In 1937, New York created taxi medallions as a way of dealing with the sheer volume of unlicensed cabs in the city. About 12,000 were initially sold. They cost $10. And you needed one, fastened to your car, in order to operate a taxi service.

In 2002, the price of a medallion had risen to about $200,000, though its value had been fairly stable since about 1995. Below is a graph from a recent NY Times investigation on taxi medallions. At their peak, in and around 2014, they were worth over $1 million.

The common narrative is that ride sharing services simply killed the value of medallions. They disrupted the taxi business. While it is certainly true that mobile apps have forever changed the way we navigate our cities, the above investigation by the NY Times has revealed something potentially more impactful:

The medallion bubble burst in late 2014. Uber and Lyft may have hastened the crisis, but virtually all of the hundreds of industry veterans interviewed for this article, including many lenders, said inflated prices and risky lending practices would have caused a collapse even if ride-hailing had never been invented.

At the market’s height, medallion buyers were typically earning about $5,000 a month and paying about $4,500 to their loans, according to an analysis by The Times of city data and loan documents. Many owners could make their payments only by refinancing when medallion values increased, which was unsustainable, some loan officers said.

So at the same time that Uber was being vilified in the media for destroying the taxi business, the industry itself was working to manipulate medallion prices and shill unaffordable debt onto new immigrants. An interesting read from the NY Times.

Architect I.M. Pei died this week in New York City. He was 102. Being a centenarian is noteworthy enough. He was born in Hong Kong in 1917. I would love to join that club. Imagine how much change he experienced throughout his life. But, of course, Pei was also a celebrated Pritzker Prize winning architect. For those of you in Toronto, you can look to Commerce Court West to see an example of his work (Page & Steele was the local architect). Completed in 1972, it was the tallest building in Canada until 1976. But perhaps his most well known project is the Louvre Pyramid in Paris (pictured above). In reading some of his obituaries, I was intrigued -- but in no way surprised -- to learn that the Louvre Pyramid was deeply hated by Parisians at the time it was being proposed and built. Supposedly, for the first few years after completion, Pei couldn't walk the streets of Paris without people berating him. However, if you surveyed Parisians today, I would bet you that the approval rating of the Pyramid would be extremely high. And I would also argue that it has since become one of Paris' most globally recognizable symbols. (Parisians, please weigh in below in the comments.) All of this, once again, suggests to me that we're often not very good at evaluating the merits of things that are new to us. Pei's Pyramid, beyond being a new circulation strategy for the broader complex, was a radically different style of architecture. Appreciating that sometimes requires a bit of time. Photo by Uriel Soberanes on Unsplash

Since we're on the topic of large-scale data collection, I thought some of you may be interested in Uber Movement's new "Speeds" product.

First launched in 2017, Uber Movement aggregates anonymized data from their ride-sharing business to create data sets and tools that can help cities make better transportation decisions.

Below is a (hex cluster) map of Toronto showing average travel times from downtown. I dropped the pin at Toronto City Hall. What is shown is the average for all days of the week during the month of January 2018.

Uber Movement's new Speeds product looks at how specific streets are performing relative to their "free-flow speed." Uber defines this as "the average speed of traffic in the absence of congestion or other adverse conditions." (The 85th percentile of all speed values.)

As of right now, Speeds is only available in 5 cities: New York City, Seattle, Cincinnati, Nairobi, and London. Here is a snapshot of London during the same time period as above, January 2018:

In comparison to what we were talking about yesterday, I have few concerns with the fact that my Uber rides around town have likely contributed to these mappings. With these use cases, the value really only emerges once you aggregate the data.