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Nelson, R.J.; DeVries, A.C. |
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Title |
Medical Hypothesis: Light at Night Is a Factor Worth Considering in Critical Care Units |
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2017 |
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Advances in Integrative Medicine |
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Advances in Integrative Medicine |
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4 |
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3 |
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115-120 |
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Human Health |
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Exposure to light at night is not an innocuous consequence of modernization. There are compelling data linking long-term exposure to occupational and environmental light at night with serious health conditions, including heart disease, obesity, diabetes, and cancer. However, far less is known about the physiological and behavioral effects of acute exposure to light at night. Among healthy volunteers, acute night-time light exposure increases systolic blood pressure and inflammatory markers in the blood, and impairs glucose regulation. Whether critically ill patients in a hospital setting experience the same physiological shifts in response to evening light exposure is not known. This paper reviews the available data on light at night effects on health and wellbeing, and argues that the data are sufficiently compelling to warrant studies of how lighting in intensive care units may be influencing patient recovery. |
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2212-9588 |
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LoNNe @ kyba @ |
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1794 |
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Author |
Liang, H.; Guo, Z.; Wu, J.; Chen, Z. |
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Title |
GDP spatialization in Ningbo City based on NPP/VIIRS night-time light and auxiliary data using random forest regression |
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Journal Article |
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Year |
2019 |
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Advances in Space Research |
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Advances in Space Research |
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in press |
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S0273117719307136 |
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Remote Sensing; GDP; gross domestic product; spatialization; VIIRS-DNB; Nighttime light; numerical methods |
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Accurate spatial distribution information on gross domestic product (GDP) is of great importance for the analysis of economic development, industrial distribution and urbanization processes. Traditional administrative unit-based GDP statistics cannot depict the detailed spatial differences in GDP within each administrative unit. This paper presents a study of GDP spatialization in Ningbo City, China based on National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL) data and town-level GDP statistical data. The Landsat image, land cover, road network and topographic data were also employed as auxiliary data to derive independent variables for GDP modelling. Multivariate linear regression (MLR) and random forest (RF) regression were used to estimate GDP at the town scale and were assessed by cross-validation. The results show that the RF model achieved significantly higher accuracy, with a mean absolute error (MAE) of 109.46 million China Yuan (CNY)·km-2 and a determinate coefficient (R2=0.77) than the MLR model (MAE=161.8 million CNY·km-2, R2=0.59). Meanwhile, by comparing with the estimated GDP data at the county level, the town-level estimated data showed a better performance in mapping GDP distribution (MAE decreased from 115.1 million CNY·km-2 to 74.8 million CNY·km-2). Among all of the independent variables, NTL, land surface temperature (Ts) and plot ratio (PR) showed higher impacts on the GDP estimation accuracy than the other variables. The GDP density map generated by the RF model depicted the detailed spatial distribution of the economy in Ningbo City. By interpreting the spatial distribution of the GDP, we found that the GDP of Ningbo was high in the northeast and low in the southwest and formed continuous clusters in the north. In addition, the GDP of Ningbo also gradually decreased from the urban centre to its surrounding areas. The produced GDP map provides a good reference for the future urban planning and socio-economic development strategies. |
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0273-1177 |
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GFZ @ kyba @ |
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2680 |
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Rybnikova, N.; Portnov, B.A. |
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Testing the generality of economic activity models estimated by merging night-time satellite images with socioeconomic data |
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2020 |
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Advances in Space Research |
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Advances in Space Research |
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66 |
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11 |
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2610-2620 |
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Economics; Remote Sensing; Artificial light at night; Cross-validation; Modelling; Quaternary industries |
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Knowledge-based economic activities (aka quaternary industries or QIs) are characterized by high concentrations of labour force and potentially high night-time light emissions. Therefore, geographic concentrations of such activities can presumably be identified using information on the amount artificial light at night (ALAN), which different geographic areas emit. Question, however, remains whether the models, incorporating ALAN data, are place-specific or whether such models are sufficiently generic, thus making it possible to apply them, once estimated, to other countries and continents. To answer this question, the analysis is performed in several phases. First, we build separate models for European NUTS3 regions and US counties. Next, we cross-validate these models and use them to predict QI concentrations worldwide. As the analysis shows, cross-validation of the models, applied to the “counterpart” continent, also results in a reasonably good fit, with R2 reaching 0.852, when the US-model is applied to the EU data, and R2 = 0.896, when the EU-model is applied to the US data. Although attempts to use ALAN data for the analysis of different socio-economic phenomena are not new, to the best of our knowledge, this is the study first that uses cross-continent validation of ALAN-based models to determine their generality. |
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Department of Natural Resources and Environmental Management, University of Haifa, Haifa, Israel; nataliya.rybnikova ( at ) gmail.com |
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Elsevier |
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English |
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0273-1177 |
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IDA @ john @ |
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3397 |
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Otchia, C. S. & Asongu, S. A. |
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Industrial Growth in Sub-Saharan Africa: Evidence from Machine Learning with Insights from Nightlight Satellite Images |
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2019 |
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African Governance and Development Institute |
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Remote Sensing |
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This study uses nightlight time data and machine learning techniques to predict industrial development in Africa. The results provide the first evidence on how machine learning techniques and nightlight data can be used to predict economic development in places where subnational data are missing or not precise. Taken together, the research confirms four groups of important determinants of industrial growth: natural resources, agriculture growth, institutions, and manufacturing imports. Our findings indicate that Africa should follow a more
multisector approach for development, putting natural resources and agriculture productivity growth at the forefront. |
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IDA @ intern @ |
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2627 |
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Author |
Shen, J.; Tower, J. |
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Title |
Effects of light on aging and longevity |
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Journal Article |
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Year |
2019 |
Publication |
Ageing Research Reviews |
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Ageing Res Rev |
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53 |
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100913 |
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Human Health; Review; Aging; longevity |
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Increasing evidence suggests an important role for light in regulation of aging and longevity. UV radiation is a mutagen that can promote aging and decrease longevity. In contrast, NIR light has shown protective effects in animal disease models. In invertebrates, visible light can shorten or extend lifespan, depending on the intensity and wavelength composition. Visible light also impacts human health, including retina function, sleep, cancer and psychiatric disorders. Possible mechanisms of visible light include: controlling circadian rhythms, inducing oxidative stress, and acting through the retina to affect neuronal circuits and systems. Changes in artificial lighting (e.g., LEDs) may have implications for human health. It will be important to further explore the mechanisms of how light affects aging and longevity, and how light affects human health. |
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Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles CA 90089-2910, United States |
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1568-1637 |
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PMID:31154014 |
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GFZ @ kyba @ |
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2514 |
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