Extraction of urban built-up area based on the fusion of night-time light data and point of interest data.

Zhiming Zhang,Xiong He,Zijiang Yang

doi:10.1098/rsos.210838

Zhiming Zhang, Xiong He + Show 1 more

Open Access

https://doi.org/10.1098/rsos.210838

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Abstract

The accurate extraction of urban built-up areas is an important prerequisite for urban planning and construction. As a kind of data that can represent urban spatial form, night-time light data has been widely used in the extraction of urban built-up areas. As one of the geographic open-source big data, point of interest (POI) data has a high spatial coupling with night-time light data, so researchers are beginning to explore the fusion of the two data in order to achieve more accurate extraction of urban built-up areas. However, the current research methods and theoretical applications of the fusion of POI data and night-time light data are still insufficient compared with the dramatically changing urban built-up areas, which needed to be further supplemented and deepened. This study proposes a new method to fuse POI data and night-time light data. The results before and after data fusion are compared, and the accuracy of urban built-up area extracted by different data and methods is analysed. The results show that the data fusion can avoid the shortage of single data and effectively improve the extraction accuracy of urban built-up areas, which is greatly helpful to supplement the study of data fusion in urban built-up areas, and also can provide decision-making guidance for urban planning and construction.

Highlights

In recent decades, with the rapid urbanization of China and the intensification of agricultural modernization in urban built-up areas, cities have undergone unlimited expansion and great changes have occurred [1,2]
From the perspective of the overall spatial distribution of point of interest (POI) point density in the study area, the distribution of POI in urban space has an obvious rule, which is that the POI density changes from dense to sparse from the urban centre to the urban edge
It is reliable to identify urban built-up areas using POI big data based on the density-graph, which objectively eliminates the subjective thresholds set arbitrarily in the previous data and the scale effect under the spatial scale and the case study of Kunming proves the value of POI big data in exploring urban space, especially in urban built-up areas

Summary

Introduction

With the rapid urbanization of China and the intensification of agricultural modernization in urban built-up areas, cities have undergone unlimited expansion and great changes have occurred [1,2]. Urban built-up areas are the royalsocietypublishing.org/journal/rsos R. Carrier of all urban activities, the main gathering areas of population and economic activities, and the 2 prominent manifestation of urbanization [3]. The implementation of ‘national spatial planning’ in China clearly proposes the demarcation of the boundary line of urban areas [4,5]. The extraction of urban built-up areas has become increasingly important

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