Tag: study

• Walking is good for creative thinking

  Here is an excellent reason for why you may want to spend more time walking:

  People have noted that walking seems to have a special relation to creativity. The philosopher Friedrich Nietzsche (1889) wrote, “All truly great thoughts are conceived by walking” (Aphorism 34). The current research puts such observations on solid footing. Four studies demonstrate that walking increases creative ideation. The effect is not simply due to the increased perceptual stimulation of moving through an environment, but rather it is due to walking. Whether one is outdoors or on a treadmill, walking improves the generation of novel yet appropriate ideas, and the effect even extends to when people sit down to do their creative work shortly after.

  The results were a bit inconclusive as to whether outdoor walking is better than other forms of walking, so for now we will just say that walking — in general — is good for creative thinking. But where my mind immediately goes is: Does this finding scale up?

  In other words, if you were to take two different cities — City A where everybody, for the most part drives, and City B where everybody, for the most part, walks — could you find any evidence that City B was on average more creative than City A?

  I guess one way you could measure this is through patents. And if you were to look at patents per capita in the US, you’d likely find cities like Princeton (NJ), Redmond (WA), and cities in Silicon Valley near the top of the list. I’m not sure there’s an obvious correlation here.

  But it is kind of interesting to think about a possible relationship between urban form and creativity.

  2022.11.11

• Housing supply in low-cost and high-cost municipalities

  Here is a housing study that looked at housing supply — in the US from 2000 to 2020 — relative to median housing values. And here is the key takeaway:

  

  What this chart is saying is that new housing is rarely added in cities with the lowest-value homes. The bar on the left represents municipalities whose median housing values are less than 50% of the metropolitan average. And this makes sense. If values are low there is likely little to no incentive to build. The math just doesn’t work.

  However, as home values increase, the incentive to build and the ability to finance new projects also increases, and that is what we see in the above chart. This also makes sense.

  But something interesting happens in the highest-value cities — housing supply once again starts to fall off. And it turns out that there is a bit of a sweet spot. Municipalities whose relative housing values are 110 to 130% of the metropolitan average actually produce the most overall housing. Any higher than that and things start to decline.

  Why is that? The answer likely has to do with restrictive land-use regulations. The highest-value cities (and wealthiest suburbs) often have a lot of large single-family lots, as well as policies to ensure that this kind of built form doesn’t change. This has the effect of both limiting supply and enshrining values.

  So when it comes to housing supply, what you don’t want are low-cost areas. But you also don’t want the highest-value areas. What you want are areas that are doing well, but no so well that they start really restricting new entrants. This is what our industry often refers to as exclusionary zoning.

  Now, one of the most common ways to respond to this problem is to develop an opposing policy, namely inclusionary zoning. But usually what this policy doesn’t do is direct more supply to these high-value and low-density areas. Instead what it typically does is force the segment that is producing the most housing — let’s call it the 110 to 130% band — to deliver more affordable housing.

  It’s a neat trick that sounds pretty cool, but it is not at no cost.

  2022.10.14

• Redfin experiment shows how home buyers react to flood-risk data

  This is a fascinating little experiment:

  From Oct. 12, 2020 to Jan. 3, 2021, Redfin ran an experiment on 17.5 million of its users across the US. As prospective homebuyers entered the site, Redfin assigned them randomly to either a group that was shown flood-risk information on each property or a group that was not.

  The flood-risk scores came from First Street Foundation, a climate and technology nonprofit that works to make climate hazards more transparent to the public. In June 2020, First Street published the first public maps that revealed flood risk for every home and property in the contiguous US. 

  First Street scores properties on a scale of 1 to 10 based on the likelihood that they will flood in the next 30 years (which is assumed to be a typical mortgage term). A score of 1 means the property has “minimal” risk and a score between 9-10 is considered “extreme” risk.

  So what happens once you start showing people flood-risk information? They, not surprisingly, start systematically looking for safer properties. After one week of users being exposed to this new information, prospective buyers who were previously looking at “extreme” homes started looking at homes that were about 7% safer.

  After 9 weeks, these same “extreme” home buyers were looking at properties that were about 25% less risky. And for some buyers, in particular those working with a Redfin agent or partner, their flood-risk tolerance dropped by over 50%. (Embedded in this data might be a sales pitch for working with a knowledgeable Redfin agent or partner).

  Also interesting is the fact that below “severe” flood risk (a score between 7-8), there was very little change in behavior. “Major” flood risk, it would seem, isn’t all that concerning to most buyers. It needs to be “severe”. Nevertheless, it is noteworthy that people will in fact make behavioral changes when presented with clear climate-risk data.

  2022.09.12

• Cars make cities less compact

  The relationship between car ownership and urban density is a fairly intuitive one. Below are two charts from a study by Francis Ostermeijer, Hans Koster, Jos van Ommeren, and Victor Nielsen, showing how urban density is inversely correlated with car ownership. In other words, the more people with cars, the less dense that a particular place is likely to be.

  

  But there’s an interesting chicken-and-egg question here. Does Atlanta, which is near the bottom right in the above chart, have a lot of cars because it wasn’t dense enough to support other modes of transport, or did the prevalence of cars in Atlanta cause the city to spread out and become less dense? And that is exactly what the above researchers set out to determine.

  To do this, they started by looking at the presence of commercial car manufacturers in the above geographies in the 1920s. One of the things they found was that having a car manufacturer in your city at this time appears to have had no effect on population density. But over the long run, rising car ownership seems to have had a sizeable effect on reducing population densities in those places.

  The conclusion they draw from this is the title of this post: cars have made cities less compact, rather than low population densities causing people to go out and buy more cars. This makes some sense to me because cities were doing just fine before we invented cars. But like all transportation innovations that allow us to move faster over longer distances, the car encouraged decentralization.

  There are, of course, all sorts of possible implications for a finding like this. But the authors specifically mention developing countries where car ownership may still be relatively low. This is something to be mindful of because if you put most people into cars, history strongly suggests that it will impact the kind of city that you end up building.

  Chart: Cars make cities less compact

  2022.03.11

• Comparing the weekly earnings of Canada’s visible minorities to white people

  We just finished up three days of snowboarding and skiing in Tremblant, Quebec and we’re now in Montreal closing out the long weekend. I am arguably Toronto’s greatest fan and supporter, but I continue to admit that Montreal is the coolest city in Canada.

  In other news, Theresa Qiu and Grant Schellenberg recently authored a Statistics Canada report looking at the weekly earnings of visible minorities and white people across the country. The study focuses on Canadian-born individuals aged 25 to 44 who were gainfully employed and making money in 2015.

  The reason why they isolated the study to Canadian-born visible minorities is that they wanted to eliminate the noise around new immigrants who may be struggling with the language(s), the recognition of their foreign credentials, or some other variable.

  In this case, every individual that factors into the study was born in Canada and, in theory, had access to similar sorts of opportunities. Of course, we know this isn’t always the case, but it’s an attempt an equal baseline.

  The findings are pretty interesting.

  Korean, Japanese, and South Asian men all tend to earn more than white males (which formed the baseline for the study). More than 60% of Chinese and Korean men also have a bachelor’s degree or higher, whereas only 24% of white males are in the same position.

  This is an important data point because we know that economic outcomes tend be positively correlated with educational attainment. The benefits of education also tend to compound later in life and this study only focuses on people aged 25 to 44. So the spreads could widen.

  One the factors that is surely influencing the above findings is that visible minorities are overwhelmingly urban. About 60% of visible minorities in Canada live in just three cities: Toronto, Montreal, and Vancouver. This compares to only 27% of white people.

  Again, an important data point given that people in big cities tend to earn more than those in smaller communities.

  For the full study, click here.

  2022.02.20

• The future of central London

  The Centre for London has just published an interesting report called, Core Values: The Future of Central London. Like most city centers, Central London (or the Central Activities Zone as the report calls it) punches well above its geographic weight.

  Central London occupies about 0.01% of the UK’s total landmass, but is responsible for about 10% of its economic output. It represents about 2% of London’s total footprint, but is responsible for about 40% of total employment and about 45% of economic output.

  Here’s another interesting stat:

  

  From 1961 to 1983, the residential population of CAZ boroughs in London fell from about 2.5 to 1.7 million. And things really didn’t begin to turnaround until the late 1980s. It took until 2018 for the population to return to 2.5 million. Makes me wonder: How concerned do you think people were in the late 1970s about housing affordability in Central London?

  To read the full report, click here.

  Chart: Centre for London

  2020.02.26

• Social and physical segregation in Singapore

  

  A recent study by the MIT Senseable City Lab has used cellphone data to map both social and physical segregation within Singapore. To start, they used residential sale prices as a proxy for socioeconomic status. They then used call and text records (presumably it was all anonymous) from 1.8 million cellphone users in Singapore (2011) to map who interacted with who. Pictured above is one of those mappings.

  What they discovered was evidence of a “rich club effect.” In other words, the richer the person the less likely they were to interact with people outside of their socioeconomic band. The study calls this their communication segregation index.

  A similar phenomenon was noted as people moved around Singapore. (This is the study’s physical segregation index.) People tend to spend time in spaces alongside people with similar socioeconomic attributes. However, they did notice that this tends to wane during the day as people move around the city — presumably for work and other such things.

  I think it would be interesting to get a bit more granular about the findings in order to try and see, among other things, if certain public spaces are more successful than others at encouraging a broader socioeconomic mix. And it’s probably only a matter of time before we start using tools like this to plan our cities. For more on the study, click here.

  Image: MIT Senseable City Lab

  2019.12.27

• Using tweets to measure social connectedness in cities

  This recent study used geotagged tweets to measure social connectedness within American cities. There are two measures: (1) concentrated mobility and (2) equitable mobility. The first measures the extent to which social connections (geotagged tweets) are concentrated in a set of places within the city. And the second looks at the degree in which people move between neighborhoods in roughly similar proportions. These measures are the y-axis and the x-axis, respectively, in this graph:

  

  So how do you read this chart?

  Well if you look at New York, you’ll see that it is relatively high in concentrated mobility, but the lowest in terms of equitable mobility. This means that social connections are highly concentrated and that there’s low connectedness to other neighborhoods within the city. Miami, on the other hand, is the opposite. It’s also an outlier. Few hubs. But its social connections appear to cross neighborhoods and spread across the city.

  Perhaps not surprisingly, the study found that the size of a city seems to have the biggest impact on social connectedness. Which makes sense — it becomes harder to get around and so people start to localize. I am reminded of this whenever my friends in Los Angeles tell me they never go to the beach because it’s simply too difficult and too time consuming to get across the city.

  This also became clear to me after I started playing around with the Moves App back in 2015. The app no longer exists, but it was an activity tracker that allowed you to map where you, well, moved. And the more time you spent in one place, the more concentrated the activity would become. They depicted this through larger and larger circles. Example maps, here. My maps revealed that I need to branch out into different neighborhoods more often.

  To download a full copy of the study, click here.

  Chart: CityLab

  2019.09.20

• Tasty data

  A recent study and research paper by the MIT Senseable City Lab — called, Tasty Data — has discovered that restaurant data alone can be used to accurately predict location-based factors such as daytime population, nighttime population, number of businesses, and overall consumer spending within a specific geography.

  They started by pulling restaurant data from Dianping (Chinese equivalent of Yelp) for 9 Chinese cities: Baoding, Beijing, Chengdu, Hengyang, Kunming, Shenyang, Shenzen, Yueyang, and Zhengzhou. They then paired their Dianping data with other available data (such as aggregated mobile phone data) and used machine learning to search for any correlations.

  Below is a diagram of “nighttime population” in Beijing. They are using a 3 km2 grid.

  

  If you’re a regular reader of this blog, you’ll know that I like these kinds of studies. By 2020, it is estimated that 1.7MB of data will be created every second by every person on earth. The numbers are staggering. And yet, “official” data sources, such as census data, remain slow and fairly limited. Studies like this one continue to show us what’s next.

  Image: MIT Senseable City Lab

  2019.08.08

• Beautiful cities are growing faster than ugly ones

  People move to cities for a whole host of reasons, whether it be for more money, more affordable housing, and/or better weather. The fastest growing cities in the US, for example, tend to be in the south where it’s warmer and where housing supply is more elastic. However, we also know that “consumer leisure amenities” increasingly factor into this decision.

  A new research paper by Gerald A. Carlino (Federal Reserve Bank of Philadelphia) and Albert Saiz (MIT) has tried to quantify this relationship by looking at the perceived beauty of a place. To do this, they analyzed the number of tourist visits and the number of “crowdsourced picturesque locations” in a metro area. Read: Instagrammable moments.

  What they found was that beauty, not surprisingly, matters (much like it does in other facets of life). Between 1990-2010, metro areas that were perceived as being “twice as picturesque” experienced greater population growth — about 10 percentage points higher. These metro areas also attracted a higher percentage of educated individuals and experienced greater housing appreciation.

  If you’d like to download a copy of Beautiful city: Leisure amenities and urban growth, click here.

  2019.05.19