Jin Shang, Xin Guo, Jicheng Wang & Hailong Su
ABSTRACT: The Central Plains Urban Agglomeration (CPUA) is a link of economic development to connect
the eastern part and western part in China. Current research on urban agglomeration integra-
tion mainly uses urban properties’ data. This paper conducts an evaluation method for urban
agglomeration integration based on multivariate big data. This method mainly applies eigen-
vector centrality to assess the integrated situation of the CPUA from four dimensions (internet
connection, industrial economy, public service and coordinated governance). The data of this
research mainly includes cell phone signaling data, Baidu index, industrial investment data and
statistical data for planning. The main innovations and contributions of this research is that (i)
on the theoretical aspect, this research proposed an index indicator evaluation system for
integration of urban agglomerations based on Analytic Hierarchy Process (AHP) and expert
rating. It contributes for the further development of regional integration and other related
theories; (ii) on the practical aspect, this study, taking the CPUA as example, presents an
assessment approach that uses multivariate big data to measure the current integrated
situation of urban agglomeration. This method provides decision-making support for the
development of urban agglomeration integration.
KEYWORDS: Central Plains Urban Agglomeration; urban agglomeration integration; multivariate big data; eigenvector centrality; index indicator evaluation system