Awesome
Peta border wilayah Indonesia per desa dalam format geojson (total 83322 desa)
Cara parsing sebagai Pandas DataFrame:
- Unzip
indonesia_villages_border.geojson.zip
- Pada Jupyter Notebook, ikuti code berikut
#import libraries
import pandas as pd
import ast
#load data
with open('file-path/indonesia_villages_border.geojson') as myfile:
data = ast.literal_eval(myfile.read())
#nitiate list containers
provinces = []
districts = []
sub_districts = []
villages = []
borders = []
#fill the lists via looping
for datapoint in data:
provinces.append(datapoint.get('province'))
districts.append(datapoint.get('district'))
sub_districts.append(datapoint.get('sub_district'))
villages.append(datapoint.get('village'))
borders.append(datapoint.get('border'))
#create as dataframe
df_village_border = pd.DataFrame({
'province' : provinces,
'district' : districts,
'sub_district' : sub_districts,
'village' : villages,
'border' : borders
})
Contoh data (5 baris pertama)
| province | district | sub_district | village | border
| ACEH | ACEH SELATAN | BAKONGAN | KEUDE BAKONGAN | [[97.4767, 2.9355], [97.4748, 2.9361], [97.471, 2.9373], [97.4657, 2.939], [97.4616, 2.9403], [97.4616, 2.9402], [97.4576, 2.9323], [97.4568, 2.9306], [97.4547, 2.9264], [97.453, 2.9231], [97.4569, 2.9234], [97.4627, 2.9231], [97.4632, 2.9231], [97.467, 2.9227], [97.4728, 2.9222], [97.4735, 2.9221], [97.4774, 2.9216], [97.4821, 2.9215], [97.4867, 2.9202], [97.4874, 2.9198], [97.4878, 2.9221], [97.4885, 2.9231], [97.4881, 2.9246], [97.4886, 2.9273], [97.4888, 2.9282], [97.4879, 2.9291], [97.4887, 2.9294], [97.4896, 2.93], [97.4888, 2.9315], [97.4887, 2.9317], [97.484, 2.9328], [97.4835, 2.9333], [97.4767, 2.9355]] |
| ACEH | ACEH SELATAN | BAKONGAN | UJUNG MANGKI | [[97.4605, 2.9413], [97.4584, 2.9431], [97.4571, 2.9448], [97.4548, 2.9472], [97.4539, 2.9497], [97.4509, 2.9543], [97.4482, 2.9599], [97.4471, 2.9622], [97.4449, 2.9684], [97.4447, 2.9686], [97.434, 2.9543], [97.4259, 2.9457], [97.4186, 2.9397], [97.4251, 2.9343], [97.4275, 2.933], [97.4307, 2.9313], [97.4374, 2.9283], [97.4439, 2.9246], [97.4473, 2.9232], [97.4506, 2.9229], [97.453, 2.9231], [97.4547, 2.9264], [97.4568, 2.9306], [97.4576, 2.9323], [97.4616, 2.9402], [97.4605, 2.9413]] |
| ACEH | ACEH SELATAN | BAKONGAN | UJUNG PADANG | [[97.4767, 2.9355], [97.4835, 2.9333], [97.484, 2.9328], [97.4887, 2.9317], [97.4888, 2.9315], [97.4983, 2.9288], [97.5001, 2.9282], [97.507, 2.9262], [97.5114, 2.925], [97.5115, 2.9248], [97.5154, 2.9231], [97.5151, 2.9237], [97.5113, 2.93], [97.5075, 2.9366], [97.5073, 2.9367], [97.5072, 2.9369], [97.5072, 2.9374], [97.5074, 2.9427], [97.506, 2.9432], [97.5001, 2.947], [97.4963, 2.9495], [97.4939, 2.9502], [97.4929, 2.9502], [97.4898, 2.9521], [97.4867, 2.9545], [97.4846, 2.9558], [97.4783, 2.9642], [97.4766, 2.9657], [97.4676, 2.9736], [97.4657, 2.9626], [97.4659, 2.9552], [97.4728, 2.9464], [97.4767, 2.9355]] |
| ACEH | ACEH SELATAN | BAKONGAN | KAMPONG DRIEN | [[97.5099, 2.9607], [97.505, 2.9648], [97.4942, 2.9766], [97.4884, 2.9827], [97.4628, 3.0105], [97.4615, 3.0096], [97.46, 3.0088], [97.4578, 3.008], [97.4569, 3.0077], [97.4565, 3.0076], [97.4551, 3.0071], [97.4544, 3.0069], [97.4522, 3.0059], [97.4504, 3.0049], [97.4479, 3.0035], [97.4478, 3.0035], [97.4469, 3.0029], [97.4461, 3.0023], [97.4446, 3.0011], [97.4406, 2.9976], [97.4676, 2.9736], [97.4766, 2.9657], [97.4783, 2.9642], [97.4846, 2.9558], [97.4867, 2.9545], [97.4898, 2.9521], [97.4929, 2.9502], [97.4939, 2.9502], [97.4963, 2.9495], [97.5001, 2.947], [97.506, 2.9432], [97.5074, 2.9427], [97.5077, 2.9491], [97.5078, 2.952], [97.5081, 2.9533], [97.5085, 2.9549], [97.5098, 2.9603], [97.5099, 2.9607]] |
| ACEH | ACEH SELATAN | BAKONGAN | DARUL IKHSAN | [[97.4447, 2.9686], [97.4384, 2.9737], [97.4253, 2.9843], [97.3967, 2.9596], [97.3988, 2.9576], [97.3989, 2.9574], [97.4002, 2.9565], [97.4058, 2.9514], [97.4116, 2.9462], [97.4147, 2.9433], [97.4177, 2.9404], [97.4186, 2.9397], [97.4259, 2.9457], [97.434, 2.9543], [97.4447, 2.9686]] |
Sumber data