The green quality of urban spatial development: A multi-dimensional and multi-regional model using big data

Abstract

Spatial development involves multi-dimensional and multi-regional objectives that might be competing, while few studies have examined these issues simultaneously. We proposed a comprehensive model to assess the green quality of urban spatial development (GQUSD), focusing on the synergy of multi-dimensional objectives and the balance of multi-regional interests. The metropolitan city of Hangzhou was taken as an example. We included land consumption, vegetation loss and infrastructure cost as the inputs and the density of production and living activities as the outputs of spatial development. We developed a categorical Data Envelope Analysis (DEA) using geospatial big data and stratified optimization to evaluate the synergy of inputs-outputs at the finer level of spatial grids. Resultsshow a “sandwich” structure of the GQUSD in Hangzhou when considering green space and vegetation loss, and characterize four spatial typologies of agglomerated development, limited development, blinded development and low-potential development through the Bivariate Local Indicator of Spatial Association (LISA) analysis. The “sandwich” structure is shaped by spatial processes of socio-economic agglomeration, industrial-driven sprawl, new-town development and suburbanization of high-end sectors. We also find green inequality in the spatial development of Hangzhou where vegetation costs were higher in manufactural industrial sites and low-income communities. We provide urban planning strategies to balance competing interests across regions, objectives, functions and communities for green growth and sustainable goals.