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
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.
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
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
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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