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Ganzi
City in Sichuan, China
Overview
Ganzi, also known as Garze or Kandze, is a remote town perched in the Tibetan Plateau of western Sichuan province. The city is famed for its lively Tibetan culture, monasteries, and spectacular mountain scenery, attracting adventure travelers and those seeking authentic local life.
Geographic Context
Jurisdiction
China
Province
Sichuan
Population
—
Time Zone
Asia/Shanghai
Coordinates
30.05°, 101.96°
Key Metrics
Safety Score · estimate
70
Female Safety
65
LGBTQ+ Safety
30
Traffic Safety
55
Food Safety
70
Monthly Cost · estimate
$703
Local Cost
$302
Local Income
$350
Cashless
60
English
10
Temperature
2.7°C
Summer Avg
9.2°C
Winter Avg
-4.2°C
Record High
23.1°C
Record Low
-26.0°C
Humidity
73%
Precipitation
1017 mm
City Vibrancy
5
Coworking
0
Cafes
3
Nightlife
0
Parks
2
Transport
14
Walk Score
14
Footways
28.0
Airport
KGT
Distance
24 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 2025-12-09.
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 2026-03-23.
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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