# cn/GB2260

中华人民共和国国家标准 GB/T 2260 行政区划代码

Repository: https://github.com/cn/GB2260
Canonical: https://ross.abutalabs.com/products/gb2260
Language: Python
License Family: other
Topics: gb2260
Last push: 2023-05-22T21:34:11+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4305, "days_push": 1199, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1543, forks 201 (observed 2026-08-28T04:05:01.199332+00:00)

## What it is
A dataset of China's national standard GB/T 2260 administrative division codes for county-level and above regions, sourced from official statistics and civil affairs ministry data. It serves as the canonical reference data with language-specific library implementations in Python, JavaScript, Ruby, Java, PHP, Go, Elixir, Swift, and .NET.

## Use cases
- look up Chinese administrative division codes by region name
- build a dropdown of Chinese provinces, cities, and counties
- validate GB/T 2260 codes in user-submitted addresses
- map region codes to province/city/county hierarchy
- seed a database with China's official division codes

## When to choose
- you need authoritative GB/T 2260 division code data for China
- you want reference data to power your own library or database in any language

## When to avoid
- you need postal codes, GPS coordinates, or street-level addresses
- you need non-Chinese country subdivision data
- you need a maintained library rather than raw data (check the per-language repos)

## Facets
- artifact type: dataset
- maturity: maintenance
- function: data-science, geospatial
- domain: e-government
- platform: cross-platform
- tags: gb2260, china, administrative-divisions, division-codes, reference-data, data-engineering, localization

## Member repositories
- cn/GB2260 (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:01.199332+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:30:40.800747+00:00, confidence not recorded.
  - readme: https://github.com/cn/GB2260 (fetched 2026-08-28T04:05:01.199332+00:00, sha 95d6520fdaae)
- Data as of 2026-08-30T08:39:29.467469+00:00.