The work of John Snow is instrumental to the field of epidemiology. In the mid-19th century, during what was the third major outbreak of cholera, he created the following map showing the clusters of cholera cases in London's Soho neighborhood. Stacked rectangles were used to indicate the number of cholera cases in a particular location. This was a major breakthrough for the fight against cholera because, at the time, it wasn't clear what was causing it. According to Wikipedia, there were two main competing theories. There was the miasma theory, which posited that cholera was caused by bad particles in the air. And there was the germ theory, which posited that cholera could be passed along through food and/or water.
By mapping the clusters of cases, Snow discovered a concentration of incidents in around the intersection of Broad Street and Cambridge Street (now Lexington Street) where a water pump was located that drew water from the Thames. This led Snow to the conclusion that it was maybe a bad idea to offer up polluted river water as drinking water. And sure enough, when the pump was shut off and residents were directed to other nearby pumps, the incidences of cholera began to decline. The germ theory had proven to be true.
The first time I saw John Snow's map was in architecture school. Perhaps many of you have seen it as well. It is often used to illustrate the potential of visual representations to not only tell a story, but to teach the creator what that story actually is. In hindsight, it may seem obvious that polluted river water is something that we maybe shouldn't drink, but it wasn't at the time. This map helped people understand that. Today, we have far more sophisticated tools available to us, but we still have a lot to learn and we're doing that every day -- particularly during a pandemic.
One other thing worth mentioning is that there are a few exceptions to Snow's findings. Supposedly, many of the workers in a nearby brewery were able to completely avoid the cholera infection during the outbreak by only drinking their own brew. Some say it is because the brewery had its own water source, whereas others say it is because the brewing process -- the water is boiled -- kills the cholera bacteria. Either way, I think the moral of this story is pretty clear: when in doubt, choose beer over water.
Map: Wikipedia
The work of John Snow is instrumental to the field of epidemiology. In the mid-19th century, during what was the third major outbreak of cholera, he created the following map showing the clusters of cholera cases in London's Soho neighborhood. Stacked rectangles were used to indicate the number of cholera cases in a particular location. This was a major breakthrough for the fight against cholera because, at the time, it wasn't clear what was causing it. According to Wikipedia, there were two main competing theories. There was the miasma theory, which posited that cholera was caused by bad particles in the air. And there was the germ theory, which posited that cholera could be passed along through food and/or water.
By mapping the clusters of cases, Snow discovered a concentration of incidents in around the intersection of Broad Street and Cambridge Street (now Lexington Street) where a water pump was located that drew water from the Thames. This led Snow to the conclusion that it was maybe a bad idea to offer up polluted river water as drinking water. And sure enough, when the pump was shut off and residents were directed to other nearby pumps, the incidences of cholera began to decline. The germ theory had proven to be true.
The first time I saw John Snow's map was in architecture school. Perhaps many of you have seen it as well. It is often used to illustrate the potential of visual representations to not only tell a story, but to teach the creator what that story actually is. In hindsight, it may seem obvious that polluted river water is something that we maybe shouldn't drink, but it wasn't at the time. This map helped people understand that. Today, we have far more sophisticated tools available to us, but we still have a lot to learn and we're doing that every day -- particularly during a pandemic.
One other thing worth mentioning is that there are a few exceptions to Snow's findings. Supposedly, many of the workers in a nearby brewery were able to completely avoid the cholera infection during the outbreak by only drinking their own brew. Some say it is because the brewery had its own water source, whereas others say it is because the brewing process -- the water is boiled -- kills the cholera bacteria. Either way, I think the moral of this story is pretty clear: when in doubt, choose beer over water.
Map: Wikipedia
I first met Monika Jaroszonek in 2017, right before she started RATIO.CITY. Since then she has developed some pretty incredible tools for the city building space.
Yesterday the company published this interactive visualization looking at development potential across the City of Toronto. The mapping looks for the following:
Land that has a Mixed Use, Apartment or Regeneration designation in the City of Toronto’s Official Plan
Land that is located within a Provincially designated Urban Growth Centre
Land that is located within 500m of a Major Transit Station
The tool then ranks each development site – AAA, AA, A – according to how many of the above criteria it meets.
It also flags land that it refers to as “Missed Opportunity.” These are lands located within 500m of a Major Transit Station, but that are designated as Neighbourhoods (considered stable) or Employment (whole other discussion).
Based on this filter, about 5.6% of the City’s land is a “Missed Opportunity” and about 1.2% is AAA.
When you look at the visualization, that is one of the first things you will probably notice; a lot of our transit infrastructure is currently underutilized as a result of land use policies.
Image: RATIO.CITY
I discovered a company yesterday called CARMERA, which just raised a $20 million Series B funding round. They call themselves a “real-time, street-level intelligence platform” and their flagship product, called Autonomous Map, provides HD maps and real-time navigation data to autonomous vehicles. That’s the way AVs work. They need maps like CARMERA’s to function. Here is an overview of what is supposedly the largest AV taxi service in the world. It is a partnership between CARMERA and Voyage.
One of the interesting things about this product is that it is cleverly powered through another one of their products: a free fleet monitoring tool for commercial operators. So fleet managers use this service to keep track of their actual human drivers and, at the same time, CARMERA uses the vehicles to collect the data it needs for its Autonomous Map. They call it “pro-sourcing” the data (a play on crowdsourcing).
It is perhaps a good example of “single user utility.” The product you’re making often has to be valuable to a single user before scale is reached. In this case, Autonomous Map would be a hard sell without a critical mass of pro-sourced data. It solves the perennial chicken-and-egg problem when creating new marketplaces.
Finally, I think many of you will be interested to know that CARMERA has also announced a partnership with the New York City Department of Transportation. As part of this, the company will be handing over the data they have on pedestrian density analytics and real-time construction detection events. Part of their mission is to “automate cities” and better street analytics will certainly help to open up a new world of city building possibilities.
Photo by Yeshi Kangrang on Unsplash
I first met Monika Jaroszonek in 2017, right before she started RATIO.CITY. Since then she has developed some pretty incredible tools for the city building space.
Yesterday the company published this interactive visualization looking at development potential across the City of Toronto. The mapping looks for the following:
Land that has a Mixed Use, Apartment or Regeneration designation in the City of Toronto’s Official Plan
Land that is located within a Provincially designated Urban Growth Centre
Land that is located within 500m of a Major Transit Station
The tool then ranks each development site – AAA, AA, A – according to how many of the above criteria it meets.
It also flags land that it refers to as “Missed Opportunity.” These are lands located within 500m of a Major Transit Station, but that are designated as Neighbourhoods (considered stable) or Employment (whole other discussion).
Based on this filter, about 5.6% of the City’s land is a “Missed Opportunity” and about 1.2% is AAA.
When you look at the visualization, that is one of the first things you will probably notice; a lot of our transit infrastructure is currently underutilized as a result of land use policies.
Image: RATIO.CITY
I discovered a company yesterday called CARMERA, which just raised a $20 million Series B funding round. They call themselves a “real-time, street-level intelligence platform” and their flagship product, called Autonomous Map, provides HD maps and real-time navigation data to autonomous vehicles. That’s the way AVs work. They need maps like CARMERA’s to function. Here is an overview of what is supposedly the largest AV taxi service in the world. It is a partnership between CARMERA and Voyage.
One of the interesting things about this product is that it is cleverly powered through another one of their products: a free fleet monitoring tool for commercial operators. So fleet managers use this service to keep track of their actual human drivers and, at the same time, CARMERA uses the vehicles to collect the data it needs for its Autonomous Map. They call it “pro-sourcing” the data (a play on crowdsourcing).
It is perhaps a good example of “single user utility.” The product you’re making often has to be valuable to a single user before scale is reached. In this case, Autonomous Map would be a hard sell without a critical mass of pro-sourced data. It solves the perennial chicken-and-egg problem when creating new marketplaces.
Finally, I think many of you will be interested to know that CARMERA has also announced a partnership with the New York City Department of Transportation. As part of this, the company will be handing over the data they have on pedestrian density analytics and real-time construction detection events. Part of their mission is to “automate cities” and better street analytics will certainly help to open up a new world of city building possibilities.
Photo by Yeshi Kangrang on Unsplash
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