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

TL;DR

This survey reviews the current state of visual analytics techniques for 3D urban data, highlighting challenges, categorizing existing work, and suggesting future research directions to improve urban data visualization.

Contribution

It provides a comprehensive categorization of existing visual analytics methods for 3D urban data and identifies research gaps and opportunities for future work.

Findings

Categorizes existing visual analytics approaches for 3D urban data.

Identifies gaps and challenges in current visualization techniques.

Suggests directions for future research in urban data visualization.

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.