The State of the Art in Visual Analytics for 3D Urban Data

TL;DR

This survey identifies and systematizes the state of visual analytics for 3D urban data by organizing contributions through the Why/What/How framework. It classifications papers into visualization and domain types, analyzes use cases, data origins, spatial scales, encodings, occlusion handling, and evaluation practices, and highlights gaps such as limited empirical validation and scarce open-tool ecosystems. The work reveals three broad use-case themes (natural phenomena, human-factor driven, built-environment only) and six common tasks (browse, identify, compare, summarize, etc.), underscoring the need for better data handling, metaphors, and guided explorations in multi-scale, immersive contexts. By outlining a roadmap with recommendations for metamodels, uncertainty visualization, and community-driven tool development, the paper aims to accelerate cross-disciplinary progress in urban visual analytics and digital-twin applications.

Abstract

Urbanization has amplified the importance of three-dimensional structures in urban environments for a wide range of phenomena that are of significant interest to diverse stakeholders. With the growing availability of 3D urban data, numerous studies have focused on developing visual analysis techniques tailored to the unique characteristics of urban environments. However, incorporating the third dimension into visual analytics introduces additional challenges in designing effective visual tools to tackle urban data's diverse complexities. In this paper, we present a survey on visual analytics of 3D urban data. Our work characterizes published works along three main dimensions (why, what, and how), considering use cases, analysis tasks, data, visualizations, and interactions. We provide a fine-grained categorization of published works from visualization journals and conferences, as well as from a myriad of urban domains, including urban planning, architecture, and engineering. By incorporating perspectives from both urban and visualization experts, we identify literature gaps, motivate visualization researchers to understand challenges and opportunities, and indicate future research directions.

Figures (15)

• Figure 1: Overview of the survey, centered around paper type and three main dimensions asking Why, What, and How visualization supports 3D urban data analytics. Each dimension corresponds to a section of the survey, and major subsections highlight the specific tags considered in each category. We also characterize papers according to their primary display modality and evaluation method. Top left: an illustration of the thematic layer (3D urban data with sunlight access information) and the physical layer (physical form of Downtown New York City).
• Figure 2: In the selection, filtering, and tagging steps, each paper was reviewed by at least two of the authors. In the consolidation step, each paper was reviewed by one of the authors. We also highlight the number of papers considered at each step.
• Figure 3: Distribution of tags across Paper type, Why, What and How dimensions.
• Figure 4: Example of papers from each one of the types considered in the survey. Left: papers with visualization contributions (system doraiswamy_topology-based_2015, technique cornel_interactive_2019, design study waser_many_2014, evaluation mota_comparison_2022). Right: papers with domain contributions (data creation liang_visualization-oriented_2014 and application studies koch_compact_2018).
• Figure 5: Distribution of surveyed papers according to Why and How dimensions, with shades denoting tag occurrence.