Badmos, Olabisi Sakirat: An Integrated Remote Sensing and Urban Growth Model Approach to Curb Slum Formation in Lagos Megacity. - Bonn, 2019. - Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn.
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author = {{Olabisi Sakirat Badmos}},
title = {An Integrated Remote Sensing and Urban Growth Model Approach to Curb Slum Formation in Lagos Megacity},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2019,
month = apr,

note = {Rapid urbanization with limited development has led to slum proliferation in many sub-Saharan African cities. Slums are recognized as a menace to planned cities, as they do not conform to planning standards, thus the need to curb their growth. However, this proves to be a challenge for many of these cities due to unavailability of data on the existing situation. It is against this background that this study aims to contribute ground information and a spatial planning tool to support urban planning to better manage slum formation in Lagos, Nigeria.
Slum growth can be described as spatial or as population growth; hence this study first analyzed and quantified the spatial growth of slums in Lagos using remote sensing techniques and intensity analysis. Then the influence of residential choices of slum dwellers on population growth in Lagos slums was assessed using ethnographic survey approach through questionnaires and focus group discussions. An urban growth model coupling logistic regression with modified cellular automata SLEUTH was used to simulate scenarios of the patterns of slum development in Lagos by 2035.
RapidEye imagery from 2009 and 2015 was used to create maps for each time point for six land-use categories (water, vegetated area, open space, road, slum, and other urban) in the study area. Intensity analysis was applied to quantify the annual intensity of changes at the category and transition level. An overall accuracy (and kappa coefficient) of 94% (0.9) and 89% (0.86) was achieved for the 2009 and 2015 land-use and land-cover maps. The results of this study show that slums in Lagos increased spatially between 2009 and 2015 gaining a land area of 9.14 km2 influenced by in-migration. However, the intensity analysis reveals slum as an active land-use category, losing some of its land area but also gaining new land area during this period. The annual gain and loss was 10.08% and 6.41%, respectively, compared to the uniform intensity of 3.15%. A systematic process of transition was observed between slums and other urban areas and open space in the interval studied, and this process was mainly influenced by the Lagos state government. The transition from slum to other land-use categories, such as other urban, is attributed to gentrification and demolition processes, while the transition from other land-use categories to slum is due to poor maintenance of existing buildings and encroachment on available spaces in the city.
Questionnaires administration and focus group discussion were conducted in four communities (Ajegunle, Iwaya, Itire and Ikorodu) in Lagos to investigate the factors influencing the residential choices and reasons of the people to remain in the Lagos slums. Descriptive statistics was used to analyze and describe the factors influencing the residential location choice, and logistic regression was applied to determine the extent to which the neighborhood and household attributes influence slum dwellers’ decisions to remain in the slums. Over 70% of the respondents were migrants from neighboring geopolitical zones (in Nigeria). The movement patterns of slum dwellers in Lagos support two theories of human mobility in slums: slum as a sink and slum as a final destination. Also, the factors that attracted most of the slum dwellers to the slums (cheap housing, proximity to work, etc.) differ from those that made them stay (duration of stay, housing status, etc.).
A hybrid land-use model, which involves the coupling of logistic regression with cellular automata SLEUTH, implemented in XULU, was utilized for the simulation of scenarios of slum growth in Lagos. The scenarios were designed based on the modification of the exclusion layer and the transition rules. The scenario 1 was business as usual with slum development similar to the present trend. The scenario 2 was based on the future population projection for the city, while the scenario 3 was based on limited interference by the government in slum development in the city. Distance to markets, shoreline, and local government administrative buildings, and land prices, etc., were predictors of slum development in Lagos. An overall accuracy of 79.17% and a relative operation characteristics (ROC) value of 0.85 were achieved for the prediction of slum development, based on the logistic regression model. The probability map generated from fitting the coefficients of the estimates in the logistic regression shows that slums can develop within the city and at the fringe, and also in places mostly inaccessible to the Lagos state government. Scenarios 1, 2 and 3 predict that the slum area will increase by 1.18 km2, 4.02 km2 and 1.28 km2, respectively, in 2035 through further densification of the existing slums and new development at the south-eastern fringe of the city. The limited growth is due to the high population density in the city, and thus it is assumed that new slums will probably develop in the neighboring cities due to spill over of the Lagos population.
The outcome of this research shows that the landscape is very dynamic in Lagos, and even over an interval of a few years, changes can be observed. It also shows that the integration of remote sensing, social science method and spatially explicit land-use model can address the challenges of data availability in the slum dynamic, especially in sub-Saharan African countries with high slum proliferation. This can support a comprehensive set of techniques important for the management of existing slums and prevention of new slum development.},

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