# VA-Blueprint: Uncovering Building Blocks for Visual Analytics System Design

arXiv: https://arxiv.org/abs/2508.07497 · HTML: https://mlsanity.com/paper/2508.07497/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 68 references that are in the map.
- First posted on arXiv 10 Aug 2025.

## Builds on (most cited first, 24 of 24)

- [Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization](https://mlsanity.com/paper/2302.08081/) (2023)
- [RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records](https://mlsanity.com/paper/1805.10724/) (2018)
- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning](https://mlsanity.com/paper/2304.02711/) (2023)
- [Summarization is (Almost) Dead](https://mlsanity.com/paper/2309.09558/) (2023)
- [Searching the Visual Style and Structure of D3 Visualizations](https://mlsanity.com/paper/1907.11265/) (2019)
- [Structure-aware Visualization Retrieval](https://mlsanity.com/paper/2202.05960/) (2022)
- [Towards Better Bus Networks: A Visual Analytics Approach](https://mlsanity.com/paper/2008.10915/) (2020)
- [IF-City: Intelligible Fair City Planning to Measure, Explain and Mitigate Inequality](https://mlsanity.com/paper/2202.07349/) (2022)
- [Reviving Static Charts into Live Charts](https://mlsanity.com/paper/2309.02967/) (2023)
- [EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control](https://mlsanity.com/paper/2007.03180/) (2020)
- [In Defence of Visual Analytics Systems: Replies to Critics](https://mlsanity.com/paper/2201.09772/) (2022)
- [VAID: Indexing View Designs in Visual Analytics System](https://mlsanity.com/paper/2211.02567/) (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)
- [Criteria for Rigor in Visualization Design Study](https://mlsanity.com/paper/1907.08495/) (2019)
- [Urban Rhapsody: Large-scale exploration of urban soundscapes](https://mlsanity.com/paper/2205.13064/) (2022)
- [The Urban Toolkit: A Grammar-based Framework for Urban Visual Analytics](https://mlsanity.com/paper/2308.07769/) (2023)
- [ProWis: A Visual Approach for Building, Managing, and Analyzing Weather Simulation Ensembles at Runtime](https://mlsanity.com/paper/2308.05019/) (2023)
- [The State of Reproducibility Stamps for Visualization Research Papers](https://mlsanity.com/paper/2408.03889/) (2024)
- [Reimagining TaxiVis through an Immersive Space-Time Cube metaphor and reflecting on potential benefits of Immersive Analytics for urban data exploration](https://mlsanity.com/paper/2402.00344/) (2024)

## Abstract (arXiv)

Designing and building visual analytics (VA) systems is a complex, iterative process that requires the seamless integration of data processing, analytics capabilities, and visualization techniques. While prior research has extensively examined the social and collaborative aspects of VA system authoring, the practical challenges of developing these systems remain underexplored. As a result, despite the growing number of VA systems, there are only a few structured knowledge bases to guide their design and development. To tackle this gap, we propose VA-Blueprint, a methodology and knowledge base that systematically reviews and categorizes the fundamental building blocks of urban VA systems, a domain particularly rich and representative due to its intricate data and unique problem sets. Applying this methodology to an initial set of 20 systems, we identify and organize their core components into a multi-level structure, forming an initial knowledge base with a structured blueprint for VA system development. To scale this effort, we leverage a large language model to automate the extraction of these components for other 81 papers (completing a corpus of 101 papers), assessing its effectiveness in scaling knowledge base construction. We evaluate our method through interviews with experts and a quantitative analysis of annotation metrics. Our contributions provide a deeper understanding of VA systems' composition and establish a practical foundation to support more structured, reproducible, and efficient system development. VA-Blueprint is available at https://urbantk.org/va-blueprint.