Abstract
In this work we aim at studying spatio-temporal patterns of the population
movement across a large city. We exploit the information on people position
collected by the smartphone application of the Earthquake Network project and we
adopt a dynamic model-based clustering approach to identify the patterns. The approach
is applied to smartphone data collected in Santiago (Chile) over the period
February-April 2016. Some preliminary results are presented and discussed.
| Titolo tradotto del contributo | [Autom. eng. transl.] Space-time clustering for identifying population patterns from smart-phone data |
|---|---|
| Lingua originale | Italian |
| Titolo della pubblicazione ospite | SIS 2017. Statistics and Data Science: New Challenges, New Generations |
| Pagine | 1-6 |
| Numero di pagine | 6 |
| Stato di pubblicazione | Pubblicato - 2017 |
| Evento | SIS 2017. Statistics and Data Science: New Challenges, New Generations - Firenze Durata: 28 giu 2017 → 30 giu 2017 |
Convegno
| Convegno | SIS 2017. Statistics and Data Science: New Challenges, New Generations |
|---|---|
| Città | Firenze |
| Periodo | 28/6/17 → 30/6/17 |
Keywords
- Finite mixture models
- Markov chain Monte Carlo
- crowd-sourcing data
- spatio-temporal modeling
- state-space