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Author Weidmann, N.; Schutte, S. url  doi
openurl 
  Title Using night light emissions for the prediction of local wealth Type Journal Article
  Year 2016 Publication Journal of Peace Research Abbreviated Journal J Peace Res  
  Volume Issue Pages (down) 0022343316630359  
  Keywords Economics; remote sensing; night lights; spatial prediction  
  Abstract Nighttime illumination can serve as a proxy for economic variables in particular in developing countries, where data are often not available or of poor quality. Existing research has demonstrated this for coarse levels of analytical resolution, such as countries, administrative units or large grid cells. In this article, we conduct the first fine-grained analysis of night lights and wealth in developing countries. The use of large-scale, geo-referenced data from the Demographic and Health Surveys allows us to cover 39 less developed, mostly non-democratic countries with a total sample of more than 34,000 observations at the level of villages or neighborhoods. We show that light emissions are highly accurate predictors of economic wealth estimates even with simple statistical models, both when predicting new locations in a known country and when generating predictions for previously unobserved countries.  
  Address Department of Politics and Public Administration, University of Konstanz, Germany; nils.weidmann(at)uni-konstanz.de  
  Corporate Author Thesis  
  Publisher SAGE 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 @ john @ Serial 1474  
Permanent link to this record
 

 
Author Bagan, H.; Borjigin, H.; Yamagata, Y. url  doi
openurl 
  Title Assessing nighttime lights for mapping the urban areas of 50 cities across the globe Type Journal Article
  Year 2018 Publication Environment and Planning B: Urban Analytics and City Science Abbreviated Journal Environment and Planning B: Urban Analytics and City Science  
  Volume Issue Pages (down) 2399808317752926  
  Keywords Remote Sensing  
  Abstract Nighttime data from the Defense Meteorological Satellite Program Operational Linescan System have been widely used to map urban/built-up areas (hereafter referred to as “built-up area”), but to date there has not been a geographically comprehensive evaluation of the effectiveness of using nighttime lights data to map urban areas. We created accurate, convenient, and scalable grid cells based on Defense Meteorological Satellite Program/Operational Linescan System nighttime light pixels. We then calculated the density of Landsat-derived built-up areas within each grid cell. We explored the relationship between Defense Meteorological Satellite Program/Operational Linescan System nighttime lights data and the density of built-up areas to assess the utility of nighttime lights for mapping urban areas in 50 cities across the globe. We found that the brightness of nighttime lights was only in moderate agreement with the density of built-up areas; moreover, correlations between nighttime lights and Landsat-derived built-up areas were weak. Even in relatively sparsely populated urban regions (where the density of the built-up area is less than 20%), the highest correlation coefficient (R2) was only 0.4. Furthermore, nighttime lights showed lighted areas that extended beyond the area of large cities, and nighttime lights reduced the area of small cities. The results suggest that it is difficult to use the regression model to calibrate the Defense Meteorological Satellite Program/Operational Linescan System nighttime lights to fit urban built up areas.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2399-8083 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number LoNNe @ kyba @; GFZ @ kyba @ Serial 1795  
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Author Liang, H.; Guo, Z.; Wu, J.; Chen, Z. url  doi
openurl 
  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 (down) S0273117719307136  
  Keywords 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 Jiang, Z.; Zhai, W.; Meng, X.; Long, Y. url  doi
openurl 
  Title Identifying Shrinking Cities with NPP-VIIRS Nightlight Data in China Type Journal Article
  Year 2020 Publication Journal of Urban Planning and Development Abbreviated Journal J. Urban Plann. Dev.  
  Volume 146 Issue 4 Pages (down) 04020034  
  Keywords Remote Sensing  
  Abstract Although there has been a rapid urbanization in China since the 1980s, the simultaneous urban shrinkage phenomenon has existed for a long time. The study of shrinking cities is particularly important for China as the current urban development has changed from physical expansion to built-up area improvement. After redefining what constitutes a city (what we term a natural city), we compared the adjusted nightlight intensity of National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data between 2013 and 2016 to accurately identify shrinking cities throughout China. The results indicate that there are 2,862 redefined natural cities in China and that the total area reaches 53,275 km2, about 0.5% of the national territory. Based on this, we identified 798 shrinking cities with a total area of 13,839 km2. After analyzing the relative position of shrinking cities and internal shrinking pixels in the geometric space, the morphological characteristics of shrinking cities were systematically classified into six patterns. The majority of shrinking cities belong to scatter shrinkage, central shrinkage, and local shrinkage; only 5% are complete shrinkage; the rest are unilateral shrinkage and peripheral shrinkage. In addition, six shrinkage causes were quantitatively classified and summarized by referring to multiple-source urban data and municipal yearbooks. To enrich the methodological system for urban shrinkage, the research provides a reminder of the need to consider the other side of urbanization (i.e., dissolution of social networks) and proposes appropriate strategies and policies to address shrinkage issues.  
  Address  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0733-9488 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 3065  
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Author Zangeneh, P.; Hamledari, H.; McCabe, B. url  doi
openurl 
  Title Quantifying Remoteness for Risk and Resilience Assessment Using Nighttime Satellite Imagery Type Journal Article
  Year 2020 Publication Journal of Computing in Civil Engineering Abbreviated Journal J. Comput. Civ. Eng.  
  Volume 34 Issue 5 Pages (down) 04020026  
  Keywords Remote Sensing  
  Abstract Remoteness has a crucial role in risk assessments of megaprojects, resilience assessments of communities and infrastructure, and a wide range of public policymaking. The existing measures of remoteness require an extensive amount of population census and of road and infrastructure network data, and often are limited to narrow scopes. This paper presents a methodology to quantify remoteness using nighttime satellite imagery. The light clusters of nighttime satellite imagery are direct yet unintended consequences of human settled populations and urbanization; therefore, the absence of illuminated clusters is considered as evidence of remoteness. The proposed nighttime remoteness index (NIRI) conceptualizes the remoteness based on the distribution of nighttime lights within radii of up to 1,000 km. A predictive model was created using machine learning techniques such as multivariate adaptive regression splines and support vector machines regressions to establish a reliable and accurate link between nighttime lights and the Accessibility/Remoteness Index of Australia (ARIA). The model was used to establish NIRI for the United States and Canada, and in different years. The index was compared with the Canadian remoteness indexes published by Statistics Canada.  
  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 0887-3801 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2937  
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