Identification and Portrait of Urban Functional Zones Based on Multisource Heterogeneous Data and Ensemble Learning

Wei Liu,Wen Dong,Jiancheng Luo,Nan Xu,Tianjun Wu,Nan Zhou

doi:10.3390/rs13030373

Abstract

Urban functional zones are important space carriers for urban economic and social function. The accurate and rapid identification of urban functional zones is of great significance to urban planning and resource allocation. However, the factors considered in the existing functional zone identification methods are not comprehensive enough, and the recognition of functional zones stops at their categories. This paper proposes a framework that combines multisource heterogeneous data to identify the categories of functional zones and draw the portraits of functional zones. The framework comprehensively describes the features of functional zones from four aspects: building-level metrics, landscape metrics, semantic metrics, and human activity metrics, and uses a combination of ensemble learning and active learning to balance the identification accuracy of functional zones and the labeling cost during large-scale generalization. Furthermore, sentiment analysis, word cloud analysis, and land cover proportion maps are added to the portraits of typical functional zones to make the image of functional zones vivid. The experiment carried out within the Fifth Ring Road, Haidian District, Beijing, shows that the overall accuracy of the method reached 82.37% and the portraits of the four typical functional zones are clear. The method in this paper has good repeatability and generalization, which is helpful to carry out quantitative and objective research on urban functional zones.

Highlights

According to people’s different social and economic activities, cities are divided into different functional zones, which are the basic units of urban planning, management, and resource allocation [1,2,3,4,5,6]
The division of urban functional zones formulated by the government usually takes administrative divisions as the unit, which only indicates the functional distribution on a macro level
The experimental results show that when the building-level metrics, the landscape metrics, the semantic metrics, and the human activity metrics are removed in turn, the overall classification accuracy of the ensemble model decreases by approximately 6.5%, 9.0%, 9.0%, and 7.2%, respectively

Summary

Introduction

According to people’s different social and economic activities, cities are divided into different functional zones, which are the basic units of urban planning, management, and resource allocation [1,2,3,4,5,6]. The accurate mapping of functional zones is of great significance for the quantitative analysis of urban traffic, the balance of workplaces and residences, and residents’ relocation [7], which are helpful to economic and demographic research [8,9]. The division of urban functional zones formulated by the government usually takes administrative divisions as the unit, which only indicates the functional distribution on a macro level. In this case, the accurate division of fine-scale urban functional zones in existing built-up areas is of great significance to the understanding and management of cities

Objectives

Methods

Results

Discussion

Conclusion