Developing an Open Computational Framework for Decision Support Across Transportation, Weather, and Public Health

Abstract

Urban environments present complex challenges in diverse domains, requiring decision-making processes that integrate diverse data sources. Current systems created to support decision-making often lack the flexibility needed for interdisciplinary applications. To address this gap, this work establishes the foundation for an open computational framework designed to support decision-making across transportation, weather, and public health domains. We began by conducting a survey to assess experts’ data needs, common tasks, and decision-making processes. Insights from this survey informed the identification and definition of foundational components necessary for building a flexible decision-support system. Additionally, we present our efforts to integrate these components into Curio, a framework for collaborative cross-domain urban visual analysis. Curio employs a dataflow model with multiple abstraction levels - including code, grammar, and graphical user interface elements - enabling experts to integrate data preprocessing, management, and visualization while maintaining provenance tracking. Curio supports cross-domain analysis of urban datasets such as city-scale noise, shadow, sidewalk infrastructure, as well as climate and weather model outputs, among others. We demonstrate Curio’s potential through a set of use cases showcasing its ability to facilitate interdisciplinary analysis and support data-driven decision-making.