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

arXiv: https://arxiv.org/abs/2508.07390 · HTML: https://mlsanity.com/paper/2508.07390/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 69 references that are in the map.
- First posted on arXiv 10 Aug 2025.

## Builds on (most cited first, 22 of 22)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [What Large Language Models Know and What People Think They Know](https://mlsanity.com/paper/2401.13835/) (2024)
- [ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language](https://mlsanity.com/paper/2311.01920/) (2023)
- [explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning](https://mlsanity.com/paper/1908.00087/) (2019)
- [Towards Natural Language Interfaces for Data Visualization: A Survey](https://mlsanity.com/paper/2109.03506/) (2021)
- [FlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System](https://mlsanity.com/paper/1908.00681/) (2019)
- [LEVA: Using Large Language Models to Enhance Visual Analytics](https://mlsanity.com/paper/2403.05816/) (2024)
- [Interactive AI Alignment: Specification, Process, and Evaluation Alignment](https://mlsanity.com/paper/2311.00710/) (2023)
- [IF-City: Intelligible Fair City Planning to Measure, Explain and Mitigate Inequality](https://mlsanity.com/paper/2202.07349/) (2022)
- [Urban Mosaic: Visual Exploration of Streetscapes Using Large-Scale Image Data](https://mlsanity.com/paper/2008.13321/) (2020)
- [Shadow Accrual Maps: Efficient Accumulation of City-Scale Shadows Over Time](https://mlsanity.com/paper/1907.04435/) (2019)
- [In Defence of Visual Analytics Systems: Replies to Critics](https://mlsanity.com/paper/2201.09772/) (2022)
- [Submerse: Visualizing Storm Surge Flooding Simulations in Immersive Display Ecologies](https://mlsanity.com/paper/2304.06872/) (2023)
- [Steering LLM Summarization with Visual Workspaces for Sensemaking](https://mlsanity.com/paper/2409.17289/) (2024)
- [No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization](https://mlsanity.com/paper/2207.07998/) (2022)
- [The State of the Art in Visual Analytics for 3D Urban Data](https://mlsanity.com/paper/2404.15976/) (2024)
- [Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics](https://mlsanity.com/paper/2408.06139/) (2024)
- [The Urban Toolkit: A Grammar-based Framework for Urban Visual Analytics](https://mlsanity.com/paper/2308.07769/) (2023)
- [Urban Pulse: Capturing the Rhythm of Cities](https://mlsanity.com/paper/1608.06949/) (2016)
- [ProWis: A Visual Approach for Building, Managing, and Analyzing Weather Simulation Ensembles at Runtime](https://mlsanity.com/paper/2308.05019/) (2023)
- [sMolBoxes: Dataflow Model for Molecular Dynamics Exploration](https://mlsanity.com/paper/2209.11771/) (2022)

## Built on by (most cited first, 1 of 1)

- [MV-Bench: Benchmarking Multimodal Large Language Models for Coordinated Multi-View Interface Construction](https://mlsanity.com/paper/2607.19910/) (2026)

## Abstract (arXiv)

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.