Slums as a self organized complex system
Updated: Jan 26, 2018
The emergence of slums: A contemporary view on simulation models. State-of-art in modeling of slum dynamics, emergence and growth.
The existence of slums or informal settlements is common to most cities of developing countries. Its role as single housing delivery mechanism has seriously challenged the popular notion held by policy makers, planners and architects. Today informality is a paradigm of city making and economic growth in Africa, Asia and Latin America.
Slum policies have evolved and urban authorities have adopted different strategies, ranging from in-situ development in slums, relocation to the resettlement colonies, and to slum evictions.
A key challenge for policy makers in implementing slum management policies is to understand the slum typology and underlying driving forces as there is unlikely a ‘one-size fits all’ solution to the problem.
Slums as a complex system
Slums, themselves are complex dynamic systems that have close symbiotic relationships with their encompassing cities. In order to identify the intricate consequences of particular policies, governments must consider a multitude of factors, and most importantly understand how these factors interact. The relationship between a slum and its parent city can be commensalistic or parasitic. Specifically, in some cases, the economic and political power of slums is so significant that cities need to maintain them. Hence, slums can be compared to a parasitic organism, thriving at the expense of the city and draining key urban resources. It grows and subdivides within itself much like an organism would when provided with restrictions.
However, another view is that slum could also be commensalistic in nature as each slum in a city serves a specific function and provides cheap labor for the development of urban infrastructure. The traditional view of slums, certainly from a policy and research perspective, is that these were “controlled” systems, maintained in an equilibrium state by negative feedbacks. The new, complexity view, considers slums as dynamic, non-equilibrium systems that are constantly changing and adapting. It is clear that slums have emerged spontaneously from the interactions of the component parts (spatial components of shelter deprivation and the dynamics of slum development), not dictated in a top-down manner. Viewing and analyzing slums as complex systems should therefore lead to new perspectives on what is an increasingly challenging problem.
Modelling and Simulation
Modeling and simulation is one useful way to understand complex slum dynamics, and knowledge derived through such tools could assist planners and policy makers in decision-making processes. The paradigm shift to a bottom-up modeling approach captures the interaction and processes at a finer scale. Agent based Modeling and Simulation (ABMS) has proved useful in representing the processes underlying a particular phenomenon. The advantage of an agent based model (ABM) lies in its capacity to simulate individual behavior and their interactions with their environment and other individuals. Geographical Information Systems (GIS) can be used to spatially represent the pattern of the phenomenon, and identify the key spatial structures which influence the micro-scale behavior and interactions. The integration of simulation models with a GIS environment has been successfully implemented in various studies to represent spatially explicit environments in ABMS. Thus, ABMS of slum dynamics coupled with the spatial representation of the geographical region would provide a promising tool to model the emergence and growth of slums as well as the processes and patterns within slums.
Review of Slum Models
In our paper published in Environmental Modelling and Software we review the current state-of-art in slum modelling. First, this paper proposes a set of universal factors which determine the emergence and growth of slums. Second, based on these factors, we define an evaluation framework for computer simulation models designed to study slum dynamics. Third, we apply this evaluation framework to existing literature and review the existing simulation models focusing on slum formation and growth. Finally, this paper identifies key open research questions in the context of slum modeling. This project is part of a larger research project with a focus on India, and lot of statistics used in this paper have been collected from Asia.