Eduardo Graells
Especialidad:
Informática urbana, visualización de información, ciencia social computacional, simulación.
Email:
egraells@dcc.uchile.cl
Eduardo Graells-Garrido es Doctorado en Tecnologías de la Información, las Comunicaciones y los Medios Audiovisuales por la Universitat Pompeu Fabra, España, obtenido en 2015.
PUBLICACIONES
The COVID-19 outbreak implied many changes in the daily life of most of the world's population for a long time, prompting severe restrictions on sociality. The Behavioral Immune System (BIS) suggests that when facing pathogens, a psychological mechanism would be activated that, among other things, would generate an increase in prejudice and discrimination towards marginalized groups, including immigrants. This study aimed to test if people tend to enhance their rejection of minorities and foreign groups under the threat of contagious diseases, using the users' attitudes towards migrants in Twitter data from Chile, for pre-pandemic and pandemic contexts. Our results appear to be mostly against the BIS hypothesis, with some faint exceptions, since threatened users increased their tweet production in the pandemic period, compared to empathetic users, but the latter grew in number and also increased the reach of their tweets between the two periods. We also found differences in the use of language between these types of users. Alternative explanations for these results may be context-dependent.
We present a data visualisation approach to support the rapid humanitarian response during the 2024 Valparaíso wildfires in Chile. Combining Meta user location data for population displacement, NASA FIRMS satellite imagery for wildfire locations, and census data, we identify key origins and destinations of displaced people during recent wildfires in Valparaíso, Chile. Our choropleth maps reveal spatial patterns of movement and socioeconomic factors, demonstrating the value of integrating diverse data sources for near real-time crisis response.
This study examines how the introduction of shared electric scooters (e-scooters) affects public transport demand in Santiago, Chile, analyzing whether they complement or substitute for existing transit services. We used smart card data from the integrated public transport system of Santiago and GPS traces from e-scooter trips during the initial deployment period. We employed a difference-in-differences approach with negative binomial regression models across three urban regions identified through k-means clustering: Central, Intermediate, and Peripheral. Results reveal spatially heterogeneous effects on public transport boardings and alightings. In the Central Region, e-scooter introduction was associated with significant substitution effects, showing a 23.87% reduction in combined bus and metro boardings, suggesting e-scooters replace short public transport trips in high-density areas. The Intermediate Region showed strong complementary effects, with a 33.6% increase in public transport boardings and 4.08% increase in alightings, indicating e-scooters successfully serve as first/last-mile connectors that enhance transit accessibility. The Peripheral Region exhibited no significant effects. Metro services experienced stronger impacts than bus services, with metro boardings increasing 9.77\% in the Intermediate Region. Our findings advance understanding of micromobility-transit interactions by demonstrating that both substitution and complementarity can coexist within the same urban system, depending on local accessibility conditions. These results highlight the need for spatially differentiated mobility policies that recognize e-scooters' variable roles across urban environments.
Work-related transportation incidents significantly impact urban mobility and productivity. These incidents include traffic crashes, collisions between vehicles, and falls that occurred during commuting or work-related transportation (e.g., falling while getting off a bus during the morning commute or while riding a bicycle for work). This study analyzes a decade of work-related transportation incident data (2012–2021) in Santiago, Chile, using records from a major worker’s insurance company. Using negative binomial regression, we assess the impact of a 2018 urban speed limit reduction law on incident injury severity. We also explore broader temporal, spatial, and demographic patterns in these incidents in urban and rural areas.
Tsundoku is a Python toolkit for analyzing social media data, focusing on text and network analysis. It offers user classification, bot detection, community identification, and topic modeling, with an active learning component to improve model accuracy. Tsundoku generates detailed reports with visualizations, making it accessible to researchers across disciplines. By streamlining the analysis pipeline from data collection to insight generation, Tsundoku helps researchers tackle the challenges of large-scale social media data analysis.
Edificio de Innovación UC, Piso 2
Vicuña Mackenna 4860
Macul, Chile
Vicuña Mackenna 4860
Macul, Chile