Modelling slum dynamics using an integrated agent based simulation framework

Today, over half of the world's population lives in urban areas and by the middle of this century 7 out of 10 people will live in a city. This increased urbanisation has also lead to more and more people residing in informal settlements generally known as slums. In India, for instance, roughly 13.7 million households, or 17.4% of urban Indian households are considered to reside in slums. Slums 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 and understand the interactions among the multitude of factors. 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. 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 thus essential to examine the practical questions related to slum formation for informed policy-making: (i) what are the differences between slum and non-slum urban households? (ii) Where and when are they formed? and (iii) What are the key factors considered by slum households when choosing a slum? This dissertation describes a novel slum growth model, namely Dynaslum that will contribute to strategic slum planning and management solutions. Dynaslum is designed to integrate Agent-Based Modelling (ABM), Discrete Choice Theory (DCT), and Geographic Information System (GIS) within a single framework. ABM coupled with DCT will provide a framework to study household dynamics and residential choices of slum inhabitants while GIS will provide the ability to create a multiscale spatial environment. Dynaslum will explore the interaction between the slum households and the encompassing spatial configuration of the city to predict the emergence of slum patterns. Dynaslum is implemented based on a case study of Bangalore, an Indian city which has experienced high levels of urbanisation and rural-urban migration in the past decade. We have used a novel and unique dataset based on the eld work from 37 slums in Bangalore combined with the NFHS data to calibrate Dynaslum and validate our findings. This dissertation presents the following key insights to address and understand the growth and emergence of slums. First, we investigate Tilly's theory on group segregation and show how segregation reproduces or reinforce inequality within the slums of Bangalore using statistical modelling (correspondence analysis and regression). Second, we nd that high rate of home leaving among young adults is the key determinants of the large variation in the life cycle of slum households. We show that reducing home leaving among young adults will reduce the formation number of new slum households and contribute to a higher but stable household size. This will lead to efficiency and higher per capita resource consumption when building capacity for slum development (resettlement colonies) as policy makers would be able to plan for a stable household size. Finally, we demonstrate criticality in the process of inter-slum migration based on social and economic satisfaction. Further, we explore how creating jobs in different occupational categories across various social groups may impact the residential choices of slum dwellers. Dynaslum serves as a decision support system for slums in general, that will help guide experts when evaluating or designing policies to improve conditions within slums. Such a model provides a virtual slum that decision-makers and researchers can use to explore how different policies would impact the growth, development or contraction of slums.Doctor of Philosophy (SCE