PurposeFirst, the key vulnerability factors from the literature are identified. Second, using the vulnerability factors as indicators, a composite index is developed. Last, from the index values, a set of vulnerability knowledge maps, showing the vulnerability hotspots, are prepared.Design/methodology/approachThis study aims to develop a pandemic vulnerability knowledge visualisation index to support the strategic decision-making efforts of authorities.FindingsTen indicators are identified as vulnerability factors that could significantly impact the virus spread risks. Verifying the identified hotspots against the recorded infected cases and deaths has evidenced the usefulness of the index. Determining and visualising the high-vulnerability locations and communities could help in informed strategic decision-making and responses of the authorities to the pandemic.Originality/valueThe study demonstrates that the developed pandemic vulnerability knowledge visualisation index is particularly appropriate in the context of Australia. Nonetheless, by replicating the methodologic steps of the study, customised versions can be developed for other country contexts.
The study proposes a framework to model the three-dimensional relationship among density, land use, and accessibility in urban areas constructively contributing to overcome the limitations noted in the domains of urban planning and transport planning. First, most of the existing studies have focused on the topological characteristics in capturing the accessibility, but a limited attention has been given on measuring the accessibility by considering both topological and roadway characteristics. Second, the existing research studies have acknowledged the relationship among density, land use, and accessibility while a limited attention has been given to develop a modeling framework to capture the three-dimensional relationship. The modelling framework was tested in three urban areas in Sri Lanka. The research first analyzed the three-dimensional relationship among density, land use, and accessibility in the case studies. Then, the study developed a set of regression models to capture the density from the land use and accesability. The proposed model recorded a satisfactory level of accuracy (i.e., R2 > 0.70) on a par with internationally accepted standards. The relationship was further elaborated through a decision tree analysis and 4D plot diagrams. Findings of the study can be utilized to model the density of a given land use and the correspondent accessibility scenarios. The proposed model is capable of quantifying the impact of the changes in the density correspondent to the accessibility and land use. Therefore, the study concludes that this will be an effective tool for decision-makers in the fields of land-use planning and transport planning for scenario building, impact analysis, and the formulation of land use zoning and urban development plans aiming at the overarching sustainability of future cities.
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