Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Top 10 algorithms in data mining
Knowledge and Information Systems
Exploring potential human activities in physical and virtual spaces: a spatio-temporal GIS approach
International Journal of Geographical Information Science
A Tale of One City: Using Cellular Network Data for Urban Planning
IEEE Pervasive Computing
Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome
IEEE Transactions on Intelligent Transportation Systems
A review of urban computing for mobile phone traces: current methods, challenges and opportunities
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
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Urban geographers, planners, and economists have long been studying urban spatial structure to understand the development of cities. Statistical and data mining techniques, as proposed in this paper, go a long way in improving our knowledge about human activities extracted from travel surveys. As of today, most urban simulators have not yet incorporated the various types of individuals by their daily activities. In this work, we detect clusters of individuals by daily activity patterns, integrated with their usage of space and time, and show that daily routines can be highly predictable, with clear differences depending on the group, e.g. students vs. part time workers. This analysis presents the basis to capture collective activities at large scales and expand our perception of urban structure from the spatial dimension to spatial-temporal dimension. It will be helpful for planers to understand how individuals utilize time and interact with urban space in metropolitan areas and crucial for the design of sustainable cities in the future.