Urbanite: A Dataflow-Based Framework for Human-AI Interactive Alignment in Urban Visual Analytics

TL;DR

Urbanite is a framework that uses a dataflow model to facilitate human-AI collaboration in urban visual analytics, enabling users to specify intent at multiple levels and ensuring alignment throughout the analytical process.

Contribution

It introduces a dataflow-based system that supports multi-level intent specification and interactive alignment, addressing challenges in urban data analysis with AI assistance.

Findings

Effective in aligning user intent with system behavior

Supports explainability and provenance in urban analytics

Demonstrated through urban expert collaboration scenarios

Abstract

With the growing availability of urban data and the increasing complexity of societal challenges, visual analytics has become essential for deriving insights into pressing real-world problems. However, analyzing such data is inherently complex and iterative, requiring expertise across multiple domains. The need to manage diverse datasets, distill intricate workflows, and integrate various analytical methods presents a high barrier to entry, especially for researchers and urban experts who lack proficiency in data management, machine learning, and visualization. Advancements in large language models offer a promising solution to lower the barriers to the construction of analytics systems by enabling users to specify intent rather than define precise computational operations. However, this shift from explicit operations to intent-based interaction introduces challenges in ensuring alignment throughout the design and development process. Without proper mechanisms, gaps can emerge between user intent, system behavior, and analytical outcomes. To address these challenges, we propose Urbanite, a framework for human-AI collaboration in urban visual analytics. Urbanite leverages a dataflow-based model that allows users to specify intent at multiple scopes, enabling interactive alignment across the specification, process, and evaluation stages of urban analytics. Based on findings from a survey to uncover challenges, Urbanite incorporates features to facilitate explainability, multi-resolution definition of tasks across dataflows, nodes, and parameters, while supporting the provenance of interactions. We demonstrate Urbanite's effectiveness through usage scenarios created in collaboration with urban experts. Urbanite is available at https://urbantk.org/urbanite.