网络空间同位模式的加色混合可视化挖掘方法

doi:10.11947/j.AGCS.2017.20160324

测绘学报 ›› 2017, Vol. 46 ›› Issue (6): 753-759.doi: 10.11947/j.AGCS.2017.20160324

网络空间同位模式的加色混合可视化挖掘方法

艾廷华^1,3, 周梦杰¹, 李晓明²

1. 武汉大学资源与环境科学学院, 湖北武汉 430079;
2. 天津市测绘院, 天津 300381;
3. 国土资源部城市土地资源监测与仿真重点实验室, 广东深圳 518034

收稿日期:2016-06-27 修回日期:2017-04-28 出版日期:2017-06-20 发布日期:2017-06-28
作者简介:艾廷华(1969—),男,教授,研究方向为地图综合,空间数据挖掘。E-mail:tinghua_ai@163.net
基金资助:
国家自然科学基金（41531180）；国家863计划（2015AA1239012）；国土资源部城市土地资源监测与仿真重点实验室开放基金（KF-2015-01-038）

Mining Co-location Pattern of Network Spatial Phenomenon Based on the Law of Additive Color Mixing

AI Tinghua^1,3, ZHOU Mengjie¹, LI Xiaoming²

1. School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China;
2. Tianjin Institute of Surveying and Mapping, Tianjin 300381, China;
3. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Presources, Shenzheng 518034, China

Received:2016-06-27 Revised:2017-04-28 Online:2017-06-20 Published:2017-06-28
Supported by:
The National Natural Science Foundation of China (No.41531180);The National High Technology Research and Development Program of China (863 Program) (No.2015AA1239012);The Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources (No. KF-2015-01-038)

摘要/Abstract

摘要： 同位模式挖掘是空间数据挖掘的热点问题之一，应用领域广泛。已有的同位模式挖掘方法一般采用统计或数据挖掘的方式，要求对复杂的数学公式、算法及相关参数等有深刻的理解，主要针对同质的欧式空间中地理现象。而城市空间中人为地理现象大多发生在网络空间，鉴于此，本文提出了一种网络空间同位模式可视化挖掘方法。该方法利用视觉语言表达网络空间现象之间的影响和交互作用。首先，利用网络空间核密度估计表达网络空间现象的分布情况和影响范围，为网络空间现象的同位模式挖掘提供支持，并建立单个地理现象分布情况与颜色之间的映射；然后基于色光加色混合原理获得两个地理现象相互影响的认知，借以挖掘空间同位模式。本文提出的方法属于形象思维，具有直观，形象和易感受等特点。

关键词: 同位模式, 可视化, 网络空间, 加色混合定律

Abstract: Mining co-location pattern is one of the hottest topics of current research in the spatial data mining community. The existing co-location mining methods belong to spatial statistics or data mining approaches, requiring much understanding of complex mathematical or statistical algorithms and parameters; and they consider events as taking place in a homogeneous and isotropic context in Euclidean space, whereas the physical movement in an urban space is usually constrained by a road network. This paper proposes a visualization method to mine co-location pattern along networks. The visual language is used to represent mutual influence between two geographic phenomena along networks. Firstly, taking Tobler's first law of geography into consideration, we use a network kernel density estimation method to express distribution pattern of geographic phenomena along networks, and construct a mapping between the distribution pattern of geographic phenomenon and color. Secondly, based on the law of additive color mixing, two colors representing two geographic phenomena are mixed to get cognition of the interaction between the two geographic phenomena. This method makes use of visual thinking, and it is intuitive and can be easily understood.

Key words: co-location pattern, visualization, network, additive color mixing

中图分类号:

P208

艾廷华, 周梦杰, 李晓明. 网络空间同位模式的加色混合可视化挖掘方法[J]. 测绘学报, 2017, 46(6): 753-759.

AI Tinghua, ZHOU Mengjie, LI Xiaoming. Mining Co-location Pattern of Network Spatial Phenomenon Based on the Law of Additive Color Mixing[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(6): 753-759.

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网络空间同位模式的加色混合可视化挖掘方法

Mining Co-location Pattern of Network Spatial Phenomenon Based on the Law of Additive Color Mixing

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