Inferring land use from mobile phone activity
This work addresses urban planning challenges by providing a cost-effective alternative to surveys, though it is incremental in applying existing machine learning methods to new data.
The authors tackled the problem of understanding urban population dynamics by using mobile phone activity data to infer land use, showing that this data can supplement traditional zoning regulations with useful information.
Understanding the spatiotemporal distribution of people within a city is crucial to many planning applications. Obtaining data to create required knowledge, currently involves costly survey methods. At the same time ubiquitous mobile sensors from personal GPS devices to mobile phones are collecting massive amounts of data on urban systems. The locations, communications, and activities of millions of people are recorded and stored by new information technologies. This work utilizes novel dynamic data, generated by mobile phone users, to measure spatiotemporal changes in population. In the process, we identify the relationship between land use and dynamic population over the course of a typical week. A machine learning classification algorithm is used to identify clusters of locations with similar zoned uses and mobile phone activity patterns. It is shown that the mobile phone data is capable of delivering useful information on actual land use that supplements zoning regulations.