Spatial interaction visualization based on the distance-similarity metaphor

doi:10.11947/j.AGCS.2026.20250377

Abstract

Abstract:

Spatial interaction provides an important perspective for understanding human-environment relationships, and intuitive and efficient visualization is crucial for identifying spatial patterns and interaction characteristics. Existing approaches are largely grounded in geographic space, representing interaction paths and overall trends through overlaid flow lines or aggregated structures. However, under high-density scenarios, these methods often struggle to support tasks such as perceiving the scale of interactions, identifying related entities, and comparing relationship strengths. To address this limitation, this study proposes a spatial interaction tag cloud (SITC) based on the distance-similarity metaphor, which reconstructs spatial interactions at the level of relational structure. In SITC, places are transformed into toponymic labels, and interaction strength is encoded through spatial proximity. The method organizes labels into compact radial clusters centered on interaction hubs, where places with stronger interactions are positioned closer to the center, while weaker relationships gradually expand outward. The framework also supports comparative analysis across multiple interaction centers. Visualization experiments and user evaluations based on intercity flight data in mainland China demonstrate that SITC effectively supports four key analytical tasks in high-density interaction contexts: rapidly perceiving the overall scale of interactions associated with a center, intuitively identifying places that interact with the center, comparing relative interaction strengths between places and the center, and discovering shared interaction structures among multiple centers. Although the approach relaxes strict geographic positional fidelity, it substantially improves the readability of object-level relational structures and analytical efficiency. SITC provides a complementary visualization paradigm for spatial interaction analysis, particularly suited for place-centered structural exploration in high-density spatial interaction data, and offers a new expressive pathway for understanding relationships and supporting decision-making in complex spatial systems.

Key words: spatial interaction, OD data, visualization, distance-similarity metaphor, toponymic labels, tag cloud, flow map

CLC Number:

P208

Xiaoqiang CHENG, Jiawei ZHAO, Pengcheng LIU. Spatial interaction visualization based on the distance-similarity metaphor[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(3): 536-547.

Figures/Tables 11

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编号	问题	准确率/（%）		放弃率/（%）
编号	问题	SITC	流地图	SITC	流地图
1	哪些城市到这3个中心城市均有直飞航班？找出3个	97.4	43.0	0.0	53.5
2	找出飞往上海航班数量最多的前3个城市	55.3	16.7	0.0	76.3
3	找出只到广州有航班的3个城市	91.2	15.0	7.9	80.7
4	杭州是否有航班飞往这3个中心城市	86.0	29.0	1.7	59.6
5	哪些城市到北京有航班，但是到广州没有航班？找出3个	91.2	2.6	3.5	89.5

编号	问题	得分
编号	问题	SITC	流地图
1	在阅读该图时，你是否能迅速理解城市之间的航班关系	5.3	2.5
2	阅读该图是否让你感到信息过载或视觉混乱	4.6	1.8
3	你认为回答问题1—5是否容易	4.9	1.8

可视化能力	传统流地图	边绑定流地图	矩阵图Maptrix	Kriskograms	SITC本文方法
节点清晰表达	✕	✕	✕	✕	√
交互关系存在性判断	✕	✕	√	√	√
整体交互模式呈现	✕	√	√	✕	✕
中心交互规模感知	✕	✕	√	√	√
具体交互对象快速识别	✕	✕	✕	✕	√
相对强度比较	√	√	√	√	√
多中心共享关系发现	✕	✕	✕	✕	√
数据规模可扩展性	✕	√	✕	✕	√
空间保真	√	√	✕	✕	✕
总评	7个✕	5个✕	5个✕	6个✕	2个✕