Big Data Application in Urban Commercial Center System Evaluation

Abstract

Big data has provided new opportunities, directions, and methods for research on urban commercial center systems. Based on a quantitative assessment of big data and public participation, the “big data + public feedback” evaluation model can objectively and scientifically quantify the scale and structural characteristics of urban commercial center systems. In this paper, socioeconomic and material spatial attributes were considered in the selection of four indexes, including commercial agglomeration centrality, commercial facility service level, commercial industry status, and industry attraction. Specifically, we based our selection on the big data of the point-of-interest network, housing price, and population. ArcGIS, SPSS, and other analytical tools were employed to conduct a comparative analysis, cluster analysis, spatial network analysis, and correlation analysis. Using these data, we constructed an assessment index system, which was then utilized to comprehensively evaluate the current commercial land use in Nanjing’s main urban area and measure the degree of commercialization. The commercial center system in the main urban area of Nanjing was found to be consistent with the spatial structure system of “one main core, five secondary cores, multiple district cores, three horizontal axes, and one vertical axis.” Meanwhile, a public questionnaire was used to evaluate the public’s perception of the commercialization level in Nanjing. Finally, the results obtained were used for comparison with the structure of the commercial center system of Nanjing commercial network planning. We discovered that the results of the public’s perception of the commercialization level in Nanjing were similar to those of the big data analysis, which confirmed the credibility of big data analysis results. In conclusion, the findings of this study provide a basis for developing urban commercial center-level positioning and propose a method for data-assisted planning research.