Statistical Correlation between Monthly Electric Power Consumption and VIIRS Nighttime Light

Lin, Jintang; Shi, Wenzhong

doi:10.3390/ijgi9010032

Cited by 20 publications

(21 citation statements)

References 40 publications

(56 reference statements)

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“…The trend of raw coal production is consistent with the trend of ALI, which is in the downward during 2015 and 2016 in most cities. Studies illustrated that nighttime light significantly positively correlates with electricity consumption [22,53,59], as well as about 70 percent of our country's electricity is produced from coal through thermal power generation [60]. Thus, we inferred that the raw coal production indirectly affects nighttime light through electricity produced by raw coal.…”

Section: Temporal Variation Mode Of Alimentioning

confidence: 77%

Analyzing Spatiotemporal Variation Modes and Industry-Driving Force Research Using VIIRS Nighttime Light in China

Song

Chen

2020

Remote Sensing

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Urbanization is a complex process closely involving the economy, society, and population. While monitoring urban development and exploring the industry-driving force in a real-time and effective way are the prerequisites for optimizing industry structure, narrowing the urban development gap, and achieving sustainable development. Nighttime light is an effective tool to monitor urban development from a macro perspective. However, the systematic research of nighttime light spatiotemporal variation modes and the industry-driving force of urban nighttime light are still unknown. Considering these issues, this paper analyzes the spatiotemporal variation modes of the average light index (ALI) and investigates the industry-driving force of ALI in 100 major prefecture-level cities across China mainland based on National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP VIIRS). The conclusions are as following three aspects. First, ALI is observed a funnel pattern among four regions in spatial dimension, with low in center and high in the surrounding, and it shows 5 variation modes (“W,” “√,” “Exponent,” “Logarithm,” and “N”) in temporal dimension, of which the “√” mode accounts for the highest proportion (60%). Second, the industry structure is closely related to ALI. Besides, the factor analysis result illustrates that the secondary and tertiary industry are the driving industries of ALI. Third, the classification result based on the industry contribution rate indicates that cities driven by different industries show significant spatial distribution differences. The three major industry-driving cities are mainly distributed in central and western regions, the secondary and tertiary industry-driving cities are evenly distributed, and the tertiary industry-driving cities are mainly distributed in provincial capitals. From 2013 to 2018, the fluctuation of city distribution driven by different industries changes obviously. The number of tertiary industry-driving cities increases steadily and the three major industry-driving cities are distributed wider spatially. Additionally, the impacts of location and raw coal on ALI are discussed. In general, these findings are essential to further research urban development mode and can be considered as the reference to narrow urban development gap.

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Section: Temporal Variation Mode Of Alimentioning

confidence: 77%

Analyzing Spatiotemporal Variation Modes and Industry-Driving Force Research Using VIIRS Nighttime Light in China

Song

Chen

2020

Remote Sensing

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“…• Examples given in Elvidge et al (1999); Hara et al (2004); Shi et al (2014); Lin & Shi (2020) correlates human activity, say electric consumption, to the energy escaped to the space. Following this example as a base, our AL pollution dataset shows a strong correlation with both population (with 0.90 confidence) and GDP (with 0.87) (see Table 1 and Fig.…”

Section: Resultsmentioning

confidence: 99%

The Temporal Analysis of Light Pollution in Turkey using VIIRS data

Yerli,

Aksaker,

Bayazit

et al. 2021

Preprint

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Artificial Light pollution (AL) in Turkey and in Turkish observatories between 2012-2020 have been studied using the archival data of Visible Infrared Imaging Radiometer Suite (VIIRS) instrument. The astroGIS database has been used in processing the data (astrogis.org) Aksaker et al. (2020a). The total energy released to space from Turkey increased by 80% in 2019 with respect to 2012. In the span of the dataset, a steady and continuous increase has been observed throughout all cities of the country. On the other hand, Dark Sky Park locations, East and Southeast Anatolian regions and mostly rural areas around the cities kept their AL level constant. Four demographic parameters have been studied and they were found to be correlated very well with AL: Population (R 0.90); GDP (R 0.87); Total Power Consumption (R 0.66) and Outdoor Lightening (R 0.67). Contrary to countries acting to prevent AL increases, Turkey seems to be at the beginning of an era where AL will arithmetically increase throughout the country and enormous amount of energy will continuously escape to space and therefore will be wasted. Therefore, a preventive legislation, especially for invaluable astronomical site locations such as TURAG, TUG, DAG and C ¸AAM where each is counted as a truly dark site due to their SQM values, has to be enacted in Turkey, in very near future.

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“…Therefore, since the 1980s, domestic and foreign scholars have used DMSP/OLS night light data to conduct a lot of research on EPC [31]. From the point of view of the research objects, it is mainly based on the global [32][33][34][35][36], China [37][38][39][40][41], and India [42][43][44]; from the perspective of the research content, it is mainly based on the estimation of EPC [45][46][47][48] and Spatialization [49][50][51][52]. By summarizing, we can clearly find that the current research on EPC based on DMSP/OLS night light data has relatively few studies on large-scale cross-regional urban agglomerations within a country.…”

Section: Introductionmentioning

confidence: 99%

Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data

et al. 2021

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In this paper, based on electrical power consumption (EPC) data extracted from DMSP/OLS night light data, we select three national-level urban agglomerations in China’s Yangtze River Economic Belt(YREB), includes Yangtze River Delta urban agglomerations(YRDUA), urban agglomeration in the middle reaches of the Yangtze River(UAMRYR), and Chengdu-Chongqing urban agglomeration(CCUA) as the research objects. In addition, the coefficient of variation (CV), kernel density analysis, cold hot spot analysis, trend analysis, standard deviation ellipse and Moran’s I Index were used to analyze the Spatio-temporal Dynamic Evolution Characteristics of EPC in the three urban agglomerations of the YREB. In addition, we also use geographically weighted regression (GWR) model and random forest algorithm to analyze the influencing factors of EPC in the three major urban agglomerations in YREB. The results of this study show that from 1992 to 2013, the CV of the EPC in the three urban agglomerations of YREB has been declining at the overall level. At the same time, the highest EPC value is in YRDUA, followed by UAMRYR and CCUA. In addition, with the increase of time, the high-value areas of EPC hot spots are basically distributed in YRDUA. The standard deviation ellipses of the EPC of the three urban agglomerations of YREB clearly show the characteristics of “east-west” spatial distribution. With the increase of time, the correlations and the agglomeration of the EPC in the three urban agglomerations of the YREB were both become more and more obvious. In terms of influencing factor analysis, by using GWR model, we found that the five influencing factors we selected basically have a positive impact on the EPC of the YREB. By using the Random forest algorithm, we found that the three main influencing factors of EPC in the three major urban agglomerations in the YREB are the proportion of secondary industry in GDP, Per capita disposable income of urban residents, and Urbanization rate.

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Statistical Correlation between Monthly Electric Power Consumption and VIIRS Nighttime Light

Cited by 20 publications

References 40 publications

Analyzing Spatiotemporal Variation Modes and Industry-Driving Force Research Using VIIRS Nighttime Light in China

Analyzing Spatiotemporal Variation Modes and Industry-Driving Force Research Using VIIRS Nighttime Light in China

The Temporal Analysis of Light Pollution in Turkey using VIIRS data

Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data

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