An Adaptive Network-Constrained Clustering (ANCC) model for fine- scale urban functional zones

Abstract

Urban functional zones are considered significant components for understanding urban landscape patterns in the socioeconomic environment. Although the spatial configuration of road networks contributes to urban function delineation at the block level, the morphological uncertainties caused by the road network structure in fine-scale urban function retrieval are ignored. This paper proposes an adaptive network-constrained clustering (ANCC) model to map urban function distributions at a finer level. By utilizing points of interest (POIs) to indicate independent functional places, the adaptive road configuration with a multilevel bandwidth selection strategy is proposed. On this basis, a term frequency-inverse document frequency-weighted latent Dirichlet allocation (TW-LDA) topic model is designed to delineate urban functions from semantic information. Taking Futian District, Shenzhen, as a case study, the results show an overall accuracy of approximately 77.10% in urban function mapping. A comparison of a block-level mapping model, a non-adaptive network-based model and the ANCC model reveals accuracies of 53.10%, 59.20% and 77.10%, respectively, indicating the advantages of the ANCC model for improving urban function mapping accuracy. The proposed ANCC model shows potential application prospects in monitoring urban land use for sustainable city planning.