Analysis of BPJS Healthcare Facilities in Indonesia 2019

15 min readFeb 1, 2024

Table of content

· Table of content
· Introduction
· Install Library and Load Dataset
· Install Library
· Preprocessing
∘ Set Kota/Kab
∘ Clear latitude and longitude
· Analize
∘ Provinces that have Health Facilities with BPJS
∘ Data to be displayed in the visualization map
· Visualization
∘ Geographical visualization of 16499 BPJS health facilities
· Visualization of all data
∘ City/Regency with the highest number of health facilities with BPJS
· Pivotable in Python
· Dashboard
· Conclusion

Introduction

BPJS Kesehatan is a public legal entity established to administer health insurance programs as stipulated in Law Number 24 of 2011 concerning the Social Security Organizing Body. In this blog, we will analyze the healthcare facilities (FasKes) provided by BPJS in Indonesia. The data for this analysis was obtained through kaggle.com.

The following is the content within the dataset:

NoLink: Sequential number or unique identification for each data row.
Provinsi: Name of the province in Indonesia.
KotaKab: Name of the city or district within the province.
Link: Hyperlink or URL that may lead to a page related to the data.
TipeFaskes: Type of healthcare facility, which could include hospitals, health centers (puskesmas), clinics, etc.
No: Sequential number or unique identification for each healthcare facility.
KodeFaskes: Unique code possibly used to identify the healthcare facility.
NamaFaskes: Full name of the healthcare facility.
LatLongFaskes: Latitude and longitude coordinates of the healthcare facility’s location.
AlamatFaskes: Physical address of the healthcare facility.
TelpFaskes: Contact telephone number for the healthcare facility.

Install Library and Load Dataset

If you haven’t already, you’ll need to install the library.

Install Library

pip install folium

pip install ipypivot

# process
import numpy as np
import pandas as pd

# viz
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns

# text process
import re

# viz maps
import folium
from folium import plugins

#pivottable
from pivottablejs import pivot_ui

df = pd.read_csv("Data Faskes BPJS 2019.csv", header=0)
df.head()

# data information
df.info()

--- output: ---

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 29157 entries, 0 to 29156
Data columns (total 11 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   NoLink         29157 non-null  int64 
 1   Provinsi       29157 non-null  object
 2   KotaKab        29157 non-null  object
 3   Link           29157 non-null  object
 4   TipeFaskes     29157 non-null  object
 5   No             29157 non-null  object
 6   KodeFaskes     29157 non-null  object
 7   NamaFaskes     29157 non-null  object
 8   LatLongFaskes  29157 non-null  object
 9   AlamatFaskes   29157 non-null  object
 10  TelpFaskes     29157 non-null  object
dtypes: int64(1), object(10)
memory usage: 2.4+ MB

This dataset comprises 29,157 entries with one column of integer type and ten columns of object type (text or mixed). The memory usage of this dataset is approximately 2.4 MB. The data provides information regarding healthcare facilities, encompassing details such as province, city/district, facility type, geographic coordinates, address, and contact numbers. All columns appear complete without any missing values, facilitating further analysis.

Preprocessing

Set Kota/Kab

def setspace(space):
    word = " ".join(space.split())
    return word

def setkotakab(kotakab):
    word1 = " ".join(kotakab.split())
    word2 = word1.replace('Kode Faskes dan Alamat Rumah Sakit BPJS di','')
    return word2

df['KotaKab'] = df.KotaKab.apply(setkotakab)
df['NamaFaskes'] = df.NamaFaskes.apply(setspace)
df['TelpFaskes'] = df.TelpFaskes.apply(setspace)
df.drop(['Link', 'NoLink'], axis=1, inplace=True)

df.head(10)

Clear latitude and longitude

def searchLatLong(row):
    str = re.search('(-?([0-9]{1}|[0-9]0|[1-8]{1,2}).[0-9]{1,6},(-?(1[0-8]{1,2}|9[1-9]{1}).[1-9]{1,6}))', row)
    if str:
        return str.group()
    return np.NaN

df['LatLongFaskes'] = df['LatLongFaskes'].apply(searchLatLong)
df.head(10)

The Python function, ‘searchLatLong’, is used to search for latitude and longitude coordinates in a specific format within the ‘LatLongFaskes’ column of a DataFrame. This function is applied to each cell in that column using the `apply` method. The coordinates found are stored in the same column, while if none are found, NaN values are applied. The results are displayed by showing the first 10 rows of the DataFrame after this transformation. Thus, the purpose of this code is to update the values in the ‘LatLongFaskes’ column based on the corresponding coordinate patterns from each row.

# Split LatLongFaskes menjadi dua kolom dan ubah ke numerik
df[['Latitude', 'Longitude']] = df['LatLongFaskes'].str.split(',', expand=True).apply(pd.to_numeric, errors='coerce')

# Filter nilai yang valid
df['Latitude'] = np.where((-90.0 <= df['Latitude']) & (df['Latitude'] <= 90.0), df['Latitude'], np.NaN)
df['Longitude'] = np.where((-180.0 <= df['Longitude']) & (df['Longitude'] <= 180.0), df['Longitude'], np.NaN)

# Hapus kolom LatLongFaskes
df.drop(['LatLongFaskes'], axis=1, inplace=True)

df.head(10)

Analize

Provinces that have Health Facilities with BPJS

province = df.Provinsi.unique().tolist()
print(province)
print('\nTotal province: ', len(province))

--- output: ---

['Nanggroe Aceh Darussalam', 'Sumatera Utara', 'Sumatera Barat',
'Riau', 'Jambi', 'Sumatera Selatan', 'Bengkulu', 'Lampung',
'Kep. Bangka Belitung', 'Dki Jakarta', 'Jawa Barat', 'Jawa Tengah',
'D I Yogyakarta', 'Jawa Timur', 'Banten', 'Bali', 'Nusa Tenggara Barat',
'Nusa Tenggara Timur', 'Kalimantan Barat', 'Kalimantan Tengah',
'Kalimantan Selatan', 'Kalimantan Timur', 'Sulawesi Utara', 'Sulawesi Tengah', 'Sulawesi Selatan', 'Sulawesi Tenggara', 'Gorontalo', 'Maluku', 'Maluku Utara', 'Papua', 'Papua Barat', 'Sulawesi Barat', 'Kepulauan Riau', 'Kalimantan Utara']

Total province:  34

These are the provinces in Indonesia with BPJS (Badan Penyelenggara Jaminan Sosial or Social Security Organizing Agency) health facilities in the year 2019.

city_regency = df['KotaKab'].unique().tolist()
print(city_regency)
print('\nTotal: ', len(city_regency))

--- output: ---

[' Kota Langsa', ' Kab. Gayo Lues', ' Kab. Aceh Selatan', ' Kab. Aceh Tenggara', ' Kab. Aceh Timur', ' Kab. Aceh Tengah', ' Kab.Aceh Barat', ' Kab. Aceh Besar', ' Kab. Pidie', 
' Kab. Aceh Tamiang', ' Kab. Aceh Utara', ' Kab. Bireun', ' Kota Lhokseumawe', ' Kab. Aceh Singkil', ' Kab. Simeulue', ' Kota Banda Aceh', ' Kota Sabang', ' Kab. Aceh Jaya',
' Kab. Nagan Raya', ' Kab. Aceh Barat Daya', ' Kab. Bener', ' Kab. Pidie Jaya', ' Kab. Subulussalam', ' Kab. Nias', ' Kab. Mandailing Natal', ' Kab. Tapanuli Selatan', 
' Kab. Tapanuli Tengah', ' Kab. Tapanuli Utara', ' Kab. Toba Samosir', ' Kab. Labuhanbatu', ' Kab. Asahan', ' Kab. Simalungun', ' Kab. Dairi', ' Kab. Karo', ' Kab. Deli Serdang', 
' Kab. Langkat', ' Kota Sibolga', ' Kota Tanjung Balai', ' Kota Pematang Siantar', ' Kota Tebing Tinggi', ' Kota Medan', ' Kota Binjai', ' Kab. Serdang Bedagai', ' Kab. Samosir',
' Kab. Pakpak Bharat', ' Kab. Nias Selatan', ' Kab. Humbang Hasundutan', ' Kota Padang Sidempuan', ' Kab. Batu Bara', ' Kab. Padang Lawas Utara', ' Kab. Padang Lawas', 
' Kota Gunungsitoli', ' Kab. Nias Utara', ' Kab. Nias Barat', ' Kab. Labuhanbatu Utara', ' Kab. Labuhanbatu Selatan', ' Kab. Kep Mentawai', ' Kab. Pesisir Selatan', ' Kab Solok', 
' Kab. Tanah Datar', ' Kab. Padang Pariaman', ' Kab. Agam', ' Kab. Lima Puluh Koto', ' Kab. Pasaman', ' Kota Padang', ' Kota Solok', ' Kota Sawahlunto', ' Kota Padang Panjang', 
' Kota Bukittinggi', ' Kota Payakumbuh', ' Kota Pariaman', ' Kab. Pasaman Barat', ' Kab. Solok Sel', ' Kab. Dharmasraya', ' Kab. Kuantan Singingi', ' Kab. Indragiri Hulu', 
' Kab. Indragiri Hilir', ' Kab. Pelalawan', ' Kab. Siak Sri Indrapura', ' Kab. Kampar', ' Kab. Rokan Hulu', ' Kab. Bengkalis', ' Kab. Rokan Hilir', ' Kota Pekan Baru', ' Kota Dumai', 
' Kab. Meranti', ' Kab. Kerinci', ' Kab. Merangin', ' Kab. Sarolangun', ' Kab. Batang Hari', ' Kab. Muara Jambi', ' Kab. Tanjab Tim', ' Kab. Tanjab Bar', ' Kab. Tebo', ' Kab. Bungo', 
' Kota Jambi', ' Kota Sungai Penuh', ' Kab. Ogan Komering Ulu', ' Kab. Ogan Komering Hilir', ' Kab. Muara Enim', ' Kab. Lahat', ' Kab. Musi Rawas', ' Kab. Musi Banyuasin', 
' Kota Lubuk Linggau', ' Kota Palembang', ' Kota Prabumulih', ' Kota Pagar Alam', ' Kab. Banyuasin', ' Kab. Ogan Ilir', ' Kab. O.K.U Timur', ' Kab. O.K.U Selatan', 
' Kab. Empat Lawang', ' Kab. Penungkalabab L Ilir', ' Kab. Musi Rawas Utara', ' Kota Bengkulu Selatan', ' Kab. Rejang Lebong', ' Kab. Bengkulu Utara', ' Kota Bengkulu', 
' Kab. Muko-Muko', ' Kab. Seluma', ' Kab. Kaur', ' Kab. Lebong', ' Kab. Kepahyang', ' Kab. Bengkulu Tengah', ' Kab. Lampung Barat', ' Kab. Tanggamus', ' Kab. Lampung Selatan', 
' Kab. Lampung Timur', ' Kab. Lampung Tengah', ' Kab. Lampung Utara', ' Kab. Way Kanan', ' Kab. Tulang Bawang', ' Kota Bandar Lampung', ' Kota Metro', ' Kab. Pesawaran', 
' Kab. Tulang Bawang Barat', ' Kab. Mesuji', ' Kab. Pringsewu', ' Kab. Pesisir Barat', ' Kab. Bangka', ' Kab. Belitung', ' Kota Pangkal Pinang', ' Kab. Bangka Selatan', 
' Kab. Bangka Barat', ' Kab. Bangka Tengah', ' Kab. Belitung Timur', ' Kota Jakarta Selatan', ' Kota Jakarta Timur', ' Kota Jakarta Pusat', ' Kota Jakarta Barat', ' Kota Jakarta Utara', 
' Kab. Kep. Seribu', ' Kota Cimahi', ' Kota Banjar', ' Kab. Bogor', ' Kab. Sukabumi', ' Kab. Cianjur', ' Kab. Bandung', ' Kab. Garut', ' Kab. Tasikmalaya', ' Kab. Ciamis', ' Kab. Kuningan', 
' Kab. Cirebon', ' Kab. Majalengka', ' Kab. Sumedang', ' Kab. Indramayu', ' Kab. Subang', ' Kab. Purwakarta', ' Kab. Karawang', ' Kab. Bekasi', ' Kota Bogor', ' Kota Sukabumi', 
' Kota Bandung', ' Kota Cirebon', ' Kota Bekasi', ' Kota Depok', ' Kota Tasikmalaya', ' Kab. Bandung Barat', ' Kab. Pangandaran', ' Kab. Cilacap', ' Kab. Banyumas', 
' Kab. Purbalingga', ' Kab. Banjarnegara', ' Kab. Kebumen', ' Kab. Purworejo', ' Kab. Wonosobo', ' Kab. Magelang', ' Kab. Boyolali', ' Kab. Klaten', ' Kab. Sukoharjo', ' Kab. Wonogiri',
' Kab. Karanganyar', ' Kab. Sragen', ' Kab. Grobogan', ' Kab. Blora', ' Kab. Rembang', ' Kab. Pati', ' Kab. Kudus', ' Kab. Jepara', ' Kab. Demak', ' Kab. Semarang', ' Kab. Temanggung', 
' Kab. Kendal', ' Kab. Batang', ' Kab. Pekalongan', ' Kab. Pemalang', ' Kab. Tegal', ' Kab. Brebes', ' Kota Magelang', ' Kota Surakarta', ' Kab. Salatiga', ' Kota Semarang', 
' Kota Pekalongan', ' Kota Tegal', ' Kab. Kulon Progo', ' Kab. Bantul', ' Kab. Gunung Kidul', ' Kab. Sleman', ' Kota Yogyakarta', ' Kab. Pacitan', ' Kab. Ponorogo', ' Kab. Trenggalek', 
' Kab. Tulungagung', ' Kab. Blitar', ' Kab. Kediri', ' Kab. Malang', ' Kab. Lumajang', ' Kab. Jember', ' Kab. Banyuwangi', ' Kab. Bondowoso', ' Kab. Situbondo', ' Kab. Probolinggo', 
' Kab. Pasuruan', ' Kab. Sidoarjo', ' Kab. Mojokerto', ' Kab. Jombang', ' Kab. Nganjuk', ' Kab. Madiun', ' Kab. Magetan', ' Kab. Ngawi', ' Kab. Bojonegoro', ' Kab. Tuban', 
' Kab. Lamongan', ' Kab. Gresik', ' Kab. Bangkalan', ' Kab. Sampang', ' Kab. Pamekasan', ' Kab. Sumenep', ' Kota Kediri', ' Kota Blitar', ' Kota Malang', ' Kota Probolinggo', 
' Kota Pasuruan', ' Kota Mojokerto', ' Kota Madiun', ' Kota Surabaya', ' Kota Batu', ' Kab. Pandeglang', ' Kab. Lebak', ' Kab. Tangerang', ' Kab. Serang', ' Kota Tangerang', 
' Kota Cilegon', ' Kota Serang', ' Kota Tangerang Selatan', ' Kab. Jembrana', ' Kab. Tabanan', ' Kab. Badung', ' Kab. Gianyar', ' Kab. Klungkung', ' Kab. Bangli', ' Kab. Karang Asem', 
' Kab. Buleleng', ' Kota Denpasar', ' Kab. Lombok Barat', ' Kab. Lombok Tengah', ' Kab. Lombok Timur', ' Kab. Dompu', ' Kab. Bima', ' Kota Mataram', ' Kab. Sumbawa Besar', 
' Kota Bima', ' Kab. Sumbawa Barat', ' Kab. Lombok Utara', ' Kab. Sumba Barat', ' Kab. Sumba Timur', ' Kab. Kupang', ' Kab. T.T.S Soe', ' Kab. T.T.U Kefamenanu', ' Kab. Belu', 
' Kab. Alor', ' Kab. Lembata', ' Kab. Flores Timur', ' Kab. Sikka', ' Kab. Ende', ' Kab. Ngada', ' Kab. Manggarai', ' Kota Kupang', ' Kab. Rote-Ndao', ' Kab. Manggarai Barat', 
' Kab. Nagekeo', ' Kab. Sumba Tengah', ' Kab. Sumba Barat Daya', ' Kab. Manggarai Timur', ' Kab. Sabu Raijua', ' Kab. Malaka', ' Kota Singkawang', ' Kab. Sambas', 
' Kab. Bengkayang', ' Kab. Landak', ' Kab. Mempawah', ' Kab. Sanggau', ' Kab. Ketapang', ' Kab. Sintang', ' Kab. Kapuas Hulu', ' Kota Pontianak', ' Kab. Melawi', ' Kab. Sekadau', 
' Kab. Kayong Utara', ' Kab. Kubu Raya', ' Kab. Pulang Pisau', ' Kab. Seruyan', ' Kab. Kotawaringin Barat', ' Kab. Kotawaringin Timur', ' Kab. Kapuas', ' Kab. Barito Selatan', 
' Kab. Barito Utara', ' Kab. Sukamara', ' Kab. Lamandau', ' Kab. Barito Timur', ' Kab. Katingan', ' Kab. Gunung Mas', ' Kab. Murung Raya', ' Kota Palangka Raya', ' Kab. Tanah Laut', 
' Kab. Kotabaru', ' Kab. Banjar', ' Kab. Barito Kuala', ' Kab. Tapin', ' Kab. Hulu Sungai Selatan', ' Kab. Hulu Sungai Tengah', ' Kab. Hulu Sungai Utara', ' Kab. Tabalong', 
' Kota Banjarmasin', ' Kota Banjar Baru', ' Kab. Tanah Bumbu', ' Kab. Balangan', ' Kab. Pasir', ' Kab. Pasir Utara', ' Kab. Kutai Barat', ' Kab. Kutai Kertanegara', ' Kab. Kutai Timur', 
' Kab. Berau', ' Kota Balikpapan', ' Kota Samarinda', ' Kota Bontang', ' Kab. Mahakam Ulu', ' Kab. Sangirtalaut', ' Kab. Minahasa', ' Kab. Sangihe', ' Kab. Boloangmongondow', 
' Kota Manado', ' Kota Bitung', ' Kab. Minahasa Selatan', ' Kota Tomohon', ' Minahasa Utara', ' Kota Kotamobagu', ' Kab. Minahasa Tenggara', ' Kab.Bolaang Mongondow Utr', 
' Kab.Kp Siao Tagulandang B', ' Kab.Bolaang Mongondow Slt', ' Kab.Bolaang Mongondow Tmr', ' Kab. Parigi Moutong', ' Kab. Banggai', ' Kab. Banggai Kepulauan', 
' Kab. Morowali', ' Kab. Poso', ' Kab. Donggala', ' Kab. Toli Toli', ' Kab. Buol', ' Kota Palu', ' Kab. Tojo Una Una', ' Kab. Sigi', ' Kab. Morowali Utara', ' Kab. Banggai Laut', 
' Kota Palopo', ' Kab. Selayar', ' Kab. Bulukumba', ' Kab. Bantaeng', ' Kab. Jeneponto', ' Kab. Takalar', ' Kab. Gowa', ' Kab. Sinjai', ' Kab. Maros', ' Kab. Pangkep', ' Kab. Barru', 
' Kab. Bone', ' Kab. Soppeng', ' Kab. Wajo', ' Kab. Sidenreng Rappang', ' Kab. Pinrang', ' Kab. Enrekang', ' Kab. Luwu', ' Kab. Tana Toraja', ' Kab. Luwu Utara', ' Kota Makassar', 
' Kab. Luwu Timur', ' Kab. Toraja Utara', ' Kota Bau-Bau', ' Kab. Buton', ' Kab. Muna', ' Kab. Konawe', ' Kab. Kolaka', ' Kota Kendari', ' Kab. Konawe Selatan', ' Kab. Kolaka Utara', 
' Kab. Wakatobi', ' Kab. Bombana', ' Kab. Konawe Utara', ' Kab. Buton Utara', ' Kab. Konawe Kepulauan', ' Kab. Kolaka Timur', ' Kab.Muna Barat', ' Kab. Buton Tengah', 
' Kab. Buton Selatan', ' Kab. Boalemo', ' Kab. Gorontalo', ' Kota Gorontalo', ' Kab. Bone Bolango', ' Kab. Pohuwato', ' Kab. Gorontalo Utara', ' Kab.Maluku Tenggara Barat',
' Kab. Maluku Tenggara', ' Kab. Maluku Tengah', ' Kab. Buru', ' Kota Ambon', ' Kab. Seram Bagian Timur', ' Kab. Seram Bagian Barat', ' Kab. Kepulauan Aru', ' Kota Tual', 
' Kab. Buru Selatan', ' Kab. Maluku Barat Daya', ' Kab. Halmahera Tengah', ' Kota Ternate', ' Kab. Halmahera Barat', ' Kab. Halmahera Utara', ' Kab. Halmahera Selatan', 
' Kab. Kepulauan Sula', ' Halmahera Timur', ' Kota Tidore Kepulauan', ' Kab. Pulau Morotai', ' Kab. Pulau Taliabu', ' Kab. Merauke', ' Kab. Jayawijaya', ' Kab. Jayapura', 
' Kab. Yapen', ' Kab. Biak Numfor', ' Kab. Paniai', ' Kab. Mimika', ' Kota Jayapura', ' Kab. Kerom', ' Kab. Sarmi', ' Kab. Puncak Jaya', ' Kab. Pegunungan Bintang', ' Kab. Yahukimo', 
' Kab. Mapi', ' Kab. Asmat', ' Kab. Bouven Digul', ' Kab. Waropen', ' Kab. Nabire', ' Kab. Tolikara', ' Kab. Supiori', ' Kab. Membramo Raya', ' Kab. Mamberamo Tengah', 
' Kab. Yalimo', ' Kab. Lanny Jaya', ' Kab. Nduga', ' Kab. Puncak', ' Kab. Dogiyai', ' Kab. Intan Jaya', ' Kab. Deiyai', ' Kab. Fak Fak', ' Kab. Sorong', ' Kab. Manokwari', ' Kota Sorong', 
' Kab. Sor Sel', ' Kab. Raja Ampat', ' Kab. Kaimana', ' Kab. Teluk Bintuni', ' Kab. Teluk Wondama', ' Kab. Tambrauw', ' Kab. Maybrat', ' Kab. Pegunungan Arfak', 
' Kab. Manokwari Selatan', ' Kab. Polewali Mandar', ' Kab. Majene', ' Kab. Mamuju', ' Kab. Mamasa', ' Kab. Mamuju Utara', ' Kab. Mamuju Tengah', ' Kota Tanjung Pinang', 
' Kab. Bintan (Kepri)', ' Kab. Tj Balai Karimun', ' Kab. Natuna', ' Kota Batam', ' Kab. Lingga', ' Kab. Kep. Anambas', ' Kab. Malinau', ' Kab. Bulungan', ' Kab. Nunukan', 
' Kota Tarakan', ' Kab. Tana Tidung']



Total:  512

These are the cities or districts in Indonesia with BPJS (Badan Penyelenggara Jaminan Sosial or Social Security Organizing Agency) health facilities in the year 2019.

Data to be displayed in the visualization map

total_data_count = df.shape[0]
displayed_data = df.dropna().shape[0]
data_not_displayed = df[df['Latitude'].isnull() | df['Longitude'].isnull()].shape[0]

print('\nTotal facility data: ', total_data_count)
print('\nTotal facilities that can be displayed on the map:', displayed_data)
print('\nTotal facilities that cannot be displayed on the map:', data_not_displayed)

--- output: ---

Total facility data:  29157

Total facilities that can be displayed on the map: 16499

Total facilities that cannot be displayed on the map: 12658

Visualization

Geographical visualization of 16499 BPJS health facilities

Visualization of geographic data with folium FastMarkCluster!

data_map = df.dropna().info()
data_map

--- output: ---

<class 'pandas.core.frame.DataFrame'>
Index: 16499 entries, 0 to 29149
Data columns (total 10 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Provinsi      16499 non-null  object 
 1   KotaKab       16499 non-null  object 
 2   TipeFaskes    16499 non-null  object 
 3   No            16499 non-null  object 
 4   KodeFaskes    16499 non-null  object 
 5   NamaFaskes    16499 non-null  object 
 6   AlamatFaskes  16499 non-null  object 
 7   TelpFaskes    16499 non-null  object 
 8   Latitude      16499 non-null  float64
 9   Longitude     16499 non-null  float64
dtypes: float64(2), object(8)
memory usage: 1.4+ MB

maps_dataset = df.dropna()
rome_lat, rome_lng = -6.200000, 106.816666       # jakarta

# init the folium map object
maps = folium.Map(location=[rome_lat, rome_lng], zoom_start=5)

# input all points from the dataset file into a map object using FastMarkerCluster
maps.add_child(plugins.FastMarkerCluster(maps_dataset[['Latitude', 'Longitude']].values.tolist()))

maps

Visualization of numbers per city/district with available latitude and longitude!

# 5 cities or districts with the largest number of BPJS facilities
city_regency = df['KotaKab'].value_counts().rename_axis('city_regrency').reset_index(name='Total')
city_regency.head()

--- output: ---

  city_regrency   Total
0 Kota Surabaya   364
1 Kota Semarang   305
2 Kab. Bogor      298
3 Kota Makassar   292
4 Kota Palembang  288

The top five cities or regency with the highest number of BPJS facilities in Indonesia are Surabaya City, Semarang City, Bogor Regency, Makassar City and Palembang City. The city of Surabaya occupies the top ranking with a total of 364 BPJS facilities, followed by the city of Semarang with a total of 305 BPJS facilities. Bogor Regency, Makassar City and Palembang City also have quite a large number of BPJS facilities, with 298, 292 and 288 BPJS facilities respectively. These areas show a significant presence of BPJS facilities, reflecting the accessibility and availability of health services in these areas.

# Retrieve latitude and longitude data per city/district
kabkota = []
prov = []
lati = []
longi = []

df1 = df.dropna()

for prv, kk, ltk, lnk in zip(df1['Provinsi'], 
                             df1['KotaKab'], 
                             df1['Latitude'], 
                             df1['Longitude']):
    if (not(kk in kabkota) and not(ltk == np.NaN) and not(lnk == np.NaN)):
        prov.append(prv)
        kabkota.append(kk)
        lati.append(ltk)
        longi.append(lnk)

print("Total: ", len(kabkota))

data_fas = pd.DataFrame(list(zip(prov, kabkota, lati, longi)),
                        columns=['Provinsi', 'city_regrency', 'Latitude', 'Langitude'])
data_fas.head()

--- output: ---

Total: 460

  Provinsi                   city_regrency       Latitude   Langitude
0 Nanggroe Aceh Darussalam   Kota Langsa         4.488058   97.947963
1 Nanggroe Aceh Darussalam   Kab. Gayo Lues      4.059445   97.463333
2 Nanggroe Aceh Darussalam   Kab. Aceh Selatan   3.111916   97.368642
3 Nanggroe Aceh Darussalam   Kab. Aceh Tenggara  3.496266   97.800000
4 Nanggroe Aceh Darussalam   Kab. Aceh Timur     4.961196   97.762670

Latitude and longitude data have been collected for 460 cities and districts in various provinces in Indonesia. This information is important for geographic mapping and location-based analysis. For example, in Nanggroe Aceh Darussalam, Langsa City is located at approximately 4.49° South Latitude and 97.95° East Latitude, Gayo Lues Regency at 4.06° South Latitude and 97.46° East Latitude, South Aceh Regency at 3 .11° South Latitude and 97.37° E. Southeast Aceh Regency’s longitude is 3.11° South Latitude and 97.37° East Longitude. 3.50° N and 97.80° E, and East Aceh Regency at 4.96° N and 97.76° E. This data allows for a more comprehensive analysis of the distribution of health facilities and can be used for various geographic insights.

Visualization of all data

City/Regency with the highest number of health facilities with BPJS

df.Provinsi.value_counts()

--- output: ---

Provinsi
Jawa Tengah                 3688
Jawa Barat                  3683
Jawa Timur                  3548
Sumatera Utara              1628
Sulawesi Selatan            1155
Dki Jakarta                  995
Sumatera Selatan             993
Banten                       913
Lampung                      784
Nanggroe Aceh Darussalam     776
Bali                         765
Riau                         729
Nusa Tenggara Timur          722
Sumatera Barat               721
Papua                        712
Kalimantan Selatan           671
Kalimantan Timur             634
Sulawesi Utara               543
D I Yogyakarta               534
Kalimantan Barat             531
Sulawesi Tenggara            500
Jambi                        446
Kalimantan Tengah            418
Nusa Tenggara Barat          393
Bengkulu                     390
Sulawesi Tengah              386
Maluku                       334
Kepulauan Riau               319
Papua Barat                  278
Maluku Utara                 253
Gorontalo                    206
Sulawesi Barat               187
Kep. Bangka Belitung         182
Kalimantan Utara             140
Name: count, dtype: int64

This dataset displays the distribution of health facilities in various provinces in Indonesia. Central Java, West Java and East Java are the provinces with the largest number of health facilities, with 3,688, 3,683 and 3,548 facilities respectively. These provinces, located on the island of Java, show a concentration of health care infrastructure. Other provinces that have adequate health facilities are North Sumatra (1,628), South Sulawesi (1,155), and DKI Jakarta (995). This distribution highlights the diverse healthcare landscape across the archipelago, with Java leading the way in terms of the number of health facilities, followed by other large islands such as Sumatra and Sulawesi. This data provides valuable insight into the regional distribution of health resources in Indonesia.

df.KotaKab.value_counts()

--- output: ---

KotaKab
 Kota Surabaya                364
 Kota Semarang                305
 Kab. Bogor                   298
 Kota Makassar                292
 Kota Palembang               288
                             ... 
 Kab.Bolaang Mongondow Tmr     12
 Kab. Banggai Laut             12
 Kab. Mamberamo Tengah         11
 Kab. Tana Tidung              11
 Kab. Manokwari Selatan        10
Name: count, Length: 512, dtype: int64

df.TipeFaskes.value_counts()

--- output: ---

TipeFaskes
Puskesmas                    9913
Klinik Pratama               6597
Klinik Utama                 5375
Apotek                       2982
Rumah Sakit                  2249
Dokter Gigi                  1410
Dokter Praktik Perorangan     631
Name: count, dtype: int64

Pivotable in Python

from pivottablejs import pivot_ui
pivot_ui(df)

Dashboard

Conclusion

In summary, the analysis of the BPJS Kesehatan healthcare facilities dataset in Indonesia reveals that major cities like Surabaya, Semarang, Bogor, Makassar, and Palembang have a substantial number of BPJS facilities, indicating widespread access to healthcare services. The inclusion of geographic coordinates for 460 cities and districts enhances the dataset’s utility for mapping and location-based analysis. The distribution of health facilities across provinces shows that Java, particularly Central, West, and East Java, leads in total numbers, followed by other key provinces. This dataset provides valuable insights for optimizing healthcare resource allocation and accessibility in Indonesia.