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This article is published as: Vermeiren K, Van Rompaey A, Loopmans M, Serwajja E, Mukwaya P
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(2012) Urban growth of Kampala, Uganda: pattern analysis and scenario development. Landscape
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and urban planning: 106: 199-206.
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Urban growth of Kampala, Uganda: pattern analysis and scenario development
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Vermeiren Karolien a, Van Rompaey Anton a, Loopmans Maarten a, Serwajja Eria b, Mukwaya
Paul b
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Abstract:
Kampala, the capital of Uganda, is one of the fastest growing African cities with annual
growth rates of 5.6%. The rapid urban growth causes major socio-economic and environmental
problems that lower the quality of life of the urban dwellers. A better insight in the controlling factors
of the urban growth pattern is necessary to develop and implement a sustainable urban planning. The
recent urban growth of Kampala was mapped using LANDSAT images of 1989, 1995, 2003 and 2010. A
spatially-explicit logistic regression model was developed for Kampala. Significant predictors in this
model included: the presence of roads, the accessibility of the city centre and the distance to existing
built-up area. These variables are used as steering handles to create future urban scenarios. Three
alternative scenarios for future urban growth were developed: a business as usual, restrictive and
stimulative scenario. Our model of growth was applied to these three scenarios to predict patterns of
urban growth to 2030. The scenarios show that the alternative policy options result in contrasting
future urban sprawl patterns with a significant impact on the local quality of life.
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Africa is rapidly urbanizing (UN, 2010). In 1950 only two African cities counted more than 1
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million inhabitants. Today there are 48 (Figure 1) and this number is predicted to rise to 68 by
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2025 (UN-Habitat, 2008). Urban growth is strongest in medium-sized cities of less than 5 million
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people (Cohen, 2004; Redman and Jones, 2005; UN-Habitat, 2010). Present urban growth in
a Geography
Research Group, Department of Earth and Environmental Science, K.U. Leuven, Celestijnenlaan
200E, 3001 Heverlee, Belgium
b Department
of Geography, Geo-informatics and Climatic Sciences, Makerere University, P.O. Box 7062,
Kampala, Uganda
Corresponding author:
Karolien Vermeiren
Karolien.vermeiren@ees.kuleuven.be
Tel. +32 16 326414
1. Introduction
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Africa is driven by both high natural growth rates of the urban population and ongoing
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immigration from the rural areas (Cohen, 2004; Kessides and Alliance, 2006; UN-Habitat, 2010).
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Moreover, burgeoning cities absorb neighbouring villages as they engulf the countryside
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(Redman and Jones, 2005).
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African urban population (in particular its marginalized and poorer segments) has largely
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depended on unplanned and informal settlements for housing provision (Bishop et al., 2000;
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Keiner et al., 2005). The dominance of informal, unmonitored, irregularly placed housing
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provision puts a strain on existing infrastructure. This can lead to inadequate sanitation,
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unreliable water supply, intermittent electricity and over-burdened transportation (Guneralp and
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Seto, 2008). As a consequence, urban living is often challenging for these new migrants.
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Urban planning systems struggling with a lack of effective tools and instruments, fail to address
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these challenges (Barredo and Demicheli, 2003; Myers, 2011)
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These mounting challenges have prompted renewed debate about planning in Africa (Todes,
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2011) and there has been a call to move away from western modernism towards a more fruitful
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dialogue with the marginalized majority (Harrison, 2006; Watson, 2007; Trefon, 2009; Myers,
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2011). Traditional planning is preventative and focuses on restrictive regulation whereby
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developments have to follow zoning laws (Harloe and Pickvance, 1990). Kamete (2011) argues
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that such a focus on punitive and rigid planning enforcement has proven to be ineffective and in
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some cases counter-productive when managing informal development. Instead of prohibiting
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unwanted constructions, innovative policy focuses on constructing and upgrading infrastructure
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and the development of urban facilities to stimulate desired urban developments (Harloe and
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Pickvance, 1990).
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Land use modelling can contribute to such a planning shift by exploring alternative approaches
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to urban planning in an African context. However, since land use modelling became embedded
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in planning in the 1950s it has been strongly tied to the Western planning paradigm. In the 1970s
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land use modelling together with rational strategic planning became criticized. In a seminal
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critique, Lee (1973) announced the fall of land use modelling as a planning tool by blaming it for
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being hyper comprehensive, gross, hungry for data, wrong headed, complicated, mechanical and
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expensive.
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However, the use of land use modelling in planning appears to have experienced a revival lately
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(Couclelis, 2005; Rabino, 2008). This revival may be partly due to technological advances which
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have rebutted some of the earlier critiques, but is also related to innovative thinking in relation to
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the applicability of land use modelling in new types of planning. Couclelis (2005) in particular,
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has identified the potential of land use modelling in planning as a means of bringing the
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normative, goal-oriented identity of planning back in touch with the complexity of everyday life.
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Modelling can contribute to planning through (i) the development of scenarios of alternative
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futures; (ii) supporting visioning processes aimed at engaging stakeholders around a common set
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of desired ends and (iii) effective storytelling to make the consequences of different alternatives
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tangible.
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Nonetheless, many of the thresholds for application of land use models in planning identified by
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Lee (1973) remain in place and inhibit its wide scale application, in particular in developing
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contexts where the cost of modelling tools, gathering data and acquiring the skills to process
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them remains difficult. Few growth models have been developed for African cities (Manu et al.,
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2003; Barredo et al., 2004; Taubenböck et al., 2011) because of a lack field-based validation data
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and a lack of GIS-knowledge to develop and implement such models. Equally, urban planners in
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many developing countries lack spatial and the appropriate GIS-software to process these data
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(Jat et al., 2008).
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To overcome these practical issues, this paper explores the possibilities of freely available spatial
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data to detect, validate and develop urban growth models for the African continent. Recent
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studies have revealed the potential of relatively cheap and archived medium-resolution satellite
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imagery (LANDSAT, SPOT and ASTER) for monitoring urban spatial dynamics (Griffiths et al.,
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2010). Such monitoring is useful, providing planners with inexpensive base maps that are often
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more reliable than outdated paper maps (Maktav and Erbek, 2005; Redman and Jones, 2005).
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This paper explores how these satellite images can be enhanced through the development and
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application of spatially-explicit models of urban change. Such spatially explicit urban expansion
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models have been developed for European and Northern-American urban areas (Engelen et al.,
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1995; Clarke and Gaydos, 1998; Poelmans and Van Rompaey, 2010). This paper explores the
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potential of such models to support innovative planning dynamics in African cities.
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The rapidly expanding metropolitan area of Kampala (Uganda) is taken as an example
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application. In a first step urban expansion is mapped on the basis of archived LANDSAT
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images of 1989, 1995, 2003 and 2010. Secondly the observed expansion pattern is correlated
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with accessibility indicators and environmental indicators. On the basis of logistic regression
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equations urban expansion probabilities for the (peri-)urban area are assessed and validated.
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Three different urban expansion scenarios for 2020 and 2030 were developed: (i) a business as
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usual scenario whereby a continued trend of recent urban growth is modelled, (ii) a restrictive
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policy scenario that prevents constructions in undesired locations and (iii) a stimulative policy
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scenario that assumes the update and expansion of present urban infrastructure to stimulate
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development in a sustainable way. These scenarios allow us to evaluate how Kampala could look
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in the near future if different planning strategies are followed.
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2. Study approach
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2.1.
Study area
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Kampala, capital of Uganda, is situated on the northern shores of Lake Victoria. Since 1970,
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Kampala has experienced exponential population growth from 330,000 to 1.5 million in 2009
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(UBOS, 2009). The average population density is 6,100 persons per km² (Figure 2) with slum
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areas rising to 30,000ppkm² (UBOS, 2009).
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During the last two decades, the city has expanded in all directions and incorporated former
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satellite towns such as Mukono, Entebbe, Mpigi and Bombo and surrounding rural areas
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(NEMA, 2009). Recent growth spatially extended beyond the administrative city boundary,
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creating an urban surface covering more than 800 km², referred to as Kampala greater
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metropolitan area in this paper (Figure 2). The paved roads in Kampala form a radial transport
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system that guides national as well as international traffic through the city centre, leading to
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heavy traffic jams. Commercial activities along these main routes have led to high population
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densities. Due to a lack of adequate investment the transport network has not been significantly
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upgraded since 1989 apart from a northern bypass funded by the European Union and the
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Uganda Government which was completed in 2009 (Figure 3). This ring road was constructed to
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allow cross-country traffic to avoid the city centre and reduce traffic pressure in the Central
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Business District.
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The UN-Habitat (2007) assessed the slum area of Kampala City to be 21% of the total city area,
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housing 39% of the total city population in 2002. Large parts of the recent residential area
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consist of informal houses that are constructed by poor immigrants in wetland areas and run
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counter to environmental planning standards in the city (Figure 3). The wetland areas are
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attractive since they are relatively free from government policing, easy accessible and they
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provide opportunities for urban farming (Kabumbuli and Kiwazi, 2009), one of the most
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accessible survival strategies for newly arriving rural immigrants. An alternative income source
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for new settlers is brick making whereby clay from the wetlands is dug out. As a consequence
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slum areas near and partially inside the wetland increase year after year. Successful immigrants
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able to acquire a formal income may escape from the slum areas and move towards more healthy
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neighbourhoods on the foot slopes.
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The main wetlands areas act as a natural buffer zone between the city and the lake through which
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all the urban waste and sediment-loaded water from the city is drained and naturally filtered
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before it is released into the lake ecosystem. The recent urban encroachments are therefore
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regularly flooded by polluted water and reduce the natural cleaning function of the wetlands
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(Lwasa, 2004; Kabumbuli and Kiwazi, 2009). Recent research (Scheren et al., 2000; Oyoo,
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2009) have shown that increasing polluted sediment influx in Lake Victoria from the urban areas
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along the lake shore threatens the lake’s ecosystem, its fishing and other lake-related industries
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and the city’s drinking water provision.
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2.2.
Land cover change maps
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Landsat TM (row 171, path 60) (1989, 1995, 2010) and Landsat ETM+ (row 171, path 60)
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(2003) images were used to create land cover maps for the Kampala metropolitan area, covering
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a 70x70km² area (Table 1). All images were geometrically registered to the Universal Transverse
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Mercator (UTM36N) coordinate system. The Landsat TM images consisted of 7 spectral bands
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with spatial resolution of 30m for bands 1 to 5 and 7. Band 6 of the Landsat TM was resampled
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from 120m to 30m resolution. The Landsat ETM+ image consisted of 8 spectral bands with
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spatial resolution of 30m for bands 1 to 7, and a spatial resolution of 15m for band 8. This band
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was resampled from 15 to 30m. By means of a supervised maximum likelihood classification
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each pixel was classified in one of the following classes: built-up land, non-built-up land and
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water surface. Built-up areas include all paved areas such as residential, commercial, industrial
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buildings, roads and parking lots.
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Training sites for each cover class were delimited by means of a visual interpretation of (i) true
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colour composites of the Landsat imagery (ii) multitemporal very high resolution imagery
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available through Google Earth and (iii) field observations. The accuracies of the classified land
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cover maps were assessed using 200 randomly selected validation points outside of the training
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sites. The land cover of the validation points was identified for each of the selected years as
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follows: (i) for 1989: a visual interpretation of the true colour composite of the Landsat TM
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image (ii) for 1995: the Kampala topographic map 1998 (Survey and Mapping Department
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Uganda, 1998) on a scale of 1: 50 000, this map is based on aerial photographs of 1995 (iii) for
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2003 and 2010: a visual interpretation of the multitemporal very high resolution imagery
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available through Google Earth. The compiled land cover maps were post processed by imposing
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that non built-up area was never built-up in a previous time period. Finally an overlay of the 4
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compiled land cover maps resulted in an urban growth map (1989-2010) as shown in Figure 4a.
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2.3.
Assessment of future urban expansion
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For each non-built pixel in the Kampala region an urbanisation probability was calculated
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through means of a multiple logistic regression. This allowed to (i) evaluate the significance of
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possible controlling factors, (ii) quantify the changing push and pull effects of these controlling
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factors and to (iii) develop spatially-explicit scenarios of future urban growth. For each of the
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observed time-periods a logistic regression model was calibrated: 1989-1995, 1995-2003 and
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2003-2010. A comparison of the model equations allowed the detection of potential changes in
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the role of the different controlling factors over time.
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P(U) was assessed on the basis of the following predicting variables: distance to roads (DR),
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distance to city centre (DC), topographic setting (TS), slope gradient (SG) and built-up potential
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(BP) (Figure S2).
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All roads accessible for motorized traffic and the city centre were digitized from Google Earth.
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For each pixel the distance to the nearest road and to the city centre was calculated. Slope
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gradients were derived from a 30m resolution ASTER digital elevation model (DEM). Based on
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the DEM and field based flood risk assessments each pixel was assigned to one of the following
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topographic settings: wetland, foot slope and hilltop. The built-up potential is a relative measure
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of accessibility to existing built-up zones and was calculated with the gravity-based formula of
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Hansen (1959):
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Where Jj represents existing built-up pixels, Dij the distance to existing built-up pixels and n is
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the number of built-up pixels in the image.
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Next the logistic regression equation of the following form was calibrated:
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Where P(U) is the urbanisation probability, DR is distance to nearest road (m), DC is distance to
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city centre (m), TS is topographic setting (low, foot slope or hill), SG is slope gradient (deg), BP
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is built-up potential (m-²) and a, b, c, d and e are regression coefficients.
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For the model calibration 10,000 pixels not built-up in a certain time period tn were randomly
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selected. Water pixels were not included in the sample. Sampled pixels that were built-up in
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between tn and the next time period tn+1 were coded as ‘1’ (urbanisation did occur), all others
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were coded as ‘0’ (urbanisation did not occur). Next, by means of a calibration module available
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in the SAS® Software (SAS Institute, 2002-2008) the regression coefficients were calibrated in
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order to maximise the agreement between P(U)-values and the observed urbanisation.
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The procedure assesses an overall model performance and the significance of individual
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predicting variables. All predicting variables that were significant at a 95% confidence level
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were included in the final model. On the basis of this model for each pixel a P(U)-value was
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assessed. The accuracy of this P(U)-map was then evaluated with a ROC-procedure (Pontius and
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Schneider, 2001). In a ROC-analysis true positives (i.e. pixels correctly predicted as new-built
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up) are plotted against false positives (i.e. pixels incorrectly predicted as new built-up) for
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different probability thresholds. For a model without any predictive power such plot results in a
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1:1 line. The ROC-curve of a significant model has more true positives for the same level of
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false positives. The area under the ROC-curve is therefore an indication for the model
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performance.
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2.4.
Scenario building for spatial planning
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On the basis of the assessed P(U)-values for the period 2003-2010, spatially-explicit urbanisation
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scenarios for 2020 and 2030 were generated by (i) assessing the necessary area for future built9
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up land and (ii) selecting the pixels with the highest P(U)-value until the required area is met.
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Three types of scenarios were developed in collaboration with the Planning Department of
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Kampala Capital City Authority: a business as usual scenario, a restrictive scenario and a
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stimulative scenario (Table 2). The business as usual scenario is based on an extrapolation of the
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observed exponential population growth while assuming a constant population density per built-
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up pixel. An exponential population growth up until 2030 is realistic taking into account that
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Uganda has a very young population (49% under 15 (UBOS, 2009)) and a strong rural urban
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migration. In the restrictive scenario the same amount of square kilometres urban growth is met,
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but zoning laws prohibit new built-up land in wetlands and in a predefined list of open zones in
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the city centre to assure the environmental urban life quality. The stimulative scenario uses the
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same constraints as the restrictive scenario but promotes development elsewhere through the
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development of satellite towns as evenly attractive centres for new built-up area as the main
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CBD, the construction of an outer ring way connecting these satellite towns and the development
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of high rise zones in the CBD and the satellite towns. In the proposed high rise zones the future
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population density was assessed at 37,500 people per km² assuming buildings of 15 stories high
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and a living area of 10 m² per person (United Nations, 2000).
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3. Results
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Figure 4a shows the observed urban expansion of Kampala between 1989 and 2010. Growth is
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concentrated along the main roads and existing constructed centres. Between 1989 and 2010 the
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total built-up area exponentially increased from 71 km² to 386 km². Table 1 reveals the pixel-to-
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pixel accuracy and the kappa index of agreement of the compiled land cover maps for the
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different years. The most important sources of classification errors are related to the
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interpretation of mixed pixels and the classification of bare soil surfaces that can be bare
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agricultural land or unpaved roads, parking lots and squares.
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Table 3 shows the calibrated coefficients of the logistic regression equations for the different
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time periods. In all time periods the location of built-up land is significantly correlated with the
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distance to the main roads. In 1989 62 km², representing 87% of all the built-up area, was
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situated within 500m of the main roads. However the pull effect of roads weakened somewhat
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over time which can be explained by saturation effects that resulted in the development of less
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accessible areas. By 2010 255 km², representing 66% of all built-up area, was situated within
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500 m from a main road.
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Kampala’s urban growth follows a largely concentric pattern throughout the studied time periods
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but locations near to the city centre are preferred for new constructions in all time periods. In
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1989 35 km² built-up land was situated within 5 km of the city centre while in 2010 this is 80
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km², representing a decrease from 49% to 21% of all built-up land.
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Foot slope areas form the largest topographic class (60% of the total study area) and comprise
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61% of the present-day built-up. The hilltop areas form green low-density, upper class
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neighbourhoods and house 18% of all built-up area. Twenty one percent of the present built-up
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area is situated in the low lying wetland zones in and around the city centre. Wetlands used to be
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avoided, but are recently encroached by high density slum areas (Figure 3).
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The existing built-up land is mainly situated on land with slope gradients between 2.5° and 10°.
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Some of the recent built-up land is nevertheless situated in flat wetland areas. No significant
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correlations between urban expansion and slope gradients could, however, are detected.
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The urban expansion clearly showed a diffusion process whereby new built-up was mostly
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constructed in the direct neighbourhood of existing built-up land. In all studied time periods
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more than 70% of new built-up area was located within 100 m of an existing built-up zone. In all
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time periods the location of built-up land is significantly correlated with the built-up potential
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(BP).
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Figure 4b shows urbanisation probabilities for all the pixels in the study area that were not built-
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up in 2010. P(U)-values were assessed on the basis of the following equation:
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The ROC-value (Figure S3) of the P(U)-map of 2003 is 82% which means that the assessed
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P(U)-values can be used for reliable predictions of the future urban expansion pattern of
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Kampala. ROC-values for the previous time periods are even higher, implying that the past urban
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growth was even more predictable than the present day urban growth pattern. This can be
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explained by the fact that in the past the expansion pattern was more strongly controlled by the
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location of roads.
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On the basis of the observed urban population growth (Figure S1) the following exponential
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trend was fitted:
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Where Pop(t) = population at year t.
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Assuming a constant population density total built-up area is expected to grow from 386 km² in
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2010 up to 653 km² in 2020 and even almost 1000 km² in 2030.
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According to the business as usual scenario urban sprawl will occur, concentrated along the main
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roads (Figure 5). While this is also prevalent in the restrictive scenario, the stimulative scenario
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reveals a limited urban sprawl.
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The proportion of urban dwellers living within 500 m distance from a road is expected to
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decrease in all scenarios (Figure S4). The restrictive scenario shuts out some nearby locations for
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new built-up land thereby pushing people to more remote areas. The stimulative scenario on the
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other hand opens up the central location for more people. This implies that relatively more
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people will live at easily accessible zones. Similar results are found when evaluating the average
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distance to the Kampala city centre.
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The pressure on hazardous land units is expected to increase: more people will end up living on
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steep slopes and in flood-prone wetlands. The total number of people on steep slopes or/and in
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wetlands is expected to double by 2020 and even triple by 2030 in case of continuation of the
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present trends. In the case of a successful restrictive or stimulative scenario no new wetland areas
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will be populated. The stimulative scenario predicts a significant decrease of percentage of
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people living in wetlands from 21 % in 2010 to 13 and 10 % by respectively 2020 and 2030.
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4. Discussion and conclusions
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A logistic regression model is carried out to detect drivers of urban growth and to develop an
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urban expansion model. A validation of the predicted scenarios showed that the developed model
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allows to predict urban expansion patterns with a relatively high accuracy. The ROC-values
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found in this study are comparable or even higher than ROC-values from similar model
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application in European study sites (Poelmans and Van Rompaey, 2009) suggesting that
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uncontrolled urban growth is easier to predict than controlled urban growth whereby zoning
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plans often overrule the primary controlling factors of urban expansion. This also implies that the
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perceived complexity and non-transparency of informal settlements can be predicted based on
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living preferences that determine the primary controlling factor.
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This study has shown that freely available remote sensing data can be used to detect and map
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urban growth patterns in an African urban context with an acceptable accuracy. Given the data
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scarcity and the lack of funding in developing countries this kind of low-budget application is of
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key importance for the development of urban policies. This urban growth model is easy
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manageable and transparent in developing scenarios what makes it suitable for a developing
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context.
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In some cases patches of bare soil were misclassified as built-up area. The number of such bare
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soil patches is, however, very limited due to the humid-tropical climate of Kampala. Moreover,
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in many cases intra-urban bare soil patches can be considered as part of the urban infrastructure
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such as dust roads and parking lots. The application of similar classification techniques for
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African cities situated in more arid climatic conditions would probably lead to less accurate land
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cover maps since more confusion would occur between non-urban and urban bare soil patches.
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The urban expansion model can be used for development of future urban expansion scenarios
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(Clarke and Gaydos, 1998). Besides trying to predict urban growth as accurately as possible
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according to past trends as shown in the business as usual scenario, an urban growth model has
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more important application possibilities (Couclelis, 2005). The urban expansion model that was
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developed in this study points out that the spatial pattern of recent urban growth (2003-2010) is
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related to the presence of major roads, existing built-up area and the distance from the city
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centre. This outcome of the model offers tools to steer urban growth according to various
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planning visions. Not only preventive planning concepts can be implemented in the model but it
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is also able to adopt initiative measurements. This makes the model a useful planning tool for
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preventive as well as initiative planning policies (Harloe and Pickvance, 1990).
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The scenarios presented in this study show that a business as usual development of Kampala is
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unsustainable. Without any policy intervention it will lead to inhuman conditions for the majority
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of the urban population in 2020 and 2030. Millions of urban inhabitants will live in flood-prone
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slum areas by 2030 suffering from epidemic diseases related to unsanitary conditions. Moreover,
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without the construction of new roads, the majority of people will have a very limited mobility
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which makes their participation to the formal economy almost impossible. Already at present,
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many urban employees travel more than 3 hours a day from one urban neighbourhood to another.
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Poor traffic conditions will impede on future economic investments in the Kampala metropolitan
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area. Finally, pollution of Lake Victoria, which is highly correlated with built-up area in the
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wetlands, would reach unacceptable levels and have a significant impact on the urban income
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and food production.
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Restrictive urban planning measures may stop wetlands encroachment but will push people into
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other, more remote and inaccessible areas. The development of satellite towns to secondary
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urban hubs, an outer ring way connecting these hubs and high rise zones in the CBD and the
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satellite towns are effective measure to slow down the urban sprawl in favour of a more
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structured metropolitan area that would increase the quality of life of many urban dwellers. The
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different scenarios can offer guidance in taking planning decisions and setting up a planning
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vision.
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An alternative way of planning policy development is goal oriented whereby focus on a desired
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target situation is set and encouraged (Couclelis 2005). Further research could concentrate on
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using visioning processes in this urban growth model to develop significant scenarios. The
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development of such ‘visioning-scenarios’ requires the definition of a set of goals for the state
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and functioning of the future city, such as a minimum overall commuting time, a minimal
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wetland encroachment and a maximal preservation of green open spaces. Scenarios could then
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serve as decision support tools to achieve the defined objectives.
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Furthermore the model that was described in this study is based on spatial correlations between
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urban expansion patterns and a set of predicting variables without any insight in the driving
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factors of the underlying human behaviour. In order to get a deeper insight in these livelihoods
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and the social interactions a better understanding of available and adopted survival strategies of
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the urban residents and new immigrants is necessary. Specific survival strategies such as urban
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farming or informal trade, require specific spatial and social boundary conditions that are not
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included in the presented models. Future research should therefore focus on the analysis of
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ongoing ways of living in African cities and their relation with the characteristics of the urban
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environment.
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18
List of Tables
SENSOR
DATE
LANDSAT TM
LANDSAT TM
LANDSAT ETM+
LANDSAT TM
27/02/1989
19/01/1995
02/02/2003
28/01/2010
CLASSIFIED MAP
Built-up 1989
Built-up 1995
Built-up 2003
Built-up 2010
pixel to pixel
accuracy
82%
81%
87%
93%
Kappa index of
agreement
0.66
0.60
0.74
0.83
Table 1: pixel to pixel and kappa accuracy of Kampala metropolitan area land cover maps
19
exponential
population
increase
business as usual
scenario
X
restrictive scenario
X
stimulative scenario
X
variable
population
density
mixed
densities
zoning
satellite
towns
outer ring
way
X
X
X
X
X
X
Table 2: Scenario characteristics
20
1989-1995
-2.66610
1995-2003
-2.16330
2003-2010
-2.69910
DR
-0.00083
-0.00049
-0.00011
DC
TS
-0.00008
n.s.
-0.00009
n.s.
-0.00005
0.14260
Foot slope
n.s.
n.s.
n.s.
Wetland
flat (<2.5°)
n.s.
n.s.
-0.51770
n.s.
0.10200
n.s.
medium (2.5-10°)
n.s.
n.s.
n.s.
steep (>10°)
n.s.
n.s.
n.s.
0.49440
0.79360
0.68130
intercept
SG
BP
Hilltop
Table 3: Logistic regression coefficients for the different time periods
21
List of Figures
Figure 1: Africa’s million+ urban agglomerations in 2010 (UN-Habitat, 2010)
22
Figure 2: Estimated population density (2010)(UBOS, 2008) of the Kampala metropolitan area at
parish level
23
Figure 3: location of slums in relation to wetlands
24
Figure 4: a) Observed urban expansion between 1989 and 2010 land in the Kampala
metropolitan area; b) Assessed probabilities for new built-up land in the Kampala metropolitan
area
25
Figure 5: Developed scenarios for the Kampala metropolitan area
26
List of Supplementary Figures
Figure S1: Observed and expected population growth of
the Kampala metropolitan area (UBOS, 2002; United Nations, 2007; UBOS, 2009)
Figure S2: Predicting variables of
the logistic regression, DR: distance to main roads, DC: distance to city centre, TS: topographic
setting, SG: slope gradient, BP: built-up potential
Figure S3: ROC curves of assessed probabilities
for new built-up land in the studied time periods.
27
Figure S4: Expected location
characteristics for the population of the Kampala metropolitan area
28