Enhancing urban data exploration: Layer Toggling and Visibility-Preserving Lenses for multi-attribute spatial analysis

This manuscript proposes two novel interaction techniques for visualization-assisted exploration of urban data, namely, Layer Toggling and Visibility-Preserving Lenses. The former mitigates visual overload by organizing information into distinct layers while enabling multi-layer comparisons through controlled overlays. The technique supports focused analyses without sacrificing spatial context and enables users to quickly switch between layers through a dedicated physical button interface. Visibility-Preserving Lenses, on the other hand, dynamically adapt their size and transparency so that users can effectively examine dense spatial regions and temporal attributes in detail. Both techniques support urban data exploration and improve prediction. Exploring urban data is essential for understanding complex phenomena related to crime, mobility, and residents’ behavior and equally important is the ability to predict and explain how they evolve over time, supporting informed urban planning and policymaking. However, navigating urban data in all their complexity is challenging, often resulting in cognitive overload, loss of spatial context, and excessive visual clutter due to the many layers that must be examined simultaneously. Although layered visualizations aim to mitigate those challenges, they face limitations with occlusion and effortless comparisons across data layers. Additionally, interaction methods are typically confined to mouse-based controls, limiting the fluidity of dynamic exploration. The visualization tool was validated through a comprehensive user study that measured user performance, cognitive load, and interaction efficiency across multiple devices. Using real-world data from São Paulo, including mobility patterns, climate conditions, and crime statistics, the way the approach enhances both exploratory and analytical tasks is demonstrated. The results also show how users perform when playing with different interactive devices, providing guidelines for future developments and improvements. • Introduces a novel approach to toggle data layers for spatial crime analysis. • Proposes visibility-preserving lenses for comparisons of urban attributes with less occlusion. • Supports multi-attribute exploration in dense urban visualizations. • User study shows improved accuracy and interaction efficiency with proposed tools. • Enables better decision-making by enhancing insight into urban spatial patterns.