Multiscale geovisual analysis of knowledge innovation patterns using big scholarly data

ABSTRACT

Knowledge innovation is a key factor in industrial development and regional economic growth. Understanding regional knowledge innovation and its dynamic changes is one of the fundamental tasks of regional policy-makers and business decision-makers. Although many existing studies have been conducted to support in understanding knowledge innovation patterns, data-driven and intuitive visual analysis of georeferenced knowledge innovation has not been sufficiently studied. In this work, we analysed knowledge innovation by visually exploring big georeferenced scholarly data. More specifically, we first applied network analysis and statistical methods to derive key measures (e.g., the number of publications and academic collaborations) of knowledge innovation with multiple spatial scales. We then designed geovisualizations to explicitly represent the multiscale spatiotemporal patterns and relations. We integrated the analytical methods and geovisualizations into an interactive tool to facilitate stakeholders’ visual learning and analysis of knowledge innovation with a spatial focus. Our work shows that geovisualizations have great potential in supporting complex geoinformation communication in knowledge innovation.

KEYWORDS:

• Geovisualization
• big scholarly data
• network analysis
• knowledge innovation

Acknowledgements

This work is funded by the project “A Visual Computing Platform for the Industrial Innovation Environment in Yangtze River Delta” supported by the Jiangsu Industrial Technology Research Institute (JITRI). We also thank Yangtze River Delta Science Data Center, National Earth System Science Data Sharing Infrastructure, National Science & Technology Infrastructure of China (http://nnu.geodata.cn:8008/). We thank the two anonymous reviewers whose comments and suggestions helped improve and clarify this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. https://dl.acm.org/ccs

2. https://nominatim.org