This project tries to analyze refugees and asylum seekers in Indonesia, and then also analyze Indonesians who are refugees and asylum seekers to find other countries to live in. This analysis aims to make new innovation graphs into visuals for open source using python. Temporary Data source collection from UNHCR in Data World in Noah Ripper's account on Refugee Host Countries. Hopefully this project will be a guide for open source and transpiration.
import csv
import pandas as pd
datarefugeesidn1115 = pd.read_csv("/Users/mymac/Desktop/Data Github/datarefugeesidn.csv", delimiter=';', header = None) # adjust columns and rows
datarefugeesidn1115.columns = ['Year', 'Country', 'Origin', 'Refugees', 'AsylumSeekers', 'Total'] # create columns
datarefugeesidn1115 = datarefugeesidn1115.iloc[1:] # delete more columns
print(datarefugeesidn1115) #view data
Year Country Origin Refugees AsylumSeekers Total
1 2011 Indonesia Afghanistan 476.0 1649.0 2125
2 2011 Indonesia Albania 0.0 1.0 1
3 2011 Indonesia Bangladesh 0.0 2.0 2
4 2011 Indonesia Central African Rep. 0.0 1.0 1
5 2011 Indonesia China 14.0 13.0 27
.. ... ... ... ... ... ...
171 2015 Indonesia Sudan 171.0 188.0 359
172 2015 Indonesia Sierra Leone 0.0 24.0 24
173 2015 Indonesia Somalia 448.0 724.0 1172
174 2015 Indonesia Syrian Arab Rep. 61.0 32.0 93
175 2015 Indonesia Yemen 6.0 153.0 159
[175 rows x 6 columns]import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import style # import theme
print(plt.style.available) # list theme
font1 = {'family':'serif','color':'black','size':20} #for title graph visual
plt.figure(figsize=(10,6))
sns.scatterplot(x = "Refugees", y ="AsylumSeekers", data = datarefugeesidn1115,
hue= "Year", palette="Set1")
plt.xlabel("Refugees")
plt.ylabel("Asylum Seekers")
plt.title("Refugees & Asylum Seekers in Indonesia (2011-2015)", fontdict = font1)
max_refugees_idx = datarefugeesidn1115["Refugees"].idxmax()
max_asylum_seekers_idx = datarefugeesidn1115["AsylumSeekers"].idxmax()
dominat_x = datarefugeesidn1115["Refugees"].iloc[max_refugees_idx]
dominant_y = datarefugeesidn1115["AsylumSeekers"].iloc[max_asylum_seekers_idx]
circle = plt.Circle((dominat_x, dominant_y), 150, color='black', fill=False, linewidth=1)
plt.gca().add_artist(circle)
plt.text(x=1681, y=3782, s="Afghanistan", fontsize=12, color='black', fontweight='bold')
plt.text(x=208, y =1649, s="Iran", fontsize=12, color='black',fontweight='bold')
plt.text(x=781, y=711, s="Myanmar", fontsize=12, color='black', fontweight='bold')
plt.text(x=448, y=724, s="Somalia", fontsize=12, color='black', fontweight='bold')
plt.text(x=217, y=1489, s="Iraq", fontsize=12, color='black', fontweight='bold')
plt.tight_layout()
plt.style.use('seaborn-v0_8-white') #theme
plt.show()
Positive correlation if we see the general trend of the number of “Refugees” increasing along with the number of “Asylum Seekers”, this shows a positive correlation between the two variables. Although, it may show most people become Refugees, they tend to also become Asylum Seekers in Indonesia. Regarding outliers if the point is far from the general pattern. Like countries with the highest refugees but relatively fewer asylum seekers vice versa. This can be interesting for further analysis. Afghanistan was the country with the highest number of refugees and asylum seekers in Indonesia in the period 2011-2015, indicating a significant humanitarian crisis at that time. In general, there may be a positive correlation between the number of refugees and asylum seekers, although further analysis is needed for countries with unusual patterns (outliers). Temporal analysis (by year) shows fluctuations, which may be related to political events or conflicts in the countries of origin of the refugees.
import geopandas as gpd
import pandas as pd
# file data from nautural earth # https://www.naturalearthdata.com/
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")
indonesianrefugeesseekers = {
'Country': ["Australia", "Canada", "Germany", "France", "United Kingdom", "Greece",
"Japan", "South Korea", "Malaysia", "Netherlands", "Papua New Guinea",
"Sweden", "United States of America"],
'Indonesian': [494, 321, 27, 6, 24, 12, 854, 7, 819, 22, 9368, 20, 4162]
}
# Dictionary country coordinate (longitude, latitude)
coords = {
"Australia": (133.7751, -25.2744),#🇦🇺
"Canada": (-106.3468, 56.1304),#🇨🇦
"Germany": (10.4515, 51.1657), #🇩🇪
"France": (2.2137, 46.6034),#🇫🇷
"United Kingdom": (-3.4360, 55.3781), #🇬🇧
"Greece": (21.8243, 39.0742), #🇬🇷
"Japan": (138.2529, 36.2048), #🗾
"South Korea": (127.7669, 35.9078),
"Malaysia": (101.9758, 4.2105), #🇲🇾
"Netherlands": (5.2913, 52.1326), #🇳🇱
"Papua New Guinea": (147.1803, -6.3149), #🇵🇬
"Sweden": (18.6435, 60.1282), #🇸🇪
"United States of America": (-95.7129, 37.0902) #🇺🇸
}
# convert to DataFrame
indonesianrefugeesseekers2015 = pd.DataFrame(indonesianrefugeesseekers)
#view data
print(indonesianrefugeesseekers2015)
Country Indonesian
0 Australia 494
1 Canada 321
2 Germany 27
3 France 6
4 United Kingdom 24
5 Greece 12
6 Japan 854
7 South Korea 7
8 Malaysia 819
9 Netherlands 22
10 Papua New Guinea 9368
11 Sweden 20
12 United States of America 4162
# Combine dataframe with world (data shapefile)
world = world.merge(indonesianrefugesseekers2015, how="left", left_on="SOVERIGNT", right_on="Country")
import matplotlib.pyplot as plt
font1 = {'family':'serif','color':'black','size':15} # text for title
# plot maps world
fig, ax = plt.subplots(1, 1, figsize=(30, 18))
# plot chorpleth
world.boundary.plot(ax=ax)
world.plot(column='Indonesian', ax=ax, legend=True,
cmap='OrRd',
missing_kwds={'color':'lightgrey'},
legend_kwds={'label':"Indonesian",
'orientation': "horizontal"})
world['centroid'] = world.geometry.centroid
plt.title("Indonesian Refugees & Asylum Seekers 2014 to 2015", fontdict=font1)
plt.show()
Total data on the number of refugees and asylum seekers in the world in 2015, most of the countries chosen by Indonesia such as Australia and the United States, which countries open migration policies for refugees and asylum seekers. Neighboring countries Australia and Malaysia are the first destinations because of easy access and strong bilateral relations. The next question is why is Papua New Guinea also a destination country for Indonesian migration?
import pandas as pd
import csv
# import dataset
indonesianrefugee = pd.read_csv("/Users/mymac/Desktop/Data Github/indonesianrefugees.csv", delimiter=';', header = None) # adjust columns and rows
indonesianrefugee = indonesianrefugee.iloc[1:]
indonesianrefugee.columns = ['Year', 'Seekers', 'Refugees'] # create colums
print(indonesianrefugee)
# Data for Refugees
indonesianrefugee = pd.DataFrame({
'Year': [2011, 2012, 2013, 2014, 2015],
'Refugees': [16079, 15523, 14786, 14393, 13942]
})
# Data for Seekers
indonesianseekers = pd.DataFrame({
'Year': [2011, 2012, 2013, 2014, 2015],
'Seekers': [429, 506, 1968, 1892, 2194]
})import pandas as pd
import plotly.graph_objects as go
# Data for Refugees
indonesianrefugee = pd.DataFrame({
'Year': [2011, 2012, 2013, 2014, 2015],
'Refugees': [16079, 15523, 14786, 14393, 13942]
})
# Data for Seekers
indonesianseekers = pd.DataFrame({
'Year': [2011, 2012, 2013, 2014, 2015],
'Seekers': [429, 506, 1968, 1892, 2194]
})
# create Figure
fig = go.Figure()
# add trace for Refugees
fig.add_trace(go.Scatter(x=indonesianrefugee['Year'],
y=indonesianrefugee['Refugees'],
mode='lines+markers',
name="Indonesian Refugees",
marker=dict(color='red'),
line=dict(color='red')))
# add trave for Seekers
fig.add_trace(go.Scatter(x=indonesianseekers['Year'],
y=indonesianseekers['Seekers'],
mode='lines+markers',
name="Indonesian Seekers",
marker=dict(color='blue'),
line=dict(color='blue')))
# Update layout for title and element or others
fig.update_layout(title="Indonesian Refugees & Asylum Seekers 2011 - 2015",
xaxis_title="Years",
yaxis_title="Indonesian",
plot_bgcolor='rgb(17,17,17)',
paper_bgcolor='rgb(17,17,17)',
font=dict(color="white"),
xaxis=dict(showgrid=False),
yaxis=dict(showgrid=False),
showlegend=True)
fig.update_xaxes(tickvals=[2011,2012,2013,2014,2015])
# show plot
fig.show()
Refugees (Red): From 2011 to 2015, the number of refugees has consistently decreased each year. This could indicate that the number of refugees entering or living in Indonesia is decreasing. Asylum Seekers (Blue): The graph shows a moderate increase from 2012 to 2015. This could indicate that more people are seeking asylum in Indonesia during this period.