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Constructing semantic interpretation of routine and anomalous mobility behaviors from big data
2015
SIGSPATIAL Special
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Temporal patterns of people's presence in the places resulted from spatio-temporal aggregation of the data by the places and hourly intervals within the weekly cycle. ...
Repeatedly visited personal and public places were extracted from trajectories by finding spatial clusters of stop points. ...
A common pattern of development in mobility analytics is the paradigm shift from syntactic [9] to semantic [11] analysis of movement data. ...
doi:10.1145/2782759.2782765
fatcat:wqdenf47pbdkpfu6u6fttor43u
Towards Privacy-Preserving Semantic Mobility Analysis
[article]
2013
International EuroVis Workshop on Visual Analytics
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approach based on transformation of the spatial component of movement data from the geographic space to an abstract semantic space, inspired by the concept of cartographic chorems. ...
By analyzing data reflecting human mobility, one can derive patterns and knowledge that are tightly linked to the underlying geography and therefore cannot be applied to another territory or even compared ...
Analysis focusing on space To analyze the temporal patterns of person's activities, which are reflected in person's presence in different semantic places, we compute the presence counts in each semantic ...
doi:10.2312/pe.eurovast.eurova13.019-023
dblp:conf/eurova-ws/AndrienkoAF13
fatcat:5pceo6bqjngbjammr3lfchrxye
Analysis of mobility behaviors in geographic and semantic spaces
2014
2014 IEEE Conference on Visual Analytics Science and Technology (VAST)
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Temporal patterns of people's presence in the places resulted from spatiotemporal aggregation of the data by the places and hourly intervals within the weekly cycle. ...
ABSTRACT Repeatedly visited personal and public places were extracted from trajectories by finding spatial clusters of stop points. ...
EXTRACTION AND INTERPRETATION OF PLACES We used an automated tool that extracts repeatedly visited personal and public places by spatial clustering of points from trajectories. ...
doi:10.1109/vast.2014.7042556
dblp:conf/ieeevast/AndrienkoAF14
fatcat:jmn7cvkmonh73loa7qwflk6qpy
Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data
2016
IEEE Transactions on Visualization and Computer Graphics
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By iteratively analyzing filtered movements, users can explore the semantics of movements, including the transportation methods, frequent visiting sequences and keyword descriptions. ...
In contrast to traditional movement data, the sparseness and irregularity of social media data increase the difficulty of extracting movement patterns. ...
This work is also supported by PKU-Qihu Joint Data Visual Analytics Research Center. ...
doi:10.1109/tvcg.2015.2467619
pmid:26340781
fatcat:yresa7zjunb3pgp7qquk5zibsu
Mining Individual Similarity by Assessing Interactions with Personally Significant Places from GPS Trajectories
2018
ISPRS International Journal of Geo-Information
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Next, we propose a new individual similarity measurement that incorporates both the spatio-temporal and semantic properties of individuals' visits to significant places. ...
individuals from the perspectives of personal behavior. ...
Conflicts of Interest: The authors declare that they have no conflicts of interest to disclose. ...
doi:10.3390/ijgi7030126
fatcat:5yebgfl3ozd7jmriuvzbcdpdmi
Visual Analysis of Movement Behavior Using Web Data for Context Enrichment
2014
2014 IEEE Pacific Visualization Symposium
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of interest) and degree of uncertainty by varying color intensity of the icons; bottom) Temporal View -showing frequent temporal daily patterns. ...
Using a density-based clustering technique we extract 1.215 frequent destinations of ∼150.000 user movements from a large e-mobility database. ...
The electric scooter data was kindly provided by EnBW Energie Baden-Württemberg AG. We would like to thank them for their collaboration. ...
doi:10.1109/pacificvis.2014.57
dblp:conf/apvis/KrugerTE14
fatcat:ryvpmznmhfbinba4lshzeag4bi
Visual Analytics Methodology for Scalable and Privacy-Respectful Discovery of Place Semantics from Episodic Mobility Data
[chapter]
2015
Lecture Notes in Computer Science
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Availability of personal traces over a long time period makes it possible to detect repeatedly visited places and identify them as home, work, place of social activities, etc. based on temporal patterns ...
The semantically abstracted data can be further analyzed without the risk of re-identifying people based on the specific places they attend. ...
Apply movement analysis methods to the semantic space trajectories. For place extraction, we have developed a special algorithm that groups position records by spatial proximity. ...
doi:10.1007/978-3-319-23461-8_25
fatcat:r4nhmxnumbfbxm4aqz3kze6zva
Modeling of Human Movement Behavioral Knowledge from GPS Traces for Categorizing Mobile Users
2017
Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion
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In this work, we present a framework which models user movement patterns containing both spatio-temporal and semantic information, generates semantic stay-point taxonomy by analysing GPS traces of all ...
users, summarizes individuals' GPS traces and clusters users based on the semantics of their movement patterns. ...
We propose a framework to model individuals' movement patterns, analyzing human movement patterns both from semantic and spatio-temporal context and extracting implicit information. ...
doi:10.1145/3041021.3054150
dblp:conf/www/GhoshG17
fatcat:cvmc4x7v5ng7loamf6adlqj2pe
A survey on next location prediction techniques, applications, and challenges
2022
EURASIP Journal on Wireless Communications and Networking
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Heterogeneous data generated from different sources, users' random movement behavior, and the time sensitivity of trajectory data are some of the challenges. ...
It is challenging to analyze and mine trajectory data due to the complex characteristics reflected in human mobility, which is affected by multiple contextual information. ...
Semantic trajectory Ying et al. proposed integrating semantic information about the places visited by individuals in addition to their location data in order to enhance prediction accuracy about future ...
doi:10.1186/s13638-022-02114-6
fatcat:s2ixs3ftibaobighbik6ikgfce
Semantic trajectories modeling and analysis
2013
ACM Computing Surveys
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trajectories from movement tracks, (ii) enriching trajectories with semantic information to enable the desired interpretations of movements, and (iii) using data mining to analyze semantic trajectories ...
In parallel, interest in movement has shifted from raw movement data analysis to more application-oriented ways of analyzing segments of movement suitable for the specific purposes of the application. ...
Behaviors extracted from semantic trajectories cannot be obtained from raw data only. ...
doi:10.1145/2501654.2501656
fatcat:g7nr36bop5eslcfmr4z34mvj4i
Location prediction on trajectory data: A review
2018
Big Data Mining and Analytics
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Then, we review existing location-prediction methods, ranging from temporal-pattern-based prediction to spatiotemporal-pattern-based prediction. ...
First, we introduce the types of trajectory data and related basic concepts. ...
This research was supported by the Foundation of Science & Technology Department of Sichuan Province (Nos. 2017JY0027 and 2016GZ0075), the National Key Research and Development Program (2016YFB0502300) ...
doi:10.26599/bdma.2018.9020010
dblp:journals/bigdatama/WuLSTP18
fatcat:3ogap5xsxffjxazjm7chcnqu3u
Where Chicagoans tweet the most: Semantic analysis of preferential return locations of Twitter users
[article]
2015
arXiv
pre-print
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In this connection, the movements of Twitter users captured by geo-located tweets were found to follow similar patterns, where a few geographic locations dominate the tweeting activity of individual users ...
Top-visited locations were identified by clustering semantic enriched tweets using a DBSCAN algorithm. ...
Insightful comments were received from members of the CyberGIS Center for Advanced Digital and Spatial Studies.
6. ...
arXiv:1512.06880v1
fatcat:lgkbxlwiffewpg57nxya5qechm
Preserving privacy in semantic-rich trajectories of human mobility
2010
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS - SPRINGL '10
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Representing the personal movements as sequences of places visited by a person during her/his movements -semantic trajectory -poses even greater privacy threats w.r.t. raw geometric location data. ...
The increasing abundance of data about the trajectories of personal movement is opening up new opportunities for analyzing and mining human mobility, but new risks emerge since it opens new ways of intruding ...
Chiara Renso acknowledges support from CNR Short Term Mobility program and Dino Pedreschi acknowledges support by Google, under the Google Research Award program. ...
doi:10.1145/1868470.1868481
dblp:conf/gis/MonrealeTRPB10
fatcat:ruttm6b5u5hwtp56jvc34f3yfe
An Analysis of Location Prediction Models
2020
International Journal of Computer Applications
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Classification of mobile users can be regular or random which can be used to ascertain the pattern of the user over a period of time which also helps in planning the movement of the user. ...
This paper places emphasizes on the relevance of location prediction models in mobile users. ...
CONCLUSION This research has analyzed various location prediction models, their objectives and also stated their various limitations. ...
doi:10.5120/ijca2020920063
fatcat:lstc4uzdrbg3pegwzd2ftnrfsy
On the properties of human mobility
2016
Computer Communications
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We show that the number of places visited by each person (Points of Interest -PoIs) is regulated by some properties that are statistically similar among individuals. ...
The current age of increased people mobility calls for a better understanding of how people move: how many places does an individual commonly visit, what are the semantics of these places, and how do people ...
The proposed classification and the PoIs and user features provide the basis for understanding human behavior by extracting the semantics of visited places. ...
doi:10.1016/j.comcom.2016.03.022
fatcat:n2kiqhey5jghjcebjwr7ui7jbu
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