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

Title: 增强城市数据探索：多属性空间分析的图层切换和可视性保留透镜

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. Show Chinese abstract

Abstract: 我们提出了两种新颖的交互技术，用于可视化辅助的城市数据探索：图层切换和可视性保持透镜。 图层切换通过将信息组织成单独的图层来减轻视觉过载，同时通过受控叠加实现比较。 该技术支持聚焦分析而不丢失空间上下文，并允许用户使用专用按钮切换图层。 可视性保持透镜动态调整其大小和透明度，从而能够详细检查密集的空间区域和时间属性。 这些技术促进了城市数据的探索并提高了预测效果。 理解与犯罪、交通和居民行为相关的复杂现象对于明智的城市规划至关重要。 然而，由于图层重叠，导航此类数据常常导致认知过载和视觉杂乱。 我们通过用户研究验证了我们的可视化工具，测量性能、认知负荷和交互效率。 使用圣保罗的真实数据，我们展示了我们的方法如何增强探索性和分析性任务，并为未来的交互系统提供了指导方针。