Neighborhood · Nangang District

Nangang Jiuru

南港・九如

Section 3 of Yanjiuyuan Road at the foot of Nangang Mountain, with Mingxin Chan Temple beside China University of Science and Technology

Nearby neighborhoods

The closest neighborhoods in the same city.

Next ›Academia Sinica & JiuzhuangAcross from the "Scholars' Cemetery", with free museums on the Academia Sinica campus HoushanpiA big park built on an old pond, with people dancing and exercising by the gateway in the morning Nangang StationThree rail lines under one roof, two malls, and the Music Center to the west Kunyang & YuchengOnce home to brick kilns, now home to a bald cypress wetland park Nangang Economic & Trade ParkNew towers full of software and IC design firms, with a riverside bike path right alongside

From people who know it

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What's here

No hospital within 1.5 km

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南港國小Nearby・Shared catchment
舊莊國小Nearby・Shared catchment
胡適國小Nearby・Shared catchment
誠正國中Nearby・Catchment covers 100%
中華科技大學

NHI-contracted hospitals, public elementary and junior high schools whose catchment covers this area (estimated from the 2026 school-year village/neighborhood catchment table, treating every neighborhood in a village as equal in size; "Shared catchment" means families there can choose between two or more schools), high schools and universities within 1 km, stations, parks of 1 ha or more, and large shopping malls and department stores.

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Maps show the shops and the sights. What a street is like, you only know by walking it. TidyMap uses AI street imagery analysis and public facility data to turn each area's street environment and everyday convenience into something you can compare. Search an address and look at the street outside first.

Street-level samplingOne street-level photo every 50 m along each street (each grid shows its capture date and source)
AI scoringAn AI vision model rates cleanliness, buildings, sidewalks and greenery; their average is the street environment score
Plus everyday convenienceAccess to transit, shopping and healthcare from public facility data, combined with the street environment into the overall score

How to read it: yellow = poor, blue = good; grey = not enough data

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