Understanding geographical space with big data: A network perspective

This paper used China’s urban clusters as case studies, and a model of the information network was constructed using Baidu index search data. In this model, the urban clusters were considered nodes, and the search intensity between them were regarded as edges. Based on complex network theory and city flow theory, the structure of geographical space was visualized in the form of a network. Analysis results show that, first, the network density of the core contact is only at 3.62%, and 69.2% of the connection strengths between urban clusters are relatively weak on a national scale. Next, the abilities of absorbing information are greater than those of giving off for more developed urban clusters. Last, compared with the ability of giving off information, the variability of absorption is more obvious for all 24 urban clusters. Based on these conclusions, we analyzed spatial influences from the perspective of urban flows and present strategies with space optimization. Subject Categories and Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval – Data mining; I.6.9 [Visualization]: Information visualization Spatial data processing General Terms Algorithm, Design, Performance