FERRES, LEONARDO ADRIÁN
Preferred name
FERRES, LEONARDO ADRIÁN
Official Name
Ferres, Leonardo Adrian
Main Affiliation
Email
lferres@udd.cl
ORCID
0000-0002-5899-9051
Scopus Author ID
14055701500
37 results
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Item type:Product, Dataset - Evaluation of home detection algorithms on mobile phone data using individual-level ground truthThis is the implementation of the 5 algorithms described in Vanhoof, M., Reis, F., Ploetz, T., & Smoreda, Z. (2018). Assessing the quality of home detection from mobile phone data for official statistics. In Journal of Official Statistics (Vol. 34, pp. 935–960). https://doi.org/10.2478/jos-2018-0046 that we used in our paper.3 - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, The social stratification of internal migration and daily mobility during the COVID-19 pandemic(2024) ;Erick Elejalde; ;NAVARRO, VÍCTOR; Emilio ZagheniThis study leverages mobile data for 5.4 million users to unveil the complex dynamics of daily mobility and longer-term relocations in and from Santiago, Chile, during the COVID-19 pandemic, focusing on socioeconomic differentials. We estimated a relative increase in daily mobility, in 2020, for lower-income compared to higher-income regions. In contrast, longer-term relocation rose primarily among higher-income groups. These shifts indicate nuanced responses to the pandemic across socioeconomic classes. Compared to 2017, economic factors in 2020 had a stronger influence on the decision to relocate and the selection of destinations, suggesting transformations in mobility behaviors. Contrary to previously held beliefs, there was no evidence supporting a preference for rural over urban destinations, despite the surge in emigration from Santiago during the pandemic. This study enhances our understanding of how varying socioeconomic conditions interact with mobility decisions during crises and provides insights for policymakers aiming to enact fair and evidence-based measures in rapidly changing circumstances.2Scopus© Citations 9 - Some of the metrics are blocked by yourconsent settings
Item type:Product, Dataset - A dataset to assess mobility changes in Chile following local quarantines(2023) ;NAVARRO ARANGUIZ, VICTOR; 10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluation of home detection algorithms on mobile phone data using individual-level ground truth(2021) ;Luca Pappalardo; ;Manuel Sacasa ;Ciro CattutoInferring mobile phone users’ home location, i.e., assigning a location in space to a user based on data generated by the mobile phone network, is a central task in leveraging mobile phone data to study social and urban phenomena. Despite its widespread use, home detection relies on assumptions that are difficult to check without ground truth, i.e., where the individual who owns the device resides. In this paper, we present a dataset that comprises the mobile phone activity of sixty-five participants for whom the geographical coordinates of their residence location are known. The mobile phone activity refers to Call Detail Records (CDRs), eXtended Detail Records (XDRs), and Control Plane Records (CPRs), which vary in their temporal granularity and differ in the data generation mechanism. We provide an unprecedented evaluation of the accuracy of home detection algorithms and quantify the amount of data needed for each stream to carry out successful home detection for each stream. Our work is useful for researchers and practitioners to minimize data requests and maximize the accuracy of the home antenna location.Scopus© Citations 35 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Predicting city poverty using satellite imagery(2019-06-01) ;Piaggesi, Simone ;Gauvin, Laetitia ;Tizzoni, Michele ;Adler, NataliaVerhulst, StefaanReliable data about socio-economic conditions of individuals, such as health indexes, consumption expenditures and wealth assets, remain scarce for most countries. Traditional methods to collect such data include on site surveys that can be expensive and labour intensive. On the other hand, remote sensing data, such as high-resolution satellite imagery, are becoming largely available. To circumvent the lack of socio-economic data at high granularity, computer vision has already been applied successfully to raw satellite imagery sampled from resource poor countries. In this work we apply a similar approach to the metropolitan areas of five different cities in North and South America, starting from pre-trained convolutional models used for poverty mapping in developing regions. Applying a transfer learning process we estimate household income from visual satellite features. The urban environment we consider is characterized by different features with respect to the resource-poor training environment, such as the high heterogeneity in population density. By leveraging both official and crowd-sourced data at city scale, we show the feasibility of estimating the socio-economic conditions of different neighborhoods from satellite data.7 - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, On the nature of real and perceived bias in the mainstream media(2018) ;Erick Elejalde; ;Eelco HerderDante R. ChialvoScopus© Citations 32 8