Identification of Urban Functional Area by Using Multisource Geographic Data: A Case Study of Zhengzhou, China

Abstract

The rational allocation of functional areas is the foundation for addressing the sustainable development of cities. Efficient and accurate identification methods of urban functional areas are of great significance to the adjustment and testing of urban planning and industrial layout optimization. Firstly, by employing multisource geographic data, an identification method of urban functional areas was developed. A quantitative measurement approach of the urban functional area was then established considering the comprehensive effects of human-land, space-time, and thematic information to present the covering area of ground objects, public awareness, and empirical research. Finally, the Zhengzhou city, which locates in Henan province of central China, was used to test the method. The results show that the developed method is efficient, accurate, and universal and can identify urban functional areas quickly and accurately. We found that the overall distribution of Zhengzhou’s functional areas presents a spatial pattern of single and multimixed coordinated development. The city’s commercial functional areas and commercial-based mixed functional areas are located in the city’s central area. The green square’s function area occupies relatively low and is mainly distributed in the city’s fringe.

A hierarchical spatial unit partitioning approach for fine‐grained urban functional region identification

“…The kernel density index is mainly based on kernel density estimation (Li et al, 2021). The basic principle of the kernel density index is to calculate the density contribution of each square point within a given radius to each grid center point using a kernel function (Lu et al, 2020).…”

Exploring Spatiotemporal Change of City and Village from Remote Sensing Using Multi-Branch Networks

“…Such as, Liu, ZJ and Wang, J et.al [12] proposed a building extraction methods from high resolution remote sensing imagery, the methods mainly using a multi-scale object classification and probabilistic Hough transform which including color feature and texture feature information. Li, Jingzhong and Xie, Xiao et.al [13] to addresses the sustainable development problem of city and effective identification of urban functional areas, combinations with multisource geographic data which a quantitative measurement method for urban functional areas has been established. Berg, Alexander C and Grabler, Floraine et.al [14] proposed a methods of parsing images of architectural scenes, where the parsing contents including roof, vegetation, Windows, building boundaries and doors etc part by training feature information of color and texture.…”

Identification of Urban Functional Areas and Their Mixing Degree Using Point of Interest Analyses

“…The spatial distribution of urban functions is an important factor to measure urban development, but the evolution of regional functions may be inconsistent with urban planning and development. Therefore, the study of urban functional zones is of great significance for effective functioning [4], the rational allocation of spatial elements [5], economic development, and the livability of a city. Information about urban functional zones is important for urban planning management and future development for relevant decision-making departments [6].…”

“…The rapid and accurate identification of urban functional areas is a necessary condition for improving urban planning and management [10]. The division of functional areas is one of the basic steps of urban planning and management [5,11]. The advent of the big-data era has led to the development of new methods for the study of urban spatial patterns.…”