Optimization with extremal dynamics
Complexity - Complex Adaptive systems: Part I
Privacy Issues in Location-Aware Mobile Devices
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 5 - Volume 5
IEEE Transactions on Knowledge and Data Engineering
Visual analytics of spatial interaction patterns for pandemic decision support
International Journal of Geographical Information Science - Geovisual Analytics for Spatial Decision Support
Cellular Census: Explorations in Urban Data Collection
IEEE Pervasive Computing
Movement data anonymity through generalization
Proceedings of the 2nd SIGSPATIAL ACM GIS 2009 International Workshop on Security and Privacy in GIS and LBS
Ocean of information: fusing aggregate & individual dynamics for metropolitan analysis
Proceedings of the 15th international conference on Intelligent user interfaces
Exploring urban characteristics using movement history of mass mobile microbloggers
Proceedings of the Eleventh Workshop on Mobile Computing Systems & Applications
Proceedings of the Eleventh Workshop on Mobile Computing Systems & Applications
Querying Spatio-temporal Patterns in Mobile Phone-Call Databases
MDM '10 Proceedings of the 2010 Eleventh International Conference on Mobile Data Management
Privacy vulnerability of published anonymous mobility traces
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Spatial Generalization and Aggregation of Massive Movement Data
IEEE Transactions on Visualization and Computer Graphics
Journal of Location Based Services - GeoVA(t) - Geospatial visual analytics: focus on time. Special issue of the ICA Commission on GeoVisualisation
Anonymization of location data does not work: a large-scale measurement study
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
A Tale of One City: Using Cellular Network Data for Urban Planning
IEEE Pervasive Computing
| Hi-index | 0.00 |
Anonymized Call Detail Records (CDRs) contain positional information of large populations and therefore have been extensively analyzed to understand human mobility. Due to the temporally sparse and spatially coarse nature of the data, most of these studies have focused on primitive aspects of movements such as travel distance and speed. Incorporating underlying geographic information in these analyses would allow analysts to put these movements into context and to gain deeper insight into how metropolitan areas function. In this paper, we present a set of procedures for inferring mobile users' mobility patterns while retaining the context of underlying geography. We apply these methods to our case study on New York City anonymized CDRs. We find that our methods verify current areal semantics and commuting rush-hour patterns, and we also derive further implications regarding geographic, demographic, and other effects on human mobility.