Using a negative spatial auto-correlation index to evaluate and improve intrinsic TagMap's multi-scale visualization capabilities

TL;DR

This paper introduces a novel method using negative spatial auto-correlation to evaluate and improve multi-scale intrinsic TagMap visualizations, enhancing their uniformity and adaptability across different scales.

Contribution

It proposes integrating a negative spatial auto-correlation index into TIN-based TagMap layouts to optimize multi-scale visualization, addressing layout uniformity issues.

Findings

Improved multi-scale visualization with higher compactness.

Trade-offs between layout compactness and time efficiency.

Effective generation of diverse TagMaps based on user preferences.

Abstract

The popularity of tag clouds has sparked significant interest in the geographic research community, leading to the development of map-based adaptations known as intrinsic tag maps. However, existing methodologies for tag maps primarily focus on tag layout at specific scales, which may result in large empty areas or close proximity between tags when navigating across multiple scales. This issue arises because initial tag layouts may not ensure an even distribution of tags with varying sizes across the region. To address this problem, we incorporate the negative spatial auto-correlation index into tag maps to assess the uniformity of tag size distribution. Subsequently, we integrate this index into a TIN-based intrinsic tag map layout approach to enhance its ability to support multi-scale visualization. This enhancement involves iteratively filtering out candidate tags and selecting optimal tags that meet the defined index criteria. Experimental findings from two representative areas (the USA and Italy) demonstrate the efficacy of our approach in enhancing multi-scale visualization capabilities, albeit with trade-offs in compactness and time efficiency. Specifically, when retaining the same number of tags in the layout, our approach achieves higher compactness but requires more time. Conversely, when reducing the number of tags in the layout, our approach exhibits reduced time requirements but lower compactness. Furthermore, we discuss the effectiveness of various applied strategies aligned with existing approaches to generate diverse intrinsic tag maps tailored to user preferences. Additional details and resources can be found on our project website: https://github.com/TrentonWei/Multi-scale-TagMap.git.