# 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: https://arxiv.org/abs/2608.07871 · HTML: https://mlsanity.com/paper/2608.07871/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 53 references that are in the map.
- First posted on arXiv 8 Aug 2026.

## Builds on (most cited first, 6 of 6)

- [Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US](https://mlsanity.com/paper/1702.06683/) (2017)
- [Micro-Estimates of Wealth for all Low- and Middle-Income Countries](https://mlsanity.com/paper/2104.07761/) (2021)
- [Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions](https://mlsanity.com/paper/2010.14374/) (2020)
- [Interpretable Poverty Mapping using Social Media Data, Satellite Images, and Geospatial Information](https://mlsanity.com/paper/2011.13563/) (2020)
- [High-Resolution Poverty Maps in Sub-Saharan Africa](https://mlsanity.com/paper/2009.00544/) (2020)
- [The Policy Implications of Economic Complexity](https://mlsanity.com/paper/2205.02164/) (2022)

## Abstract (arXiv)

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 whether the composition of crowd-sourced Google Maps Points of Interest (POIs) can serve as a high-frequency, low-cost proxy for household income across the 26,625 census sectors of the municipality of Sao Paulo. Using a theoretically motivated set of POI categories retrieved from Google Places, we represent each sector by its POI counts, decompose these high-dimensional, sparse features with principal component analysis (PCA) and non-negative matrix factorization (NMF), and train a sweep of regression models to predict census-derived income. Under a data leakage-aware spatial validation design the best model (NMF with gradient boosting) attains a held-out R^2 of 0.65, with performance stable across feature-extraction methods. Interpretable decompositions reveal which POI types carry the income signal. These results suggest that commercial, crowd-sourced geospatial data can complement conventional income statistics during intercensal periods, and we discuss extensions toward multidimensional poverty and the capabilities framework.