# eduosi/district

中国省/自治区/直辖市、市/自治州、区/县/旗数据，包含名称、拼音、拼音首字母、行政代码、区号

Repository: https://github.com/eduosi/district
Canonical: https://ross.abutalabs.com/products/district
License: Apache-2.0
License Family: permissive
Last push: 2026-01-22T09:23:13+00:00

## Health v2 (maintenance only)
Score: 51/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 63, release rhythm 8, longevity 100
- inputs: {"age_days": 4661, "days_push": 223, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1082, forks 394 (observed 2026-08-28T04:03:31.056605+00:00)

## What it is
A dataset of Chinese administrative divisions (provinces, cities, districts/counties) with names, pinyin, pinyin initials, administrative codes, and area codes. Data is distributed as SQL and CSV files for import into relational or non-relational databases.

## Use cases
- populate a cascading province/city/district selector in a web form
- look up Chinese administrative division codes and area codes
- search Chinese regions by pinyin or pinyin initials
- import China region data into MySQL or another database
- keep an app's region data up to date with administrative changes

## When to choose
- you need structured Chinese province/city/county data with pinyin and codes
- you want ready-to-import SQL or CSV files
- you need data covering mainland China plus Hong Kong, Macau, and Taiwan

## When to avoid
- you need global or non-China administrative divisions
- you need officially guaranteed accuracy for every code (some data is unofficial, e.g. Taiwan codes)
- you need a live API rather than static data files

## Facets
- artifact type: dataset
- maturity: active
- function: data-generation, geospatial
- domain: databases
- platform: cross-platform
- tags: china, administrative-divisions, pinyin, csv, sql, mysql, region-codes, area-codes, localization

## Member repositories
- eduosi/district (main) score 51

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:31.056605+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 2026-08-30T06:51:15.049997+00:00, confidence not recorded.
  - readme: https://github.com/eduosi/district (fetched 2026-08-28T04:03:31.056605+00:00, sha 78ce4f396aca)
- Data as of 2026-08-30T08:39:29.467469+00:00.