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Google Maps POIs used to estimate income in Sao Paulo

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to estimate household income at a sub-municipal level in São Paulo, Brazil, by analyzing crowd-sourced data from Google Maps Points of Interest (POIs). This approach uses POI categories from Google Places to create high-frequency, low-cost income proxies, addressing the limitations of Brazil's infrequent and costly decennial census. The best-performing model, combining Non-negative Matrix Factorization with gradient boosting, achieved an R^2 of 0.65 in predicting census-derived income, suggesting that geospatial data can effectively supplement traditional income statistics. AI

IMPACT Demonstrates novel applications of geospatial and machine learning techniques for socio-economic data analysis.

RANK_REASON Academic paper detailing a new methodology for income estimation. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Google Maps POIs used to estimate income in Sao Paulo

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Academic paper detailing a new methodology for income estimation. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · 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

    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. We test…

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