Refined Identification of Urban Functional Zones Integrating Multisource Data Features: A Case Study of Lanzhou, China

Yixuan Wang, Shuwen Yang (), Xianglong Tang, Zhiqi Ding and Yikun Li
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Yixuan Wang: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
Shuwen Yang: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
Xianglong Tang: School of Architecture and Urban Planning, Lanzhou Jiaotong University, Lanzhou 730070, China
Zhiqi Ding: School of Management, Xi’an Jiaotong University, Xi’an 710049, China
Yikun Li: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China

Sustainability, 2024, vol. 16, issue 20, 1-25

Abstract: Identifying urban functional zones is one of the important foundational activities for urban renewal and the development of high-quality urban areas. Efficient and accurate identification methods for urban functional zones are significant for smart city planning and industrial layout optimization. However, existing studies have not adequately considered the impact of the interactions between human activities and geographical space provision on the delineation of urban functional zones. Therefore, from the perspective of integrating the spatiotemporal characteristics of human activities with the distribution of urban functional facilities, by incorporating mobile signaling, POI (point of interest), and building outline data, we propose a multifactorial weighted kernel density model that integrates ‘human activity–land feature area–public awareness’ to delineate urban functional zones quantitatively. The results show that the urban functional zones in the central city area of Lanzhou are primarily characterized by dominant single functional zones nested within mixed functional zones, forming a spatial pattern of ‘single–mixed’ synergistic development. Mixed function zones are widely distributed in the center of Lanzhou City. However, the area accounted for a relatively small proportion, the overall degree of functional mixing is not high, and the inter-district differences are obvious. The confusion matrix showed 85% accuracy and a Kappa coefficient of 0.83.

Keywords: urban functional zones; mobile signaling data; building outline data; POI data; Lanzhou city (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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