolis city extent (1:250,000)

I looked at creating a 4-mile radius circle to encompass the highest-cycling area of each city (by ACS mode share data), to be able to develop more comparable metrics from city to city. I think the method will prove very useful in the data analysis portion of the work, though I’ll make some tweaks to it in the final version. For this work I selected all census tracts which intersected the circle, but the resulting extents are still quite different in land area (by almost a factor of two). For the paper I’ll probably use a 5-mile radius and select only the census tracts which are completely included in the circle. I’ll also include census tracts outside of the central city if the circle includes them (which might affect Minneapolis, but not any of the other cities).

Bike mode share, facilities, and crashes within 4-mile radius circle in central Austin (1:100,000)

Bike mode share, facilities, and crashes within 4-mile radius circle in central Austin (1:100,000)

Bike mode share, facilities, and crashes within 4-mile radius circle in central Minneapolis (1:100,000)

Bike mode share, facilities, and crashes within 4-mile radius circle in central Minneapolis (1:100,000)

There are still a lot of issues with the data, but at least I’ll be comparing rotten apples to rotten apples.

Here’s the full paper if you’re interested.

Comparing cities

A question I’m trying to answer is, why are utility cycling rates higher in some U.S. cities than others? It’s tricky to investigate for a number of reasons. The first is simple: Our data sucks. The cycling mode share number commonly used as a comparator is based on commute cycling only, not utility cycling in general, and it has a number of other issues. (For example, it’s based on the city limits, which are handled differently in each city, and which change over time). There is no generally accepted standard for collecting count data. Bikeway mileage numbers bear only a loose correlation to the utility of the street network for cycling. And so on.

The second is complex: It’s just a difficult question. Utility cycling rates are driven by countless factors: the density and nature of the built environment, the character of the street network, weather, demographics, socioeconomic status, culture, and more. Each city has its own unique combination of these factors; extracting causal relationships from the paltry data is quite challenging.

Still, I have to start somewhere. My plan is to look at pairs of cities with relatively similar demographic profiles, but significantly different cycling rates as reported by ACS mode share, to see if it’s possible to get a little closer to what’s going on. Using some overview statistics and a bit of hand waving, I paired Charlotte with Austin, TX. Here are some of the reasons why:

Austin Charlotte
Population 912,791 809,958
Pop. change since Y2K 39.0% 49.8%
Median household income $56,351 $51,034
Median resident age 32 33.3
Land area (sq. mi.) 242.3 251.5
Density (persons/sq. mi.) 3520.2 3272.7
Housing density (units/sq. ml.) 1099.8 951.7
White Non-Hispanic 49.7% 42.9%
Black 7.2% 35.2%
Hispanic 34.0% 13.9%
Asian 6.1% 5.4%
Two or more 2.6% 2.1%

(source: City Data)

As cities 1,000 miles apart go, this is about as close as it gets. The Black and Hispanic ethnicity numbers are flipped, but other than that it’s pretty close.

A qualitative thing I like about this pairing is that Austin and Charlotte are different examples of the new South: Charlotte now being the second-biggest banking center after NYC, and Austin being possibly the fastest-growing tech hub in the U.S. These industries are driving population expansion (and a ton of construction) in both places, driving up median incomes, and creating neighborhood change.

In terms of biking, Charlotte’s ACS mode share is 0.2%, among the lowest in the country. Austin’s is 1.2%, about double the national average of 0.62% (2014 numbers). Counting bikes in Austin should be a lot more fun than it was in Charlotte.

Golden Age

On Friday night I did a bike/ped count at the Birdsong Brewery, which I’d been told was the place to see lots of bikes. The count turned out to be as just as fruitless as the others I’d done (a total of four bike riders in two hours), but the trip was fruitful. Even though I’d only been in town a few days, there were several people at the bar who I had already met. While I was there, a couple more people from the cycling community joined us, and we had some good conversations about cycling, Charlotte, and my impressions of my findings so far.

Birdsong Brewery

Birdsong Brewery

After a while, the group checked Strava to find their other cycling friends, who turned out to be at a different brewery. Most of the group went off to meet up with them there. Near the end of my count time, Pam rolled up, and we rode back to her place so I could drop off the bike she’d loaned me.

On our ride, Pam asked what I’d found so far, and I reflected on the idea that the cycling scene in Charlotte feels a little like it’s in a Golden Age. It is a tight-knit group of people bound together by a common interest which separates them from the mainstream culture of the place. They all know each other, spend time together, and work towards shared goals. They don’t always agree; the vehicular cycling vs. infrastructure advocacy divide is notable. But they identify as part of the same community in a way that is no longer as true in the Bay Area, or other places where utility cycling may be more mature. The community is marginalized, but that brings them closer together.

A Golden Age creates many opportunities to do great things. I’ll be interested to see how it progresses in Charlotte.


Another thing I’m doing in my travel is bike and pedestrian counts at selected parts of the city transportation network. The data that exists on cycling volumes in different cities is very spotty; the national American Community Survey data only covers work commute cycling and isn’t spatially located within the city. Some cities also collect count data, but there is no national standard, and different cities have very different count programs. The National Bike and Pedestrian Documentation project is an attempt to regularize the collection and storage of count information, and I’m using NBPD’s methods and forms to conduct counts in the cities I’m visiting.

Basically, that involves sitting somewhere for two hours and counting the bicyclists and pedestrians (broken down by gender) who pass by an imaginary line across the street and sidewalk. Count instances are divided into 15-minute periods. After completion, counts will be uploaded to the NBPD database, where they can contribute to comparisons within the city and between cities.

Example NBDP screenline count form

Example NBDP screenline count form

One shortcoming I’m motivated by is the relative lack of data on non-commute utility cycling. It feels to me like cycling to cafes, restaurants, grocery stores, and other short-range trips has increased significantly in the past 10 years in the Bay Area, and my hypothesis is that this increase is connected to the rise of bike culture in the U.S.

So in Charlotte, in addition to conducting counts downtown, I tried a hip restaurant/cafe district and a Bike Benefits brew pub which supports cycling events (and lights up on Strava’s heat maps).

Unfortunately, the location didn’t seem to matter much; in none of the places I conducted a count in Charlotte did I see more than 5 bikes in a 2-hour period. This was consistent with my experience on the road. I rode over 100km around the city, and seeing another cyclist was a rare event in my surveys. ACS has Charlotte’s mode share at 0.4%, and I didn’t find any evidence that there were secret pockets of utility cycling that ACS is missing.


Turn onto Little Sugar Creek Greenway

Turn onto Little Sugar Creek Greenway

Charlotte is putting a lot of effort into developing greenways with multi-use paths. It seems like their political climate makes it easier to sell creek daylighting and greenway projects than bike-oriented road projects, but for utility cyclists, the creek paths are a mixed bag. One thing I immediately noticed in surveying them is that they’re poorly connected to the street grid. Leaving downtown on Third Street, a designated bikeway, the bridge crosses over the greenway, but doesn’t interact with it. There’s no signage as you pass by, and to get onto it you have to do an awkward hairpin turn into a driveway and down a sidewalk.

This kind of disconnection continues as you use the greenway. I was heading to Target to buy a GoPro battery (those things drain faster than I expected, even shooting stills), and from the greenway, even though you’re passing right by the shopping center, there is no obvious connection to bike up to the street–just several sets of stairs. Eventually I found a narrow sidewalk through a playground area that let me get to the shopping center without hauling the bike up stairs. The design almost seems intentionally keeping the greenway traffic separate from the street traffic.

Debris pile on greenway

Debris pile on greenway

There were also discontinuities on the path itself. A pile of construction rocks blocked half of the path at one point; mud from an earlier flood filled the path underneath one of the bridges. (Pam said that portions of the pathway are often closed for flooding).

The impression I get is that the idea of a greenway is more politically palatable than the idea of a utility bikeway, and that the languaging and design of the greenway is oriented towards recreational cyclists. Utility cyclists may use portions of the greenway where they are effective, but in general, utility cyclists want their routes to be reliable. If these facilities are going to be identified as part of the transportation network, they need to be treated as any other transportation facility would be; that would require enforcement of obstruction codes, for example.

Mud under bridge on greenway

Mud under bridge on greenway

The flooding problem is more fundamental. The creek is an attractive route because waterways tend to connect interesting places, and creek daylighting projects can be marketed based on being green/sustainable/resilient. But the creek is crossed by multiple arterials, and at each one the pathway has to go either over, under, or through. Over is too expensive, through (at-grade crossings) create difficult and unpleasant intersections, so often the design settles for going under. The problem with that is the road bed height is set based on the creek flood stage, so a pathway 10 feed below the road bed will be underwater a lot.

Charlotte DOT

After the breakfast meetup on Friday, I rode in to the government center with Matt Magnasco, one of the designers for Charlotte’s Department of Transportation. There, I met with Matt and his colleague Ben Miller, also a cyclist. Matt and Ben do most of the design on bike-related projects in Charlotte.

Charlotte does not have a funded bike infrastructure program. They have a bicycle and pedestrian coordinator position, but it isn’t allocated specific staffing or funding. (The person in that role was retiring the same week I was there, so unfortunately I missed being able to meet with him). Lacking dedicated funding, bike infrastructure projects have been approached on an opportunistic basis; as road paving or development projects come up, they’ll try to put in a bike lane stripe or other accommodation, but there isn’t a coherent plan for facilitating utility cycling. This may be partly because Pam, the most influential member of the cycling community, advocates for education over infrastructure, but it’s also because of city politics.

The Charlotte city council adopted a Complete Streets policy some years ago, but clearly hasn’t provided funding for it. It has affected building codes but not really city practices. Matt took me on a ride after our meeting, and one of the streets we took was Park Road, a 6-lane “high cycling stress” arterial south of downtown. I mentioned that on this stretch, you could probably narrow the grass verge to carve out a decent space for bikes; Matt’s response was that developers (which are going up all over Charlotte–new buildings and cranes everywhere) are required by the Complete Streets policy to have a grass verge to provide an “environment for pedestrians.”


Grass verge on Park Road. “Complete Street”?

Now, while an extra six feet of building setback is better for pedestrians than having the building right up against the street, this little bit of greenish-brown grass and a token tree isn’t really providing an environment for pedestrians; it’s still going to be an unpleasant place to walk. A real Complete Street project on this stretch would look at reducing the traffic lanes (the road is legitimately busy but still probably overbuilt), and providing dedicated, separated space for non-motorized users.NACTO guidelines can tell you where to put curbs and lane stripes, but they can’t navigate the politics for you. Right now the bike and pedestrian volumes are quite low (more on counts later), which makes it hard to justify a project which would surely be controversial. (Pam speculates that CDOT avoids doing bike counts because they’re worried the low numbers would cause problems for future projects).

I felt like Matt and Ben were both guys who understand the issues and want to do the right thing, but Charlotte hasn’t committed to a real investment in utility cycling, so they’re pretty limited in what they can affect. Perhaps a new bike and pedestrian coordinator will find a way to exert more influence.


There are two reasons I’m visiting cities to ride the infrastructure. One is that I want to get a better sense of the subjective experience of the place; I feel like I can’t really understand a city unless I’ve ridden through it. The other is that I want to identify specific issues with the infrastructure. Bikeway mileage is a poor measure for a number of reasons, and one of those is that poor bikeway design can create more problems than it solves. Some paths and lanes provide a great user experience and improve safety, some are more or less neutral, and others are actively dangerous. But no matter how good or bad the facility is, it’s counted in the bikeway mileage. I’d like to get to a measure like “value over replacement facility” which counts mileage only for clearly bike-oriented improvements, and discounts those facilities which include hazardous conditions.

So, I’m riding the bikeways with a GPS tracker (phone-based, PixTrack) and a GoPro shooting time-lapse photos. Here’s the routes I rode in Charlotte, with a few segments missing due to glitches of one sort or another. It came out to just under 100km:

Charlotte GPX image

I use those tracks to geo-tag the time lapse photos, add date, filename, and geotag labels to them, and turn them into a time-lapse video. I wound up with almost 30,000 images from this trip, which at one per second is about 500 minutes of riding, just over 8 hours. That’s a lot of images, and the GUI tools were having some difficulty digesting that fat wad of data, so I dug into my bag of Unix command-line tricks and wrote a hacky Perl script using exiftool and ImageMagick to do the tagging and labeling.

The idea is to be able to use the video to go back and look through the videos to identify problematic areas; pinch points, poor intersections, discontinuous facilities. This will at some point get pulled into GIS, with the facility map overlain with identified problems. That will be connected with some sort of measure of neighborhood change, probably a combination of median income, land value, ethnic makeup, and educational attainment.

The issue is that lots of cities have lots of bikeway mileage that doesn’t provide value. (I’d say that includes most bikeway mileage in most U.S. cities). When trying to dig into causal relationships between bikeways and neighborhood change, it would be useful to be able to identify the areas where value-added facilities exist, and discount the places where there’s just a white stripe in the gutter of a busy arterial.

Here’s the video from Charlotte. It’s not really designed for human consumption but you can see where I’m going with this.


I didn’t happen to be in town on Tuesday, so I couldn’t do the big PMTNR, but the Spokes People have encouraged numerous social rides, one of which was Thursday Night Lights, which I rolled by to participate in (on a bike borrowed from Pam).

There’s not quite an equivalent of these social rides in the Bay Area. We have lycra club rides, and Bike Party, but not an exact analogue to these no-drop, baggy pant (mostly), casual get-together rides. Perhaps it’s because our bike culture is more mature, and people do casual rides with their friends instead of as a specific event. Or else that our event rides get too big (like East Bay Bike Party) to really be social.

Thursday Night Lights ride

We wound up having about 25 riders, ranging from weekend road warriors, to a mother and daughter on department store bikes. It was clear that most people knew each other from previous rides. We rolled out at slow pace, with the leaders modeling Cycling Savvy “control and release” road behaviors. The group stopped to deal with a couple mechanicals (a flat tire and a loose handlebar). Midpoint of the ride was at a brew pub (a bit overwhelmed with 25 people dropping in), after which we rolled back to the bike store. Most had driven there; most of the people I conversed with during the evening expressed fear of riding alone on Charlotte’s roads.

The next day I met up with the Friday morning breakfast group. This was a tradition started by Dianna Ward, executive director of Charlotte’s B-Cycle (bike share) program. As a strategy to normalize utility cycling, B-Cycle provided free breakfast for bike commuters for several months. The sponsorship ran out, but a group of folks decided to keep the tradition alive, and I joined them for bagels and coffee at the Common Market on the south side of downtown. The Common Market itself is about to become a victim of Charlotte’s rapid growth; it’s going to be torn down for a new 8-story office building with ground-floor retail.

We had lots of good conversations about neighborhood change and riding in the city. This was again clearly a tight-knit community. At the end of breakfast, I was gifted a “bullet”; a 30-oz plastic beer container from Unknown Brewing Company, an award typically given for lap primes and other minor achievements at local races and bike events. Unknown sponsors a number of local bike events in Charlotte; beer culture and bike culture are strongly intertwined in the city. (More on that later).


Natural bikeways

Little Sugar Creek (green) parallelling natural bikeway (yellow)

Little Sugar Creek (green) parallelling natural bikeway (yellow). From the Charlotte Cycling Guide.

One of the things I noticed while riding around Charlotte was that certain streets, or collections of streets, are what I would call “natural” bikeways. These are relatively flat roads which carry relatively low traffic volumes for their width, and which allow you to ride for significant distances with limited major intersections and limited stops. Some of these natural bikeways are officially signed routes, but they really don’t need to be; if they connect interesting places, utility cyclists will use them whether or not they are signed. Way-finding, which can be an issue with off-street pathways, is much less of a problem on roads.

For example, in south Charlotte there is a series of roads (Wesfield, Irby, Jameston, Sterling) which parallels the Little Sugar Creek Greenway for about two  miles near Freedom Park. The greenway provides traffic protection to these roads; there is very little cross-traffic. Most auto traffic uses the arterial on the other side of the creek (Park Road). This route for bikes is easy to find, fairly direct, and relatively pleasant.

The concept of the natural bikeway has several implications for my research. One is that as a group, these routes are under-counted; some cities like Portland have miles and miles of natural bikeways, while most Southern cities (or anything built post-WWII in the U.S.) are limited in natural bikeway mileage. This is one of the reasons why Portland’s models for utility cycling (not to mention Amsterdam’s) don’t automatically fit into other U.S. cities. Natural bikeway mileage must be accounted for when comparing the facilities available in different cities.

And while natural bikeways are under-counted as a group, specific routes which become officially designated as bike routes can double-count bikeway mileage in terms of utility. The Little Sugar Creek Greenway is a designated off-street recreational pathway which parallels this natural bikeway. Both routes are identified on the Charlotte cycling map, and both connect the same places. In the data this counts as four miles of cycling facility, whereas in reality there is only two miles, with different options for different types of riders (street or greenway). The greenway path is narrow and twisty, involving several 90-degree turns onto narrow bridges and pathways; appropriate for low-speed recreational riding, especially for young children, but utility cyclists (I hypothesize) will tend to prefer the on-street route. In any case, this overlap of facilities should be considered duplicative for the purposes of utility cycling.

Charlotte Spokes People


Pam Murray, Charlotte Spokes People

The first person who responded to me about meeting up while I was in Charlotte was Pam Murray, co-founder of the Charlotte Spokes People. Pam turned out to be incredibly helpful and enthusiastic about cycling. Her philosophy is that education should be the primary tool to get people riding; she runs a “Cycling Savvy” class to teach vehicular cycling methods, and she organizes a number of social rides, most notably the Plaza Midwood Tuesday Night Ride (PMTNR), which can get over 100 people when the weather is good.

Social rides are an important part of cycling culture in Charlotte (and in Austin, it turns out). These are low-speed rides focused on building community, generally starting or ending at “Bike Benefits” businesses. Bike Benefits is another program of the Charlotte Spokes People; businesses volunteer to give discounts to people who come in with their bike helmets, and the Spokes People help encourage cyclists to frequent those businesses.

Because Pam is focused on education rather than infrastructure, she doesn’t generally advocate for new facilities. As we rode around town, she regularly pointed out that Charlotte’s roads were “just fine” once you knew how to ride them safely. As a long-time vehicular-style cyclist myself, I appreciate her points, and while in Charlotte I met a number of individuals who had been through CyclingSavvy and felt it had made a huge difference in their confidence on the road. However, I’m personally not sure that education can ever move the needle on cycling rates on its own; by the time someone has committed to a 10-hour class, they already must be disposed towards cycling.

Pam was a great person to meet to start this work, because the tension between separate-infrastructure advocates and vehicular cyclists has characterized bike advocacy in the U.S. for decades, and clearly it’s still something that cities struggle with.