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Hang Dong
City in Chiang Mai, Thailand
Overview
Hang Dong is a tranquil district just south of Chiang Mai, known for its lush scenery, local markets, and emerging expat communities. It combines rural charm with modern conveniences, making it attractive for families, retirees, and remote workers. The area is less crowded than central Chiang Mai while offering excellent access to nature and amenities.
Geographic Context
Jurisdiction
Thailand
Province
Chiang Mai
Population
7,291
Time Zone
Asia/Bangkok
Coordinates
18.69°, 98.92°
Key Metrics
Safety Score · estimate
80
Female Safety
70
LGBTQ+ Safety
55
Traffic Safety
50
Food Safety
70
Monthly Cost · estimate
$617
Local Cost
$350
Local Income
$430
Cashless
50
English
35
Temperature
24.2°C
Summer Avg
25.2°C
Winter Avg
21.2°C
Record High
41.8°C
Record Low
5.4°C
Humidity
72%
Precipitation
1183 mm
Internet Speed
259 Mbps
Upload
223 Mbps
Latency
8 ms
City Vibrancy
11
Coworking
0
Cafes
2
Nightlife
1
Parks
8
Transport
1
Walk Score
1
Footways
2.0
Airport
CNX
Distance
10 km
Explore
Data sources
Source-backed data
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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