Selected work

Representative Papers

Crowd-Sourced Geographies of Income: Using Google Maps Points of Interest as High-Frequency Proxies for Sub-Municipal Income Estimation in Sao Paulo, Brazil

Aug 07, 2026

This study addresses the scarcity of high-frequency, low-cost sub-municipal income data in middle-income countries between censuses, which hampers effective social policy design. It presents the first systematic validation of Google Maps points of interest (POIs) as a high-frequency proxy for household income at the census tract level across São Paulo, covering 26,625 areas. The authors reduce the dimensionality of sparse POI features using principal component analysis (PCA) and non-negative matrix factorization (NMF), then integrate these with gradient boosting regression models. Employing spatially aware cross-validation to prevent data leakage, the optimal NMF-enhanced gradient boosting model achieves an R² of 0.65, demonstrating robust predictive performance. The analysis further identifies specific POI categories significantly associated with income, offering a novel paradigm for fine-grained socioeconomic monitoring.

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The NetMob26 Dataset: A High-Resolution Multi-Source View of Public Bus Mobility in Niterói

May 18, 2026

This study addresses the scarcity of high-quality public transit ridership and passenger demand data by integrating heterogeneous multi-source datasets—including bus GPS trajectories, 7.2 million fare transactions, route and stop information, weather records, urban infrastructure, and sociodemographic statistics—at the scale of a single city to construct a high-resolution, supply-and-demand-oriented public transit dataset. Rigorous data cleaning, anomaly detection, standardization, and differential privacy-based anonymization ensure both data quality and individual privacy. A controlled-access mechanism is implemented to balance open data sharing with privacy preservation. The resulting dataset enables research on transit efficiency evaluation, passenger flow forecasting, accessibility analysis, and weather impact assessment, thereby providing a robust foundation for intelligent urban transportation governance.

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How People Manage Knowledge in their "Second Brains"- A Case Study with Industry Researchers Using Obsidian

Sep 24, 2025

Information overload intensifies challenges in personal knowledge management (PKM), necessitating deeper understanding of how users construct and navigate knowledge bases across diverse contexts (e.g., work and personal life). This study employs a qualitative, in-depth case approach—combining semi-structured interviews and behavioral observation—to examine how researchers in a Brazilian laboratory use Obsidian to build and maintain personal knowledge bases. We identify “retrieval-driven construction” as a core behavioral pattern: users’ knowledge organization and annotation practices are fundamentally shaped by anticipated retrieval needs. Based on these empirical insights, we propose three AI-assisted design directions for personalized PKM tools: dynamic bidirectional link recommendation, semantic search enhancement, and context-aware annotation support. The findings provide empirically grounded, actionable guidelines for developing intelligent knowledge management systems that align with real-world user practices. (149 words)

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Recent publications

Latest Papers

Crowd-Sourced Geographies of Income: Using Google Maps Points of Interest as High-Frequency Proxies for Sub-Municipal Income Estimation in Sao Paulo, Brazil

Aug 07, 2026

This study addresses the scarcity of high-frequency, low-cost sub-municipal income data in middle-income countries between censuses, which hampers effective social policy design. It presents the first systematic validation of Google Maps points of interest (POIs) as a high-frequency proxy for household income at the census tract level across São Paulo, covering 26,625 areas. The authors reduce the dimensionality of sparse POI features using principal component analysis (PCA) and non-negative matrix factorization (NMF), then integrate these with gradient boosting regression models. Employing spatially aware cross-validation to prevent data leakage, the optimal NMF-enhanced gradient boosting model achieves an R² of 0.65, demonstrating robust predictive performance. The analysis further identifies specific POI categories significantly associated with income, offering a novel paradigm for fine-grained socioeconomic monitoring.

0 citationsRead paper

The NetMob26 Dataset: A High-Resolution Multi-Source View of Public Bus Mobility in Niterói

May 18, 2026

This study addresses the scarcity of high-quality public transit ridership and passenger demand data by integrating heterogeneous multi-source datasets—including bus GPS trajectories, 7.2 million fare transactions, route and stop information, weather records, urban infrastructure, and sociodemographic statistics—at the scale of a single city to construct a high-resolution, supply-and-demand-oriented public transit dataset. Rigorous data cleaning, anomaly detection, standardization, and differential privacy-based anonymization ensure both data quality and individual privacy. A controlled-access mechanism is implemented to balance open data sharing with privacy preservation. The resulting dataset enables research on transit efficiency evaluation, passenger flow forecasting, accessibility analysis, and weather impact assessment, thereby providing a robust foundation for intelligent urban transportation governance.

0 citationsRead paper

How People Manage Knowledge in their "Second Brains"- A Case Study with Industry Researchers Using Obsidian

Sep 24, 2025

Information overload intensifies challenges in personal knowledge management (PKM), necessitating deeper understanding of how users construct and navigate knowledge bases across diverse contexts (e.g., work and personal life). This study employs a qualitative, in-depth case approach—combining semi-structured interviews and behavioral observation—to examine how researchers in a Brazilian laboratory use Obsidian to build and maintain personal knowledge bases. We identify “retrieval-driven construction” as a core behavioral pattern: users’ knowledge organization and annotation practices are fundamentally shaped by anticipated retrieval needs. Based on these empirical insights, we propose three AI-assisted design directions for personalized PKM tools: dynamic bidirectional link recommendation, semantic search enhancement, and context-aware annotation support. The findings provide empirically grounded, actionable guidelines for developing intelligent knowledge management systems that align with real-world user practices. (149 words)

0 citationsRead paper