A discursive, many-objective approach for selecting more-evolved urban vulnerability assessment models

Abstract

The development of more-evolved urban vulnerability assessment (UVA) models has become an increasingly important issue for both policy agendas and academia. Several requirements have already been set for this goal; they should be pursued simultaneously. However, methods with such integration are yet to be developed. The present paper addresses this integration via a discursive process in which interactions between decision makers and the method contribute to the selection of a model fulfilling these requirements. That model yields a UVA built upon both qualitative information and quantitative data from indicators selected for the neighbourhood, city, province, region and country political-administrative scales. The characteristics demanded are encoded both into the UVA assessment model and in the optimization and control modules governing the process. While the optimization produces compromise solutions, the control module supervises the process, provides dynamic control and enables the interactions. Interactions are informed with knowledge derived from the cognitive approach entailed by the method and afford a better understanding of the process dynamics. We conclude that the goodness of fit and time dynamics objectives are aligned. Therefore, UVA methods performing well for these objectives are available, although at the expense of medium to poor preferences and robustness of performance.

VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain

“…Compromise solutions are those satisfying several objectives simultaneously, such that there are no other solutions improving any of these objectives without worsening some of the others. In prior work, Salas and Yepes [11] employed the multi-objective approach to find out sets of indicators better representing urban vulnerability accordingly to some characteristics. These characteristics were closeness to expert judgment, goodness of fit of the statistical model representing vulnerability's evolution along time, and the robustness of each set of indicators against data uncertainty (Figure 1, Optimization).…”

“…According to this, an entity is vulnerable when its value, for any of the basic indicators, goes beyond the reference value, calculated as 1.5 (vulnerability threshold) times the national average (base of vulnerability). The basic indicators considered by the Ministry [26] Based on this first basic characterisation, classifying the entities as vulnerable or non-vulnerable, the proposed assessment framework deepens in the analysis, establishing, using principal component analysis, an assessment of the state of vulnerability (SV) at a given time [11]. The higher the relationship, the more vulnerable the entity considered will be.…”

“…This paper presents a Decision Support System (DSS) implementing a methodology previously developed [11] to provide an urban vulnerability assessment of Spain at regional, provincial, and city scales that can be integrated into strategic planning tools for ameliorating this problem [8]. The DSS is a Multi Objective Optimization (MOO) decision-making tool [12] for simultaneously addressing all the requirements pointed out for vulnerability assessment, as well as for searching the set of indicators best representing urban vulnerability accordingly to several objectives.…”

“…The DSS is a Multi Objective Optimization (MOO) decision-making tool [12] for simultaneously addressing all the requirements pointed out for vulnerability assessment, as well as for searching the set of indicators best representing urban vulnerability accordingly to several objectives. These objectives were closeness to expert judgment, maximise goodness of fit of the statistical model representing vulnerability's evolution over time, and the robustness of each set of indicators against data uncertainty [11]. As a result, the method rendered several sets of indicators among which the decision-makers had to choose.…”

“…This particularity more patently affects many-objective configurations [15], especially in the presence of conflicting or not aligned objectives, as in the case of selectin urban UVAMs. The large amount of solutions, also called the "curse of dimensionality" by Kukkonen and Lampinen [16] (Figure 1, Step 1), requires a specific treatment enabling DMs to focus their attention on those alternatives found relevant [11]. The employment of data analytics, such as cluster analysis [13,17,18], or visual analytics, such as "brushing" solutions [19,20], have previously been used for alleviating the problem of The presented decision support system addresses this problem by extensive usage of visual analytics all along the decision process with the aim of selecting a proper UVAM (Figure 1, Step 3) for the assessment of UV in Spanish regions, provinces, and cities larger than 100,000 people, capitals of provinces, and all municipalities in the province of Valencia.…”