An Improved Method of Determining Human Population Distribution Based on Luojia 1-01 Nighttime Light Imagery and Road Network Data-A Case Study of the City of Shenzhen.

Qiang Zhou,Jinyuan Shao,Haowei Wang,Yuanmao Zheng,Yinglun Lin

doi:10.3390/s20185032

Abstract

Previously published studies on population distribution were based on the provincial level, while the number of urban-level studies is more limited. In addition, the rough spatial resolution of traditional nighttime light (NTL) data has limited their fine application in current small-scale population distribution research. For the purpose of studying the spatial distribution of populations at the urban scale, we proposed a new index (i.e., the road network adjusted human settlement index, RNAHSI) by integrating Luojia 1-01 (LJ 1-01) NTL data, the enhanced vegetation index (EVI), and road network density (RND) data based on population density relationships to depict the spatial distribution of urban human settlements. The RNAHSI updated the high-resolution NTL data and combined the RND data on the basis of human settlement index (HSI) data to refine the spatial pattern of urban population distribution. The results indicated that the mean relative error (MRE) between the population estimation data based on the RNAHSI and the demographic data was 34.80%, which was lower than that in the HSI and WorldPop dataset. This index is suitable primarily for the study of urban population distribution, as the RNAHSI can clearly highlight human activities in areas with dense urban road networks and can refine the spatial heterogeneity of impervious areas. In addition, we also drew a population density map of the city of Shenzhen with a 100 m spatial resolution for 2018 based on the RNAHSI, which has great reference significance for urban management and urban resource allocation.

Highlights

Population data are the basic data that reflect the social and economic situation of a country or region, but are some of the most important basic data in social and geographical research [1]
This study has introduced the field of high-resolution population modeling by utilizing an innovative combination of remote sensing and traffic road network data to refine population distribution
Our research verified that the road network adjusted human settlement index (RNAHSI) can reduce errors and improve the accuracy of the results from simulating the urban population distribution

Summary

Introduction

Population data are the basic data that reflect the social and economic situation of a country or region, but are some of the most important basic data in social and geographical research [1]. Population data have been extensively used for social resource allocation, environmental protection, and city planning [2]. Existing population data are often collected step-by-step with administrative divisions as units, with a long update cycle and low spatial and temporal resolution. The above problems can be effectively solved by the spatialization of population data, which is one of the most important methods to realize the coupling of population and other socio-economic, resource, and environmental data. This carries important implications to enhance the comprehensive management capacity of populations, resources, and the environment [3].

Methods

Results

Discussion

Conclusion