arXiv subjects

Stefania Rubrichi

Publications and source records attributed to Stefania Rubrichi.

7 recordsLinked to original sources

arXiv2024

A Highly Granular Temporary Migration Dataset Derived From Mobile Phone Data in Senegal

Understanding temporary migration is crucial for addressing various socio-economic and environmental challenges in developing countries. However, traditional surveys often fail to capture such movements effectively, leading to a scarcity of reliable data, particularly in sub-Saharan Africa. This article introduces a detailed and open-access dataset that leverages mobile phone data to capture temporary migration in Senegal with unprecedented spatio-temporal detail. The dataset provides measures of migration flows and stock across 151 locations across the country and for each half-month period from 2013 to 2015, with a specific focus on movements lasting between 20 and 180 days. The article presents a suite of methodological tools that not only include algorithmic methods for the detection of temporary migration events in digital traces, but also addresses key challenges in aggregating individual trajectories into coherent migration statistics. These methodological advancements are not only pivotal for the intrinsic value of the dataset but also adaptable for generating systematic migration statistics from other digital trace datasets in other contexts.

cs.CY↗

arXiv2024

Early Detection of Critical Urban Events using Mobile Phone Network Data

Network Signalling Data (NSD) have the potential to provide continuous spatio-temporal information about the presence, mobility, and usage patterns of cell phone services by individuals. Such information is invaluable for monitoring large urban areas and supporting the implementation of decision-making services. When analyzed in real time, NSD can enable the early detection of critical urban events, including fires, large accidents, stampedes, terrorist attacks, and sports and leisure gatherings, especially if these events significantly impact mobile phone network activity in the affected areas. This paper presents empirical evidence that advanced NSD can detect anomalies in mobile traffic service consumption, attributable to critical urban events, with fine spatial and temporal resolutions. We introduce two methodologies for real-time anomaly detection from multivariate time series extracted from large-scale NSD, utilizing a range of algorithms adapted from the state-of-the-art in unsupervised machine learning techniques for anomaly detection. Our research includes a comprehensive quantitative evaluation of these algorithms on a large-scale dataset of NSD service consumption for the Paris region. The evaluation uses an original dataset of documented critical or unusual urban events. This dataset has been built as a ground truth basis for assessing the algorithms performance. The obtained results demonstrate that our framework can detect unusual events almost instantaneously and locate the affected areas with high precision, largely outperforming random classifiers. This efficiency and effectiveness underline the potential of NSD-based anomaly detection in significantly enhancing emergency response strategies and urban planning.

cs.CY↗

arXiv2023

Human spatial dynamics for electricity demand forecasting: the case of France during the 2022 energy crisis

Accurate electricity demand forecasting is crucial to meet energy security and efficiency, especially when relying on intermittent renewable energy sources. Recently, massive savings have been observed in Europe, following an unprecedented global energy crisis. However, assessing the impact of such crisis and of government incentives on electricity consumption behaviour is challenging. Moreover, standard statistical models based on meteorological and calendar data have difficulty adapting to such brutal changes. Here, we show that mobility indices based on mobile network data significantly improve the performance of the state-of-the-art models in electricity demand forecasting during the sobriety period. We start by documenting the drop in the French electricity consumption during the winter of 2022-2023. We then show how our mobile network data captures work dynamics and how adding these mobility indices outperforms the state-of-the-art during this atypical period. Our results characterise the effect of work behaviours on the electricity demand.

stat.AP↗

arXiv2023

The NetMob23 Dataset: A High-resolution Multi-region Service-level Mobile Data Traffic Cartography

Digital sources have been enabling unprecedented data-driven and large-scale investigations across a wide range of domains, including demography, sociology, geography, urbanism, criminology, and engineering. A major barrier to innovation is represented by the limited availability of dependable digital datasets, especially in the context of data gathered by mobile network operators or service providers, due to concerns about user privacy and industrial competition. The resulting lack of reference datasets curbs the production of new research methods and results, and prevents verifiability and reproducibility of research outcomes. The NetMob23 dataset offers a rare opportunity to the multidisciplinary research community to access rich data about the spatio-temporal consumption of mobile applications in a developed country. The generation process of the dataset sets a new quality standard, leading to information about the demands generated by 68 popular mobile services, geo-referenced at a high resolution of $100\times100$ $m^2$ over 20 metropolitan areas in France, and monitored during 77 consecutive days in 2019.

cs.NI↗

arXiv2023

Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal

Explainable artificial intelligence (XAI) provides explanations for not interpretable machine learning (ML) models. While many technical approaches exist, there is a lack of validation of these techniques on real-world datasets. In this work, we present a use-case of XAI: an ML model which is trained to estimate electrification rates based on mobile phone data in Senegal. The data originate from the Data for Development challenge by Orange in 2014/15. We apply two model-agnostic, local explanation techniques and find that while the model can be verified, it is biased with respect to the population density. We conclude our paper by pointing to the two main challenges we encountered during our work: data processing and model design that might be restricted by currently available XAI methods, and the importance of domain knowledge to interpret explanations.

cs.CY↗

arXiv2020

The impact of electrification on rural attractiveness in Senegal

A reliable and affordable access to electricity has become one of the basic needs for humans and is, as such, at the top of the development agenda. It contributes to socio-economic development by transforming the whole spectrum of people's lives - food, education, health care; it spurs new economic opportunities and thus improves livelihoods. Using a comprehensive dataset of pseudonymised mobile phone records, provided by the market share leader, we analyse the impact of electrification on the attractiveness of rural areas in Senegal. We extract communication and mobility flows from the call detail records (CDRs) and show that electrification has a small, yet positive and specific, impact on centrality measures within the communication network and on the volume of incoming visitors. This increased influence is however circumscribed to a limited spatial extent, creating a complex competition with nearby areas. Nevertheless, we found that the volume of visitors between any two sites could be well predicted from the level of electrification at the destination combined with the living standard at the origin. In view of these results, we discuss how to obtain the best outcomes from a rural electrification planning strategy. We determine that electrifying clusters of rural sites is a better solution than attempting to centralise electricity supplies to maximise the development of specifically targeted sites.

physics.soc-ph↗

arXiv2018

A Comparison of Spatial-based Targeted Disease Containment Strategies using Mobile Phone Data

Epidemic outbreaks are an important healthcare challenge, especially in developing countries where they represent one of the major causes of mortality. Approaches that can rapidly target subpopulations for surveillance and control are critical for enhancing containment processes during epidemics. Using a real-world dataset from Ivory Coast, this work presents an attempt to unveil the socio-geographical heterogeneity of disease transmission dynamics. By employing a spatially explicit meta-population epidemic model derived from mobile phone Call Detail Records (CDRs), we investigate how the differences in mobility patterns may affect the course of a realistic infectious disease outbreak. We consider different existing measures of the spatial dimension of human mobility and interactions, and we analyse their relevance in identifying the highest risk sub-population of individuals, as the best candidates for isolation countermeasures. The approaches presented in this paper provide further evidence that mobile phone data can be effectively exploited to facilitate our understanding of individuals' spatial behaviour and its relationship with the risk of infectious diseases' contagion. In particular, we show that CDRs-based indicators of individuals' spatial activities and interactions hold promise for gaining insight of contagion heterogeneity and thus for developing containment strategies to support decision-making during country-level pandemics.

cs.SI↗