Size distributions of slums across the globe using different data and classification methods

ABSTRACT More than 900 million people worldwide live in slums. These slums mainly can be found in cities of the global south and are characterized by poor living conditions and usually insufficient access to basic infrastructure such as water or energy. In order to improve the living conditions of slum inhabitants, information about the number, location and size of the slums is required to plan supply infrastructure. We therefore identify morphological slums in eight different cities in Africa, South America and Asia, using remote sensing data and analyse their size distributions. We show that 84.6% of all observed morphological slums have a size between 0.001 and 0.1 km2. These results rely on a consistent approach using a clear ontology and conceptual frame for classification. However, classification methods for these underserved areas differ. We show slum classifications based on different methods reveal a strong dependency between the particular method and the resulting size distribution. The study shows the relevance of remote sensing for the investigation of slums and the results can be used for infrastructure planning, as infrastructure improvement projects are often limited to the large known slums. Whereas, the large number of small slums distributed across the city is often neglected.

[1]  John Friesen,et al.  A Holistic Concept to Design Optimal Water Supply Infrastructures for Informal Settlements Using Remote Sensing Data , 2018, Remote. Sens..

[2]  P. Agouris,et al.  The study of slums as social and physical constructs: challenges and emerging research opportunities , 2016 .

[3]  D. Rink,et al.  Der Umzug der Menschheit: Die transformative Kraft der Städte , 2018 .

[4]  T. Robinson,et al.  Sustainable Development Goals , 2016 .

[5]  H. Taubenböck,et al.  The similar size of slums , 2018 .

[6]  John Friesen,et al.  Providing water for the poor - towards optimal water supply infrastructures for informal settlements by using remote sensing data , 2017, 2017 Joint Urban Remote Sensing Event (JURSE).

[7]  David E Bloom,et al.  The psychological toll of slum living in Mumbai, India: a mixed methods study. , 2014, Social science & medicine.

[8]  Alfred Stein,et al.  Uncertainty analysis for image interpretations of urban slums , 2016, Comput. Environ. Urban Syst..

[9]  Anthony Capon,et al.  The history, geography, and sociology of slums and the health problems of people who live in slums , 2017, The Lancet.

[10]  B. Bruggen,et al.  Causes of Water Supply Problems in Urbanised Regions in Developing Countries , 2010 .

[11]  Monika Kuffer,et al.  Coupling Uncertainties with Accuracy Assessment in Object-Based Slum Detections, Case Study: Jakarta, Indonesia , 2017, Remote. Sens..

[12]  H. Taubenböck,et al.  The morphology of the Arrival City - A global categorization based on literature surveys and remotely sensed data , 2018 .

[13]  H. Taubenböck,et al.  Detecting social groups from space – Assessment of remote sensing-based mapped morphological slums using income data , 2018 .

[14]  H. Taubenböck,et al.  The physical face of slums: a structural comparison of slums in Mumbai, India, based on remotely sensed data , 2014 .

[15]  Monika Kuffer,et al.  Slums from Space - 15 Years of Slum Mapping Using Remote Sensing , 2016, Remote. Sens..

[16]  Alfred Stein,et al.  An ontology of slums for image-based classification , 2012, Comput. Environ. Urban Syst..

[17]  Andrew Crooks,et al.  A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities , 2018 .

[18]  Monika Kuffer,et al.  Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai , 2017, Remote. Sens..

[19]  Patrick Hostert,et al.  Mapping the Slums of Dhaka from 2006 to 2010 , 2014 .

[20]  Kristian Giesen,et al.  The size distribution across all cities - Double Pareto lognormal strikes , 2010 .

[21]  María Vera-Cabello,et al.  Size Distributions for All Cities: Which One is Best? , 2015 .

[22]  Anthony Capon,et al.  Improving the health and welfare of people who live in slums , 2017, The Lancet.

[23]  Hannes Taubenböck,et al.  Spatial patterns of slums: Comparing African and Asian cities , 2017, 2017 Joint Urban Remote Sensing Event (JURSE).