Multiscale geovisual analysis of knowledge innovation patterns using big scholarly data

Abstract

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.