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BACKGROUND
The research presented in this report is part of a larger, inter-disciplinary, multi-institutional,
international development research centre (IDRC) funded project “Coastal Cities at Risk” or CCaR. It is a
collaborative international research initiative to enhance adaptation to climate change in coastal cities
around the globe. The four international project cities include Vancouver, Canada; Manila, Philippines;
Lagos, Nigeria; and Bangkok, Thailand. Each of these project cities has their own research team. These
four cities were selected for this project, in part, because of their coastal locations, presence of river
delta, high density urban populations and diverse economic, physical, social, and political environments.
This report builds on the quantitative framework for the assessment of disaster resilience (Peck and
Simonovic, 2013; and Simonovic and Peck, 2013) which moves beyond qualitative conceptualizations
and provides a measure that can be used to evaluate various decisions that affect resilience of a city.
The quantitative resilience measure introduced by Peck and Simonovic (2013) and Simonovic and Peck
(2013) allows for better identification and understanding of those factors which contribute to resilience.
In understanding what systems are contributing most to disaster resilience, it will hopefully be possible
to take or create opportunities to emerge from a disaster stronger than before, with improved levels of
resilience to combat and adapt to future disasters. This research considers a Space-time Dynamic
Resilience Measure (ST-DRM), which is designed to capture the complex relationships between the main
drivers of resilience. This report introduces the method for linking time and space in the implementation
of ST-DRM. Detailed presentation of generic coupling programming concepts designed for use as part of
the City Resilience Simulator (CRS) is presented.
The remainder of the report is laid out as follows: Chapter 2 provides an introduction to spatial system
dynamics (SSD) approach and current research and methodologies in literature. This chapter also
describes the theory behind coupling temporal simulation using Systems Dynamics (SD) and spatial
analysis Geographic Information System (GIS) and gives the basic principles and generic approach for
coupling different programs using Python programming language. Chapter 3 presents the research
methodology. Chapter 4 describes in detail the functionality, development and technical requirements
(hardware and software) of the coupling program. This chapter is particularly important for people who
will be modifying or using the coupling program. Chapter 5 provides a sample application of the coupling
program within the context of CCaR project. Finally, Chapter 6 presents a few key messages and
conclusions of the work.
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