Space-time clustering for identifying population patterns from smart-phone data

Francesco Finazzi; Lucia Paci

Space-time clustering for identifying population patterns from smart-phone data

Francesco Finazzi
, Lucia Paci

Risultato della ricerca: Contributo in libro › Contributo a conferenza

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

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title = "Space-time clustering for identifying population patterns from smart-phone data",

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.",

keywords = "Finite mixture models, Markov chain Monte Carlo, crowd-sourcing data, spatio-temporal modeling, state-space, Finite mixture models, Markov chain Monte Carlo, crowd-sourcing data, spatio-temporal modeling, state-space",

author = "Francesco Finazzi and Lucia Paci",

year = "2017",

language = "Italian",

isbn = "9788864535210",

pages = "1--6",

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note = "SIS 2017. Statistics and Data Science: New Challenges, New Generations ; Conference date: 28-06-2017 Through 30-06-2017",

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TY - GEN

T1 - Space-time clustering for identifying population patterns from smart-phone data

AU - Finazzi, Francesco

AU - Paci, Lucia

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Finite mixture models

KW - Markov chain Monte Carlo

KW - crowd-sourcing data

KW - spatio-temporal modeling

KW - state-space

KW - Finite mixture models

KW - Markov chain Monte Carlo

KW - crowd-sourcing data

KW - spatio-temporal modeling

KW - state-space

UR - http://hdl.handle.net/10807/105368

M3 - Contributo a conferenza

SN - 9788864535210

SP - 1

EP - 6

BT - SIS 2017. Statistics and Data Science: New Challenges, New Generations

T2 - SIS 2017. Statistics and Data Science: New Challenges, New Generations

Y2 - 28 June 2017 through 30 June 2017

ER -

Space-time clustering for identifying population patterns from smart-phone data

Abstract

Convegno

Keywords

Altri file e collegamenti

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