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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 (up) Advances in Space Research Abbreviated Journal Advances in Space Research  
  Volume in press Issue Pages 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.  
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  ISSN 0273-1177 ISBN Medium  
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  Call Number GFZ @ kyba @ Serial 2680  
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Author Otchia, C. S. & Asongu, S. A. url  openurl
  Title Industrial Growth in Sub-Saharan Africa: Evidence from Machine Learning with Insights from Nightlight Satellite Images Type Journal Article
  Year 2019 Publication (up) African Governance and Development Institute Abbreviated Journal  
  Volume Issue Pages  
  Keywords Remote Sensing  
  Abstract 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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  Call Number IDA @ intern @ Serial 2627  
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Author Briggs, R. C. url  openurl
  Title Why does aid not target the poorest? Type Journal Article
  Year 2019 Publication (up) AIDDATA Abbreviated Journal  
  Volume Issue Pages  
  Keywords Remote Sensing  
  Abstract Foreign aid projects typically have local effects, so if they are to reduce poverty then they need to be placed close to the poor. I show that, conditional on local population, World Bank (WB) project aid targets richer parts of countries. This relationship holds over time and across world regions. I test five explanations for pro-rich targeting using a pre-registered conjoint experiment on WB task team leaders (TTLs). TTLs perceive aid-receiving governments as most interested in targeting aid politically and controlling implementation. They also believe that aid works better in poorer or more remote areas, but that implementation in these areas is uniquely difficult. These results speak to debates in distributive politics, international bargaining over aid, and principal-agent issues in international organizations. The results also suggest that tweaks to WB incentive structures to make ease of project implementation less important may encourage aid to flow to poorer parts of countries.  
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  Notes Approved no  
  Call Number IDA @ intern @ Serial 2719  
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Author Archila Bustos, M.F.; Hall, O.; Andersson, M. url  doi
openurl 
  Title Nighttime lights and population changes in Europe 1992-2012 Type Journal Article
  Year 2015 Publication (up) Ambio Abbreviated Journal Ambio  
  Volume Issue Pages  
  Keywords Remote Sensing  
  Abstract Nighttime satellite photographs of Earth reveal the location of lighting and provide a unique view of the extent of human settlement. Nighttime lights have been shown to correlate with economic development and population but little research has been done on the link between nighttime lights and population change over time. We explore whether population decline is coupled with decline in lighted area and how the age structure of the population and GDP are reflected in nighttime lights. We examine Europe between the period of 1992 and 2012 using a Geographic Information System and regression analysis. The results suggest that population decline is not coupled with decline in lighted area. Instead, human settlement extent is more closely related to the age structure of the population and to GDP. We conclude that declining populations will not necessarily lead to reductions in the extent of land development.  
  Address Department of Human and Economic Geography, Lund University, Solvegatan 10, 223 62, Lund, Sweden  
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  ISSN 0044-7447 ISBN Medium  
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  Notes PMID:25773533 Approved no  
  Call Number LoNNe @ christopher.kyba @ Serial 1138  
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Author Szpir, M. ( url  openurl
  Title Clickworkers on Mars Type Journal Article
  Year 2002 Publication (up) American Scientist Abbreviated Journal  
  Volume 90 Issue 3 Pages 226  
  Keywords Remote Sensing  
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  Notes Approved no  
  Call Number LoNNe @ kagoburian @ Serial 975  
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