Estimating Rural Electric Power Consumption Using NPP-VIIRS Night-Time Light, Toponym and POI Data in Ethnic Minority Areas of China

Abstract

Aiming at the problem that the estimation of electric power consumption (EPC) by using night-time light (NTL) data is mostly concentrated in large areas, a method for estimating EPC in rural areas is proposed. Rural electric power consumption (REPC) is a key indicator of the national socio-economic development. Despite an improved quality of life in rural areas, there is still a big gap between electricity consumption between rural residents and urban residents in China. The experiment takes REPC as the research target, selects Dehong (DH) Dai Jingpo Autonomous Prefecture of Yunnan Province as an example, and uses the NTL data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) carried by the Suomi National Polar-orbiting Partnership (NPP) Satellite from 2012 to 2017, toponym and points-of-interest (POI) data as the main data source. By performing kernel density estimation to extract the urban center and rural area boundaries in the prefecture, and combining the county-level boundary data and electric power data, a linear regression model of the total rural NTL intensity and REPC is estimated. Finally, according to the model, the EPC in ethnic minority rural areas is estimated at a 1-km spatial resolution. The results show that the NPP-REPC model can simulate REPC within a small average error (17.8%). Additionally, there are distinct spatial differences of REPC in ethnic minority areas.

Using Multi-Source Nighttime Lights Data to Proxy for County-Level Economic Activity in China from 2012 to 2019

“…In order to provide some corroboration for the main results, this section reports results that use an alternative benchmark-monthly electricity consumption-for assessing the predictive power of NTL data for studying short-run changes in economic activity. Many previous studies have used NTL data to proxy for spatiotemporal dynamics of electricity consumption [52][53][54]. Therefore, the use of electricity data should be a widely accepted benchmark in the case of any doubts about using China's local GDP data as a benchmark.…”

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

“…Therefore, we used wavelet transform to fuse POI and NTL data at the pixel scale to delineate urbanrural boundaries. Compared to the results before fusion, after data fusion, the accuracy of delineating urban-rural boundaries was 93.20%, which surpasses the accuracy of 90% obtained by studies such as the Global Artificial Impervious Area [60,61]. Accurate fusion of urban and rural development level and urban infrastructure differences can effectively improve the results of urban-rural boundary delineation.…”

Illuminated border: Spatiotemporal analysis of COVID-19 pressure in the Sino-Burma border from the perspective of nighttime light

“…Both contain exact location information (i.e., latitude and longitude) and additional details such as names and categories ( McKenzie et al, 2015 ). However, POI data contain more urban information, while toponym data contain more rural information ( Zhao et al, 2020 ). We searched for POI and toponym data from BigeMap ( https://www.bigemap.com/ ) and the Chinese National Database of Geographical Names ( https://dmfw.mca.gov.cn/online/map.html ).…”