A framework for modelling spatio-temporal informal settlement growth prediction
Introduction
The world's population growth rate is expected to decrease from 1.24% in 2000 to 0.5% in 2050, while the population of many developing regions is expected to nearly double over the next 30 years (United Nations, 2015). As of 2007, more than half of the world's population lives in cities (Friesen, Rausch, Pelz, & Fürnkranz, 2018). Due to the projected global population growth, these cities continue to attract thousands of new residents from rural areas every year in search of employment and a better standard of life (Abbott, 2002; Kohli, Sliuzas, Kerle, & Stein, 2012). Urban housing backlogs coupled with shortages in housing subsidies in developing regions, such as in Africa (United Nations, 2002), South America (Friesen, Taubenböck, Wurm, & Pelz, 2019; Kamalipour & Dovey, 2019) and South-East Asia (Friesen et al., 2019), for example, have led to large numbers of urban dwellers having no alternative but to live on the peripheries of cities in informal settlements. Many urban areas in developing regions are, in fact, anticipated to contain 60% of the global population by 2030, with 90% of the projected urbanisation expected to occur in less developed countries (He, Okada, Zhang, Shi, & Li, 2008).
The emergence or expansion of urban areas is related to their topographical features, the local transport infrastructure, the current surrounding land-uses, the surrounding social structure, and economic activities associated with the underlying area. As the populations of cities increasingly require additional space to live in, it is generally accepted that the demographies and economies of these populations will be the primary driving factors of spatial urban and informal settlement emergence and expansion (He et al., 2008).
Although developed areas currently only cover a small fraction of the earth's land surface, there is evidence that spatial urban and informal settlement expansion has significantly altered the earth's natural landscape, resulting in a very significant impact on the surrounding environment and ecosystems at all geographical scales (Li, Zhu, Sun, & Wang, 2010; Seto, Güneralp, & Hutyra, 2012; Wei & Ye, 2014). Moreover, temporal urban and informal settlement growth is expected to contribute to global environmental changes, including land-use and land cover changes, changes in biogeochemical and hydrological cycles, climate, and biodiversity (McDonald, Kareiva, & Forman, 2008; Seto et al., 2012).
In order to predict land-use changes and settlement growth quantitatively and in a spatial fashion, cellular automata (CA) have typically been adopted as the primary modelling paradigm in the literature. In CA models, the landscape is represented by cellular land parcels that can develop into different land-use types according to local transition rules, in much the same way as fractal growth occurs. Global patterns of urban growth then emerge as a result of rural to urban conversions. Whereas previous urban development modelling and analyses of the associated impacts of such development have taken place mainly in the context of developed countries (Linard, Tatem, & Gilbert, 2013), it may be argued that developing countries (which exhibit the highest urbanisation rates) are more in need of such modelling and analysis projects.
A data-driven framework for modelling spatio-temporal informal settlement emergence and growth is proposed in this paper which leverages the considerable power of machine learning (ML) instead of relying on researchers to postulate the factors influencing settlement development subjectively. This framework is deemed unbiased as the factors driving informal settlement emergence and growth are identified and weighted from a large pool of data presented by a user, resulting in a black-box emergence of prominent driving factors to be employed within a predictive CA model.
The remainder of this paper is structured as follows. A brief overview of existing frameworks and models for settlement emergence and growth is given in §2, emphasising the strengths and shortcomings of these frameworks and models. The framework proposed in this paper is then presented in some detail in §3. The practical workability of the framework is showcased thereafter, in §4, in the form of a real-world case study. The paper finally closes with a short appraisal of its contributions and some proposals for future work in §5 and §6.
Section snippets
Background and literature review
Urbanisation and the emergence and growth of informal settlements have been the subject of research interest for decades in several disciplines (Batty & Longley, 1986; Batty & Xie, 1994; Couclelis, 1985; Sietchiping, 2005; Torrens & O'Sullivan, 2001). A relatively recent realisation that urbanisation and informal settlement development are largely dynamic processes have led to an adoption in the literature of a variety of modelling paradigms aimed at explaining the driving forces behind
Framework
The review of previous settlement expansion models presented in §2 indicates that, to the best of the authors' knowledge, no model or framework exists for investigating the intricate nature of informal settlement emergence and expansion based on dynamic spatial feature selection by an ML approach. A data-driven framework, called the informal settlement emergence and growth (ISEG) framework, in proposed in this paper for predicting the future temporal population growth and spatial population
Illustrative case study
In order to demonstrate the practical workability of the proposed ISEG framework, a concept demonstration is carried out in this section according to the framework with a view to model informal settlement emergence and growth in a developing country context. Although a South African case study is conducted in this paper for demonstration purposes, the generic nature of the ISEG framework allows for informal settlement growth and emergence analysis in any area of interest.
Discussion
In this paper, a generic framework for modelling spatio-temporal informal settlement emergence and growth was proposed. This framework addresses two major shortcomings of similar, existing frameworks. The first pertains to comprehensiveness and generalisation requirements in a variety of contexts. The second is that it renders irrelevant the typical subjective selection of factors driving informal settlement emergence and growth in existing models and frameworks, and then incorporating these
Conclusion
The generic data-driven ISEG framework proposed in this paper provides an objective feature selection procedure which proves to be more desirable than the subjective feature selection approaches (i.e. feature selection approaches in which a user selects certain spatial features based on their preference) typically employed in existing, similar modelling frameworks. During the objective feature selection procedure the ML component of the ISEG framework identifies and selects an appropriate
Author statement
(CRediT author statement as per Elsevier website https://www.elsevier.com/authors/policies-and-guidelines/credit-author-statement)
CRediT authorship contribution statement
P. Cilliers: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administratio. J.H. van Vuuren: Conceptualization, Formal analysis, Resources, Writing – original draft, Writing – review & editing, Supervision, Project administration, Funding acquisitio. Q. van Heerden: Resources, Data curation.
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