Data ini diambil dari situs Pemutakhiran MFD dan MBS Badan Pusat Statistik (http://mfdonline.bps.go.id/) pada 11 Januari 2018.
The data were taken from Central Agency on Statistics (BPS) - MFD and MBS Update (http://mfdonline.bps.go.id/) on 11th January 2018.
The data were curl-ed from BPS site:
curl http://mfdonline.bps.go.id/index.php?link=hasil_pencarian --data "pilihcari=desa&kata_kunci="
with a, i, u, e and o as the keywords.
+------+---------------------------+----------------+-----------+----------------+
| Kode | Provinsi | Kabupaten/Kota | Kecamatan | Desa/Kelurahan |
+------+---------------------------+----------------+-----------+----------------+
| 11 | ACEH | 23 | 289 | 6509 |
| 12 | SUMATERA UTARA | 33 | 448 | 6102 |
| 13 | SUMATERA BARAT | 19 | 179 | 1160 |
| 14 | RIAU | 12 | 169 | 1876 |
| 15 | JAMBI | 11 | 141 | 1562 |
| 16 | SUMATERA SELATAN | 17 | 236 | 3263 |
| 17 | BENGKULU | 10 | 128 | 1515 |
| 18 | LAMPUNG | 15 | 228 | 2642 |
| 19 | KEPULAUAN BANGKA BELITUNG | 7 | 47 | 366 |
| 21 | KEPULAUAN RIAU | 7 | 70 | 395 |
| 31 | DKI JAKARTA | 6 | 44 | 254 |
| 32 | JAWA BARAT | 27 | 627 | 5832 |
| 33 | JAWA TENGAH | 35 | 573 | 8008 |
| 34 | DI YOGYAKARTA | 5 | 78 | 414 |
| 35 | JAWA TIMUR | 38 | 666 | 7856 |
| 36 | BANTEN | 8 | 155 | 1501 |
| 51 | BALI | 9 | 57 | 653 |
| 52 | NUSA TENGGARA BARAT | 10 | 116 | 1062 |
| 53 | NUSA TENGGARA TIMUR | 22 | 307 | 3202 |
| 61 | KALIMANTAN BARAT | 14 | 174 | 2073 |
| 62 | KALIMANTAN TENGAH | 14 | 136 | 1537 |
| 63 | KALIMANTAN SELATAN | 13 | 152 | 1971 |
| 64 | KALIMANTAN TIMUR | 10 | 103 | 1002 |
| 65 | KALIMANTAN UTARA | 5 | 53 | 466 |
| 71 | SULAWESI UTARA | 15 | 171 | 1790 |
| 72 | SULAWESI TENGAH | 13 | 175 | 1953 |
| 73 | SULAWESI SELATAN | 24 | 307 | 2975 |
| 74 | SULAWESI TENGGARA | 17 | 222 | 2301 |
| 75 | GORONTALO | 6 | 77 | 722 |
| 76 | SULAWESI BARAT | 6 | 69 | 639 |
| 81 | MALUKU | 11 | 118 | 1180 |
| 82 | MALUKU UTARA | 10 | 116 | 1155 |
| 91 | PAPUA BARAT | 13 | 217 | 1730 |
| 94 | PAPUA | 29 | 567 | 4866 |
+------+---------------------------+----------------+-----------+----------------+
Note:
The data was provided as-it-is and looks like there are two anomalies:
There was entries that not in ASCII format.
72 SULAWESI TENGAH 09 KABUPATEN TOJO UNA-UNA 070 TOGEAN 016 TITIRIí POPOLION
94 PAPUA 33 KABUPATEN PUNCAK 042 MAGEÁBUME
There are duplicate village id.
91 PAPUA BARAT 07 KABUPATEN SORONG 182 WEMAK 005 KAMLIN
91 PAPUA BARAT 07 KABUPATEN SORONG 182 WEMAK 005 KWARI
91 PAPUA BARAT 09 KABUPATEN TAMBRAUW 070 KEBAR 015 ANARUM
91 PAPUA BARAT 09 KABUPATEN TAMBRAUW 070 KEBAR 015 JAMBUANI
The above statistics was generated by MySQL version and uses INSERT IGNORE statements.
This will skip the duplicate items.
In order to generate new data:
cd scripts
pip install -r requirements.txt
./run.sh
We use mysql as default database hostname and root as default database username, but if you have different database setup on your local machine, you can simply run it with argument:
./run -h [my-db-host] -u [my-db-username]
You can also run docker-compose (more preferred):
docker-compose build
docker-compose up
- The scripts are license under: MIT.
- The data (CSV and SQL) are under: ODBL v1.0.
- The source data is attributed to Badan Pusat Statistik (BPS) Indonesia.
- Fork it (https://github.com/edwardsamuel/Wilayah-Administratif-Indonesia/fork).
- Create your feature branch (
git checkout -b my-new-feature). - Commit your changes (
git commit -am 'Add some feature'). - Push to the branch (
git push origin my-new-feature). - Create a new Pull Request.