Forecasting China’s GDP at the pixel level using nighttime lights time series and population images

Zhao, Naizhuo; Liu, Ying; Cao, Guofeng; Samson, E.; Zhang, Jingqi

doi:10.1080/15481603.2016.1276705

Cited by 119 publications

(58 citation statements)

References 44 publications

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“…Via values presented in Graphics No. 4 we can see that the Series proves an obvious exponential trend as the series variability is not constant. Ordinary probability is relatively distanced from the expected values (the red growing curve), mostly at the beginning and at the end of the CZ GDP observed period.…”

Section: Box-jenkins Methodsmentioning

confidence: 88%

“…Another possibility are predictions of future GDP via Holt-Winters static tool smoothing based on the division of individual provinces, and not from the perspective of a national scale, as stated by Zhao, Liu and Cao [4]. Ways to predict economic indicators are also demonstrated by Jerabkova´s article [5], who has, using the Box-Jenkins Method, predicted the unemployment development in the Czech Republic.…”

Section: Introductionmentioning

confidence: 99%

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CZ GDP Prediction via neural networks and Box-Jenkins Method

Dvořáková

2017

SHS Web Conf.

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Abstract. Economic indicators are nowadays ones of the most observed, their development does not only serve for comparing individual countries among each other but also show how the given country is prospering. That is why economists are trying to predict also the future development of these indicators via different statistical instruments. Neural networks or Box-Jenkins Method, able to predict future development based on data from the past, are one of the many instruments. The aim of this contribution is to find CZ GDP prediction per individual quarters using neural networks and Box-Jenkins Method, compare them mutually, and evaluate which of them is better.

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Section: Box-jenkins Methodsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

CZ GDP Prediction via neural networks and Box-Jenkins Method

Dvořáková

2017

SHS Web Conf.

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“…This issue occurs in particular in large urban areas and on specific surfaces, such as sand and water (Elvidge et al, 2004;Small et al, 2005). As a consequence of saturation, socioeconomic indicators scale rather exponentially than linearly with nightlight intensity (Sutton and Costanza, 2002;Zhao et al, 2015Zhao et al, , 2017. To counteract the saturation effect, Gettelman et al (2017) and Aznar-Siguan and Bresch (2019) used exponentially scaled nightlight intensity as a basis for GDP disaggregation for tropical cyclone risk assessments.…”

Section: Introductionmentioning

confidence: 99%

“…Saturation and blooming can also be mitigated by combining nightlights with other data types: Sutton et al (2007) combined the areal extent of lit area with population data to estimate GDP at a subnational level. Zhao et al (2017) enhanced nightlight intensity values with population data to get a more accurate estimation of spatial economic activity in China. This is based on the observation that there is also an exponential relationship between nightlight intensity and population density.…”

Section: Introductionmentioning

confidence: 99%

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Asset exposure data for global physical risk assessment

Eberenz

Stocker

Röösli

et al. 2020

Earth Syst. Sci. Data

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Abstract. One of the challenges in globally consistent assessments of physical climate risks is the fact that asset exposure data are either unavailable or restricted to single countries or regions. We introduce a global high-resolution asset exposure dataset responding to this challenge. The data are produced using “lit population” (LitPop), a globally consistent methodology to disaggregate asset value data proportional to a combination of nightlight intensity and geographical population data. By combining nightlight and population data, unwanted artefacts such as blooming, saturation, and lack of detail are mitigated. Thus, the combination of both data types improves the spatial distribution of macroeconomic indicators. Due to the lack of reported subnational asset data, the disaggregation methodology cannot be validated for asset values. Therefore, we compare disaggregated gross domestic product (GDP) per subnational administrative region to reported gross regional product (GRP) values for evaluation. The comparison for 14 industrialized and newly industrialized countries shows that the disaggregation skill for GDP using nightlights or population data alone is not as high as using a combination of both data types. The advantages of LitPop are global consistency, scalability, openness, replicability, and low entry threshold. The open-source LitPop methodology and the publicly available asset exposure data offer value for manifold use cases, including globally consistent economic disaster risk assessments and climate change adaptation studies, especially for larger regions, yet at considerably high resolution. The code is published on GitHub as part of the open-source software CLIMADA (CLIMate ADAptation) and archived in the ETH Data Archive with the link https://doi.org/10.5905/ethz-1007-226 (Bresch et al., 2019b). The resulting asset exposure dataset for 224 countries is archived in the ETH Research Repository with the link https://doi.org/10.3929/ethz-b-000331316 (Eberenz et al., 2019).

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Contribution of urbanization to the increase of extreme heat events in an urban agglomeration in east China

Yang

Leung

Zhao

et al. 2017

Geophysical Research Letters

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186

124

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The urban agglomeration of Yangtze River Delta (YRD) is emblematic of China's rapid urbanization during the past decades. Based on homogenized daily maximum and minimum temperature data, the contributions of urbanization to trends of summer extreme temperature indices (ETIs) in YRD are evaluated. Dynamically classifying the observational stations into urban and rural, this study presents unexplored changes in temperature extremes during the past four decades in YRD and quantifies the amplification of the positive trends in ETIs by the urban heat island effect. Overall, urbanization contributes to more than one third of the increase of intensity of extreme heat events in the region, which is comparable to the contribution of greenhouse gases. Compared to rural stations, more notable shifts to the right in the probability distribution of temperature and ETIs are found in urban stations. The rapid urbanization in YRD has resulted in large increases in the risk of heat extremes.

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Forecasting China’s GDP at the pixel level using nighttime lights time series and population images

Cited by 119 publications

References 44 publications

CZ GDP Prediction via neural networks and Box-Jenkins Method

CZ GDP Prediction via neural networks and Box-Jenkins Method

Asset exposure data for global physical risk assessment

Contribution of urbanization to the increase of extreme heat events in an urban agglomeration in east China

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