arXiv Machine Learning August 11, 2026 By Adrienne C. Kinney, Anya Workman, Ademar Takeo Akabane, Jenna Barac, Paulo Fernando Braga Carvalho, Jeova Farias, Fernando Nascimento, Paulo Ricardo da Silva Oliveira

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

Read the original on arXiv Machine Learning →

arXiv:2608. 07871v1 Announce Type: cross Abstract: Accurate, up-to-date income data at the sub-municipal scale is essential for social policy in middle-income countries, yet in Brazil it depends on a costly decennial census whose intercensal gap recently exceeded a decade.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Measuring Poverty and Inequality with Reduced Data: A Machine Learning Approach Using Nigerian Household Data

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