Inferring land use from mobile phone activity
arXiv:1207.1115
Abstract
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.
To be presented at ACM UrbComp2012
References in corpus (1)
Cited by in corpus (7)
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- Urban Magnetism Through The Lens of Geo-tagged Photography
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- Urban Anomaly Analytics: Description, Detection, and Prediction
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- Quantitative Comparison of Open-Source Data for Fine-Grain Mapping of Land Use
- Global Patterns of Synchronization in Human Communications