Refugees in Undeclared Employment—A Case Study in Turkey

Bruckschen, Fabian; Koebe, Till; Ludolph, Melina; Marino, Maria Francesca; Schmid, Timo

doi:10.1007/978-3-030-12554-7_17

Cited by 6 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [30] (also see Chap. 19 in the present volume), the main finding was that the adaptation of Syrian children into the public education system is a significant determinant of Syrian refugee integration in Turkey. Specifically, the authors have proposed a model that successfully identifies the regions with congested public education services.…”

Section: Educationmentioning

confidence: 62%

“…Mobile health services can be employed to visit seasonal working fields, such as farms, to check the working environments, to inform the workers about the services that can be provided, and to screen their physical abilities for the tasks they are carrying out (e.g., to check if there are pregnant women who do physical work, etc.). Data from various sources, including mobile CDR, can be used for predicting the density of seasonal workers in particular regions and around particular dates [7,19,27]. The predictions can be shared with local migrant health centers (MHCs) and local authorities in advance to allow efficient resource allocation.…”

Section: Healthmentioning

confidence: 99%

See 1 more Smart Citation

Policy Implications of the D4R Challenge

Salah

Altuncu

Balcısoy

et al. 2019

Guide to Mobile Data Analytics in Refugee Scenarios

View full text Add to dashboard Cite

The Data for Refugees (D4R) Challenge resulted in many insights related to the movement patterns of the Syrian refugees within Turkey. In this chapter, we summarize some of the important findings, and suggest policy recommendations for the main areas of the challenge. These recommendations are sometimes broad suggestions, as the policy interventions involve many factors that are difficult to take into account. We give examples of such issues to help policy-makers.

show abstract

Section: Educationmentioning

confidence: 62%

Section: Healthmentioning

confidence: 99%

Policy Implications of the D4R Challenge

Salah

Altuncu

Balcısoy

et al. 2019

Guide to Mobile Data Analytics in Refugee Scenarios

View full text Add to dashboard Cite

show abstract

“…Each disaster is mapped to a class (e.g., drought, extreme temperature, storm and others). Finally, it contains an identifier that allows researchers to easily integrate information from another popular dataset that records natural disasters: EM-DAT 7…”

Section: Climate-based Migrationmentioning

confidence: 99%

“…Bruckschen et al [7] proposed a framework to detect fine-grained socio-economic occurrences with a limited training dataset. The authors used CDRs to spot potentially undeclared employment of refugees in Turkey by analyzing seasonal migration patterns in two scenarios: hazelnut harvest in Ordu and the construction of Istanbul's airport.…”

Section: Labor Migrationmentioning

confidence: 99%

Leveraging Mobile Phone Data for Migration Flows

Luca¹,

Barlacchi²,

Oliver³

et al. 2021

Preprint

View full text Add to dashboard Cite

Statistics on migration flows are often derived from census data, which suffer from intrinsic limitations, including costs and infrequent sampling. When censuses are used, there is typically a time gap -up to a few years -between the data collection process and the computation and publication of relevant statistics. This gap is a significant drawback for the analysis of a phenomenon that is continuously and rapidly changing. Alternative data sources, such as surveys and field observations, also suffer from reliability, costs, and scale limitations. The ubiquity of mobile phones enables an accurate and efficient collection of up-to-date data related to migration. Indeed, passively collected data by the mobile network infrastructure via aggregated, pseudonymized Call Detail Records (CDRs) is of great value to understand human migrations. Through the analysis of mobile phone data, we can shed light on the mobility patterns of migrants, detect spontaneous settlements and understand the daily habits, levels of integration, and human connections of such vulnerable social groups. This Chapter discusses the importance of leveraging mobile phone data as an alternative data source to gather precious and previously unavailable insights on various aspects of migration. Also, we highlight pending challenges that would need to be addressed before we can effectively benefit from the availability of mobile phone data to help make better decisions that would ultimately improve millions of people's lives.

show abstract

“…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]. While most of these studies have mapped mobile phone metadata and groundtruth data using point-to-polygon allocation or voronoi tessellation, very few studies have applied more elaborate approximation schemes.…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Refugees in Undeclared Employment—A Case Study in Turkey

Cited by 6 publications

References 7 publications

Policy Implications of the D4R Challenge

Policy Implications of the D4R Challenge

Leveraging Mobile Phone Data for Migration Flows

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

Contact Info

Product

Resources

About