Modeling Homeowners Post-flood Reconstruction Decisions

In the past we have developed agent-based models to explore a wide variety of applications and even to explored at humanitarian assistance after a natural disaster, however we have not explored how people might decide to rebuild or not after a natural disaster. Well that was until now. In a new paper with Kim McEligot, Peggy Brouse and myself entitled "Sea Bright, New Jersey Reconstructed: Agent-Based Protection Theory Model Responses to Hurricane Sandy" which was published as part of the 2019 Winter Simulation Conference. In the paper we use a hindcast (aka backtesting) of Hurricane Sandy’s damage to Sea Bright, NJ and explore homeowners post-flood reconstruction decisions. Below we provide the abstract to the paper, a short movie of the model running, along with a link to access the source code and data of the model, and finally a link to the full paper.

Abstract:

Coastal flooding is the most expensive type of natural disaster in the United States. Policy initiatives to mitigate the effects of these events are dependent upon understanding flood victim responses at an individual and municipal level. Agent-Based Modeling (ABM) is an effective tool for analyzing community-wide responses to natural disaster, but the quality of the ABM’s performance is often challenging to determine. This paper discusses the complexity of the Protective Action Decision Model (PADM) and Protection Motivation Theory (PMT) for human decision making regarding hazard mitigations. A combined (PADM/PMT) model is developed and integrated into the MASON modeling framework. The ABM implements a hind-cast of Hurricane Sandy’s damage to Sea Bright, NJ and homeowner post-flood reconstruction decisions. It is validated against damage assessments and post-storm surveys. The contribution of socio-economic factors and built environment on model performance is also addressed and suggests that mitigation for townhouse communities will be challenging.

The model source code (utilizing MASON Version 17) and data is available on CoMSES.net: http://bit.ly/SEABrightABM.

Our adaptation of the Protection Motivation Theory and Protective Action Decision Model.

Full Reference:

McEligot, K. Brouse, P. and Crooks A.T. (2019), Sea Bright, New Jersey Reconstructed: Agent-Based Protection Theory Model Responses to Hurricane Sandy, in Mustafee, N., Bae, K.-H.G., Lazarova-Molnar, S., Rabe, M., Szabo, C., Haas, P. and Son, Y-J. (eds.), Proceedings of the 2019 Winter Simulation Conference, National Harbor, MD, pp 251-262 (pdf)

New Paper: Measuring Slum Severity in Mumbai and Kolkata

In a move to understand slums, we have switch gears slightly from agent-based modeling to a more statistical study of slums.  To this end we have just received word that our paper entitled "Measuring Slum Severity in Mumbai and Kolkata: A Household-based Approach" has just been Habitat International. Specifically we propose a new household level enumeration of slums and develop a Slum Severity Index as shown Table 4 below. Moreover, the paper estimates number of slum households in Mumbai and Kolkata as shown in Figure 1 (below). Furthermore, the paper demonstrates that varying slum definitions result into stark differences in slum population estimates. Paper abstract:

"Slums pose a significant challenge for urban planning and policy as they provide shelter to a third of urban residents. UN-Habitat reports that, in 2001, approximately 924 million people lived in slums or informal settlements across the world (UN-Habitat, 2003). However, varying definitions of what constitutes a slum result in different slum population estimates. Most definitions treat a slum as a community of several households, rarely recognizing that housing conditions differ for each individual household within the area. Moreover, definitions of slums usually take a dichotomous approach whereby a place is either a slum or not. Little attempt is made to go beyond this slum/non-slum dichotomy. This paper moves beyond the traditional ways of defining a slum by proposing a new household level enumeration of slums and developing Slum Severity Index (SSI), which measures the level of deprivation on a continuous scale based on the UN-Habitat's slum definition. We apply this new approach of analyzing slums to a household survey dataset to estimate the total number of slum households in Mumbai and Kolkata, two megacities in India. To contrast our approach, we compare these estimates with the Census of India's. The comparison highlights stark differences in the two estimates and the slum/non-slum household classifications. The estimates by the Census are considerably smaller than those based on the UN-Habitat definition in both cities. By applying the SSI, we also demonstrate intra-urban variability in housing conditions within our study cities. The analysis highlights differences in slum profiles measured in terms of both housing deprivation levels and housing deprivation types in both cities. The main objective of this study is to demonstrate the usefulness of the household level analysis of slums in drawing implications for designing and implementing slum policies."

Keywords: Slums, Definition, Deprivation, India, Housing.

Full Reference: 

Patel, A., Koizumi, N. and Crooks, A.T. (2014), Measuring Slum Severity in Mumbai and Kolkata: A Household-based Approach, Habitat International, 41: 300-306. (pdf)

If you would like to read the paper and don't have access to Habitat International, feel free to send us an email and we can send you an early version of the paper.

Modeling Housing Markets: Update

A while ago I blogged about the a simple NetLogo model by Nigel Gilbert et al. which explores how some of the main features of the English housing market emerges from the interactions between buyers, realtors and sellers (click here to run the model). I have just come across an audio recording with accompanying slide presentation by Nigel Gilbert’s which enhances the previous post and thought it was worth sharing. Click here to listen and see the slides. The talk is hosted on the SIMIAN (Simulation Innovation: a Node) project website which aims to promote and develop social simulation methods in the UK.

Reference:

Gilbert, N, Hawksworth, J C, and Sweeney, P (2008) 'An Agent-based Model of the UK Housing Market'. University of Surrey http://cress.soc.surrey.ac.uk/housingmarket/ukhm.html or click here to read the paper.

Modelling Housing Markets

After reading about land markets I come across this simple NetLogo model by Nigel Gilbert et al. The model explores how some of the main features of the English housing market emerges from the interactions between buyers, realtors and sellers (click here to run the model).

There is a paper that accompanies the model (click here) which discusses the potential advantages of agent-based modeling over more traditional 'top down' techniques for exploring the housing market. While the model is not based on any particular geographical location or attempts to represent either the characteristics of individual housing units, nor spatial attributes such as proximity to services. It is still a very interesting paper investigating the complicated interactions that create a housing market in a simple model.

Reference:
Gilbert, N, Hawksworth, J C, and Sweeney, P (2008) 'An Agent-based Model of the UK Housing Market'. University of Surrey http://cress.soc.surrey.ac.uk/housingmarket/ukhm.html or click here to read the paper.