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Author Abay, K.A.; Amare, M.
Title Night light intensity and women's body weight: Evidence from Nigeria Type Journal Article
Year 2018 Publication Economics and Human Biology Abbreviated Journal Econ Hum Biol
Volume 31 Issue Pages 238-248
Keywords (down) Remote Sensing; Human Health; Adolescent; Adult; Body Mass Index; *Body Weight; Cross-Sectional Studies; Female; Health Surveys; Humans; Lighting/*statistics & numerical data; Middle Aged; Nigeria/epidemiology; Obesity/epidemiology; Overweight/*epidemiology; Prevalence; *Urbanization; Young Adult; *Bmi; *Nigeria; *Night light; *Obesity; *Overweight; *Urbanization
Abstract The prevalence of overweight and obesity are increasing in many African countries and hence becoming regional public health challenges. We employ satellite-based night light intensity data as a proxy for urbanization to investigate the relationship between urbanization and women's body weight. We use two rounds of the Demographic and Health Survey data from Nigeria. We employ both nonparametric and parametric estimation approaches that exploit both the cross-sectional and longitudinal variations in night light intensities. Our empirical analysis reveals nonlinear relationships between night light intensity and women's body weight measures. Doubling the sample's average level of night light intensity is associated with up to a ten percentage point increase in the probability of overweight. However, despite the generally positive relationship between night light intensity and women's body weight, the strength of the relationship varies across the assorted stages of night light intensity. Early stages of night light intensity are not significantly associated with women's body weight, while higher stages of nightlight intensities are associated with higher rates of overweight and obesity. Given that night lights are strong predictors of urbanization and related economic activities, our results hint at nonlinear relationships between various stages of urbanization and women's body weight.
Address International Food Policy Research Institute (IFPRI), USA. Electronic address: M.Amare@cgiar.org
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1570-677X ISBN Medium
Area Expedition Conference
Notes PMID:30312904 Approved no
Call Number GFZ @ kyba @ Serial 2714
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Author Falchi, F.; Furgoni, R.; Gallaway, T.A.; Rybnikova, N.A.; Portnov, B.A.; Baugh, K.; Cinzano, P.; Elvidge, C.D.
Title Light pollution in USA and Europe: The good, the bad and the ugly Type Journal Article
Year 2019 Publication Journal of Environmental Management Abbreviated Journal Journal of Environmental Management
Volume 248 Issue Pages 109227
Keywords (down) Remote Sensing; gross domestic product; light pollution; Economics
Abstract Light pollution is a worldwide problem that has a range of adverse effects on human health and natural eco-systems. Using data from the New World Atlas of Artificial Night Sky Brightness, VIIRS-recorded radiance and Gross Domestic Product (GDP) data, we compared light pollution levels, and the light flux to the population size and GDP at the State and County levels in the USA and at Regional (NUTS2) and Province (NUTS3) levels inEurope. We found 6800-fold differences between the most and least polluted regions in Europe, 120-fold differences in their light flux per capita, and 267-fold differences influx per GDP unit. Yet, we found even greater differences between US counties: 200,000-fold differences in sky pollution, 16,000-fold differences in light flux per capita, and 40,000-fold differences in light flux per GDP unit. These findings may inform policy-makers, helping to reduce energy waste and adverse environmental, cultural and health consequences associated with light pollution.
Address STIL – Istituto di Scienza e Tecnologia dell'Inquinamento Luminoso, Light Pollution Science and Technology Institute, Thiene, Italy; Italy. falchi@lightpollution.it(at)istil.it
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0301-4797 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number IDA @ john @ Serial 2593
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Author Gaughan, A. E., Oda, T., Sorichetta, A., Stevens, F. R., Bondarenko, M., Bun, R., Krauser, L., Yetman, G., & Nghiem, S. V.
Title Evaluating nighttime lights and population distribution as proxies for mapping anthropogenic CO2 emission in Vietnam, Cambodia and Laos Type Journal Article
Year 2019 Publication Environmental Research Communications Abbreviated Journal
Volume 1 Issue 9 Pages 091006
Keywords (down) Remote Sensing; greenhouse gas emissions; GHG; Asia; Vietnam; Cambodia; Laos; nighttime light
Abstract Tracking spatiotemporal changes in GHG emissions is key to successful implementation of the United Nations Framework Convention on Climate Change (UNFCCC). And while emission inventories often provide a robust tool to track emission trends at the country level, subnational emission estimates are often not reported or reports vary in robustness as the estimates are often dependent on the spatial modeling approach and ancillary data used to disaggregate the emission inventories. Assessing the errors and uncertainties of the subnational emission estimates is fundamentally challenging due to the lack of physical measurements at the subnational level. To begin addressing the current performance of modeled gridded CO2 emissions, this study compares two common proxies used to disaggregate CO2 emission estimates. We use a known gridded CO2 model based on satellite-observed nighttime light (NTL) data (Open Source Data Inventory for Anthropogenic CO2, ODIAC) and a gridded population dataset driven by a set of ancillary geospatial data. We examine the association at multiple spatial scales of these two datasets for three countries in Southeast Asia: Vietnam, Cambodia and Laos and characterize the spatiotemporal similarities and differences for 2000, 2005, and 2010. We specifically highlight areas of potential uncertainty in the ODIAC model, which relies on the single use of NTL data for disaggregation of the non-point emissions estimates. Results show, over time, how a NTL-based emissions disaggregation tends to concentrate CO2 estimates in different ways than population-based estimates at the subnational level. We discuss important considerations in the disconnect between the two modeled datasets and argue that the spatial differences between data products can be useful to identify areas affected by the errors and uncertainties associated with the NTL-based downscaling in a region with uneven urbanization rates.
Address University of Louisville, Department of Geography and Geosciences, Louisville, KY, United States of America; ae.gaughan(at)louisville.edu
Corporate Author Thesis
Publisher IOP Place of Publication Editor
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number IDA @ intern @ Serial 2727
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Author Liang, H.; Guo, Z.; Wu, J.; Chen, Z.
Title GDP spatialization in Ningbo City based on NPP/VIIRS night-time light and auxiliary data using random forest regression Type Journal Article
Year 2019 Publication Advances in Space Research Abbreviated Journal Advances in Space Research
Volume in press Issue Pages S0273117719307136
Keywords (down) Remote Sensing; GDP; gross domestic product; spatialization; VIIRS-DNB; Nighttime light; numerical methods
Abstract 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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0273-1177 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2680
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Author Lopez-Ruiz, H., Nezamuddin, N., Al Hassan, R., & Muhsen, A.
Title Estimating Freight Transport Activity Using Nighttime Lights Satellite Data in China, India and Saudi Arabia Type Journal Article
Year 2019 Publication EconPapers Abbreviated Journal
Volume ks--2019-mp07 Issue Pages
Keywords (down) Remote Sensing; Freight; shipping; freight transport activity; FTA; China; India; Saudi Arabia; Transportation; nighttime lights; NTL
Abstract This paper focuses on the methodology for estimating total freight transport activity (FTA) for three countries — China, India and Saudi Arabia — with the objective of building on current state-of-the-art transportation modeling in three key areas: Studying the relationship between nighttime lights (NTL) and FTA allows for an estimation of full transportation datasets for countries where only a few observation points exist or where data is unavailable. Establishing the foundation for future work on how to use this approach in transport flow estimation (origin-destination matrices). Determining whether this approach can be used globally, given the coverage of the satellite data used. The paper uses the KAPSARC Transport Analysis Framework (KTAF), which estimates transport activity from freely available global data sources, satellite images and NTL. It is a tool for estimating freight transport activity that can be used in models to measure the impact of an accelerated transport policy planning approach. The methodology offers a solution to inadequate data access and allows for scenario building in policy planning for transportation. This approach allows for quick estimation of the effects of policy measures and economic changes on transportation activities at a global level. The paper also includes a detailed guide on how to replicate the methodology used in this analysis.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number IDA @ intern @ Serial 2639
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