Abstract
In this work, the focus is on location data collected by smartphone applications. Specically, we propose and compare a set of models of increasing complexity to estimate individual location at any time, uncertainty included. Unlike classic tracking for high spatio-temporal resolution data, the approaches are suitable when location data are sparse in
time and are affected by non negligible errors. The approaches build upon mixtures of densities that describe past and future locations; the model parameters are estimated by maximum likelihood. The approaches are applied to smartphone location data collected by the Earthquake Network citizen science project.
Lingua originale | English |
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Titolo della pubblicazione ospite | Proceedings of ITISE 2018 |
Pagine | 1471-1482 |
Numero di pagine | 12 |
Stato di pubblicazione | Pubblicato - 2018 |
Evento | ITISE 2018 International Conference on Time Series and Forecasting - Granada Durata: 19 set 2018 → 21 set 2018 |
Convegno
Convegno | ITISE 2018 International Conference on Time Series and Forecasting |
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Città | Granada |
Periodo | 19/9/18 → 21/9/18 |
Keywords
- location-based applications
- maximum likelihood
- normal mixtures
- spatio-temporal patterns