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Sacavém
City in Lisbon, Portugal
Overview
Sacavém is a busy suburb north of Lisbon, known for its pottery heritage and proximity to the Tagus River. With efficient transport links to central Lisbon, Sacavém offers a blend of residential tranquility, local commerce, and riverfront leisure. Its neighborhoods are ideal for settling while enjoying accessible urban amenities.
Geographic Context
Jurisdiction
Portugal
District
Lisbon
Population
17,718
Time Zone
Europe/Lisbon
Coordinates
38.79°, -9.11°
Key Metrics
Safety Score · estimate
78
Female Safety
80
LGBTQ+ Safety
75
Traffic Safety
82
Food Safety
90
Monthly Cost · estimate
$1,815
Local Cost
$1,160
Local Income
$1,200
Cashless
70
English
65
Temperature
16.0°C
Summer Avg
19.9°C
Winter Avg
11.8°C
Record High
36.3°C
Record Low
0.0°C
Humidity
80%
Precipitation
574 mm
Internet Speed
312 Mbps
Upload
189 Mbps
Latency
4 ms
City Vibrancy
93
Coworking
0
Cafes
62
Nightlife
1
Parks
30
Transport
0
Walk Score
0
Footways
—
Airport
LIS
Distance
3 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: 73/100; this is not a measured accuracy score. Model: derived+gpt-4.1. Record updated 2026-03-26.
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