Enhancing Urban Data Exploration: Layer Toggling and Visibility-Preserving Lenses for Multi-Attribute Spatial Analysis

TL;DR

This paper introduces Layer Toggling and Visibility-Preserving Lenses, two innovative visualization techniques that improve urban data exploration by reducing clutter and enhancing focus, validated through a user study with real-world data.

Contribution

The paper presents two novel interaction techniques for urban data visualization that enhance exploration and analysis by managing visual complexity and detail.

Findings

Improved task performance and efficiency in urban data analysis.

Reduced cognitive load during complex spatial exploration.

Enhanced ability to analyze dense and multi-attribute spatial data.

Abstract

We propose two novel interaction techniques for visualization-assisted exploration of urban data: Layer Toggling and Visibility-Preserving Lenses. Layer Toggling mitigates visual overload by organizing information into separate layers while enabling comparisons through controlled overlays. This technique supports focused analysis without losing spatial context and allows users to switch layers using a dedicated button. Visibility-Preserving Lenses adapt their size and transparency dynamically, enabling detailed inspection of dense spatial regions and temporal attributes. These techniques facilitate urban data exploration and improve prediction. Understanding complex phenomena related to crime, mobility, and residents' behavior is crucial for informed urban planning. Yet navigating such data often causes cognitive overload and visual clutter due to overlapping layers. We validate our visualization tool through a user study measuring performance, cognitive load, and interaction efficiency. Using real-world data from Sao Paulo, we demonstrate how our approach enhances exploratory and analytical tasks and provides guidelines for future interactive systems.