Population living in slums 2009
This statistic measures the percentage of a country's population living in slums, highlighting urban poverty and living conditions. Understanding slum populations is crucial for addressing housing challenges and improving the quality of life.
Interactive Map
Complete Data Rankings
Rank | ||
|---|---|---|
1 | South Sudan | 98.405 % |
2 | Sudan | 91.461 % |
3 | Chad | 86.511 % |
4 | Madagascar | 80.629 % |
5 | Ethiopia | 78.243 % |
6 | Guinea-Bissau | 75.2 % |
7 | Mozambique | 74.284 % |
8 | Congo, Democratic Republic of the | 73.757 % |
9 | Central African Republic | 73.176 % |
10 | Liberia | 71.918 % |
11 | Niger | 70.299 % |
12 | Benin | 70.096 % |
13 | Mauritania | 69.499 % |
14 | Congo | 69.111 % |
15 | Uganda | 68.801 % |
16 | Nicaragua | 67.2 % |
17 | Comoros | 67.158 % |
18 | Djibouti | 66.113 % |
19 | Eritrea | 66.113 % |
20 | Mauritius | 66.113 % |
21 | Seychelles | 66.113 % |
22 | Somalia | 66.113 % |
23 | Sierra Leone | 65.703 % |
24 | Malawi | 65.598 % |
25 | Mali | 65 % |
26 | Pakistan | 64.36 % |
27 | Afghanistan | 64.204 % |
28 | Burundi | 63.637 % |
29 | Nigeria | 62.801 % |
30 | Tanzania | 62.169 % |
31 | Equatorial Guinea | 60.228 % |
32 | Cabo Verde | 60.192 % |
33 | Côte d'Ivoire | 60.144 % |
34 | Cambodia | 59.45 % |
35 | Sao Tome and Principe | 59.428 % |
36 | Ecuador | 57.8 % |
37 | Burkina Faso | 57.164 % |
38 | Zambia | 56.798 % |
39 | Kenya | 56.226 % |
40 | Rwanda | 55.755 % |
41 | Bangladesh | 55.412 % |
42 | Haiti | 55.408 % |
43 | Lebanon | 54.748 % |
44 | Togo | 54.677 % |
45 | Bhutan | 54.087 % |
46 | Iran | 54.087 % |
47 | Sri Lanka | 54.087 % |
48 | Nepal | 53.296 % |
49 | Yemen | 52.015 % |
50 | Timor-Leste | 52 % |
51 | Senegal | 51.157 % |
52 | Cameroon | 50.652 % |
53 | Botswana | 49.141 % |
54 | Gabon | 49.01 % |
55 | Gambia | 48.808 % |
56 | Lesotho | 48.271 % |
57 | Bolivia | 47.758 % |
58 | Guatemala | 45.431 % |
59 | Ghana | 45.248 % |
60 | Guinea | 44.419 % |
61 | Myanmar | 43.84 % |
62 | Philippines | 43.646 % |
63 | Namibia | 41.886 % |
64 | Iraq | 40.844 % |
65 | Maldives | 40.42 % |
66 | Mongolia | 39.728 % |
67 | Peru | 39.094 % |
68 | Laos | 39 % |
69 | El Salvador | 37.487 % |
70 | Honduras | 37.39 % |
71 | Eswatini | 36.792 % |
72 | Angola | 34.989 % |
73 | Algeria | 30.8 % |
74 | Brunei Darussalam | 29.67 % |
75 | Libya | 29.428 % |
76 | Panama | 29.274 % |
77 | Paraguay | 28.54 % |
78 | Syrian Arab Republic | 27.766 % |
79 | Indonesia | 27.682 % |
80 | Kyrgyzstan | 27.035 % |
81 | Azerbaijan | 26.927 % |
82 | State of Palestine | 26.211 % |
83 | South Africa | 25.896 % |
84 | Venezuela | 25.7 % |
85 | Zimbabwe | 24.843 % |
86 | Mexico | 24.445 % |
87 | Vietnam | 23.069 % |
88 | Guyana | 21.2 % |
89 | Papua New Guinea | 20.7 % |
90 | Morocco | 20.609 % |
91 | Brazil | 19.858 % |
92 | Dominican Republic | 19.521 % |
93 | Turkey | 18.308 % |
94 | Egypt | 17.43 % |
95 | Argentina | 17.027 % |
96 | Uzbekistan | 17.022 % |
97 | Albania | 16.65 % |
98 | Belize | 15.747 % |
99 | Montenegro | 15.5 % |
100 | Colombia | 15.127 % |
101 | Uruguay | 14.361 % |
102 | Republic of Moldova | 13.7 % |
103 | Fiji | 13 % |
104 | Kiribati | 12.45 % |
105 | Kazakhstan | 11.93 % |
106 | Georgia | 11.297 % |
107 | Suriname | 11.197 % |
108 | Armenia | 10.595 % |
109 | Tunisia | 10.272 % |
110 | South Korea | 10.175 % |
111 | Turkmenistan | 9.882 % |
112 | Hungary | 9.75 % |
113 | Trinidad and Tobago | 9.11 % |
114 | Costa Rica | 8.578 % |
115 | Ireland | 7.35 % |
116 | Chile | 7.302 % |
117 | Solomon Islands | 6.775 % |
118 | Cuba | 4.775 % |
119 | Bosnia and Herzegovina | 4.6 % |
120 | Vanuatu | 4.3 % |
121 | Cayman Islands | 4.15 % |
122 | Austria | 3.584 % |
123 | Russia | 3.25 % |
124 | Romania | 3.1 % |
125 | Saint Lucia | 3.05 % |
126 | Samoa | 2.875 % |
127 | Ukraine | 2.85 % |
128 | Lithuania | 2.2 % |
129 | British Virgin Islands | 2.135 % |
130 | Tuvalu | 2 % |
131 | Oman | 1.975 % |
132 | Latvia | 1.725 % |
133 | Nauru | 1.6 % |
134 | Marshall Islands | 1.5 % |
135 | Tonga | 1.35 % |
136 | Palau | 1.175 % |
137 | North Macedonia | 0.9 % |
138 | United Arab Emirates | 0.725 % |
139 | Poland | 0.7 % |
140 | Serbia | 0.525 % |
141 | Portugal | 0.475 % |
142 | Canada | 0.475 % |
143 | Croatia | 0.35 % |
144 | Cyprus | 0.25 % |
145 | United States | 0.2 % |
146 | Sweden | 0.175 % |
147 | Bulgaria | 0.15 % |
148 | Greece | 0.125 % |
149 | Bermuda | 0.1 % |
150 | Estonia | 0.1 % |
151 | United Kingdom | 0.1 % |
152 | Australia | 0.095 % |
153 | Czech Republic | 0.05 % |
154 | Slovakia | 0.05 % |
155 | Malta | 0.038 % |
156 | Italy | 0.02 % |
157 | Luxembourg | 0.007 % |
158 | Belarus | 0.004 % |
159 | Andorra | 0 % |
160 | Aruba | 0 % |
161 | Belgium | 0 % |
162 | Denmark | 0 % |
163 | Finland | 0 % |
164 | France | 0 % |
165 | Germany | 0 % |
166 | Iceland | 0 % |
167 | Kuwait | 0 % |
168 | Monaco | 0 % |
169 | Netherlands | 0 % |
170 | New Zealand | 0 % |
171 | Norway | 0 % |
172 | Singapore | 0 % |
173 | Switzerland | 0 % |
↑Top 10 Countries
- #1
South Sudan
- #2
Sudan
- #3
Chad
- #4
Madagascar
- #5
Ethiopia
- #6
Guinea-Bissau
- #7
Mozambique
- #8
Congo, Democratic Republic of the
- #9
Central African Republic
- #10
Liberia
Analysis: These countries represent the highest values in this dataset, showcasing significant scale and impact on global statistics.
↓Bottom 10 Countries
- #173
Switzerland
- #172
Singapore
- #171
Norway
- #170
New Zealand
- #169
Netherlands
- #168
Monaco
- #167
Kuwait
- #166
Iceland
- #165
Germany
- #164
France
Context: These countries or territories have the lowest values, often due to geographic size, administrative status, or specific characteristics.
Analysis & Context
The statistic "Population living in slums" offers a crucial lens through which to understand urban poverty and living conditions worldwide. In 2009, this metric highlighted the ongoing challenges faced by urban areas in providing adequate housing and the necessity for sustainable urban development. While global discussions often emphasize countries with high slum populations, the available data for 2009 is intriguing, particularly because it showcases Belarus as having a remarkably low percentage of its population living in slums.
Global Significance of Slum Population Metrics
Understanding slum populations is of particular importance when considering global efforts to improve urban living conditions. Slums are typically characterized by inadequate housing and poor living conditions, including limited access to essential services. Monitoring the percentage of populations living in such conditions provides insight into socioeconomic disparities and informs policy aimed at bridging these gaps. In 2009, global efforts were ongoing to address these issues, reflecting the commitments made during the Millennium Development Goals to improve the lives of slum dwellers by 2020.
Surprising Statistics from 2009: The Belarus Example
The 2009 data reveals an interesting anomaly with Belarus reporting a slum population percentage of only 0.004. This figure stands out, especially when compared to many other countries facing significant urban housing challenges. Belarus's low slum population percentage suggests effective urban planning and housing policies that have successfully mitigated the growth of informal settlements. Such statistics indicate not only the importance of governance in urban development but also highlight Belarus as a potential model for other nations striving to minimize slum populations.
Policy and Governance Influence on Slum Populations
The remarkably low slum population in Belarus in 2009 underscores the influence of effective governance and policy frameworks. The country's success in virtually eliminating slum conditions can be attributed to a combination of factors, including strong governmental control over urban planning and robust social policies that ensure housing availability and affordability. These strategies contrast sharply with nations where rapid urbanization has outpaced the development of infrastructure and housing, leading to expansive slum areas.
Historical Context and the Impact of Urbanization
Historically, the rapid urbanization experienced by many countries has been a double-edged sword. While providing economic opportunities, it has also led to the proliferation of slums due to inadequate housing and infrastructure development. The historical urban planning approach of Belarus, however, appears to have mitigated these risks, emphasizing the importance of strategic urban development from an early stage. This serves as a lesson in how urbanization policies can be structured to prevent slum formation, balancing growth with sustainable development.
Future Lessons and Global Trends
As the world continues to urbanize, the lessons from Belarus in 2009 provide valuable insights. Future urban development must prioritize sustainable housing solutions that align with population growth. Globally, the trends indicate a need for comprehensive policies that not only address existing slum conditions but also prevent the emergence of new ones. International collaboration and knowledge sharing, inspired by successful models like Belarus’s, can significantly aid countries struggling with high slum populations, aiming for a future where urban areas are equitable and sustainable.
In conclusion, the 2009 data on "Population living in slums" offers a unique perspective, especially considering Belarus’s standout performance in this area. This underscores the critical role of governance and policy in shaping urban living conditions and highlights the potential for countries to learn from successful case studies to tackle urban poverty and improve housing conditions globally.
Data Source
UN Habitat
The Data and Analytics Section (DAS) is the specialized statistics unit within UN-Habitat. The data section is responsible for overall data oversight across all urban monitoring domains within UN-Habitat, methodological developments, supporting member states in their monitoring efforts around global agenda such as the SDGs, implementing direct data collection and compilation, providing data to UN-Habitat global reports, and publicly and openly disseminating urban data through its urban indicators programme.
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