Machine Learning
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Eigenplaces: Segmenting Space through Digital Signatures
IEEE Pervasive Computing
Automated land use identification using cell-phone records
HotPlanet '11 Proceedings of the 3rd ACM international workshop on MobiArch
Estimating Origin-Destination Flows Using Mobile Phone Location Data
IEEE Pervasive Computing
Discovering regions of different functions in a city using human mobility and POIs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards fine-grained urban traffic knowledge extraction using mobile sensing
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Stochastic agent-based simulations of social networks
Proceedings of the 46th Annual Simulation Symposium
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
From data to knowledge: city-wide traffic flows analysis and prediction using bing maps
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
A comparison of Foursquare and Instagram to the study of city dynamics and urban social behavior
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
Proceedings of The First ACM SIGSPATIAL International Workshop on Computational Models of Place
Temporal decomposition and semantic enrichment of mobility flows
Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks
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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.