Urban Growth Boundaries Delineation under Multi-Objective Constraints from the Perspective of Humanism and Low-Carbon Concept

Abstract

Urban growth boundaries (UGBs) play an important role in controlling urban sprawl and protecting natural ecosystems. Traditional methods mainly focus on the heterogeneity of regional resources and environment rather than residents’ behavioral activities. However, residents’ behavioral activities are one of the most important factors influencing urban spatial form. Fortunately, the emergence of big data, especially phone signaling data, provides alternative data sources to understand the dynamic resident behavior activity space, which is significant for people-oriented urban development. Therefore, we propose a novel framework for UGB delineation based on multi-source big data and multi-objective constraints, which emphasizes humanism and the low-carbon concept in urban expansion simulation. The multi-objective constraints are constructed from the evaluation of resident activity space expansion potential, the evaluation of urban construction suitability, the evaluation of ecological conservation importance, and the human survival materials limitation. We apply the framework to Ningbo, and the results show that the framework under multi-objective constraints from a people-oriented and low-carbon perspective is more reliable and comprehensive than that without constraints. The findings also show that the UGB delineation based on multi-source big data has higher accuracy and better performance. The conceptual and methodological advances of this study are also applicable to other cities to help UGBs delineation.