FERRES, LEONARDO ADRIÁN
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FERRES, LEONARDO ADRIÁN
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Ferres, Leonardo Adrian
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lferres@udd.cl
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Scopus Author ID
14055701500

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Predicting city poverty using satellite imagery

2019-06-01 , Piaggesi, Simone , Gauvin, Laetitia , Tizzoni, Michele , Adler, Natalia , Verhulst, Stefaan , Young, Andrew , Price, Rihannan , FERRES, LEONARDO ADRIÁN , Cattuto, Ciro , Panisson, André

Reliable 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.

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News and the city: understanding online press consumption patterns through mobile data

2020 , Salvatore Vilella , Daniela Paolotti , Giancarlo Ruffo , FERRES, LEONARDO ADRIÁN

The always increasing mobile connectivity affects every aspect of our daily lives, including how and when we keep ourselves informed and consult news media. By studying a DPI dataset, provided by one of the major Chilean telecommunication companies, we investigate how different cohorts of the population of Santiago De Chile consume news media content through their smartphones. We find that some socio-demographic attributes are highly associated to specific news media consumption patterns. In particular, education and age play a significant role in shaping the consumers behaviour even in the digital context, in agreement with a large body of literature on off-line media distribution channels.

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Publication

Parallel construction of wavelet trees on multicore architectures

2016 , José Fuentes-Sepúlveda , Erick Elejalde , FERRES, LEONARDO ADRIÁN , Diego Seco

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The social stratification of internal migration and daily mobility during the COVID-19 pandemic

2024 , Erick Elejalde , FERRES, LEONARDO ADRIÁN , NAVARRO, VÍCTOR , BRAVO CELEDÓN, MARÍA LORETO , Emilio Zagheni

This 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.

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Fast and Compact Planar Embeddings

2017 , FERRES, LEONARDO ADRIÁN , José Fuentes , Travis Gagie , Meng He , Gonzalo Navarro

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Quantifying the ecological diversity and health of online news

2018 , Erick Elejalde , FERRES, LEONARDO ADRIÁN , Eelco Herder , Johan Bollen

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Power structure in Chilean news media

2018 , Jorge Bahamonde , Johan Bollen , Erick Elejalde , FERRES, LEONARDO ADRIÁN , Barbara Poblete , Dante R. Chialvo

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Problems and Opportunities of Working with a Telco's Large Data Sets of Mobile Data*

2019 , FERRES, LEONARDO ADRIÁN

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The effect of Pokémon Go on the pulse of the city: a natural experiment

2017 , Eduardo Graells-garrido , FERRES, LEONARDO ADRIÁN , Diego Caro , BRAVO CELEDÓN, MARÍA LORETO

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Evaluation of home detection algorithms on mobile phone data using individual-level ground truth

2021 , Luca Pappalardo , FERRES, LEONARDO ADRIÁN , Manuel Sacasa , Ciro Cattuto , BRAVO CELEDÓN, MARÍA LORETO

Inferring 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.

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