Mining Urban Land Use in Space and Time

Mining Urban Land Use in Space and Time

Variations from the average activity detected in blocks of the city with different land use during one week (starting on a Sunday), one can detect movement of the global activity from residential areas to commercial and parks at certain times of the day. We propose a deeper analysis of this data to reveal salient patterns in urban dynamic land use.

Variations from the average activity detected in blocks of the city with different land use during one week (starting on a Sunday), one can detect movement of the global activity from residential areas to commercial and parks at certain times of the day. We propose a deeper analysis of this data to reveal salient patterns in urban dynamic land use.

We are interested in quantifying “how human allocate time to different activities as part of a spatial, temporal socio-economic system “ for city, mobility and infrastructure planning.

Source: Inferring land use from mobile phone activity (JL. Toole, M. Ulm, M.C. González, D. Bauer), In Proceedings of the ACM SIGKDD International Workshop on Urban Computing,2012  (“BEST PAPER AWARD”) [bibtex][[pdf]

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 classi cation 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.

Source: Clustering daily patterns of human activities in the city (S. Jiang, J. Ferreira Jr, M.C. González), In Data Mining and Knowledge Discovery, volume 25, 478-510, 2012. [bibtex][pdf]

Detailed data on activities by time of day were collected from more than 30,000 individuals (and 10,552 households) who participated in a 1-day or 2-day survey implemented from January 2007 to February 2008. We examine this large-scale data in order to explore three critical issues: (1) the inherent daily activity structure of individuals in a metropolitan area, (2) the variation of individual daily activities—how they grow and fade over time, and (3) clusters of individual behaviors and the revelation of their related socio-demographic information. We find that the population can be clustered into 8 and 7 representative groups according to their activities during weekdays and weekends, respectively. Our results enrich the traditional divisions consisting of only three groups (workers, students and non-workers) and provide clusters based on activities

Group Members: J. L. Toole (ESD-PhD Student, MIT) and Shan Jiang (DUSP-PhD Student, MIT)