UVDAT: Urban Visualization and Data Analysis Toolkit

High concentrations of underserved populations with low social capital in urban areas, along with assets and ecosystems in vulnerable zones, present high risks of cascading failures and expected loss of lives and property. A key gap is the lack of urban models that can help to address and visualize threats, especially under a changing climate and with growing urbanization and “coastalization” as well as aging lifeline systems and protective infrastructures. We propose to build UVDAT: Urban Visualization and Data Analysis Toolkit that will support data-driven research for the growth, progress, and welfare of urban areas, as well as to enable and inform decisions and policy. It will provide access to an extensive data library in a secure computing environment, support for data science and AI methods to enhance and merge data specifically for analyzing the effects of natural hazards on infrastructure, and neighborhood-scale high-resolution visualization that offers visual representations to present the data and uncertainty at different levels of navigation. In Phase I, we will create a software system to understand climatic threats (in this case, floods) and their influence on critical infrastructure in the Boston area. We will analyze extreme tidal water levels in the future, create a flood vulnerability map analyzing the fragile nodes, and observe the impacts of tidal flooding on existing networks. To achieve this, we will build an urban data library, make advances toward a comprehensive intuitive multi-scale visualization system, and implement workflows to highlight the important trends and relationships between layers represented in the model outputs. The proposed system will aid planners, policymakers, and logistics engineers in grappling with data that is growing in size, complexity, and diversity. By placing climate data alongside other infrastructure, sensor, and demographic data in new ways with our proposed analytic and visualization tools, planners will make data-driven decisions that result in better holistic outcomes for urban areas.