Population living in slums 2005
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.
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Complete Data Rankings
Rank | ||
|---|---|---|
1 | South Sudan | 99.8 % |
2 | Sudan | 99.094 % |
3 | Lebanon | 92.405 % |
4 | Chad | 88.766 % |
5 | Madagascar | 85.432 % |
6 | Ethiopia | 84.433 % |
7 | Congo | 81.574 % |
8 | Mozambique | 81.311 % |
9 | Central African Republic | 78.86 % |
10 | Mauritania | 76.252 % |
11 | Liberia | 75.042 % |
12 | Burundi | 74.362 % |
13 | Uganda | 74.179 % |
14 | Mali | 73.389 % |
15 | Malawi | 72.626 % |
16 | Congo, Democratic Republic of the | 72.145 % |
17 | Sierra Leone | 71.179 % |
18 | Djibouti | 71.125 % |
19 | Eritrea | 71.125 % |
20 | Mauritius | 71.125 % |
21 | Seychelles | 71.125 % |
22 | Somalia | 71.125 % |
23 | Benin | 70.883 % |
24 | Cambodia | 70.75 % |
25 | Tanzania | 70.684 % |
26 | Niger | 70.183 % |
27 | Nicaragua | 68.443 % |
28 | Burkina Faso | 68.296 % |
29 | Nigeria | 67.826 % |
30 | Pakistan | 67.41 % |
31 | Comoros | 65.974 % |
32 | Cabo Verde | 65.046 % |
33 | Côte d'Ivoire | 63.225 % |
34 | Rwanda | 62.718 % |
35 | Sao Tome and Principe | 61.4 % |
36 | Togo | 61.164 % |
37 | Zambia | 59.903 % |
38 | Kenya | 59.319 % |
39 | Nepal | 59.08 % |
40 | Equatorial Guinea | 59.001 % |
41 | Senegal | 58.267 % |
42 | Haiti | 58.019 % |
43 | Ecuador | 57.8 % |
44 | Yemen | 57.258 % |
45 | Cameroon | 57.191 % |
46 | Bangladesh | 56.7 % |
47 | Lesotho | 56.532 % |
48 | Timor-Leste | 56.1 % |
49 | Bhutan | 55.624 % |
50 | Iran | 55.624 % |
51 | Sri Lanka | 55.624 % |
52 | Botswana | 53.386 % |
53 | Gabon | 52.752 % |
54 | Gambia | 52.414 % |
55 | Bolivia | 52.285 % |
56 | Ghana | 51.968 % |
57 | Eswatini | 51.64 % |
58 | Guatemala | 49.904 % |
59 | El Salvador | 47.996 % |
60 | Mongolia | 47.673 % |
61 | Philippines | 46.458 % |
62 | Laos | 45.8 % |
63 | Peru | 42.791 % |
64 | Guinea | 42.751 % |
65 | Namibia | 42.184 % |
66 | Honduras | 42.07 % |
67 | Maldives | 41.535 % |
68 | Libya | 38.381 % |
69 | Panama | 37.897 % |
70 | Iraq | 37.755 % |
71 | Myanmar | 37.422 % |
72 | Kyrgyzstan | 35.979 % |
73 | Azerbaijan | 35.932 % |
74 | Paraguay | 34.534 % |
75 | Brunei Darussalam | 33.243 % |
76 | Vietnam | 32.957 % |
77 | Indonesia | 30.99 % |
78 | Syrian Arab Republic | 30.872 % |
79 | State of Palestine | 29.536 % |
80 | Egypt | 28.451 % |
81 | Mexico | 27.875 % |
82 | South Africa | 26.658 % |
83 | Brazil | 26.472 % |
84 | Morocco | 26.185 % |
85 | Zimbabwe | 26.033 % |
86 | Venezuela | 25.7 % |
87 | Guyana | 24.494 % |
88 | Dominican Republic | 24.247 % |
89 | Angola | 22.726 % |
90 | Uzbekistan | 22.579 % |
91 | Uruguay | 21.813 % |
92 | Albania | 21.75 % |
93 | Turkey | 21.096 % |
94 | Papua New Guinea | 19.925 % |
95 | Republic of Moldova | 19.4 % |
96 | Argentina | 18.478 % |
97 | Montenegro | 17.9 % |
98 | Colombia | 17.816 % |
99 | Kazakhstan | 17.498 % |
100 | Belize | 15.755 % |
101 | Kiribati | 14.95 % |
102 | Fiji | 14.75 % |
103 | Georgia | 12.831 % |
104 | Hungary | 12.25 % |
105 | Armenia | 11.573 % |
106 | Trinidad and Tobago | 10.67 % |
107 | Costa Rica | 10.408 % |
108 | Turkmenistan | 10.382 % |
109 | Solomon Islands | 9.675 % |
110 | Suriname | 9.523 % |
111 | Chile | 7.312 % |
112 | Ireland | 6.8 % |
113 | Bosnia and Herzegovina | 5 % |
114 | Vanuatu | 4.5 % |
115 | Austria | 4.4 % |
116 | Ukraine | 4.25 % |
117 | Samoa | 4.225 % |
118 | Saint Lucia | 4.2 % |
119 | Cayman Islands | 4.15 % |
120 | Tuvalu | 4 % |
121 | Romania | 3.575 % |
122 | Russia | 3.425 % |
123 | Oman | 3.225 % |
124 | Lithuania | 3.075 % |
125 | British Virgin Islands | 2.724 % |
126 | Cuba | 2.678 % |
127 | Nauru | 2.5 % |
128 | Latvia | 2.275 % |
129 | Marshall Islands | 2.1 % |
130 | Tonga | 1.725 % |
131 | Palau | 1.6 % |
132 | Poland | 1.4 % |
133 | North Macedonia | 1.225 % |
134 | United Arab Emirates | 0.95 % |
135 | Portugal | 0.85 % |
136 | Serbia | 0.6 % |
137 | Croatia | 0.375 % |
138 | Canada | 0.275 % |
139 | Sweden | 0.25 % |
140 | Cyprus | 0.225 % |
141 | Greece | 0.225 % |
142 | United States | 0.2 % |
143 | Australia | 0.135 % |
144 | Bulgaria | 0.1 % |
145 | United Kingdom | 0.1 % |
146 | Estonia | 0.075 % |
147 | Bermuda | 0.066 % |
148 | Czech Republic | 0.05 % |
149 | Slovakia | 0.05 % |
150 | Malta | 0.038 % |
151 | Italy | 0.02 % |
152 | Luxembourg | 0.002 % |
153 | Andorra | 0 % |
154 | Aruba | 0 % |
155 | Belgium | 0 % |
156 | Denmark | 0 % |
157 | Finland | 0 % |
158 | France | 0 % |
159 | Germany | 0 % |
160 | Iceland | 0 % |
161 | Kuwait | 0 % |
162 | Monaco | 0 % |
163 | Netherlands | 0 % |
164 | New Zealand | 0 % |
165 | Norway | 0 % |
166 | Singapore | 0 % |
167 | Switzerland | 0 % |
- #1
South Sudan
- #2
Sudan
- #3
Lebanon
- #4
Chad
- #5
Madagascar
- #6
Ethiopia
- #7
Congo
- #8
Mozambique
- #9
Central African Republic
- #10
Mauritania
Analysis: These countries represent the highest values in this dataset, showcasing significant scale and impact on global statistics.
- #167
Switzerland
- #166
Singapore
- #165
Norway
- #164
New Zealand
- #163
Netherlands
- #162
Monaco
- #161
Kuwait
- #160
Iceland
- #159
Germany
- #158
France
Context: These countries or territories have the lowest values, often due to geographic size, administrative status, or specific characteristics.
Analysis & Context
In 2005, South Sudan had the highest Population living in slums at 99.8%, while the global range for this metric spanned from 0.00% to 99.80%. The global average was 32.25%, providing a stark contrast between countries with severe urban poverty and those with none.
Economic Disparities and Slum Populations
The substantial variation in the Population living in slums across countries often reflects underlying economic disparities. Countries like South Sudan (99.8%), Sudan (99.09415%), and Lebanon (92.4051%) illustrate the extreme end of urban poverty. These nations face significant economic challenges, including limited infrastructure investment and political instability, which contribute to high slum populations. In contrast, countries such as Iceland, Denmark, and Singapore report a slum population of 0%, indicating robust economic frameworks and effective urban planning policies.
Urbanization and Infrastructure Development
Rapid urbanization without corresponding infrastructure development often leads to increased slum populations. In Chad (88.76555%) and Madagascar (85.4318%), urban migration exceeds the capacity of cities to provide adequate housing, resulting in informal settlements. Conversely, nations with minimal slum populations, such as New Zealand and Switzerland, demonstrate how strategic urban planning and investment in infrastructure can mitigate the pressures of urbanization.
Policy Interventions and Slum Reduction
Policy interventions play a crucial role in reducing slum populations. Eswatini experienced the largest decrease in slum populations with a reduction of 3.71% (-6.7%), reflecting successful policy measures and investment in housing. Similarly, Libya and Congo saw decreases of 3.21% (-7.7%) and 3.12% (-3.7%), respectively, indicative of governmental efforts to improve living conditions and urban infrastructure.
Trends and Anomalies in Year-over-Year Changes
Examining year-over-year changes reveals intriguing trends and anomalies. Angola, for instance, saw the largest increase in slum populations at 3.07% (15.6%), highlighting challenges in managing rapid urban growth. Meanwhile, Myanmar and Iraq experienced increases of 1.60% (4.5%) and 0.77% (2.1%), respectively, suggesting that economic or political factors may be impeding effective urban management. These changes underscore the dynamic nature of slum populations and the need for adaptive urban policies.
In summary, the Population living in slums in 2005 highlights significant global disparities driven by economic conditions, urbanization rates, and policy effectiveness. Countries with high slum populations often face economic challenges and lack adequate infrastructure, while those with low percentages benefit from strategic planning and robust economies. Addressing these disparities requires targeted interventions and policies that promote sustainable urban development and improve living conditions for the urban poor.
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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