Identifying seasonal mobility profiles from anonymized and aggregated mobile phone data. Application in food security

Zufiria, Pedro J.; Pastor-Escuredo, David; Úbeda-Medina, Luis; Hernández-Medina, Miguel Á.; Barriales-Valbuena, Iker; Morales, Alfredo Jose; Jacques, Damien Christophe; Nkwambi, Wilfred; Diop, Mohamed; Quinn, John A.; Hidalgo-Sanchís, Paula; Luengo-Oroz, Miguel

doi:10.1371/journal.pone.0195714

Cited by 25 publications

(13 citation statements)

References 33 publications

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“…CDR data was used by Lu, Bengtsson, and Holme [31] to estimate displacement after the Haiti earthquake; the study found that individual movements were highly predictable, and correlated with the regions to which the individuals were connected according to their call history and past travel patterns. CDR data has also been used to track displacement in Nepal [32]; the impact of flooding in Mexico [33]; responses to drought in Colombia [34]; seasonal migration in Senegal [35]; and the mobility patterns of Syrian refugees in Turkey [36].…”

Section: Cell Phone Datamentioning

confidence: 99%

Predictive modeling of movements of refugees and internally displaced people: Towards a computational framework

Pham¹,

Luengo-Oroz²

2022

Preprint

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Predicting forced displacement is an important undertaking of many humanitarian aid agencies, which must anticipate flows in advance in order to provide vulnerable refugees and Internally Displaced Persons (IDPs) with shelter, food, and medical care. While there is a growing interest in using machine learning to better anticipate future arrivals, there is little standardized knowledge on how to predict refugee and IDP flows in practice. Researchers and humanitarian officers are confronted with the need to make decisions about how to structure their datasets and how to fit their problem to predictive analytics approaches, and they must choose from a variety of modeling options. Most of the time, these decisions are made without an understanding of the full range of options that could be considered, and using methodologies that have primarily been applied in different contexts -and with different goals -as opportunistic references. In this work, we attempt to facilitate a more comprehensive understanding of this emerging field of research by providing a systematic model-agnostic framework, adapted to the use of big data sources, for structuring the prediction problem. As we do so, we highlight existing work on predicting refugee and IDP flows. We also draw on our own experience building models to predict forced displacement in Somalia, in order to illustrate the choices facing modelers and point to open research questions that may be used to guide future work.

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Section: Cell Phone Datamentioning

confidence: 99%

Predictive modeling of movements of refugees and internally displaced people: Towards a computational framework

Pham¹,

Luengo-Oroz²

2022

Preprint

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“…Other high impact applications include social and economical development [45,57], disaster events management such as cyclones landfall [38] or earthquakes [64], and food security [27,68].…”

Section: Applicationsmentioning

confidence: 99%

Big Data Processing, Analysis and Applications in Mobile Cellular Networks

Brdar

Novovic

Grujić

et al. 2019

Lecture Notes in Computer Science

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When coupled with spatio-temporal context, location-based data collected in mobile cellular networks provide insights into patterns of human activity, interactions, and mobility. Whilst uncovered patterns have immense potential for improving services of telecom providers as well as for external applications related to social wellbeing, its inherent massive volume make such 'Big Data' sets complex to process. A significant number of studies involving such mobile phone data have been presented, but there still remain numerous open challenges to reach technology readiness. They include efficient access in privacy-preserving manner, high performance computing environments, scalable data analytics, innovative data fusion with other sources-all finally linked into the applications ready for operational mode. In this chapter, we provide a broad overview of the entire workflow from raw data access to the final applications and point out the critical challenges in each step that need to be addressed to unlock the value of data generated by mobile cellular networks.

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“…For example, Salat, Smoreda, and Schläpfer developed methods for extrapolating population densities from CDRs from tracing weekly, monthly, and yearly patterns of mobile phone use in Senegal [14]. Zufiria et al also utilized mobile phone data from Senegal and found that aggregated mobility profiles based on likely livelihoods can shed light on economic activity, agricultural cycles, and precipitation, and thus, seasonal migration [15]. Lai et al further directly applied CDR data and found that it can complement national statistics, especially in countries with high rates of internal migration, to ensure that public services are appropriately deployed [16].…”

Section: Introductionmentioning

confidence: 99%

Detecting Destroyed Communities in Remote Areas with Personal Electronic Device Data: A Case Study of the 2017 Puebla Earthquake

Marx

Poynor

Kim

et al. 2020

IJGI

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Large-scale humanitarian disasters often disproportionately damage poor communities. This effect is compounded when communities are remote with limited connectivity and response is slow. While humanitarian response organizations are increasingly using a wide range of satellites to detect damaged areas, these images can be delayed days or weeks and may not tell the story of how many or where people are affected. In order to address the need of identifying severely damaged communities due to humanitarian disasters, we present an algorithmic approach to leverage pseudonymization locational data collected from personal cell phones to detect the depopulation of localities severely affected by the 2017 Puebla earthquake in Mexico. This algorithm capitalizes on building a pattern of life for these localities, first establishing which pseudonymous IDs are a resident of the locality and then establishing what percent of those residents leave those localities after the earthquake. Using a study of 15 localities severely damaged and 15 control localities unaffected by the earthquake, this approach successfully identified 73% of severely damaged localities. This individual-focused system provides a promising approach for organizations to understand the size and severity of a humanitarian disaster, detect which localities are most severely damaged, and aid them in prioritizing response and reconstruction efforts.

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Identifying seasonal mobility profiles from anonymized and aggregated mobile phone data. Application in food security

Cited by 25 publications

References 33 publications

Predictive modeling of movements of refugees and internally displaced people: Towards a computational framework

Predictive modeling of movements of refugees and internally displaced people: Towards a computational framework

Big Data Processing, Analysis and Applications in Mobile Cellular Networks

Detecting Destroyed Communities in Remote Areas with Personal Electronic Device Data: A Case Study of the 2017 Puebla Earthquake

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