A comparison of statistical methods for estimating individual location densities from smartphone data

Lucia Paci, Francesco Finazzi

Risultato della ricerca: Contributo in libroContributo a convegno

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 originaleEnglish
Titolo della pubblicazione ospiteProceedings of ITISE 2018
Pagine1471-1482
Numero di pagine12
Stato di pubblicazionePubblicato - 2018
EventoITISE 2018 International Conference on Time Series and Forecasting - Granada
Durata: 19 set 201821 set 2018

Convegno

ConvegnoITISE 2018 International Conference on Time Series and Forecasting
CittàGranada
Periodo19/9/1821/9/18

Keywords

  • location-based applications
  • maximum likelihood
  • normal mixtures
  • spatio-temporal patterns

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