Big data is not new to us any longer, which has been applied to many industries. It has also been implemented into the urban studies, for monitoring the people flow in the city, analysing the trend of population density, also predicting the demographic changes.
It has shown its great ability in the foregoing fields, however, I will wonder more about how to use it in helping with criticizing and designing urban spaces, in a space scale, rather than a city scale. What is my interest now is, for instance, use the tool of big data to see how many people are using the so-called public spaces planned and built by the government in China, and to see where people would have their public activities like gathering.
Not long ago, the giant data-holder Tencent has released one new product named Easygo, using its own data, which I believe are collected from the their own popular apps such as QQ, Wehcat, Tencent Map, Jingdong, Dianping as well as Didi. These apps will collect real-time information from users’ mobile devices. After encryption, the data on the Easygo would not contain any personal information, but just show the overall situation. The bad news is, the new product Easygo is strictly only allowed to use inside Wechat for personal trip planning, and it’s not available on computers.
So, I tried to transfer the data and visualize it on the computer for further studies.
One thing that attracts me to spend time into this is, I would like to prove how bad the civic center/plaza in Shenzhen is, which means how many people would stay there and use the space as a public space, as many people are criticizing the ironic civic center/plaza for its unfriendly environment of being a real public space. To make a comparison, I would also like to know how many people are living in the urban villages in Shenzhen, and using the spaces there as their public space. For now, at least I can see the civic center is always like a void on the map, which strongly supports the critics’ ideas.
Besides, it would also be quite helpful to calculate and monitor the population density in certain points like the mixed-use complexes, and train stations (even for one particular station exit), which will surely offer suggestions to designers and planners.
As it’s not an open project, the data is not available all the time, and you may need to contact me for a key to use this map for academic uses.
Shenzhen Civic center 2015.11.15 11:15
Shenzhen Civic center 2015.11.15 12:45
Shenzhen Gangxia Village 2015.11.15 11:15
Shenzhen Gangxia Village 2015.11.15 12:45
Shanghai People’s Square 2015.11.15 11:15
Tian’anmen Square 2015.11.15 11:15