• 地理空间分析综合应用 •

### 科学合作地域倾向性研究——以中国雾霾研究为例

1. 1. 信息工程大学地理空间信息学院,郑州 450052
2. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
3. 75711部队,广州 510515
• 收稿日期:2016-06-01 修回日期:2016-10-10 出版日期:2017-02-28 发布日期:2017-02-17
• 通讯作者: 袁烨城 E-mail:wangsh@lreis.ac.cn;yuanyc@lreis.ac.cn
• 作者简介:

作者简介：王双（1983-）,女,河南洛阳人,博士生,研究方向为时空数据挖掘与可视化、地图认知与设计。E-mail: wangsh@lreis.ac.cn

• 基金资助:
国家自然科学基金项目（41171353）;资源与环境信息系统国家重点实验室青年人才培养基金项目（08R8B6IOYA）;国家“863”计划项目（2012AA12A404）

### Research on Geographical Preference of Scientific Collaboration : A Case Study of Haze Research Network in China

WANG Shuang1(), CHEN Yufen1, YUAN Yecheng2,*(), LI Wei1,3, WANG Chengshun1

1. 1. Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450052, China
2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
3. 75711 Troops, Guangzhou 510515, China
• Received:2016-06-01 Revised:2016-10-10 Online:2017-02-28 Published:2017-02-17
• Contact: YUAN Yecheng E-mail:wangsh@lreis.ac.cn;yuanyc@lreis.ac.cn

Abstract:

Scientific collaboration is an important way of knowledge dissemination and sharing. Researches have showed that geographic factor is one of the main factors that influencing scientific collaboration. However, most of related researches have just quantitatively described the functional relationship between collaboration strength and geographic distance from the perspective of Scientometrics. As a result, it can hardly detect the spatial characteristics and relationship of scientific collaboration. In this paper, for the purpose of mining spatial patterns in scientific collaboration network, geographical preference of scientific collaboration was studied from the view of geography. Taking the haze research network in China for example, the location information was extracted from bibliographic data and then the virtual scientific collaboration network can be mapped into geo-collaboration network by using geocoding service. Based on this, a distance-based method for community detection of scientific collaboration network was proposed to explore the spatial cluster pattern in scientific collaboration. Using modified Louvain community detection algorithm, two different variables were used as weight factor to detect communities. The results showed that, the community detection algorithm considering collaboration frequency and geographic distance can make the average geographic distance minimum and the Salton index maximum inside community, which both reflect the geographical preference and collaboration strength of scientific collaboration. This method can effectively explore the spatial pattern and relationship in scientific collaboration network, and represent geographical preference of scientific collaboration in a quantitative and qualitative way. In addition, it is a novel method of introducing geographic location and geographic distance into complex network analysis. We hope that it will not only be helpful for scientific collaboration network, but also can be applied to other complex network for geographic community detection.