This statistical data simulation analysis or demo data on population and demographics in Indonesia aims to study the quantity and distribution of society. Data collection from Worldometer with direct data, but my project only makes visual innovation of data.
import pandas as pd
import csv
# import data from csv
datapopulation = pd.read_csv("/Users/mymac/Desktop/Data Github/datapopulation.csv", delimiter=';', header = None) # adjust columns and rows
datapopulation.columns = ['Cities', 'Population'] create columns
datapopulation = datapopulation.iloc[1:]print(datapopulation)
Cities Population
1 Jakarta 8,540,121
2 Surabaya 2,874,314
3 Bekasi 2,564,940
4 Bandung 2,444,160
5 Medan 2,435,252
6 Depok 2,056,400
7 Tangerang 1,911,914
8 Palembang 1,668,848
9 Semarang 1,653,524
10 Makassar 1,423,877
11 South Tangerang 1,365,688
12 Jepara 1,257,912
13 Bandar Lampung 1,166,066
14 Batam 1,164,352
15 Bogor 1,099,422
16 Pekanbaru 1,085,000
17 Padang 90,904
18 Malang 84,381
19 Samarinda 83,146
20 Denpasar 725,314
21 Balikpapan 695,287
22 Serang 692,101
23 Situbondo 685,967
24 Pontianak 658,685
25 Banjarmasin 657,663
26 Jambi City 6,062
27 Cimahi 5,684
28 Surakarta 522,364
29 Manado 451,916
30 Kupang 442,758import geopandas as gpd
# download data from natural data earth site
world = gpd.read_file("/Users/mymac/Desktop/Data Github/ne_110m_admin_0_countries.shp")
world = gpd.read_file("/Users/mymac/Desktop/Data Github/ne_110m_admin_0_countries.dbf")
world = gpd.read_file("/Users/mymac/Desktop/Data Github/ne_110m_admin_0_countries.shx")
# filter data to Indonesia geo
Indonesia = world[world['SOVEREIGNT'] == 'Indonesia']
#indonesia geometry centroid data
Indonesia_centroid = indonesia.geometry.centroid
latitude = Indonesia_centroid.y.values[0]
longitude = Indonesia_centroid.x.values[0]
# coordinate indonesia
print("Latitude:", latitude)
Latitude: -2.221737936520542
print("Longitude:", longitude)
Longitude: 117.42340756227364import geopandas as gpd
citiesidn = gpd.read_file("/Users/mymac/Desktop/Data Github/ne_10m_populated_places_simple.shp")
citiesidn['Latitude'] = cities.geometry.y
citiesidn['Longitude'] = cities.geometry.x
cities_df = cities[['NAME(cities)', 'Latitude', 'Longitude']]
print(cities_df)
No Cities Latitude Longitude
<dbl> <chr> <chr> <chr>
1 Jakarta -6.175247 106.827049
2 Surabaya -7.245972 112.737827
3 Bekasi -6.234986 106.994544
4 Bandung -6.921553 107.611021
5 Medan 3.589665 98.673826
6 Depok -6.407190 106.815837
7 Tangerang -6.176192 106.638216
8 Palembang -2.988824 104.756851
9 Semarang -6.990399 110.422910
10 Makassar -5.134296 119.412428
# ℹ 20 more rows
# Combine data population with data longitude and latitude
# Data cities population and Latitude,Longitude
datapopulation = {
'Cities': ['Jakarta', 'Surabaya', 'Bekasi', 'Bandung', 'Medan', 'Depok', 'Tangerang', 'Palembang', 'Semarang',
'Makassar', 'South Tangerang', 'Jepara', 'Bandar Lampung', 'Batam', 'Bogor', 'Pekanbaru', 'Padang',
'Malang', 'Samarinda', 'Denpasar', 'Balikpapan', 'Serang', 'Situbondo', 'Pontianak', 'Banjarmasin',
'Jambi City', 'Cimahi', 'Surakarta', 'Manado', 'Kupang'],
'Latitude': [-6.175247, -7.245972, -6.234986, -6.921553, 3.589665, -6.407190, -6.176192, -2.988824, -6.990399,
-5.134296, -6.322702, -6.589915, -5.446071, 1.103082, -6.596299, 0.526246, -0.924759, -7.977131,
-0.501780, -8.652497, -1.239871, -6.105497, -7.706784, -0.022690, -3.324740, -0.037687, -6.873153,
-7.569249, 1.490058, -10.163221],
'Longitude': [106.827049, 112.737827, 106.994544, 107.611021, 98.673826, 106.815837, 106.638216, 104.756851,
110.422910, 119.412428, 106.708574, 110.667299, 105.264374, 104.038370, 106.797242, 101.451573,
100.363256, 112.634027, 117.139309, 115.216667, 116.859338, 105.988471, 114.005414, 109.344749,
114.597505, 109.314475, 107.542310, 110.828448, 124.840871, 123.601776],
'Population': [8540121, 2874314, 2564940, 2444160, 2435252, 2056400, 1911914, 1668848, 1653524, 1423877,
1365688, 1257912, 1166066, 1164352, 1099422, 1085000, 909040, 843810, 831460, 725314, 695287,
692101, 685967, 658685, 657663, 606200, 568400, 522364, 451916, 442758]
}
cities_df = pd.DataFrame(datapopulation)import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(50, 30)) # size
# Plot Indonesia
Indonesia.plot(ax=ax, color='#a6cee3', edgecolor='black')
# colormap
cmap = plt.colormaps['plasma']
# normalization data population
norm = plt.Normalize(vmin=gdf_cities['Population'].min(), vmax=gdf_cities['Population'].max())
# plot for city with deffent such as population
gdf_cities.plot(ax=ax, color=cmap(norm(gdf_cities['Population'])),
marker='o', markersize=gdf_cities['Population'] / 100000,
label='Population', alpha=0.7)
# add colorbar for showing skala population
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
cbar = fig.colorbar(sm, ax=ax, orientation='horizontal', fraction=0.03, pad=0.04)
cbar.set_label('Population')
# add title
plt.title("Population of Major Cities in Indonesia", fontsize=20)
#legenda delete text label city and just show legend
plt.legend(['City Population'], loc='lower left')
# show map
plt.show()
Distribution of Cities by Population: Notice how cities with higher populations (indicated by larger dots and lighter colors) are spread across Indonesia. Based on the map, large cities with high populations are generally located in Java, Sumatra, and Sulawesi. Concentration in Java: It appears that many cities with large populations are located in Java (such as Jakarta, Surabaya, and Bandung). This indicates a concentration of population on this island.
This section makes a basic analysis to display the population census in Indonesia from 2016 to 2024, data collection from the Central Statistics Agency of Indonesia.
import pandas as pd
import csv
# import dataset
datapopulationcensus = pd.read_csv("/Users/mymac/Desktop/Data Github/datapopulationcensus.csv", delimiter=';', header = None)
datapopulationcensus = datapopulationcensus.iloc[1:]
datapopulationcensus.columns = ['Years', 'Census']
print(datapopulationcensus)
Years Census
1 2015 255.587
2 2016 258.496
3 2017 261.355
4 2018 264.161
5 2019 266.911
6 2020 270.209
7 2021 272.682
8 2022 275.773
9 2023 278.696
10 2024 281.603
# if create data frame
datapopulationcensus = pd.Data.Frame({
'Year': [2015,2016,2017,2018,2019,2020,2021,2022,2023,2024],
'Census':[255.587,258.496,261.355,264.161,266.911,270.209,272.682,275.773,278.696,281.603]
})## if want create line visual basic graph
import pandas as pd
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 6))
plt.plot(datapopulationcensus['Years'], datapopulationcensus['Census'],
color ='b', linewidth=2)
plt.fill_between(datapopulationcensus['Years'], datapopulationcensus['Census'],
color ='blue', alpha = 0.1)
plt.title('Population Growth 2015-2024')
plt.xlabel('Year')
plt.ylabel('Population')
plt.show()
# visual from plotly.graph
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(
x=datapopulationcensus['Years'],
y=datapopulationcensus['Census'],
mode='lines',
line=dict(color='red'),
fill = 'tozeroy',
name ='Population Growth'))
fig.update_layout(title="Indonesia Population Growth 2015-2024",
xaxis_title="Years",
yaxis_title="Population",
template="simple_white")
fig.show()
The population of Indonesia from 2015 to 2024 continues to increase, with a stable growth trend. The government needs to prepare policies to accommodate growth, especially those that impact people's welfare.
This section also makes an analysis of the population projection of Indonesia from 2020 to 2100, data collection form Country Meters Purpose of Population Projections Population projections in the field of demography are estimates of the population in the future.
import pandas as pd
import csv
# import data
populationprojection = pd.read_csv("/Users/mymac/Desktop/Data Github/populationprojection.csv", delimiter=';', header = None)
populationprojection = populationprojection.iloc[1:]
populationprojection.columns = ["Years", "Population"]
populationprojection = pd.DataFrame({
'Years': [2020, 2025, 2030, 2035, 2040, 2045, 2050, 2055, 2060, 2065,
2070, 2075, 2080, 2085, 2090, 2095, 2100],
'Population': [272222982, 284751033, 295595241, 304758617, 312134190,
317731990, 321550683, 323790913, 324704160, 324574724,
323652781, 322033596, 319781249, 316961616, 313660545,
309025670, 306025531]
})
print(populationprojection)
Years Population
0 2020 272222982
1 2025 284751033
2 2030 295595241
3 2035 304758617
4 2040 312134190
5 2045 317731990
6 2050 321550683
7 2055 323790913
8 2060 324704160
9 2065 324574724
10 2070 323652781
11 2075 322033596
12 2080 319781249
13 2085 316961616
14 2090 313660545
15 2095 309025670
16 2100 306025531# Creaate visual basic graph
import pandas as pd
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 6))
plt.plot(populationprojection['Years'],populationprojection['Population'],
color= 'b', linewidth=2)
plt.fill_between(populationprojection['Years'],populationprojection['Population'],
color = 'blue', alpha =0.1)
plt.title("Population Projection 2020-2100")
plt.xlabel('Year')
plt.ylabel('Population')
plt.show()
import pandas as pd
import plotly.graph_objects as go
# graph with plotly
fig = go.Figure()
fig.add_trace(go.Scatter(
x=populationprojection['Years'],
y=populationprojection['Population'],
mode='lines', # Garis dan titik data
line=dict(color='red'),
fill='tozeroy', # Mengisi area di bawah garis
name='Population Growth'
))
# setting layout
fig.update_layout(
title="Indonesia Population Projection 2020-2100",
xaxis_title="Years",
yaxis_title="Population",
template="simple_white"
)
fig.update_xaxes(
tickmode='linear',
dtick=5
)
# show graph
fig.show()
This graph shows that Indonesia will experience rapid population growth almost in the middle of the century, but after that the population will decline. For example, from 2020 to 2060 population growth will be very stable, and from 2060 to 2100 the population will decline, which has implications for employment challenges, social needs, and public policy.
This section is for the analysis of the population gender between Men and Women in Indonesia from 2018 to 2023. And I collect data from Central Bureau of Statistics of Indonesia.
import pandas as pd
genderpopulation = {
'Year': [2018, 2019, 2020, 2021, 2022, 2023],
'Man': [133.136, 143.657, 136.611, 137.871, 139.388, 140.786],
'Woman': [131.879, 133.416, 133.545, 134.811, 136.384, 137.909]
}
genderpopulation = pd.DataFrame(genderpopulation)import plotly.graph_objects as go
fig = go.Figure()
# add man population horizontal bar (negative point for section left)
fig.add_trace(go.Bar(
y=genderpopulation['Year'],
x=[-1 * val for val in genderpopulation['Man']], # negative point for bar left
orientation='h',
name='Man',
marker=dict(color='blue'),
text=[f"{val} M" for val in genderpopulation['Man']], #add population label
textposition='inside' # text position in the bar
))
# add woman population for bar horizontal
fig.add_trace(go.Bar(
y=genderpopulation['Year'],
x=genderpopulation['Woman'], # positive point in right
orientation='h',
name='Woman',
marker=dict(color='red'),
text=[f"{val} M" for val in genderpopulation['Woman']], # add label population
textposition='inside' # position text in the bar
))
# setting
fig.update_layout(
title='Gender Populationin Indonesia 2018-2023',
xaxis=dict(
title='Population (in millions)',
tickvals=[-150, -100, -50, 0, 50, 100, 150],
ticktext=['150', '100', '50', '0', '50', '100', '150'],
),
yaxis=dict(
title='Year',
tickmode='array',
tickvals=genderpopulation['Year'],
ticktext=genderpopulation['Year']
),
barmode='relative',
bargap=0.1,
plot_bgcolor='white'
)
# show plot
fig.show()
By looking at this visual, you can calculate the gender or compression between the Male and Female population in each year. If you have data from 2018, the male population was 133.136 million and the female population was 131.879 million. In 2019, the male population increased to 143.657 million and the female population to 133.416 million. In 2020 to 2023 there was a fluctuation, but the trend shows a slight increase in both sexes.
library(readxl)
populationgroupage <- read_excel("~/Desktop/Data Github/populationgroupage.xlsx")
View(populationgroupage)
print(populationgroupage, n=96) # view all rows and columns
| Year|Group Age | Man| Women|
|----:|:---------|------:|-------:|
| 2023|0-4 | 11499 | 11012 |
| 2023|5-9 | 11237 | 10762 |
| 2023|10-14 | 11316 | 10746 |
| 2023|15-19 | 11316 | 10714 |
| 2023|20-24 | 11489 | 10871 |
| 2023|25-29 | 11509 | 10986 |
| 2023|30-34 | 11243 | 10845 |
| 2023|35-39 | 10834 | 10570 |
| 2023|40-44 | 10274 | 10130 |
| 2023|45-49 | 9544 | 9514 |
| 2023|50-54 | 8415 | 8450 |
| 2023|55-59 | 7133 | 7231 |
| 2023|60-64 | 5676 | 5813 |
| 2023|65-69 | 4183 | 4344 |
| 2023|70-74 | 2695 | 2920 |
| 2023|75+ | 2314 | 2993 |
| 2022|0-4 | 11303| 10790 |
| 2022|5-9 | 11242| 10771 |
| 2022|10-14 | 11356| 10732 |
| 2022|15-19 | 11432| 10730 |
| 2022|20-24 | 11553| 10937 |
| 2022|25-29 | 11485| 10977 |
| 2022|30-34 | 11215| 10851 |
| 2022|35-39 | 10743| 10504 |
| 2022|40-44 | 10207| 10088 |
| 2022|45-49 | 9378| 9348 |
| 2022|50-54 | 8240| 8259 |
| 2022|55-59 | 6945| 7016 |
| 2022|60-64 | 5486| 5575 |
| 2022|65-69 | 4035| 4163 |
| 2022|70-74 | 2530| 2739 |
| 2022|75+ | 2232| 2897 |
| 2021|0-4 | 11280| 10765 |
| 2021|5-9 | 11249| 10775 |
| 2021|10-14 | 11392| 10723 |
| 2021|15-19 | 11445| 10755 |
| 2021|20-24 | 11588| 10989 |
| 2021|25-29 | 11434| 10947 |
| 2021|30-34 | 11155| 10818 |
| 2021|35-39 | 10633| 10412 |
| 2021|40-44 | 10109| 10009 |
| 2021|45-49 | 9191| 9163 |
| 2021|50-54 | 8050| 8061 |
| 2021|55-59 | 6740| 6791 |
| 2021|60-64 | 5280| 5337 |
| 2021|65-69 | 3860| 3968 |
| 2021|70-74 | 2345| 2546 |
| 2021|75+ | 2112| 2748 |
| 2020|0-4 | 11293| 10778 |
| 2020|5-9 | 11295| 10799 |
| 2020|10-14 | 11449| 10746 |
| 2020|15-19 | 11495| 10816 |
| 2020|20-24 | 11632| 11050 |
| 2020|25-29 | 11410| 10945 |
| 2020|30-34 | 11109| 10795 |
| 2020|35-39 | 10556| 10354 |
| 2020|40-44 | 10014| 9928 |
| 2020|45-49 | 9025| 8996 |
| 2020|50-54 | 7872| 7874 |
| 2020|55-59 | 6546| 6574 |
| 2020|60-64 | 5091| 5117 |
| 2020|65-69 | 3681| 3772 |
| 2020|70-74 | 2179| 2374 |
| 2020|75+ | 2007| 2617 |
| 2019|0-4 | 12044| 11560 |
| 2019|5-9 | 12234| 11739 |
| 2019|10-14 | 11824| 11232 |
| 2019|15-19 | 11406| 10888 |
| 2019|20-24 | 11167| 10750 |
| 2019|25-29 | 10690| 10537 |
| 2019|30-34 | 10320| 10261 |
| 2019|35-39 | 10058| 10207 |
| 2019|40-44 | 9679| 9687 |
| 2019|45-49 | 8876| 8817 |
| 2019|50-54 | 7699| 7739 |
| 2019|55-59 | 6314| 6435 |
| 2019|60-64 | 4895| 4923 |
| 2019|65-69 | 3337| 3394 |
| 2019|70-74 | 2027| 2357 |
| 2019|75+ | 2080| 2886 |
| 2018|0-4 | 12107| 11622 |
| 2018|5-9 | 12199| 11679 |
| 2018|10-14 | 11732| 11146 |
| 2018|15-19 | 11378| 10864 |
| 2018|20-24 | 11097| 10726 |
| 2018|25-29 | 10630| 10494 |
| 2018|30-34 | 10270| 10258 |
| 2018|35-39 | 10017| 10164 |
| 2018|40-44 | 9594| 9551 |
| 2018|45-49 | 8718| 8657 |
| 2018|50-54 | 7488| 7536 |
| 2018|55-59 | 6120| 6205 |
| 2018|60-64 | 4688| 4663 |
| 2018|65-69 | 3141| 3224 |
| 2018|70-74 | 1938| 2279 |
| 2018|75+ | 2012| 2804 |
populationgroupage <-populationgroupage %>%
mutate(`Group Age` = factor(`Group Age`, levels = c("0-4", "5-9", "10-14", "15-19", "20-24", "25-29", "30-34",
"35-39", "40-44", "45-49", "50-54", "55-59", "60-64",
"65-69", "70-74", "75")))
library(dplyr)
library(ggplot2)
library(patchwork)
# Filter data each year
data_2018 <-populationgroupage %>% filter(Year==2018)
data_2019 <-populationgroupage %>% filter(Year==2019)
data_2020 <-populationgroupage %>% filter(Year==2020)
data_2021 <-populationgroupage %>% filter(Year==2021)
data_2022 <-populationgroupage %>% filter(Year==2022)
data_2023 <-populationgroupage %>% filter(Year==2023)
plot_2018 <- ggplot(data_2018, aes(x = reorder(`Group Age`, -as.numeric(gsub("\\+", "", `Group Age`))))) +
geom_bar(aes(y = Man, fill = "Man"), stat = "identity", position = "identity") +
geom_bar(aes(y = -Women, fill = "Women"), stat = "identity", position = "identity") +
scale_y_continuous(labels = abs) +
labs(title = "2018",
x = "Age Group",
y = "Population",
fill = "Gender") +
theme_minimal() +
coord_flip() +
scale_fill_manual(values = c("Man" = "darkblue", "Women" = "darkred"))
plot_2019 <- ggplot(data_2019, aes(x = reorder(`Group Age`, -as.numeric(gsub("\\+", "", `Group Age`))))) +
geom_bar(aes(y = Man, fill = "Man"), stat = "identity", position = "identity") +
geom_bar(aes(y = -Women, fill = "Women"), stat = "identity", position = "identity") +
scale_y_continuous(labels = abs) +
labs(title = "2019",
x = "Age Group",
y = "Population",
fill = "Gender") +
theme_minimal() +
coord_flip() +
scale_fill_manual(values = c("Man" = "darkblue", "Women" = "darkred"))
plot_2020 <- ggplot(data_2020, aes(x = reorder(`Group Age`, -as.numeric(gsub("\\+", "", `Group Age`))))) +
geom_bar(aes(y = Man, fill = "Man"), stat = "identity", position = "identity") +
geom_bar(aes(y = -Women, fill = "Women"), stat = "identity", position = "identity") +
scale_y_continuous(labels = abs) +
labs(title = "2020",
x = "Age Group",
y = "Population",
fill = "Gender") +
theme_minimal() +
coord_flip() +
scale_fill_manual(values = c("Man" = "darkblue", "Women" = "darkred"))
plot_2021 <- ggplot(data_2021, aes(x = reorder(`Group Age`, -as.numeric(gsub("\\+", "", `Group Age`))))) +
geom_bar(aes(y = Man, fill = "Man"), stat = "identity", position = "identity") +
geom_bar(aes(y = -Women, fill = "Women"), stat = "identity", position = "identity") +
scale_y_continuous(labels = abs) +
labs(title = "2021",
x = "Age Group",
y = "Population",
fill = "Gender") +
theme_minimal() +
coord_flip() +
scale_fill_manual(values = c("Man" = "darkblue", "Women" = "darkred"))
plot_2022 <- ggplot(data_2022, aes(x = reorder(`Group Age`, -as.numeric(gsub("\\+", "", `Group Age`))))) +
geom_bar(aes(y = Man, fill = "Man"), stat = "identity", position = "identity") +
geom_bar(aes(y = -Women, fill = "Women"), stat = "identity", position = "identity") +
scale_y_continuous(labels = abs) +
labs(title = "2022",
x = "Age Group",
y = "Population",
fill = "Gender") +
theme_minimal() +
coord_flip() +
scale_fill_manual(values = c("Man" = "darkblue", "Women" = "darkred"))
plot_2023 <- ggplot(populationgroupage %>% filter(Year == 2023), aes(x = reorder(`Group Age`, -as.numeric(gsub("\\+", "", `Group Age`))))) +
geom_bar(aes(y = Man, fill = "Man"), stat = "identity", position = "identity") +
geom_bar(aes(y = -Women, fill = "Women"), stat = "identity", position = "identity") +
scale_y_continuous(labels = abs) +
labs(title = "2023", x = "Age Group", y = "Population", fill = "Gender") +
theme_minimal() + coord_flip() +
scale_fill_manual(values = c("Man" = "darkblue", "Women" = "darkred"))
# Arrange all plots in a single view with a main title
(plot_2018 | plot_2019) / (plot_2020 | plot_2021) / (plot_2022 | plot_2023) +
plot_annotation(title = "Gender Population Indonesia 2018-2023",
theme = theme(plot.title = element_text(size = 15, face = "bold", hjust = 0.5)))
From the visual pyramid, it shows the population distribution from the youngest age group (0-4) to the oldest (75+). The shape of the pyramid shows the proportion of young age, productive age, and old age in the population. If there are more young people such as 0-14 years old, the population growth is greater and the proportion is greater in the elderly population.
This section explains about the number of rural and urban population in Indonesia from 1955 to 2024, data collection from World Meters. Then make a simple visual analysis.
import pandas as pd
import csv
# import data
ruralurbanidn = pd.read_csv("/Users/mymac/Desktop/Data Github/ruralurbanidn.csv", delimiter=';', header = None)
ruralurbanidn = ruralurbanidn.iloc[1:]
ruralurbanidn.columns = ["Years", "Urban", "Rural"]
print(ruralurbanidn)
# or create data frame manual
data = {
"Years": [1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2024],
"Urban": [12805184, 15838170, 19603959, 25252096, 32601807, 43049412, 55491343, 71053577, 88851487, 104155654, 121052980, 137634761, 154188546, 167176839],
"Rural": [75490886, 85526960, 96053536, 106591752, 116348733, 123726773, 128009755, 128834480, 127226303, 126715996, 125252342, 124164488, 120626320, 116311092]
}
ruralurbanidn = pd.DataFrame(data)
# Setting column
Years = ruralurbanidn["Years"]
Urban_Population = ruralurbanidn["Urban"]
Rural_Population = ruralurbanidn["Rural"]fig = go.Figure()
# Menambahkan data Rural
fig.add_trace(go.Scatter(
x=Years,
y=Rural_Population,
mode='none',
stackgroup='one',
name='Rural',
fillcolor='blue'
))
# Menambahkan data Urban
fig.add_trace(go.Scatter(
x=Years,
y=Urban_Population,
mode='none',
stackgroup='one',
name='Urban',
fillcolor='red'
))
# Menambahkan layout
fig.update_layout(
title='Indonesia: Urban & Rural Population',
xaxis_title='Years',
yaxis_title='Population in Millions',
template='plotly_white',
xaxis=dict(
tickmode='array', # array for show labels
tickvals=Years, # show all years in x
ticktext=[str(year) for year in Years] # show label in text
)
)
# show figure
fig.show()
This graph shows an accurate picture of demographic growth in Indonesia, showing that in recent decades, Indonesia has experienced significant urbanization with a larger urban population and a larger rural population.
This section explains about population data in each region of Jakarta such as Central Jakarta, South Jakarta, North Jakarta, West Jakarta and East Jakarta. Data collection from Central Statistics Agency
library(tidyverse)
library(sf)
library(osmdata) # package for open street map
jakarta_bb <-getbb("Jakarta, Indonesia")
jakarta_osm <- opq(jakarta_bb) %>%
add_osm_feature(key = "admin_level", value = "6") %>%
osmdata_sf()
jakarta_boundaries <- jakarta_osm$osm_multipolygons
library(ggplot)
jakartapopulation <- data.frame(
Region = c("South Jakarta", "East Jakarta", "Central Jakarta", "West Jakarta", "North Jakarta"),
Population = c(2235606, 3079618, 1049314, 2470054, 1808985)
)
jakarta_boundaries$Region <- c("South Jakarta", "East Jakarta", "Central Jakarta", "West Jakarta", "North Jakarta")
jakarta_boundaries <- merge(jakarta_boundaries, jakartapopulation, by = "Region")
ggplot() +
geom_sf(data = jakarta_boundaries, aes(fill = Population), color = "black", size = 2) +
scale_fill_viridis_b(option = "B") +
theme_minimal() +
labs(title = "Jakarta Population in 2023",
fill = "Population")
If South Jakarta, which is colored yellow, has a larger population, conversely Central Jakarta, which is colored dark purple, has a relatively smaller population compared to other areas of Jakarta.