Population living in slums 2012
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 | 94.202 % |
2 | Chad | 84.82 % |
3 | Sudan | 83.833 % |
4 | Madagascar | 77.026 % |
5 | Guinea-Bissau | 75.248 % |
6 | Congo, Democratic Republic of the | 75.138 % |
7 | Ethiopia | 73.6 % |
8 | Niger | 70.386 % |
9 | Benin | 69.506 % |
10 | Liberia | 69.24 % |
11 | Mozambique | 69.014 % |
12 | Central African Republic | 68.913 % |
13 | Comoros | 68.046 % |
14 | Afghanistan | 67.248 % |
15 | Uganda | 64.768 % |
16 | Mauritania | 64.434 % |
17 | Djibouti | 62.17 % |
18 | Eritrea | 62.17 % |
19 | Mauritius | 62.17 % |
20 | Seychelles | 62.17 % |
21 | Somalia | 62.17 % |
22 | Pakistan | 62.073 % |
23 | Sierra Leone | 61.597 % |
24 | Equatorial Guinea | 61.417 % |
25 | Malawi | 60.327 % |
26 | Congo | 59.763 % |
27 | Nigeria | 59.032 % |
28 | Mali | 58.708 % |
29 | Côte d'Ivoire | 57.833 % |
30 | Ecuador | 57.8 % |
31 | Sao Tome and Principe | 56.52 % |
32 | Cabo Verde | 56.461 % |
33 | Tanzania | 55.782 % |
34 | Burundi | 55.593 % |
35 | Zambia | 54.469 % |
36 | Bangladesh | 54.446 % |
37 | Kenya | 53.906 % |
38 | Haiti | 53.45 % |
39 | Bhutan | 52.944 % |
40 | Iran | 52.944 % |
41 | Sri Lanka | 52.944 % |
42 | Cambodia | 51 % |
43 | Rwanda | 50.533 % |
44 | Togo | 49.812 % |
45 | Nepal | 48.958 % |
46 | Burkina Faso | 48.815 % |
47 | Myanmar | 48.654 % |
48 | Yemen | 48.083 % |
49 | Bolivia | 46.626 % |
50 | Gabon | 46.204 % |
51 | Gambia | 46.104 % |
52 | Peru | 46.083 % |
53 | Timor-Leste | 46 % |
54 | Botswana | 45.957 % |
55 | Senegal | 45.825 % |
56 | Cameroon | 45.749 % |
57 | Guinea | 45.67 % |
58 | Angola | 44.187 % |
59 | Iraq | 43.161 % |
60 | Guatemala | 42.076 % |
61 | Lesotho | 42.075 % |
62 | Namibia | 41.663 % |
63 | Philippines | 41.537 % |
64 | Ghana | 40.207 % |
65 | Maldives | 38.748 % |
66 | Honduras | 33.88 % |
67 | Laos | 33.8 % |
68 | Mongolia | 33.769 % |
69 | Algeria | 28.887 % |
70 | El Salvador | 28.483 % |
71 | Brunei Darussalam | 27.042 % |
72 | Azerbaijan | 26.927 % |
73 | Venezuela | 25.7 % |
74 | Eswatini | 25.656 % |
75 | Syrian Arab Republic | 25.434 % |
76 | South Africa | 25.325 % |
77 | Indonesia | 25.201 % |
78 | Libya | 24.3 % |
79 | Paraguay | 24.044 % |
80 | Zimbabwe | 23.95 % |
81 | State of Palestine | 23.351 % |
82 | Panama | 22.807 % |
83 | Mexico | 21.873 % |
84 | Papua New Guinea | 21.3 % |
85 | Kyrgyzstan | 20.327 % |
86 | Guyana | 18.729 % |
87 | Lebanon | 17.085 % |
88 | Morocco | 16.427 % |
89 | Turkey | 16.217 % |
90 | Dominican Republic | 15.976 % |
91 | Argentina | 15.938 % |
92 | Belize | 15.741 % |
93 | Vietnam | 15.654 % |
94 | Brazil | 14.897 % |
95 | Montenegro | 13.6 % |
96 | Colombia | 13.11 % |
97 | Albania | 12.9 % |
98 | Uzbekistan | 12.835 % |
99 | Suriname | 12.452 % |
100 | Fiji | 11.4 % |
101 | Kiribati | 10.6 % |
102 | Georgia | 10.147 % |
103 | Armenia | 9.862 % |
104 | Tunisia | 9.644 % |
105 | Turkmenistan | 9.507 % |
106 | Republic of Moldova | 9.4 % |
107 | Egypt | 9.165 % |
108 | Uruguay | 8.773 % |
109 | Trinidad and Tobago | 7.94 % |
110 | Hungary | 7.9 % |
111 | Kazakhstan | 7.754 % |
112 | Ireland | 7.7 % |
113 | Chile | 7.307 % |
114 | Nicaragua | 7.255 % |
115 | Costa Rica | 7.206 % |
116 | South Korea | 7 % |
117 | Cuba | 6.348 % |
118 | Solomon Islands | 4.6 % |
119 | Bosnia and Herzegovina | 4.3 % |
120 | Cayman Islands | 4.15 % |
121 | Vanuatu | 4.15 % |
122 | Russia | 3.15 % |
123 | Austria | 2.767 % |
124 | Romania | 2.65 % |
125 | Antigua and Barbuda | 2.646 % |
126 | Saint Lucia | 2.2 % |
127 | Samoa | 1.85 % |
128 | Ukraine | 1.8 % |
129 | British Virgin Islands | 1.639 % |
130 | Lithuania | 1.55 % |
131 | Latvia | 1.3 % |
132 | Tonga | 1.1 % |
133 | Marshall Islands | 1 % |
134 | Oman | 1 % |
135 | Nauru | 0.9 % |
136 | Palau | 0.85 % |
137 | North Macedonia | 0.65 % |
138 | Canada | 0.55 % |
139 | Serbia | 0.5 % |
140 | United Arab Emirates | 0.5 % |
141 | Tuvalu | 0.35 % |
142 | Croatia | 0.3 % |
143 | Cyprus | 0.25 % |
144 | Poland | 0.25 % |
145 | Portugal | 0.25 % |
146 | United States | 0.2 % |
147 | Bulgaria | 0.15 % |
148 | Sweden | 0.15 % |
149 | Bermuda | 0.1 % |
150 | Estonia | 0.1 % |
151 | United Kingdom | 0.1 % |
152 | Australia | 0.065 % |
153 | Czech Republic | 0.05 % |
154 | Greece | 0.05 % |
155 | Slovakia | 0.05 % |
156 | Malta | 0.038 % |
157 | Italy | 0.02 % |
158 | Luxembourg | 0.011 % |
159 | Belarus | 0.004 % |
160 | Andorra | 0 % |
161 | Aruba | 0 % |
162 | Belgium | 0 % |
163 | Denmark | 0 % |
164 | Finland | 0 % |
165 | France | 0 % |
166 | Germany | 0 % |
167 | Iceland | 0 % |
168 | Kuwait | 0 % |
169 | Monaco | 0 % |
170 | Netherlands | 0 % |
171 | New Zealand | 0 % |
172 | Norway | 0 % |
173 | Qatar | 0 % |
174 | Singapore | 0 % |
175 | Switzerland | 0 % |
↑Top 10 Countries
- #1
South Sudan
- #2
Chad
- #3
Sudan
- #4
Madagascar
- #5
Guinea-Bissau
- #6
Congo, Democratic Republic of the
- #7
Ethiopia
- #8
Niger
- #9
Benin
- #10
Liberia
Analysis: These countries represent the highest values in this dataset, showcasing significant scale and impact on global statistics.
↓Bottom 10 Countries
- #175
Switzerland
- #174
Singapore
- #173
Qatar
- #172
Norway
- #171
New Zealand
- #170
Netherlands
- #169
Monaco
- #168
Kuwait
- #167
Iceland
- #166
Germany
Context: These countries or territories have the lowest values, often due to geographic size, administrative status, or specific characteristics.
Analysis & Context
The statistic of "Population living in slums" for the year 2012 serves as a critical indicator of urban poverty, housing inadequacies, and living conditions across the globe. By examining the percentage of people in each country who reside in slum areas, we can gain insight into the socioeconomic challenges faced by urban populations and the necessity for policy interventions aimed at improving living conditions in rapidly urbanizing regions.
Global Trends in Urban Poverty
In 2012, the issue of urban poverty was starkly highlighted by the alarming statistics of slum living conditions. Approximately 26.95% of people globally resided in slum areas, with a median of 17.09%, underscoring the widespread nature of this challenge. South Sudan reported the highest percentage of its population living in slums at a staggering 94.20%, reflecting the severe socioeconomic and political hurdles it faced as a newly independent nation. Meanwhile, other countries like Chad (84.82%) and Sudan (83.83%) followed closely, showcasing the pervasive nature of slum conditions in regions plagued by ongoing conflict and economic instability.
Regional Comparisons and Variations
Regional analysis reveals significant disparities in slum populations. African nations dominated the list of countries with the highest percentages of slum dwellers, with eight of the top ten countries being from the continent. This highlights Africa's unique challenges concerning rapid urbanization, lack of infrastructure, and limited access to affordable housing. In contrast, European countries like Finland, Denmark, and Norway reported a 0% slum population, indicating a stark contrast in living conditions and effective urban planning policies. Such comparisons underline the necessity for targeted international support and policy adaptations for countries struggling with high slum populations.
Policy Implications and Urban Planning
The widespread existence of slums in 2012 brought to light the urgent need for reformative urban planning and housing policies. Nations with high slum populations, such as Madagascar (77.03%) and Ethiopia (73.60%), were in dire need of effective strategies to address housing shortages and improve infrastructure. Meanwhile, countries with negligible slum populations demonstrated the benefits of comprehensive urban planning initiatives that prioritize sustainable development and equitable resource distribution. The data from 2012 serves as a pivotal reference point for policymakers aiming to design interventions that reduce urban poverty and enhance living standards globally.
Environmental and Socioeconomic Effects
Living in slums has far-reaching implications not only for the individuals and families directly affected but also for broader socioeconomic and environmental systems. Overcrowding, inadequate sanitation, and insufficient access to clean water are prevalent issues in slum areas, which can exacerbate public health challenges and environmental degradation. The 2012 statistics reveal the pressing need for integrated approaches that address these multifaceted issues, as slum conditions often hinder economic productivity and contribute to cycles of poverty. Understanding these dynamics is crucial for crafting policies that foster sustainable urban environments and improve quality of life.
Historical Context and Long-term Trends
The persistence of slum populations over time points to longstanding historical and structural factors that perpetuate inequality and hinder development. The data from 2012 indicates a continuation of trends observed in previous years, where developing regions consistently report higher percentages of slum dwellers. Addressing these issues requires a long-term commitment to structural reforms, international collaboration, and investment in sustainable development goals. The focus must be on creating inclusive cities that proffer equitable opportunities and resources for all, thus reducing the necessity for slum settlements.
Overall, the 2012 data on the population living in slums provides a critical lens through which to examine the complexities of urbanization and poverty. It underscores the urgent need for comprehensive and coordinated efforts to tackle the root causes of slum proliferation and highlights the essential role of policy innovation in fostering sustainable urban development on a global scale.
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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