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Kibera’s morphologic transformation JURSE2019 Poster Nicolas J Kraff



Urban morphologies change over time. The dynamics and nature of morphological changes in informal settlements or slums have largely not been scientifically investigated. Consequently, it is necessary to fill the gap of the international demand for timeline analysis. In this work, earth observation (EO) is used to discover morphologic changes within eight years (2006-2014) in Nairobi’s major slum Kibera. Research mostly handles automated detection but in this study the classical visual image interpretation is applied on a very high level of detail capturing buildings in three dimensions. Consistencies and deviations in time are measured according to morphological variables. We find dynamics in the slum area high in terms of a 77% rise in number of buildings due to arising, splitting, upgrading or demolishing; at the same time, density increases only by 10%. Overall, the general pattern of complex, organic structure remains mostly unchanged.
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Motivation and background
• 1.6 bio. people expected to live in slums
by the year 2025 (Woetzel et al., 2014)
• International appeal
to reduce poverty,
Sustainable Development
Goal 1 (United Nations, 2017)
Temporal dynamics of complex
building alignments have
hardly been subject of research
(Kuer, Pfeer & Sliuzas, 2016)
Classication of 3D buildings (LoD1) by visual
image interpretation (Taubenböck & Kra, 2014)
• Using 6 detailed spatial variables to measure
organic dynamics entirely, blockwise
and for single constructions:
1. building height
2. building size
3. building density
4. orientation of buildings
5. heterogeneity of the pattern
6. distance to neighbouring buildings
Application of categorization (A-D) of
morphologic forms of urban poverty
(Taubenböck, Kra, & Wurm, 2018) i n K i b e r a f o r
2006 and 2014
Kibera‘s morphologic transformation
05/24/2014 © Google Earth
600 m1500
t1 + t2
Change in heterogeneity
< -0,5 Std. Dev.
-0,5 - 0,5 Std. Dev.
0,5 - 1,5 Std. Dev.
1,5 - 2,5 Std. Dev.
> 2,5 Std. Dev.
Max. (%)
Min. (%)
Change in
no. of buildings
building size
building height
• Signicant increase in number of
buildings (76.9%) and building
density (10.1%)
• Signicant decrease in
building sizes (-37.8%)
• General morphologic patterns
and category remain stable
• Population growth aecting the urban
morphology by splitting, re-building and re-
organizing buildings
Nicolas J. Kra, Hannes Taubenböck & Michael Wurm
German Remote Sensing Data Center (DFD) , German Aerospace Center (DLR), Wessling, Germany
Woetzel, J., Ram, S., Mischke, J., Garemo, N., Sankhe, S. (2014): A blueprint for addressing the global af-
fordable housing challenge. McKinsey Global Institute (2014)
United Nations (2017): The Sustainable Development Goals Report 2017. New York.
Kuer, M., Pfeer, K., & Sliuzas, R. (2016): Slums from space—15 years of slum mapping using remote sen-
sing. Remote Sensing, 8(6), 455.
Taubenböck, H., & Kra, N. J. (2014): The physical face of slums: A structural comparison of slums in Mum-
bai, India, based on remotely sensed data. Journal of Housing and the Built Environment, 29(1), 15–38 [On-
line rst 2013], print 03/2014) March 2014.
Taubenböck, H., Kra, N. J., & Wurm, M. (2018): The morphology of the Arrival City-A global categorization
based on literature surveys and remotely sensed data. Applied Geography, 92, 150-167.
11 Change in density (%)
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