Delineation of the Urban-Rural Boundary through Data Fusion: Applications to Improve Urban and Rural Environments and Promote Intensive and Healthy Urban Development

Abstract

As one of the most important methods for limiting urban sprawl, the accurate delineation of the urban–rural boundary not only promotes the intensive use of urban resources, but also helps to alleviate the urban issues caused by urban sprawl, realizing the intensive and healthy development of urban cities. Previous studies on delineating urban–rural boundaries were only based on the level of urban and rural development reflected by night-time light (NTL) data, ignoring the differences in the spatial development between urban and rural areas; so, the comprehensive consideration of NTL and point of interest (POI) data can help improve the accuracy of urban–rural boundary delineation. In this study, the NTL and POI data were fused using wavelet transform, and then the urban–rural boundary before and after data fusion was delineated by multiresolution segmentation. Finally, the delineation results were verified. The verification result shows that the accuracy of delineating the urban–rural boundary using only NTL data is 84.20%, and the Kappa value is 0.6549; the accuracy using the fusion of NTL and POI data on the basis of wavelet transform is 93.2%, and the Kappa value is 0.8132. Therefore, we concluded that the proposed method of using wavelet transform to fuse NTL and POI data considers the differences between urban and rural development, which significantly improves the accuracy of the delineation of urban–rural boundaries. Accurate delineation of urban–rural boundaries is helpful for optimizing internal spatial structure in both urban and rural areas, alleviating environmental problems resulting from urban development, assisting the formulation of development policies for urban and rural fringes, and promoting the intensive and healthy development of urban areas.

A New Perspective for Urban Development Boundary Delineation Based on the MCR Model and CA-Markov Model

“…The report of the 19th National Congress of the Communist Party of China (CPC) proposed to "scientifically delineate urban development boundaries (UDBs)". UDB research provides scientific references for land-use planning and urban planning, which is one of the key approaches for the government to control the spread of urban construction land and manage urban development boundaries [15][16][17].…”

Spatial Expansion of Built-Up Areas in the Beijing–Tianjin–Hebei Urban Agglomeration Based on Nighttime Light Data: 1992–2020

“…Based on the harmonized DMSP-like nighttime light datasets, this paper determines the optimal threshold of nighttime light using the spatial comparison method, which can optimally reflect the economic dynamism and spatial heterogeneity of urban areas. This laid an indispensable foundation for grasping the spatial evolution of urban agglomerations and realizing the healthy and sustainable development of the region [ 35 ]. There is an upper limit on the value of DMSP / OLS nighttime light data due to the limitation of DN value.…”

Using Food Delivery Data to Identify Urban -Rural Areas: A Case Study of Guangzhou, China

“…In order to assess the accuracy of traditional NTL data and food delivery data in identifying urban and rural spatial space, this study adopted the verification method of random pixel confusion matrix to verify the identification accuracy of different data. Referring to existing studies, it can be found that the random pixel verification of single city is generally 1,000, and that of urban agglomeration is 3,000 (He et al, 2021b;Zhang et al, 2021). Therefore, in order to assess the identification of urban and rural spatial space more accurately in this study, ArcGIS was used to create 1,000-5,000 random pixel verification points within the spatial space of Guangzhou, in which the ratio of training data to verification data was 1:1.…”