Measuring Fine-Grained Multidimensional Integration Using Mobile Phone Metadata: The Case of Syrian Refugees in Turkey

Piracha, Daoud A.; Lu, Patricia J.; Bejgo, Keis; Bahrami, Mohsen; Leng, Yan; Balsa-Barreiro, José; Ricard, Julie; Morales, Alfredo J.; Singh, Vivek Kumar; Bozkaya, Burçin; Balcısoy, Selim; Pentland, Alex

doi:10.1007/978-3-030-12554-7_7

Cited by 9 publications

(9 citation statements)

References 20 publications

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“…Central to this, as Paskov et al (2013) argue, is that there is more social competition and status anxiety amongst both the rich and the poor in more unequal societies. Income inequality is also related to reduced socioeconomic diversity in social networks (Bakker et al, 2019;Tammaru et al, 2020). In the short term, this is evident in the restricted social networks that are evident in the mobile phone data of migrant communities for example (Bakker et al, 2019).…”

Section: Challenges Associated With Studying the Negative Consequence...mentioning

confidence: 99%

State level income inequality affects cardiovascular stress responses: Evidence from the Midlife in the United States (MIDUS) study

Ryan

Gallagher

Jetten

et al. 2022

Social Science & Medicine

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Section: Challenges Associated With Studying the Negative Consequence...mentioning

confidence: 99%

State level income inequality affects cardiovascular stress responses: Evidence from the Midlife in the United States (MIDUS) study

Ryan

Gallagher

Jetten

et al. 2022

Social Science & Medicine

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“…Mobility aspects such as commuting and travelling routines have been looked at in more detail by [31][32][33][34][35][36]. By exploiting both mobility and (social) network characteristics of mobile phone metadata, [37][38][39][40][41][42] and [43,44] use mobile phone metadata to model disease spreading and integration, respectively. Mobile usage patterns have been explored to provide fine granular insights on socio-demographic indicators such as multi-dimensional poverty [2,3], literacy [1,45] and economic vulnerability [46,47].…”

Section: Plos Onementioning

confidence: 99%

Better coverage, better outcomes? Mapping mobile network data to official statistics using satellite imagery and radio propagation modelling

Koebe

2020

PLoS ONE

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Mobile sensing data has become a popular data source for geo-spatial analysis, however, mapping it accurately to other sources of information such as statistical data remains a challenge. Popular mapping approaches such as point allocation or voronoi tessellation provide only crude approximations of the mobile network coverage as they do not consider holes, overlaps and within-cell heterogeneity. More elaborate mapping schemes often require additional proprietary data operators are highly reluctant to share. In this paper, I use human settlement information extracted from publicly available satellite imagery in combination with stochastic radio propagation modelling techniques to account for that. I show in a simulation study and a real-world application on unemployment estimates in Senegal that better coverage approximations do not necessarily lead to better outcome predictions.

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“…Mobility aspects such as commuting and travelling routines have been looked at in more detail by [29,30,31,32,33,34]. By exploiting both mobility and (social) network characteristics of mobile phone metadata, [35,36,37,38,39,40] and [41,42] use mobile phone metadata to model disease spreading and integration, respectively. Mobile usage patterns have been explored to provide fine granular insights on socio-demographic indicators such as multi-dimensional poverty [2,3], literacy [1,43] and economic vulnerability [44,45].…”

Section: Related Workmentioning

confidence: 99%

Better coverage, better outcomes? Mapping mobile network data to official statistics using satellite imagery and radio propagation modelling

Koebe

2020

Preprint

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Mobile sensing data has become a popular data source for geo-spatial analysis, however, mapping it accurately to other sources of information such as statistical data remains a challenge. Popular mapping approaches such as point allocation or voronoi tessellation provide only crude approximations of the mobile network coverage as they do not consider holes, overlaps and within-cell heterogeneity. More elaborate mapping schemes often require additional proprietary data operators are highly reluctant to share. In this paper, I use human settlement information extracted from publicly available satellite imagery in combination with stochastic radio propagation modelling techniques to account for that. I investigate in a simulation study and a real-world application on unemployment estimates in Senegal whether better coverage approximations lead to better outcome predictions. The good news is: it does not have to be complicated.

show abstract

Measuring Fine-Grained Multidimensional Integration Using Mobile Phone Metadata: The Case of Syrian Refugees in Turkey

Cited by 9 publications

References 20 publications

State level income inequality affects cardiovascular stress responses: Evidence from the Midlife in the United States (MIDUS) study

State level income inequality affects cardiovascular stress responses: Evidence from the Midlife in the United States (MIDUS) study

Better coverage, better outcomes? Mapping mobile network data to official statistics using satellite imagery and radio propagation modelling

Better coverage, better outcomes? Mapping mobile network data to official statistics using satellite imagery and radio propagation modelling

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