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Senhora da Hora

City in Porto, Portugal

Overview

Senhora da Hora is a dynamic suburban city just north of Porto, offering a blend of convenient urban living and tranquil residential zones. Known for NorteShopping and strong public transport links, it's ideal for visitors looking to explore the Porto metropolitan area beyond the tourist crowds.

Geographic Context

Jurisdiction
Portugal
District
Porto
Population
28,930
Time Zone
Europe/Lisbon
Coordinates
41.19°, -8.65°

Key Metrics

Safety Score
87
Female Safety
83
LGBTQ+ Safety
85
Traffic Safety
78
Food Safety
95
Monthly Cost
$1,732
Local Cost
$1,102
Local Income
$1,100
Cashless
78
English
65
Temperature
15.2°C
Summer Avg
19.5°C
Winter Avg
10.7°C
Record High
35.2°C
Record Low
0.6°C
Humidity
79%
Precipitation
1008 mm
Internet Speed
241 Mbps
Upload
113 Mbps
Latency
3 ms
City Vibrancy
111
Coworking
0
Cafes
72
Nightlife
5
Parks
34
Transport
0
Walk Score
0
Footways
Airport
OPO
Distance
7 km

Neighborhoods

Partial

Coverage is incomplete — additional neighborhoods are being added as more source data is processed.

Coverage is incomplete — more neighborhoods are being added.

View neighborhoods

Frequently Asked Questions

Data sources

Source-backed data

NASA POWER - Climate normals and monthly weather measurements rendered as city weather metrics. Updated 2026-03-22.
Ookla Open Data - Internet speed aggregates for 2024Q3 from 31 tests. Updated 2025-12-05.
OpenStreetMap - Nearby amenities counted from OpenStreetMap-derived city extracts. Updated 2025-12-06.
OpenStreetMap transport extract - Walkability, footway density, and airport access metrics derived from mapped transport features. Updated 2025-12-05.
Oyster List city price model - City price estimates calibrated from local price surveys and jurisdiction-level cost signals. Updated 2026-01-14.
Overture Maps and OpenStreetMap - 2 source-backed neighborhood entities are available for this city. Updated 2026-04-05.

Estimated data

Oyster List AI-assisted estimates - Safety, lifestyle, income, and practical scores are model-assisted estimates derived from source-backed country and city signals. Confidence 82/100.

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