Abstract
Producing gridded electric power consumption (EPC) maps at a fine geographic scale is critical for rational deployment and effective utilization of electric power resources. Brightness of nighttime light (NTL) has been extensively adopted to evaluate the spatial patterns of EPC at multiple geographical scales. However, the blooming effect and saturation issue of NTL imagery limit its ability to accurately map EPC. Moreover, limited sectoral separation in applying NTL leads to the inaccurate spatial distribution of EPC, particularly in the case of industrial EPC, which is often a dominant portion of the total EPC in China. This study pioneers the separate estimation of spatial patterns of industrial and nonindustrial EPC over mainland China by jointly using points of interest (POIs) and multiple remotely sensed data in a random forests (RF) model. The POIs provided fine and detailed information about the different socioeconomic activities and played a significant role in determining industrial and nonindustrial EPC distribution. Based on the RF model, we produced industrial, non-industrial, and overall EPC maps at a 1 km resolution in mainland China for 2011. Compared against statistical data at the county level, our results showed a high accuracy (R2 = 0.958 for nonindustrial EPC estimation, 0.848 for industrial EPC estimation, and 0.913 for total EPC). This study indicated that the proposed RF-based method, integrating POIs and multiple remote sensing data, can markedly improve the accuracy for estimating EPC. This study also revealed the great potential of POIs in mapping the distribution of socioeconomic parameters.
Highlights
As the most widely used secondary energy source, electricity is indispensable to modern society and plays a vital role in supporting socioeconomic activities and human life.the spatial pattern of electric power consumption (EPC) can be used as an essential indicator in signifying socioeconomic development [1] and energy use, which, in turn, are closely associated with CO2 emissions and global warming [2]
nonindustrial EPC (NEPC) is used as the dependent variable, and the aggregated values of 19 points of interest (POIs) kernel density layers at the prefecture level are used as explanatory variables [35]
Without POIs, nighttime light (NTL) and NDVImax, which are significantly correlated with human s tlements and impervious surfaces [68,69,70], are the most important predictors in the model for total electric power consumption (TEPC) estimation
Summary
As the most widely used secondary energy source, electricity is indispensable to modern society and plays a vital role in supporting socioeconomic activities and human life. The better quality of the VIIRS data compared with DMSP/OLS data enhance the ability of NTL for estimating EPC [23]. Both NTL products are not directly indicative of human activities. The advent of social sensing big data, which closely relate to various human activities, provides great opportunities to further refine EPC estimation, especially in complex urban areas. Industrial activities that account for most EPC in the majority of Chinese cities are mainly located in suburban areas This phenomenon cannot be captured well merely on the basis of NTL data and may cause substantial misdistribution in EPC over China. To the best of our knowledge, this study is the first attempt to integrate POIs in EPC estimation
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