基于距离-相似性隐喻的空间交互可视化

doi:10.11947/j.AGCS.2026.20250377

测绘学报 ›› 2026, Vol. 55 ›› Issue (3): 536-547.doi: 10.11947/j.AGCS.2026.20250377

基于距离-相似性隐喻的空间交互可视化

成晓强¹^,²(), 赵家威¹, 刘鹏程³^,⁴()

^1.湖北大学地理科学学院，湖北　武汉　430062
^2.湖北大学区域开发与环境响应湖北省重点实验室，湖北　武汉　430062
^3.华中师范大学地理过程分析与模拟湖北省重点实验室，湖北　武汉　430079
^4.华中师范大学城市与环境科学学院，湖北　武汉　430079

收稿日期:2025-08-28 修回日期:2026-03-03 出版日期:2026-04-16 发布日期:2026-04-16
通讯作者: 刘鹏程 E-mail:carto@hubu.edu.cn;liupc@ccnu.edu.cn
作者简介:成晓强（1985—），男，博士，副教授，研究方向为地理信息可视化。E-mail：carto@hubu.edu.cn
基金资助:
国家自然科学基金(42471486; 42071455)

Spatial interaction visualization based on the distance-similarity metaphor

Xiaoqiang CHENG¹^,²(), Jiawei ZHAO¹, Pengcheng LIU³^,⁴()

^1.School of Geographical Sciences, Hubei University, Wuhan 430062, China
^2.Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China
^3.Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China
^4.College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

Received:2025-08-28 Revised:2026-03-03 Online:2026-04-16 Published:2026-04-16
Contact: Pengcheng LIU E-mail:carto@hubu.edu.cn;liupc@ccnu.edu.cn
About author:CHENG Xiaoqiang (1985—), male, PhD, associate professor, majors in geographic information visualization. E-mail: carto@hubu.edu.cn
Supported by:
The National Natural Science Foundation of China(42471486; 42071455)

摘要/Abstract

摘要：

空间交互是理解人地关系的重要视角，而构建直观高效的可视化对于识别空间模式和交互特征至关重要。现有方法大多以地理空间为基础，通过叠加流线或聚合结构表达交互路径与整体趋势，但在高密度场景下难以支持交互规模感知、关联对象识别及关系强度比较等任务。本文提出一种基于距离-相似性隐喻的空间交互标签云（SITC），将地点转化为地名标签，并以空间邻近性编码交互强度关系，从关系结构层面对空间交互进行重构表达。SITC围绕交互中心形成紧凑的径向标签簇，使交互强度强的地点自然靠近中心，交互强度弱的逐渐外扩，同时支持多中心并置分析。基于中国大陆城市航班数据的可视化试验与用户认知评估表明，SITC能够在高密度交互场景下有效支持4类关键分析能力：快速感知交互中心所涉及的整体交互规模；直观识别与中心存在交互关系的具体地点；比较不同地点与中心之间的相对交互强度；发现多个中心之间共享的交互对象结构。尽管该方法弱化了地理位置保持，但显著提升了对象级关系结构的可读性与分析效率。SITC为空间交互分析提供了一种互补性的可视化范式，尤其适用于高密度空间交互数据中以地点为核心的结构探索任务，为复杂空间系统的关系理解与决策支持提供了一种表达路径。

关键词: 空间交互, OD数据, 可视化, 距离-相似性隐喻, 地名标签, 标签云, 流地图

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

中图分类号:

P208

成晓强, 赵家威, 刘鹏程. 基于距离-相似性隐喻的空间交互可视化[J]. 测绘学报, 2026, 55(3): 536-547.

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.

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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✕

基于距离-相似性隐喻的空间交互可视化

Spatial interaction visualization based on the distance-similarity metaphor

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