Simulating slum growth in Lagos: An integration of rule based and empirical based model

• Published in: Computers, environment and urban systems environment and urban systems, 2019-09, Vol.77, p.101369, Article 101369
• Main Authors: Badmos, Olabisi S., Rienow, Andreas, Callo-Concha, Daniel, Greve, Klaus, Jürgens, Carsten
• Format: Article
• Language: English
• Subjects: Cellular automata; Cities; Computer simulation; Correlation analysis; Demographics; Densification; Empirical analysis; Forecasting; Ghettos; Imagery; Lagos; Local government; Logistic regression; Population growth; Prediction models; Public administration; Regression analysis; Regression models; SLEUTH; Slum growth; Slums; Statistical analysis; Urban development; Urban growth; Urban planning
• ISSN: 0198-9715; 1873-7587
• DOI: 10.1016/j.compenvurbsys.2019.101369

Demographic forecasts put Lagos as one of the cities with the highest population growth. Past trends show correlations between urban growth and slum growth, thereby creating a major challenge for sustainable city planning. This study explores the drivers of slum development in Lagos, and simulates scenarios for slum growth through coupling logistic regression with the cellular automata-based SLEUTH model. RapidEye (2009 and 2015) and Sentinel-2 (2015) imagery were used to create slum extents maps for each time point, and then used for the calibration and prediction, respectively, of the model. The driving forces of slum development in Lagos were analyzed, and the correlated spatial drivers compiled to create a probability map of slum development using the logistic regression model. The probability map was incorporated with the exclusion layer of the modified SLEUTH to simulate scenarios of slum growth in Lagos by 2035. Three scenarios were designed based on the modification of the exclusion layer and the transition rules. The Scenario 1 ‘business as usual’, depicts slum development following the present trend; the scenario 2 ‘excessive growth’, considers the demographic projection for the city; while the scenario 3 ‘limited government influence’, asserts limited interference by the government in slum management/control. Factors including distance to markets, distance to shoreline, distance to local government administrative buildings, land prices, etc. were predictors of slum development in Lagos. The prediction model, based on the logistic regression, reached an overall accuracy of 79.17% and a relative operation characteristics value of 0.85. The three scenarios show further densification of the existing slums, and increase in their area by 1.18 km2 (scenario 1), 4.02 km2 (scenario 2), and 1.28 km2 (scenario 3). New slums are predicted at the fringe of the south-eastern part of the city. The foreseen limited spatial growth of the slums is due to the high density of the city, thus new slums may likely develop in the neighboring zones to Lagos when land in the city is no longer available. [Display omitted] •We explore the drivers of slum development and simulate scenarios for slum growth in Lagos•Distance to shoreline, distance to Government buildings, land prices, etc. influences of slum development in Lagos•New slums are predicted at the fringe of the south-eastern part of the city.•The forecasted limited spatial growth of the slums is due to the high de