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Senador Elói de Souza
City in Rio Grande do Norte, Brazil
Overview
Senador Elói de Souza is a small, close-knit town in Rio Grande do Norte, Brazil, with a relaxed rural vibe and a strong sense of community. Life here revolves around local traditions, friendly gatherings, and the beautiful surrounding countryside.
Geographic Context
Jurisdiction
Brazil
State
Rio Grande do Norte
Population
6,027
Time Zone
America/Sao_Paulo
Coordinates
-6.04°, -35.66°
Key Metrics
Safety Score · estimate
50
Female Safety
45
LGBTQ+ Safety
35
Traffic Safety
60
Food Safety
65
Monthly Cost · estimate
$527
Local Cost
$307
Local Income
$280
Cashless
15
English
10
Temperature
26.2°C
Summer Avg
27.5°C
Winter Avg
24.4°C
Record High
38.0°C
Record Low
17.9°C
Humidity
74%
Precipitation
973 mm
City Vibrancy
—
Coworking
0
Cafes
0
Nightlife
0
Parks
0
Transport
0
Walk Score
0
Footways
—
Airport
NAT
Distance
44 km
Explore
Data sources
Source-backed data
World Air Quality Index - Latest available city-level air quality station measurement. Record updated 2025-12-05.
OpenStreetMap - Nearby amenities counted from OpenStreetMap-derived city extracts. Record updated 2025-12-07.
OpenStreetMap transport extract - Walkability, footway density, and airport access metrics derived from mapped transport features. Record updated 2025-12-06.
Estimates and unverified origins
Oyster List climate data - Climate origins are unverified. Available methods include historical APIs and AI estimates; stored rows do not identify the original dataset, reference period, or model for each value. Treat these summaries as estimates until independently verified. Record updated 2026-03-22.
Oyster List city price estimates - Price origins are unverified. Available generation methods include AI estimates; stored records do not identify a survey, observation period, or model for each price. Record updated 2026-01-14.
Oyster List AI-assisted estimates - Safety, lifestyle, income, and practical scores include model-assisted estimates. Stored records do not identify supporting observations for each score. Model-reported confidence: 65/100; this is not a measured accuracy score. Model: derived+gpt-4.1. Record updated 2026-03-26.
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