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Mashan
City in Guangxi, China
Overview
Mashan is a small county town set in Guangxi, China, known for its scenic countryside, relaxed pace, and welcoming local population. While not a major tourist destination, it offers insight into authentic rural life in southern China and access to the region's picturesque landscapes.
Geographic Context
Jurisdiction
China
Autonomous Region
Guangxi
Population
17,301
Time Zone
Asia/Shanghai
Coordinates
23.71°, 108.17°
Key Metrics
Safety Score · estimate
75
Female Safety
60
LGBTQ+ Safety
30
Traffic Safety
65
Food Safety
55
Monthly Cost · estimate
$577
Local Cost
$292
Local Income
$400
Cashless
65
English
10
Temperature
20.8°C
Summer Avg
26.8°C
Winter Avg
12.6°C
Record High
40.4°C
Record Low
-6.7°C
Humidity
78%
Precipitation
1466 mm
City Vibrancy
1
Coworking
0
Cafes
0
Nightlife
0
Parks
1
Transport
1
Walk Score
1
Footways
1.0
Airport
—
Distance
—
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 2025-12-06.
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: 60/100; this is not a measured accuracy score. Model: derived+gpt-4.1. Record updated 2026-03-26.
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