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
The latest project out of MIT's Senseable City Lab examines the "sensing power of taxis" in various cities around the world. Looking at traffic data, they determined how many circulating taxis you would need to equip with sensors if you wanted to capture comprehensive street data across a particular city. This might be useful if you wanted to measure things like air quality, weather, traffic patterns, road quality, and so on.
What they found is that the sensing power of taxis starts out unexpectedly high. It would only take 10 taxis to cover 1/3 of Manhattan's streets in a single day. However, because taxis tend to have convergent routes, they also discovered rapid diminishing returns. It would take 30 taxis (or 0.3% of all taxi trips) to cover half of Manhattan in a day, and over 1,000 taxis to cover 85% of it. A similar phenomenon was observed in the other cities that they studied: Singapore, Chicago, San Francisco, Vienna, and Shanghai.
However, if you look at the percentage of trips needed to scan half of the streets in a city, Manhattan has the lowest rate at 0.3%. Vienna is the highest at 9%. But I'm not sure if this is a function of the utilization rate of their taxis or if it has something to do with urban form. Singapore has a similarly low rate (0.44%), but its street grid looks nothing like that of New York's.
Here's a short video explaining the project:
https://youtu.be/Vs3q3jQaM9Q
MIT's Self Assembly Lab and Invena (which is an organization based out of the Maldives) are trying to invent a system of underwater devices that naturally harness wave energy to restore and/or create new beaches, sandbars, and islands. The hope is that this line of thinking could be scaled up and eventually used a response to sea level rise, as well as other coastal challenges.
Here's a short video explaining the initiative:
https://vimeo.com/322246170
With over 40% of the world's population supposedly living in a coastal area, this is a problem that will need to be addressed. Already we are seeing these concerns start to rear their head in the real estate markets of some particularly vulnerable cities. The team installed their first field experiment in the Maldives this past February and a second one is expected in Q4-2019.
For more information on the "Growing Islands" project, click here.
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
The latest project out of MIT's Senseable City Lab examines the "sensing power of taxis" in various cities around the world. Looking at traffic data, they determined how many circulating taxis you would need to equip with sensors if you wanted to capture comprehensive street data across a particular city. This might be useful if you wanted to measure things like air quality, weather, traffic patterns, road quality, and so on.
What they found is that the sensing power of taxis starts out unexpectedly high. It would only take 10 taxis to cover 1/3 of Manhattan's streets in a single day. However, because taxis tend to have convergent routes, they also discovered rapid diminishing returns. It would take 30 taxis (or 0.3% of all taxi trips) to cover half of Manhattan in a day, and over 1,000 taxis to cover 85% of it. A similar phenomenon was observed in the other cities that they studied: Singapore, Chicago, San Francisco, Vienna, and Shanghai.
However, if you look at the percentage of trips needed to scan half of the streets in a city, Manhattan has the lowest rate at 0.3%. Vienna is the highest at 9%. But I'm not sure if this is a function of the utilization rate of their taxis or if it has something to do with urban form. Singapore has a similarly low rate (0.44%), but its street grid looks nothing like that of New York's.
Here's a short video explaining the project:
https://youtu.be/Vs3q3jQaM9Q
MIT's Self Assembly Lab and Invena (which is an organization based out of the Maldives) are trying to invent a system of underwater devices that naturally harness wave energy to restore and/or create new beaches, sandbars, and islands. The hope is that this line of thinking could be scaled up and eventually used a response to sea level rise, as well as other coastal challenges.
Here's a short video explaining the initiative:
https://vimeo.com/322246170
With over 40% of the world's population supposedly living in a coastal area, this is a problem that will need to be addressed. Already we are seeing these concerns start to rear their head in the real estate markets of some particularly vulnerable cities. The team installed their first field experiment in the Maldives this past February and a second one is expected in Q4-2019.
For more information on the "Growing Islands" project, click here.
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