GraphLoc: a graph based approach for automatic detection of significant locations from GPS trajectory data
| Autor: | Bita Shams, Saman Haratizadeh |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Atmospheric Science
Mobility mining business.industry Geography Planning and Development Boundary problem Graph based 02 engineering and technology Machine learning computer.software_genre Gps trajectory General Energy Geography Location prediction 020204 information systems Gps data 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) 020201 artificial intelligence & image processing Artificial intelligence Data mining business computer |
| Zdroj: | Journal of Spatial Science. 63:115-134 |
| ISSN: | 1836-5655 1449-8596 |
| DOI: | 10.1080/14498596.2017.1327374 |
| Popis: | Automatic discovery of significant locations from row GPS data is the first phase of mining mobility pattern and developing location-aware services. Unfortunately, current location discovery algorithms are ineffective when locations have different local properties such as density. Moreover, these algorithms suffer from the sharp boundary problem that is assigning some close points to different locations while they intuitively belong to one location. This article presents a novel framework, GraphLoc that formulates location discovery as a network community detection problem to address these issues. Experimental results show that GraphLoc’s locations lead to higher performance in mobility mining and location prediction. |
| Databáze: | OpenAIRE |
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