Inferring Social Ties from Geographic Coincidences

Proceedings of the National Academy of Sciences, 2010 107 (52) 22436-22441; published ahead of print December 8, 2010

David J. Crandall, Lars Backstrom, Dan Cosley, Siddharth Suri, Daniel Huttenlocher, and Jon Kleinberg

“We investigate the extent to which social ties between people can be inferred from co-occurrence in time and space: Given that two people have been in approximately the same geographic locale at approximately the same time, on multiple occasions, how likely are they to know each other? Furthermore, how does this likelihood depend on the spatial and temporal proximity of the co-occurrences? Such issues arise in data originating in both online and offline domains as well as settings that capture interfaces between online and offline behavior. Here we develop a framework for quantifying the answers to such questions, and we apply this framework to publicly available data from a social media site, finding that even a very small number of co-occurrences can result in a high empirical likelihood of a social tie. We then present probabilistic models showing how such large probabilities can arise from a natural model of proximity and co-occurrence in the presence of social ties. In addition to providing a method for establishing some of the first quantifiable estimates of these measures, our findings have potential privacy implications, particularly for the ways in which social structures can be inferred from public online records that capture individuals’ physical locations over time.”

Real-time Environment Representation Based on Occupancy Grid Temporal Analysis using a Dense Stereo-Vision System

Proceedings of the 2010 IEEE 6th International Conference on Intelligent Computer Communication and Processing, Cluj-Napoca, Romania, 26-28 August 2010, pp.203-209

Andrei Vatavu, Sergiu Nedevschi, Florin Oniga

“We propose an environment representation technique by Temporal Analysis of the Occupancy Grid using a Dense Stereo-Vision System. The proposed method takes into account both the 3D information provided by the Occupancy Grid and the ego-car parameters. We use a method for computing the differences between the previous and current frames and compute an evidence space called Occupancy Grid Difference Map. Based on the difference map we created a reasoning component to generate an improved 2.5D model by representing the environment as a set of polylines with the associated static and dynamic features.”

Evolving Residential and Employment Locations and Patterns of Commuting under Hyper Growth: The Case of Guangzhou, China

Urban Studies, July 2010; vol. 47, 8: pp. 1643-1661., first published on February 8, 2010

Si-ming Li

“Chinese cities have experienced rapid growth and restructuring in recent times. This paper examines the evolving residential and employment locations and the changes in the patterns of commuting in Guangzhou, China. Tabulations derived from household surveys conducted in 2001 and 2005 show rapid suburbanisation of both residence and employment. Intrazone traffic today dominates the commuting scene in both the central core and the suburbs. The mean commute distance and mean commute time have increased, but the increases are quite modest. Estimation of residential and employment density gradients reveals differential decentralisation of different population groups. Multivariate analysis indicates that commute distance generally increases with income and occupational status. Males in Guangzhou used to have appreciably shorter commutes than females; but the difference has decreased in recent years, suggesting convergence in commuting behaviour between the Chinese and Western cases.”