Project Lachesis: Parsing and Modeling Location Histories

By Ramaswamy Hariharan, Kentaro Toyama. International Conference Geographic Information Science, 2004

321 citations recorded by the source corpus.

Subfields: Geography, Planning and Development, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ca0fccd49c7afd8273e7b843bd75333c83da21ee

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Citations in the corpus

Location-Based Activity Recognition using Relational Markov Networks (2005, methodology): “Our system builds upon previous work on extracting places from traces of users’ movements, gathered by GPS or other localization technologies [Ashbrook and Starner, 2003; Hariharan and Toyama, 2004; Liao et al., 2004].”
Measuring Large-Scale Social Networks with High Resolution (2014, methodology): “From the raw location data we can extract stop locations as groups of locations clustered within distance D and time T [162–165].”
Mining significant semantic locations from GPS data (2010, background): “Extracting locations from GPS trajectories: There has been a host of studies on extracting locations from single users’ GPS trajectories [3, 8, 10, 13, 20, 24].”
Mining interesting locations and travel sequences from GPS trajectories (2009, background): “These works include detecting significant locations of a user [5][9], predicting the user’s movement among these locations [5][13], and recognizing user-specific activities at each location [15].”
Extracting places from traces of locations (2004, result): “Recently, we have found parallel work by Hariharan and Toyama [5] that uses an approach that is very similar to ours in that they use time information to determine important places.”
Inference Attacks on Location Tracks (2007, methodology): “Hariharan & Toyama[11] created a time-and location-sensitive clustering technique to hierarchically represent " stays " and " destinations " .”
Learning travel recommendations from user-generated GPS traces (2011, unknown): “These works include detecting signi.cant locations of a user [Ashbrook and Starner 2003; Hariharan and Toyama 2004], predicting the user s movement among these locations [Liao et al 2005], and recognizing userspeci.c activities at each location [Liao et al. 2004; Patterson et al.”
On the validity of geosocial mobility traces (2013, methodology): “While this used to require GPS devices, recent efforts leverage the rich collection of sensors on smartphones to improve location accuracy [11,12,15,18].”
Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2017, methodology): “Inspired by the algorithm introduced by Hariharan and Toyama [40] which was tested previously using triangulated mobile phone data [24], [29], we modified the algorithm for the”
SensLoc: sensing everyday places and paths using less energy (2010, background): “[10] that finds places by defining temporal and spatial stay thresholds without depending on GPS disappearances.”
Origin-destination trips by purpose and time of day inferred from mobile phone data (2015, methodology): “To do so, we develop a method based in the work of Hariharan and Toyama (2004) for processing GPS traces.”
Predestination: Inferring Destinations from Partial Trajectories (2006, background): “In Project Lachesis, Hariharan and Toyama[8] infer potential destinations in a similar way, but explicitly account for variations in scale of a destination's size and duration of stay.”
Recruitment Framework for Participatory Sensing Data Collections (2010, background): “For instance, [18] models transitions of individuals using Markov processes, and [19] incorporates factors such as land-use information to help with destination prediction.”
A survey of computational location privacy (2009, unknown): “Hariharan and Toyama [20] created a time-and location-sensitive clustering technique to hierarchically represent “stays” and “destinations”.”