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Cafezal do Sul
City in Paraná, Brazil
Overview
Cafezal do Sul is a small, tranquil town in Paraná that offers a snapshot of rural Brazilian life. Its close-knit community and laid-back vibe make it an ideal destination for travelers seeking authenticity and peace. The town is surrounded by farmland and natural beauty.
Geographic Context
Jurisdiction
Brazil
State
Paraná
Population
4,580
Time Zone
America/Sao_Paulo
Coordinates
-23.96°, -53.61°
Key Metrics
Safety Score · estimate
55
Female Safety
50
LGBTQ+ Safety
35
Traffic Safety
60
Food Safety
60
Monthly Cost · estimate
$556
Local Cost
$373
Local Income
$350
Cashless
30
English
15
Temperature
22.7°C
Summer Avg
26.4°C
Winter Avg
17.9°C
Record High
40.8°C
Record Low
-1.4°C
Humidity
76%
Precipitation
1464 mm
City Vibrancy
—
Coworking
0
Cafes
0
Nightlife
0
Parks
0
Transport
0
Walk Score
0
Footways
—
Airport
UMU
Distance
35 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: 75/100; this is not a measured accuracy score. Model: derived+gpt-4.1. Record updated 2026-03-26.
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