Open Data Based Urban For-Profit Music Venues Spatial Layout Pattern Discovery

• Main Authors: Xueqi Wang, Zhichong Zou
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: Sustainability
• Subjects: music venues; spatial layout pattern; internet-accessible data; hierarchical clustering
• Online Access: https://www.mdpi.com/2071-1050/13/11/6226

The spatial pattern of music venues is one of the key decision-making factors for urban planning and development strategies. Understanding the current configurations and future demands of music venues is fundamental to scholars, planners, and designers. There is an urgent need to discover the spatial pattern of music venues nationwide with high precision. This paper aims at an open data solution to discover the hidden hierarchical structure of the for-profit music venues and their dynamic relationship with urban economies. Data collected from the largest two public ticketing websites are used for clustering-based ranking modeling and spatial pattern discovery of music venues in 28 cities as recorded. The model is based on a multi-stage hierarchical clustering algorithm to level those cities into four groups according to the website records which can be used to describe the total music industry scale and activity vitality of cities. Data collected from the 2018 China City Statistical Year Book, including the GDP per capita, disposable income per capita, the permanent population, and the number of patent applications, are used as socio-economic indicators for the city-level potential capability of music industry development ranking. The Spearman’s rank correlation coefficient and the Kendall rank correlation coefficient are applied to test the consistency of the above city-level rankings. The results are 0.782 and 0.744 respectively, which means there is a relatively significant correlation between the scale level of current music venue configuration and the potential to develop the music industry. Average nearest neighbor index (ANNI), quadrate analysis, and Moran’s I are used to identify the spatial patterns of music venues of individual cities. The results indicate that music venues in urban centers show more spatial aggregation, where the spatial accessibility of music activity services takes the lead significantly, while a certain amount of venues with high service capacity distribute in suburban areas. The findings can provide decision support for urban planners to formulate effective policies and rational site-selection schemes on urban cultural facilities, leading to smart city rational construction and sustainable economic benefit.