Spatial disparity of urban performance from a scaling perspective: a study of industrial features associated with economy, infrastructure, and innovation

Abstract

The power-law scaling theory has been widely applied in urban studies. Industrial development is tied to cities. However, current research on scaling pace development of industrial features in cities, and industrial features’ association with urban indicators in cities are limited. This study develops power-law scaling regression and robust geographical detector-based spatial models to (i) identify spatial disparities of urban performance from a scaling perspective and (ii) explore the spatial association between industrial scaling features and other urban features. The developed models are implemented in three essential industries, including mining, manufacturing, and utility supply and waste services, in Australia. This study investigates urban scaling features in 101 Australian significant urban areas (SUAs) and assesses the spatial association between industrial features and indicators from the perspectives of the economy, infrastructure design, and innovation. This study has general results on Australian urban scaling development and specific spatial association results on industrial scaling features. In general, the study validates the consistency of the scaling development among Australian cities with the power-law theory and the similarity of scaling disparity features among top-populated cities. In specific, the urban innovation indicator and the income level are predominantly and positively associated factors with industrial companies and employees, indicating that the innovation growth and economic development in Australian cities would stimulate the performance of industrial companies and the employment scale. The synergy between urban innovation and industrial company performance is especially significant in major capital cities. The developed spatial models have a broad potential to address spatial and scaling characteristics of industrial features.