An approach for urban agglomerations integration evaluation based on multivariate big data: case of the Central Plains Urban Agglomeration

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 integration mainly uses urban properties’ data. This paper conducts an evaluation method for urban agglomeration integration based on multivariate big data. This method mainly applies eigenvector 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