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.
Highlights
Rapid urbanization processes have led to drastic socioeconomic evolution [1], [2]
Since the traditional kernel density estimation (KDE) with fixed bandwidth fails to capture urban functional zones driven by road networks, the adaptive road configuration approach with a multilevel bandwidth selection strategy is proposed
WORK This paper introduces an Adaptive Network-Constrained Clustering (ANCC) model for urban functional zones
Summary
In the process of urban development, the potential impacts and interactions of population, road, social and economic activities have been revealed by many studies [3], [4]. The significant socioeconomic changes have improved transportation and have further led to the diversity of urban functions such as residential, industrial, commercial and sports areas [5]. Identifying these functional areas and delineating their distribution characteristics are essential to understanding urban spatial structures and guiding sustainable city development [6]–[8]. Geotagged data have been widely utilized in urban function retrieval in previous studies. Yunliang et al [18] calculated the overlap rate between a POI distribution with obvious
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