Understanding Geographical Space with Big Data: A Network Perspective
Lei Mao, Bohong Zheng, Guihu Zhao, Linlin Liu
Journal of Digital Information Management, Vol. 13, No. 5, 2015.
DOI: 
Link: http://dline.info/fpaper/jdim/v13i5/v13i5_6.pdf
Abstract:
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.