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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
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.
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.
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
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