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Author |
Tripathy, B.R.; Sajjad, H.; Elvidge, C.D.; Ting, Y.; Pandey, P.C.; Rani, M.; Kumar, P. |
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Title |
Modeling of Electric Demand for Sustainable Energy and Management in India Using Spatio-Temporal DMSP-OLS Night-Time Data |
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Journal Article |
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Year |
2018 |
Publication |
Environmental Management |
Abbreviated Journal |
Environ Manage |
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61 |
Issue |
4 |
Pages |
615-623 |
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Keywords |
Remote Sensing |
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Abstract |
Changes in the pattern of electric power consumption in India have influenced energy utilization processes and socio-economic development to greater extent during the last few decades. Assessment of spatial distribution of electricity consumption is, thus, essential for projecting availability of energy resource and planning its infrastructure. This paper makes an attempt to model the future electricity demand for sustainable energy and its management in India. The nighttime light database provides a good approximation of availability of energy. We utilized defense meteorological satellite program-operational line-scan system (DMSP-OLS) nighttime satellite data, electricity consumption (1993-2013), gross domestic product (GDP) and population growth to construct the model. We also attempted to examine the sensitiveness of electricity consumption to GDP and population growth. The results revealed that the calibrated DMSP and model has provided realistic information on the electric demand with respect to GDP and population, with a better accuracy of r (2) = 0.91. The electric demand was found to be more sensitive to GDP (r = 0.96) than population growth (r = 0.76) as envisaged through correlation analysis. Hence, the model proved to be useful tool in predicting electric demand for its sustainable use and management. |
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Department of Geography, Jamia Millia Islamia, New Delhi, 110025, India. pavan.jamia@gmail.com |
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0364-152X |
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PMID:29282533 |
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GFZ @ kyba @ |
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2484 |
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Tan, M.; Li, X.; Li, S.; Xin, L.; Wang, X.; Li, Q.; Li, W.; Li, Y.; Xiang, W. |
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Title |
Modeling population density based on nighttime light images and land use data in China |
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Journal Article |
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2018 |
Publication |
Applied Geography |
Abbreviated Journal |
Applied Geography |
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90 |
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239-247 |
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Remote Sensing |
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Population change is a key variable that influences climate change, ecological construction, soil and water use, and economic growth. Census data are always point data, whereas planar data are often required in scientific research. By using nighttime light (NTL) images and land use data, combined with the fifth and sixth census data of China at the county level, we carried out spatial matching on the population of each county, respectively, and established population density diagrams of China for 2000 and 2010, which had a spatial resolution of 1 × 1 km. The method proposed in this paper is relatively simple and has a high simulation precision. The results showed that during the first ten years of the 21st century, there are some remarkable characteristics in Chinese population spatial pattern change: 1) the “disappearance” of intermediate-density regions; namely, areas with a population density between 500 and 1500 persons/km2 have decreased by 41% during the ten years; 2) continuous growth of high-density regions; namely, areas with a population density of more than 1500 persons/km2 have increased by 76%; 3) an expansion tendency of low-density regions similar to high-density regions. |
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0143-6228 |
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GFZ @ kyba @ |
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2481 |
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Wang, L.; Wang, S.; Zhou, Y.; Liu, W.; Hou, Y.; Zhu, J.; Wang, F. |
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Title |
Mapping population density in China between 1990 and 2010 using remote sensing |
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Journal Article |
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2018 |
Publication |
Remote Sensing of Environment |
Abbreviated Journal |
Remote Sensing of Environment |
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210 |
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269-281 |
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Remote Sensing |
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Knowledge of the spatial distribution of populations at finer spatial scales is of significant value and fundamental to many applications such as environmental change, urbanization, regional planning, public health, and disaster management. However, detailed assessment of the population distribution data of countries that have large populations (such as China) and significant variation in distribution requires improved data processing methods and spatialization models. This paper described the construction of a novel population spatialization method by combining land use/cover data and night-light data. Based on the analysis of data characteristics, the method used partial correlation analysis and geographically weighted regression to improve the distribution accuracy and reduce regional errors. China's census data for the years 1990, 2000, and 2010 were assessed. The results showed that the method was better at population spatialization than methods that use only night-light data or land use/cover data and global linear regression. Evaluation of overall accuracies revealed that the coefficient of correlation R-square was >0.90 and increased by >0.13 in the years 1990, 2000, and 2010. Moreover, the local R-square of over 90% of the samples (counties) was higher than the adjusted R-square of the general linear regression model. Furthermore, the gridded population density datasets obtained by this method can be used to analyse spatial-temporal patterns of population density and provide population distribution information with increased accuracy and precision compared to conventional models. |
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0034-4257 |
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GFZ @ kyba @ |
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2480 |
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Author |
Petrova, S. |
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Title |
Illuminating austerity: Lighting poverty as an agent and signifier of the Greek crisis |
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Journal Article |
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2018 |
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European Urban and Regional Studies |
Abbreviated Journal |
Eur Urban Reg Stud |
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25 |
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4 |
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360-372 |
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Economics; Society |
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Light – whether natural or artificial – plays multiple roles in the home: both as a material enabler of everyday life and as a device for exercising a variety of social relations. The post-2008 Greek economic crisis has endangered those roles by limiting people's ability to access or afford adequate energy services. This paper focuses on the enforced lack of illumination in the home, and the strategies and tactics undertaken by households to overcome this challenge. I connect illumination practices and discourses to the implementation of austerity, by arguing that the threat of darkness has become a tool for compelling vulnerable groups to pay their electricity bills. The evidence presented in the paper is based on two sets of interviews with 25 households (including a total of 55 adult members) living in and around Thessaloniki – Greece's second largest city, and one that has suffered severe economic consequences as a result of the crisis. I have established that the under-consumption of light is one of the most pronounced expressions of energy poverty, and as such endangers the ability to participate in the customs that define membership of society. But the emergence of activist-led amateur electricians and the symbolic and material mobilization of light for political purposes have also created multiple opportunities for resistance. |
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The University of Manchester, UK |
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0969-7764 |
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PMID:30369725; PMCID:PMC6187059 |
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GFZ @ kyba @ |
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2453 |
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Author |
Hiltunen, A. P., Kumpula, T., &Tykkyläinen, M. |
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Title |
Yövalaistuksen ja valopäästöjen alueellinen jakautuminen |
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Journal Article |
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2018 |
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Geoinformatiikka Yhteiskunnassa |
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130 |
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4 |
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Remote Sensing |
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Remotely-sensed night-time lights (NTL) reveal the occurrence of human development while excessive light emissions cause ecological impacts and may create human health hazards. The aim of this research is to find out the factors affecting the quantity of remotely-sensed NTLs in Finland at 2015. We also aim to unveil how much NTLs have changed in Finland from 1993 to 2012 and what is the share of NTLs for different land use types in Finland in 2015. Answers to these questions are achieved with satellite radiance data and data on spatial structure, multiple linear regression (MLR), and change-detection methods. National and regional MLR models were produced to explain NTL and to compare the suitability of this modelling approach in different regions. Radiance is explained by population density, industrial building density, and lit roads density. Surprisingly, the brightest areas in Finland seem to be in Närpiö, a rural area with low population density but where greenhouse farming is common. Based on change-detection, new light sources have emerged because of the expansion of mining and tourism industries. |
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Finnish |
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IDA @ intern @ |
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2354 |
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