Riviera-Pays-d'Enhaut District
City in Vaud, Switzerland
Overview
The Riviera-Pays-d'Enhaut District in Switzerland is a scenic region set between the Alps and Lake Geneva, blending glamorous lakeside towns with charming mountain villages. The area is known for its cultural festivals, outdoor activities, and breathtaking landscapes, making it an internationally beloved destination.
Geographic Context
Jurisdiction
Switzerland
Canton
Vaud
Population
85,976
Time Zone
Europe/Zurich
Coordinates
46.46°, 6.88°
Key Metrics
Safety Score · estimate
92
Female Safety
88
LGBTQ+ Safety
82
Traffic Safety
84
Food Safety
97
Monthly Cost · estimate
$3,430
Local Cost
$2,180
Local Income
$4,100
Cashless
93
English
68
Temperature
7.3°C
Summer Avg
16.2°C
Winter Avg
-1.8°C
Record High
33.1°C
Record Low
-26.0°C
Humidity
79%
Precipitation
1174 mm
Internet Speed
396 Mbps
Upload
289 Mbps
Latency
7 ms
City Vibrancy
11
Coworking
0
Cafes
4
Nightlife
1
Parks
6
Transport
0
Walk Score
0
Footways
—
Airport
VIP
Distance
42 km
Explore
Data sources
Source-backed data
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 2025-12-05.
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: 77/100; this is not a measured accuracy score. Model: derived+gpt-4.1. Record updated 2026-03-26.