Night lights and regional GDP

Bickenbach, Frank; Bode, Eckhardt; Nunnenkamp, Peter; Söder, Mareike

doi:10.1007/s10290-016-0246-0

Cited by 82 publications

(63 citation statements)

References 11 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given the fact that some studies have looked into predicting GDP with nighttime lights (Chen and Nordhaus, 2010;Elvidge et al, 2007;Sutton et al, 2007), our findings suggest that caution must be taken when interpreting monthly nighttime lights as a proxy for economic activity, because there are additional factors which drive the emissions night lights besides economic activity. Indeed, Bickenbach et al (2013) concluded that night lights data may be poor proxies for regional GDP. Due to the phenological cycle of vegetation and seasonal changes in snow cover, variations which are not related to the emission of nighttime lights can be introduced into nighttime light time series.…”

Section: Discussionmentioning

confidence: 99%

A global analysis of factors controlling VIIRS nighttime light levels from densely populated areas

Levin

Zhang

2017

Remote Sensing of Environment

163

View full text Add to dashboard Cite

Remote sensing of nighttime lights has been shown as a good surrogate for estimating population and economic activity at national and sub-national scales, using DMSP satellites. However, few studies have examined the factors explaining differences in nighttime brightness of cities at a global scale. In this study, we derived quantitative estimates of nighttime lights with the new VIIRS sensor onboard the Suomi NPP satellite in January 2014 and in July 2014, with two variables: mean brightness and percent lit area. We performed a global analysis of all densely populated areas (n = 4,153, mostly corresponding to metropolitan areas), which we defined using high spatial resolution Landscan population data. National GDP per capita was better in explaining nighttime brightness levels (0.60 < Rs < 0.70) than GDP density at a spatial resolution of 0.25 degrees (0.25 < Rs < 0.43), or than a city-level measure of GDP per capita (in proportion to each city's fraction of the national population; 0.49 < Rs < 0.62). We found that in addition to GDP per capita, the nighttime brightness of densely populated areas was positively correlated with MODIS derived percent urban area (0.46 < Rs < 0.60), the density of the road network (0.51 < Rs < 0.67), and with latitude (0.31 < Rs < 0.42) at p < 0.001. NDVI values (representing vegetation cover) were found to be negatively correlated with cities' brightness in winter time (-0.48 < Rs <-0.22), whereas snow cover (enhancing artificial light reflectance) was found to be positively correlated with cities' brightness in winter time (0.17 < Rs < 0.35). Overall, the generalized linear model we built was able to explain more than 45% of the variability in cities' nighttime brightness, when both physical and socioeconomic variables were included. Within the generalized linear model, the percent of national GDP derived from income (rents) from natural gas 3 and oil, was also found as one of the statistically significant variables. Our findings show that cities' nighttime brightness can change with the seasons as a function of vegetation and snow cover, two variables affecting surface albedo. Explaining cities' nighttime brightness is therefore affected not only by country level factors (such as GDP), but also by the built environment and by climatic factors.

show abstract

Section: Discussionmentioning

confidence: 99%

A global analysis of factors controlling VIIRS nighttime light levels from densely populated areas

Levin

Zhang

2017

Remote Sensing of Environment

163

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge, we are the first to introduce G rate to extend the current disease measurement systems in modern epidemiology. The size of geographic area has been considered as an important measurement component in other fields, including environmental science ( Levin and Kerster, 1969 , Nambinina et al, 2016 ), demography ( Langford and Unwin, 1994 , Pearl and Parker, 1922 ), and economics ( Bickenbach et al, 2016 , Gallup et al, 1999 ). However, no reported studies have ever factored this component into any formal rate indicators in epidemiology.…”

Section: Discussionmentioning

confidence: 99%

Geographic area-based rate as a novel indicator to enhance research and precision intervention for more effective HIV/AIDS control

Chen

Wang

2017

Preventive Medicine Reports

View full text Add to dashboard Cite

Ending the HIV epidemic needs additional methods to better assess the incidence and prevalence of HIV infection. In this study, a new indicator — G-rate was developed for the evaluation of HIV epidemics across regions with regard to geographic area size. Different from the commonly used incidence and prevalence rates that assess the HIV epidemic with reference to population (termed as P rate in this study), G rate measures the number of new infections (incidence) or cases (prevalence) over a unit land area in one year. We demonstrated the utility of G rates using officially reported data on new HIV infections and persons living with HIV in the United States during 2000–2012. Findings of our analysis indicate that relative to P rates, G rates indicated a quicker increase in the HIV epidemic in the United States during the study period. In 2012, 4.6 persons were newly infected and 101.4 persons lived with HIV per 1000 km2 land area. The five states with both highest P prevalence rates and highest G prevalence rates were Florida, Maryland, New York, New Jersey and Washington DC, which included New Jersey ranked 8th by P rate and excluded Massachusetts ranked 5th by G rate. In conclusion, adding G rates extends the conventional measurement system that consists of case count and P rate. Combining G rates with P rates provides a new approach for information extraction to support precision intervention strategy toward the goal of creating an AIDS-Free Generation.

show abstract

“…Panel data estimate that condition on state and year fixed effects indicate that a 1 per cent increase in night lights is associated with a 0.12 per cent increase in GDP (see Table A.3). Using Indian district level GDP data that are available for a few recent years, Bickenbach et al (2013) estimate an elasticity of 0.107. Using global data, Henderson et al (2012) estimate an elasticity of about 0.3.…”

Section: Night Lights Datamentioning

confidence: 99%

Women legislators and economic performance

Baskaran¹,

Bhalotra²,

Min³

et al. 2018

WIDER Working Paper

View full text Add to dashboard Cite

provides economic analysis and policy advice with the aim of promoting sustainable and equitable development. The Institute began operations in 1985 in Helsinki, Finland, as the first research and training centre of the United Nations University. Today it is a unique blend of think tank, research institute, and UN agency-providing a range of services from policy advice to governments as well as freely available original research. The Institute is funded through income from an endowment fund with additional contributions to its work programme from Finland, Sweden, and the United Kingdom as well as earmarked contributions for specific projects from a variety of donors.

show abstract

Night lights and regional GDP

Cited by 82 publications

References 11 publications

A global analysis of factors controlling VIIRS nighttime light levels from densely populated areas

A global analysis of factors controlling VIIRS nighttime light levels from densely populated areas

Geographic area-based rate as a novel indicator to enhance research and precision intervention for more effective HIV/AIDS control

Women legislators and economic performance

Contact Info

Product

Resources

About