world population is the number of human beings living on earth. The current world population is 7.9 billion as per february 2022 according to the most recent united nations estimates elaborated by worldometer.
The aim of this project is to analyze world population data.In doing this, we will come up with the highly populated countries,low populated countries,the death rates etc.
To achieve this goal we are going to use python libraries like
pandas
,numpy'
andmatplotlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# reading our dataset
world_pop_data = pd.read_csv("world_population.xls")
#displayng a few rows
world_pop_data.head(10)
id | code | name | area | area_land | area_water | population | population_growth | birth_rate | death_rate | migration_rate | |
---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | af | Afghanistan | 652230.0 | 652230.0 | 0.0 | 32564342.0 | 2.32 | 38.57 | 13.89 | 1.51 |
1 | 2 | al | Albania | 28748.0 | 27398.0 | 1350.0 | 3029278.0 | 0.30 | 12.92 | 6.58 | 3.30 |
2 | 3 | ag | Algeria | 2381741.0 | 2381741.0 | 0.0 | 39542166.0 | 1.84 | 23.67 | 4.31 | 0.92 |
3 | 4 | an | Andorra | 468.0 | 468.0 | 0.0 | 85580.0 | 0.12 | 8.13 | 6.96 | 0.00 |
4 | 5 | ao | Angola | 1246700.0 | 1246700.0 | 0.0 | 19625353.0 | 2.78 | 38.78 | 11.49 | 0.46 |
5 | 6 | ac | Antigua and Barbuda | 442.0 | 442.0 | 0.0 | 92436.0 | 1.24 | 15.85 | 5.69 | 2.21 |
6 | 7 | ar | Argentina | 2780400.0 | 2736690.0 | 43710.0 | 43431886.0 | 0.93 | 16.64 | 7.33 | 0.00 |
7 | 8 | am | Armenia | 29743.0 | 28203.0 | 1540.0 | 3056382.0 | 0.15 | 13.61 | 9.34 | 5.80 |
8 | 9 | as | Australia | 7741220.0 | 7682300.0 | 58920.0 | 22751014.0 | 1.07 | 12.15 | 7.14 | 5.65 |
9 | 10 | au | Austria | 83871.0 | 82445.0 | 1426.0 | 8665550.0 | 0.55 | 9.41 | 9.42 | 5.56 |
removing rows and columns which are not helpful
world_pop_data.drop("id",axis=1,inplace=True )# the id column is not useful so we remove it
world_pop_data.drop("code",axis=1,inplace=True )
world_pop_data.head()#printing the first five without the id column
name | area | area_land | area_water | population | population_growth | birth_rate | death_rate | migration_rate | |
---|---|---|---|---|---|---|---|---|---|
0 | Afghanistan | 652230.0 | 652230.0 | 0.0 | 32564342.0 | 2.32 | 38.57 | 13.89 | 1.51 |
1 | Albania | 28748.0 | 27398.0 | 1350.0 | 3029278.0 | 0.30 | 12.92 | 6.58 | 3.30 |
2 | Algeria | 2381741.0 | 2381741.0 | 0.0 | 39542166.0 | 1.84 | 23.67 | 4.31 | 0.92 |
3 | Andorra | 468.0 | 468.0 | 0.0 | 85580.0 | 0.12 | 8.13 | 6.96 | 0.00 |
4 | Angola | 1246700.0 | 1246700.0 | 0.0 | 19625353.0 | 2.78 | 38.78 | 11.49 | 0.46 |
#location of population over two billion
pop_over_2_billion=world_pop_data[world_pop_data['population']>2000000000]
pop_over_2_billion
name | area | area_land | area_water | population | population_growth | birth_rate | death_rate | migration_rate | |
---|---|---|---|---|---|---|---|---|---|
260 | World | NaN | NaN | NaN | 7.256490e+09 | 1.08 | 18.6 | 7.8 | NaN |
world_pop_data.drop([260],axis=0,inplace=True)
#removig countries with population over two billion because there is country called world
pop_over_2_billion=world_pop_data[world_pop_data['population']>2000000000]
pop_over_2_billion
#after removing the countris with population over two billion our data frame is empty
name | area | area_land | area_water | population | population_growth | birth_rate | death_rate | migration_rate |
---|
pop_0=world_pop_data[world_pop_data['population']==0] # lets confirm countries with population zero
pop_0
name | area | area_land | area_water | population | population_growth | birth_rate | death_rate | migration_rate | |
---|---|---|---|---|---|---|---|---|---|
249 | Antarctica | NaN | 280000.0 | NaN | 0.0 | NaN | NaN | NaN | NaN |
We can see that the poulation of Antarctica is recorded to be zero. This is because Antarctica is unique continent in that it does not have a native population. There are no countries in Antartica,although seven nations claim different parts of it:New Zealand, Australia, France, Norway, the United Kingdom, Chile and Argentina.it is also larger than Oceania and Europe.
world_pop_data.drop([249],axis=0,inplace=True)#we drop the country with zero population because its not helpful in our anaysis
world_pop_data.head()
name | area | area_land | area_water | population | population_growth | birth_rate | death_rate | migration_rate | |
---|---|---|---|---|---|---|---|---|---|
0 | Afghanistan | 652230.0 | 652230.0 | 0.0 | 32564342.0 | 2.32 | 38.57 | 13.89 | 1.51 |
1 | Albania | 28748.0 | 27398.0 | 1350.0 | 3029278.0 | 0.30 | 12.92 | 6.58 | 3.30 |
2 | Algeria | 2381741.0 | 2381741.0 | 0.0 | 39542166.0 | 1.84 | 23.67 | 4.31 | 0.92 |
3 | Andorra | 468.0 | 468.0 | 0.0 | 85580.0 | 0.12 | 8.13 | 6.96 | 0.00 |
4 | Angola | 1246700.0 | 1246700.0 | 0.0 | 19625353.0 | 2.78 | 38.78 | 11.49 | 0.46 |
pop_0=world_pop_data[world_pop_data['population']==0]
pop_0 #afer dropping the countries with zero population our data frame is empty
name | area | area_land | area_water | population | population_growth | birth_rate | death_rate | migration_rate |
---|
#top 30 highly populated countries
top30_highly_populated=world_pop_data.sort_values("population",ascending=False)
top30_highly_populated[['name','population','birth_rate']].head(30)
name | population | birth_rate | |
---|---|---|---|
36 | China | 1.367485e+09 | 12.49 |
76 | India | 1.251696e+09 | 19.55 |
196 | European Union | 5.139494e+08 | 10.20 |
185 | United States | 3.213689e+08 | 12.49 |
77 | Indonesia | 2.559937e+08 | 16.72 |
23 | Brazil | 2.042598e+08 | 14.46 |
131 | Pakistan | 1.990858e+08 | 22.58 |
128 | Nigeria | 1.815621e+08 | 37.64 |
13 | Bangladesh | 1.689577e+08 | 21.14 |
142 | Russia | 1.424238e+08 | 11.60 |
84 | Japan | 1.269197e+08 | 7.93 |
113 | Mexico | 1.217368e+08 | 18.78 |
137 | Philippines | 1.009984e+08 | 24.27 |
57 | Ethiopia | 9.946582e+07 | 37.27 |
191 | Vietnam | 9.434884e+07 | 15.96 |
52 | Egypt | 8.848740e+07 | 22.90 |
78 | Iran | 8.182427e+07 | 17.99 |
64 | Germany | 8.085441e+07 | 8.47 |
178 | Turkey | 7.941427e+07 | 16.33 |
39 | Congo, Democratic Republic of the | 7.937514e+07 | 34.88 |
172 | Thailand | 6.797640e+07 | 11.19 |
60 | France | 6.655377e+07 | 12.38 |
184 | United Kingdom | 6.408822e+07 | 12.17 |
82 | Italy | 6.185512e+07 | 8.74 |
27 | Burma | 5.632021e+07 | 18.39 |
160 | South Africa | 5.367556e+07 | 20.75 |
171 | Tanzania | 5.104588e+07 | 36.39 |
90 | Korea, South | 4.911520e+07 | 8.19 |
162 | Spain | 4.814613e+07 | 9.64 |
37 | Colombia | 4.673673e+07 | 16.47 |
# let's visualize top ten most populated country
most_pop=top30_highly_populated[['name','population']].head(10)
plt.bar(most_pop['name'],most_pop['population'])
plt.xticks(rotation=45)
plt.xlabel('Countries')
plt.ylabel('population(in billions)')
plt.title('most populated countries')
plt.show()
plt.pie(most_pop['population'],labels=most_pop['name'])
plt.xticks(rotation='45')
plt.title('most populated countries')
Text(0.5, 1.0, 'most populated countries')
Overpopulation in China began after world war 2 in 1949 when Chinese families were encouraged to have as many children as possible in hopes of bringing more money to the country, building a better army and producing more food. In China like much of east Asia is in grip of a population crisis with lowering birthrates and prediction of imminent negative population growth and an ageing population.Proportion of over 60's rose from 18.7% in 2020 to 18.9% in 17th january 2022. In helping reduce the population China,s one child policy was announced in late 2015 and it formally ended in 2016.Beginning in 2016 the Chinese goverment allowed all families to have two children and in 2021 all mrried couples wewre permitted to have as many as threee children.
In Africa, Nigeria is the highly populated country.A s the most populous nation in Africa,Nigeria is home to over 206 million inhabitants. Each year its population increases by nearly 5.5 million.
#top 30 most dense countries
world_pop_data["dense_area"]=world_pop_data['population']/world_pop_data['area_land']
top30=world_pop_data.sort_values("dense_area",ascending=False)
top30=top30[['name','dense_area','area_land','population']].head(30)
top30
name | dense_area | area_land | population | |
---|---|---|---|---|
189 | Holy See (Vatican City) | inf | 0.0 | 8.420000e+02 |
204 | Macau | 2.116896e+04 | 28.0 | 5.927310e+05 |
116 | Monaco | 1.526750e+04 | 2.0 | 3.053500e+04 |
155 | Singapore | 8.259785e+03 | 687.0 | 5.674472e+06 |
203 | Hong Kong | 6.655271e+03 | 1073.0 | 7.141106e+06 |
250 | Gaza Strip | 5.191819e+03 | 360.0 | 1.869055e+06 |
232 | Gibraltar | 4.876333e+03 | 6.0 | 2.925800e+04 |
12 | Bahrain | 1.771859e+03 | 760.0 | 1.346613e+06 |
107 | Maldives | 1.319641e+03 | 298.0 | 3.932530e+05 |
109 | Malta | 1.310016e+03 | 316.0 | 4.139650e+05 |
226 | Bermuda | 1.299926e+03 | 54.0 | 7.019600e+04 |
13 | Bangladesh | 1.297978e+03 | 130170.0 | 1.689577e+08 |
217 | Sint Maarten | 1.167324e+03 | 34.0 | 3.968900e+04 |
233 | Guernsey | 8.471795e+02 | 78.0 | 6.608000e+04 |
234 | Jersey | 8.387414e+02 | 116.0 | 9.729400e+04 |
195 | Taiwan | 7.258254e+02 | 32260.0 | 2.341513e+07 |
14 | Barbados | 6.758233e+02 | 430.0 | 2.906040e+05 |
112 | Mauritius | 6.600133e+02 | 2030.0 | 1.339827e+06 |
215 | Aruba | 6.231222e+02 | 180.0 | 1.121620e+05 |
96 | Lebanon | 6.045651e+02 | 10230.0 | 6.184701e+06 |
212 | Saint Martin | 5.880370e+02 | 54.0 | 3.175400e+04 |
148 | San Marino | 5.413115e+02 | 61.0 | 3.302000e+04 |
143 | Rwanda | 5.132858e+02 | 24668.0 | 1.266173e+07 |
90 | Korea, South | 5.067602e+02 | 96920.0 | 4.911520e+07 |
124 | Netherlands | 5.000414e+02 | 33893.0 | 1.694790e+07 |
253 | West Bank | 4.938592e+02 | 5640.0 | 2.785366e+06 |
122 | Nauru | 4.542857e+02 | 21.0 | 9.540000e+03 |
76 | India | 4.209937e+02 | 2973193.0 | 1.251696e+09 |
28 | Burundi | 4.183129e+02 | 25680.0 | 1.074228e+07 |
180 | Tuvalu | 4.180385e+02 | 26.0 | 1.086900e+04 |
#Let's visualize top 10 most dense countries
most_dense=top30[['name','dense_area']].head(10)
plt.bar(most_dense['name'],most_dense['dense_area'])
plt.xticks(rotation=45)
plt.xlabel('name')
plt.ylabel('dense-area')
plt.title('most dense countries in the world')
plt.show
<function matplotlib.pyplot.show(close=None, block=None)>
with a population density of 21340 people per square kilometer.Macau is the most densely populated region in the world click here
#less dense countries
world_pop_data["less_dense"]=world_pop_data['population']/world_pop_data['area_land']
least30=world_pop_data.sort_values("less_dense")
least30=least30[['name','less_dense','area_land','population']].head(10)
least30
name | less_dense | area_land | population | |
---|---|---|---|---|
206 | Greenland | 0.026653 | 2166086.0 | 57733.0 |
223 | Svalbard | 0.030172 | 62045.0 | 1872.0 |
231 | Falkland Islands (Islas Malvinas) | 0.276103 | 12173.0 | 3361.0 |
237 | Pitcairn Islands | 1.021277 | 47.0 | 48.0 |
117 | Mongolia | 1.926489 | 1553556.0 | 2992908.0 |
254 | Western Sahara | 2.146113 | 266000.0 | 570866.0 |
121 | Namibia | 2.687154 | 823290.0 | 2212307.0 |
8 | Australia | 2.961485 | 7682300.0 | 22751014.0 |
75 | Iceland | 3.310903 | 100250.0 | 331918.0 |
111 | Mauritania | 3.489572 | 1030700.0 | 3596702.0 |
world_data=least30[['name','less_dense']]
plt.bar(world_data['name'],world_data['less_dense'])
plt.xticks(rotation=90)
plt.xlabel('name')
plt.ylabel('less_dense')
plt.title('less dense countries')
plt.show()
The island of Greenland is the world least populated. Greenland is sparsely populated because the weather in Greenland is frigid and freezing cick here
# top 30 countries with high death rates
top_30_with_high_death_rates=world_pop_data.sort_values("death_rate", ascending=False)
top_30_with_high_death_rates[['name','death_rate', 'birth_rate', 'population']].head(30)
name | death_rate | birth_rate | population | |
---|---|---|---|---|
97 | Lesotho | 14.89 | 25.47 | 1947701.0 |
182 | Ukraine | 14.46 | 10.72 | 44429471.0 |
25 | Bulgaria | 14.44 | 8.92 | 7186893.0 |
70 | Guinea-Bissau | 14.33 | 33.38 | 1726170.0 |
95 | Latvia | 14.31 | 10.00 | 1986705.0 |
34 | Chad | 14.28 | 36.60 | 11631456.0 |
101 | Lithuania | 14.27 | 10.10 | 2884433.0 |
121 | Namibia | 13.91 | 19.80 | 2212307.0 |
0 | Afghanistan | 13.89 | 38.57 | 32564342.0 |
33 | Central African Republic | 13.80 | 35.08 | 5391539.0 |
142 | Russia | 13.69 | 11.60 | 142423773.0 |
152 | Serbia | 13.66 | 9.08 | 7176794.0 |
159 | Somalia | 13.62 | 40.45 | 10616380.0 |
166 | Swaziland | 13.56 | 24.67 | 1435613.0 |
22 | Botswana | 13.39 | 20.96 | 2182719.0 |
15 | Belarus | 13.36 | 10.70 | 9589689.0 |
61 | Gabon | 13.12 | 34.49 | 1705336.0 |
128 | Nigeria | 12.90 | 37.64 | 181562056.0 |
108 | Mali | 12.89 | 44.99 | 16955536.0 |
74 | Hungary | 12.73 | 9.16 | 9897541.0 |
193 | Zambia | 12.67 | 42.13 | 15066266.0 |
115 | Moldova | 12.59 | 12.00 | 3546847.0 |
127 | Niger | 12.42 | 45.45 | 18045729.0 |
56 | Estonia | 12.40 | 10.51 | 1265420.0 |
43 | Croatia | 12.18 | 9.45 | 4464844.0 |
120 | Mozambique | 12.10 | 38.58 | 25303113.0 |
141 | Romania | 11.90 | 9.14 | 21666350.0 |
26 | Burkina Faso | 11.72 | 42.03 | 18931686.0 |
4 | Angola | 11.49 | 38.78 | 19625353.0 |
64 | Germany | 11.42 | 8.47 | 80854408.0 |
27th august 2019 by far most important of the ten facts about life expectancy in Lesotho is that it has high rate of HIV/AIDs in the world. Lesotho have high death rates due to various lifestyle risk factors like smoking, alcohol consumption, high blood sugar and obesity. The most common non-communicable diseases are cardiovasclar diseases, diabetes and cancer.In 2014,non-communicable diseases accounted to 27% of total deaths in 16th feb 2017.
#top 30 countris with low death rates
top30_low_death_rates=world_pop_data.sort_values("death_rate")
top30_low_death_rates[['name','death_rate','birth_rate','population']].head(30)
name | death_rate | birth_rate | population | |
---|---|---|---|---|
140 | Qatar | 1.53 | 9.84 | 2194817.0 |
183 | United Arab Emirates | 1.97 | 15.43 | 5779760.0 |
92 | Kuwait | 2.18 | 19.91 | 2788534.0 |
12 | Bahrain | 2.69 | 13.66 | 1346613.0 |
250 | Gaza Strip | 3.04 | 31.11 | 1869055.0 |
240 | Turks and Caicos Islands | 3.10 | 16.13 | 50280.0 |
150 | Saudi Arabia | 3.33 | 18.51 | 27752316.0 |
130 | Oman | 3.36 | 24.44 | 3286936.0 |
155 | Singapore | 3.43 | 8.27 | 5674472.0 |
253 | West Bank | 3.50 | 22.99 | 2785366.0 |
24 | Brunei | 3.52 | 17.32 | 429646.0 |
99 | Libya | 3.58 | 18.03 | 6411776.0 |
244 | Northern Mariana Islands | 3.71 | 18.32 | 52344.0 |
79 | Iraq | 3.77 | 31.45 | 37056169.0 |
85 | Jordan | 3.79 | 25.37 | 8117564.0 |
158 | Solomon Islands | 3.85 | 25.77 | 622469.0 |
107 | Maldives | 3.89 | 15.75 | 393253.0 |
169 | Syria | 4.00 | 22.17 | 17064854.0 |
188 | Vanuatu | 4.09 | 25.04 | 272264.0 |
110 | Marshall Islands | 4.21 | 25.60 | 72191.0 |
204 | Macau | 4.22 | 8.88 | 592731.0 |
114 | Micronesia, Federated States of | 4.23 | 20.54 | 105216.0 |
2 | Algeria | 4.31 | 23.67 | 39542166.0 |
217 | Sint Maarten | 4.51 | 13.00 | 39689.0 |
50 | Dominican Republic | 4.55 | 18.73 | 10478756.0 |
41 | Costa Rica | 4.55 | 15.91 | 4814144.0 |
225 | Anguilla | 4.57 | 12.67 | 16418.0 |
135 | Paraguay | 4.68 | 16.37 | 6783272.0 |
241 | American Samoa | 4.75 | 22.89 | 54343.0 |
52 | Egypt | 4.77 | 22.90 | 88487396.0 |
Qatar has the absolute lowest low death rate in the world with 1.53 death rates per 1000 people annually. One of the major reasons fot its low number of annual desth is its improved health care system. It has one of the best and most widely recognized health care systems and as compared to other nations, Qatar health sector is renowned fot its technologically advanced medical facilities and offering of the best patient care. The country affluence has enabled the provision of medical care to most of its citizen. Qatar has the world's highest per_capita coronvirus infection rate but the lowest death rates due to extensive testing, a young population and lavish healthcare spending.
#To estabish countries with more area land than water land
world_pop_data["land_water"]=world_pop_data["area_land"]/world_pop_data["area_water"]
world_pop_data.head(10)
name | area | area_land | area_water | population | population_growth | birth_rate | death_rate | migration_rate | dense_area | less_dense | land_water | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Afghanistan | 652230.0 | 652230.0 | 0.0 | 32564342.0 | 2.32 | 38.57 | 13.89 | 1.51 | 49.927697 | 49.927697 | inf |
1 | Albania | 28748.0 | 27398.0 | 1350.0 | 3029278.0 | 0.30 | 12.92 | 6.58 | 3.30 | 110.565662 | 110.565662 | 20.294815 |
2 | Algeria | 2381741.0 | 2381741.0 | 0.0 | 39542166.0 | 1.84 | 23.67 | 4.31 | 0.92 | 16.602211 | 16.602211 | inf |
3 | Andorra | 468.0 | 468.0 | 0.0 | 85580.0 | 0.12 | 8.13 | 6.96 | 0.00 | 182.863248 | 182.863248 | inf |
4 | Angola | 1246700.0 | 1246700.0 | 0.0 | 19625353.0 | 2.78 | 38.78 | 11.49 | 0.46 | 15.741841 | 15.741841 | inf |
5 | Antigua and Barbuda | 442.0 | 442.0 | 0.0 | 92436.0 | 1.24 | 15.85 | 5.69 | 2.21 | 209.131222 | 209.131222 | inf |
6 | Argentina | 2780400.0 | 2736690.0 | 43710.0 | 43431886.0 | 0.93 | 16.64 | 7.33 | 0.00 | 15.870225 | 15.870225 | 62.610158 |
7 | Armenia | 29743.0 | 28203.0 | 1540.0 | 3056382.0 | 0.15 | 13.61 | 9.34 | 5.80 | 108.370812 | 108.370812 | 18.313636 |
8 | Australia | 7741220.0 | 7682300.0 | 58920.0 | 22751014.0 | 1.07 | 12.15 | 7.14 | 5.65 | 2.961485 | 2.961485 | 130.385268 |
9 | Austria | 83871.0 | 82445.0 | 1426.0 | 8665550.0 | 0.55 | 9.41 | 9.42 | 5.56 | 105.107041 | 105.107041 | 57.815568 |
more_land=world_pop_data[world_pop_data["land_water"]>1.0]
more_water=world_pop_data[world_pop_data["land_water"]<1]
land_water=world_pop_data[world_pop_data["land_water"]==1]
land_water
name | area | area_land | area_water | population | population_growth | birth_rate | death_rate | migration_rate | dense_area | less_dense | land_water |
---|
The dataframe is empyty because there is no country which have land area equal to the water area
more_land.head(10)
name | area | area_land | area_water | population | population_growth | birth_rate | death_rate | migration_rate | dense_area | less_dense | land_water | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Afghanistan | 652230.0 | 652230.0 | 0.0 | 32564342.0 | 2.32 | 38.57 | 13.89 | 1.51 | 49.927697 | 49.927697 | inf |
1 | Albania | 28748.0 | 27398.0 | 1350.0 | 3029278.0 | 0.30 | 12.92 | 6.58 | 3.30 | 110.565662 | 110.565662 | 20.294815 |
2 | Algeria | 2381741.0 | 2381741.0 | 0.0 | 39542166.0 | 1.84 | 23.67 | 4.31 | 0.92 | 16.602211 | 16.602211 | inf |
3 | Andorra | 468.0 | 468.0 | 0.0 | 85580.0 | 0.12 | 8.13 | 6.96 | 0.00 | 182.863248 | 182.863248 | inf |
4 | Angola | 1246700.0 | 1246700.0 | 0.0 | 19625353.0 | 2.78 | 38.78 | 11.49 | 0.46 | 15.741841 | 15.741841 | inf |
5 | Antigua and Barbuda | 442.0 | 442.0 | 0.0 | 92436.0 | 1.24 | 15.85 | 5.69 | 2.21 | 209.131222 | 209.131222 | inf |
6 | Argentina | 2780400.0 | 2736690.0 | 43710.0 | 43431886.0 | 0.93 | 16.64 | 7.33 | 0.00 | 15.870225 | 15.870225 | 62.610158 |
7 | Armenia | 29743.0 | 28203.0 | 1540.0 | 3056382.0 | 0.15 | 13.61 | 9.34 | 5.80 | 108.370812 | 108.370812 | 18.313636 |
8 | Australia | 7741220.0 | 7682300.0 | 58920.0 | 22751014.0 | 1.07 | 12.15 | 7.14 | 5.65 | 2.961485 | 2.961485 | 130.385268 |
9 | Austria | 83871.0 | 82445.0 | 1426.0 | 8665550.0 | 0.55 | 9.41 | 9.42 | 5.56 | 105.107041 | 105.107041 | 57.815568 |
Afghanistan has more land than water area. image of part of afghanistan
Clearly we can see that there is no country which has water area equal to land area
more_water
name | area | area_land | area_water | population | population_growth | birth_rate | death_rate | migration_rate | dense_area | less_dense | land_water | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
227 | British Indian Ocean Territory | 54400.0 | 60.0 | 54340.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.001104 |
246 | Virgin Islands | 1910.0 | 346.0 | 1564.0 | 103574.0 | 0.59 | 10.31 | 8.54 | 7.67 | 299.346821 | 299.346821 | 0.221228 |
There are only two countries with more water than land that is British Indian Ocean Territory and Virgin Islands. the image shows part of virgin islands while the other shows part of british indian ocean territory
From this analysis we can claerly see that China is the most populated country in the world with about 1.4 billion people in 2015. one of the reason is that after world war 2 the goverment adviced the citizens to have large families. We also found out that most countries in Africa have got high death rates led by Lesotho due to poor facilities. The most dense country is Macau where 1 kilometer of land accommodate atleast 2000 people. In terms of land area, Afghanistan is the country in the world with more land coverage compared to water coverage