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Felsenstein, D., & Lichter, M. (2014). Social and economic vulnerability of coastal communities to sea-level rise and extreme flooding. Natural Hazards, 71(1), 463-491.

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This paper assesses the socioeconomic consequences of extreme coastal flooding events.  Wealth and income impacts associated with different social groups in coastal communities in Israel are estimated. A range of coastal flood hazard zones based on different scenarios are identified.  These are superimposed on a composite social vulnerability index to highlight the spatial variation in the socioeconomic structure of those areas exposed to flooding.  Economic vulnerability is captured by the exposure of wealth and income.  For  the  former,  we  correlate  the  distribution  of  housing  stock  at  risk  with  the socioeconomic  characteristics  of  threatened  populations.  We also estimate the value of residential assets exposed under the different scenarios.  For the latter, we calculate the observed change in income distribution of the population under threat of inundation. We interpret the change in income distribution as an indicator of recovery potential.

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Felsenstein, D., & Mas, E., (2018). Introduction to SI: Modeling urban resilience to disasters, International Journal of Disaster Risk Reduction 31:602-603.

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Felsenstein, D., Vernik, M., & Israeli, Y. (2018). Household insurance expenditure as an indicator of urban resilience. International Journal of Disaster Risk Reduction, 31, 102-111

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Demand for household insurance is intuitively perceived as contributing to household and community resilience. However the causality in this relationship is not clear. This paper examines household insurance expenditure and the generation of urban resilience as jointly determined. Potential endogeneity is purged by estimating this relationship as a system and using an instrumental variable approach. Empirical analysis based on aggregated Israeli household expenditure data is used. Results show that instrumenting makes a difference, that a distinction needs to be drawn between personal resilience and environmental resilience and that insurance coverage has an independent effect on resilience different to that of classic social (personal) and economic (property and place-based) characteristics. The policy contexts of the findings are discussed.

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Fischhendler, I. (2017). The Use of Intangible Benefits for Promoting Contested Policies: The Case of Geopolitical Benefits and the Israeli Gas Policy. Geopolitics, 1-25.

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The evaluation of many contested projects and policies often includes intangible benefits. Geopolitics represents one type of intangible benefit. Despite a few examples on the use of geopolitics to promote contested projects, there is a gap in the literature on how geopolitical argumentation is constructed for the purpose of promoting resource-based policies. Hence, the aim of this study is to use an Israeli case study to examine how geopolitical constructs are used to promote competing energy policies concerning recent gas discoveries and to provide rudimentary insights on the implications for policy making. It was found that the geopolitical rationale was an appealing rhetorical device for all players as it is both unquantifiable and hence difficult to disprove and is rooted in the Israeli societal context. As a result, coalitions built their own geopolitical rationales, each with its own rhetorical tools. These literary tools were often embedded in narratives of power and geographical language with emotional resonance. Yet, the Israeli case demonstrates that geopolitical constructs come at a detrimental price as they promote censorship and exclusion of the public from the process.

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Grinberger, A. Y., & Felsenstein, D. (2014). Bouncing Back or Bouncing Forward? Simulating Urban Resilience and Policy in the Aftermath of an Earthquake. Proc. Institution of Civil Engineers: Urban Design and Planning, 167(3).

Abstract:

While the direct physical effects of an urban catastrophe are relatively straightforward to assess, indirect and long-term impact on the urban system is more circumspect. A large-scale shock such as an earthquake derails the complex urban system from its equilibrium path onto an unknown trajectory. Consequently, assessing the effect of policy intervention that aims to mitigate this shock and increase urban resilience is fraught with complexity. This paper presents the implementation of dynamic agent-based simulation to test long-run effects of a hypothetical earthquake in Jerusalem, Israel. It focuses on investigating the effectiveness of policy choices aimed at restoring the urban equilibrium. Cities are found to have a self-organizing market-based mechanism that strives to attain a new equilibrium. They therefore may not always bounce back – they may also bounce forward. Decision-makers, engineers, emergency and urban planners need to be cognizant of this tendency when designing policy interventions. Otherwise, well-intentioned efforts may inhibit urban rejuvenation and delay the onset of city recovery.

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Grinberger, A. Y., & Felsenstein, D. (2016). Dynamic agent based simulation of welfare effects of urban disasters. Computers, Environment and Urban Systems, 59, 129-141.

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An agent based model for assessing the welfare impacts of urban disasters is presented. This couples a population allocation algorithm with a simulation platform. The fully articulated model fuses both bottom-up (locational choice for workplace, residence and daily activities) and top-down (land use and housing price) protocols. This study moves beyond current research by addressing economic welfare consequences of urban disasters. The resilience capabilities of different income groups are identified. This is illustrated for the Jerusalem central business district. Empirical results at the micro-scale suggest that physical destruction leads to a zero-sum game within the housing market in which wealthier residents hold an advantage over the poor. This results in the transformation of neighborhoods and displacement of poor and vulnerable populations. Low income groups lose both physical ground and the social support systems that go with location. Policy implications of these findings are discussed.

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Grinberger A. Y., Felsenstein D. (2017). A Tale of Two Earthquakes: Dynamic Agent-Based Simulation of Urban Resilience. In: Lombard G., Stern E. and Clarke G. (Eds). Applied Spatial Modeling and Planning. Taylor and Francis; 2017. pp. 134-154.‏

Abstract:

As cities increase in size and complexity they also become increasing vulnerable to unanticipated events, both natural and anthropogenic (Deppisch and Schaerffer, 2011; Godschalk, 2003). Large scale disasters such as the 1995 Kobe earthquake, hurricane Katrina, the Tohoku earthquake and tsunami and Superstorm Sandy have elicited research interest in the way cities cope with such shocks. This work tends to highlight either mitigation measures (Fleischauer, 2008; Godschalk, 2003) or ‘bouncing back’ strategies (Campanella, 2008; Chang, 2010; Chang & Rose, 2012; Olshansky, Hopkins & Johnson, 2012). It also tends to imply that urban recovery should be directly related to the magnitude of the disaster with larger shocks to the urban system requiring more drastic mediation or rejuvenation measures.

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Grinberger A. Y., Felsenstein D. (2018). Using Big (Synthetic) Data for Identifying Local Housing Market Attributes, pp 109-120 in Schintler L and Chen Z (Eds) Big Data for Regional and Urban Science, Routledge.‏

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Recent advances in data disaggregation and the generation of synthetic spatial microdata are exploited for identifying local housing market attributes. Traditional public sector ‘small’ data is disaggregated to yield big (synthetic) spatial microdata using a three-stage approach. First, an allocation algorithm is used to attach synthetic socio-economic attributes to residential buildings. Second, inconsistencies between housing values and the ascribed socio-economic attributes of the resident population are identified. These indicate incipient clusters of housing market change. Third, clusters are typologized using the synthetic socio-economic microdata coupled with building attributes data. This yields information on housing market attributes such as segmentation and dynamics such as gentrification. The approach is operationalized for the entire stock of residential units and households in Israel and can be easily reproduced in other national contexts.

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Grinberger, A. Y., Lichter, M., & Felsenstein, D. (2015). Simulating urban resilience: Disasters, dynamics and (synthetic) data. In Planning support systems and smart cities (pp. 99-119). Springer, Cham.

Abstract:

An agent based (AB) simulation model of urban dynamics following a disaster is presented. Data disaggregation is used to generate ‘synthetic’ data with accurate socio-economic profiling. Entire synthetic populations are extrapolated at the building scale from survey data. This data is coupled with the AB model. The disaggregated baseline population allows for the bottom-up formulation of the behavior of an entire urban system. Agent interactions with each other and with the environment lead to change in residence and workplace, land use and house prices. The case of a hypothetical earthquake in the Jerusalem CBD is presented as an illustrative example. Dynamics are simulated for a period up to 3 years, post-disaster. Outcomes are measured in terms of global resilience measures, effects on residential and non-residential capital stock and population dynamics. The visualization of the complex outputs is illustrated using dynamic web-mapping.

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Grinberger, A. Y., Lichter, M., & Felsenstein, D. (2017). Dynamic agent based simulation of an urban disaster using synthetic big data. In Seeing Cities Through Big Data (pp. 349-382). Springer, Cham.‏

Abstract:

This paper illustrates how synthetic big data can be generated from standard administrative small data. Small areal statistical units are decomposed into households and individuals using a GIS buildings data layer. Households and individuals are then profiled with socio-economic attributes and combined with an agent based simulation model in order to create dynamics. The resultant data is ‘big’ in terms of volume, variety and versatility. It allows for different layers of spatial information to be populated and embellished with synthetic attributes. The data decomposition process involves moving from a database describing only hundreds or thousands of spatial units to one containing records of millions of buildings and individuals over time. The method is illustrated in the context of a hypothetical earthquake in downtown Jerusalem. Agents interact with each other and their built environment. Buildings are characterized in terms of land-use, floor-space and value. Agents are characterized in terms of income and socio-demographic attributes and are allocated to buildings. Simple behavioral rules and a dynamic house pricing system inform residential location preferences and land use change, yielding a detailed account of urban spatial and temporal dynamics. These techniques allow for the bottom-up formulation of the behavior of an entire urban system. Outputs relate to land use change, change in capital stock and socio-economic vulnerability.

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