The data generated by smartphones and mobile devices has an enormous potential to capture the temporal and spatial dimensions of the city. Once collected and processed, this urban data can be used to recreate the daily dynamics of the city and its inhabitants. In this case, Blake Shaw used the data collected by Foursquare to show the exact time and places frequented by the users of that online platform.
The urban data presented has evidently a very narrow scope, namely the users of Foursquare. Nevertheless, it is still a quite interesting portrait of this focus group and its spatial behavior. Based on their preferences it is possible to compare different sectors of the city and find similarities within them, even if they are far away. In the same way it is possible to contrast different uses concentrated in zones adjacent zones.
Blake Shaw also presented the dynamic behind a coffee shop’s opening. How many check-ins does this place report? Who are the visitors of this place? Are they all related? He managed to show the word of mouth still influences (is it going to disappear anyhow?) the places that the people visit and like.
While some of the conclusions are obvious, (people eat more ice cream when the temperature increases), the urban data collected, still let us glimpse into the actual spatial practice of the inhabitants. Regardless of the privacy concerns, a widespread use of this technique can provide excellent information for urban designers (and planners perhaps?) about the actual nature of that “black square”.
This conference was originally presented in the urban data conference called DataGotham.
“Blake Shaw is currently a Data Scientist at Foursquare, a location-based service that helps people keep up with friends and discover new places. At this NYC startup, Shaw applies machine-learning algorithms to large spatiotemporal datasets in order to better understand patterns of human mobility. Shaw holds a Ph.D. in Computer Science from Columbia University, and his research has appeared at a variety of conferences including NIPS, ICML and AISTAT. Shaw was also the lead developer of CabSense, a mobile app for predicting the best street corners in New York City for catching taxicabs.”