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2 pages, 154 KiB

Open AccessEditorial

Everynight Accounting: Nighttime Lights as a Proxy for Economic Performance of Regions

by Nataliya Rybnikova

Remote Sens. 2022, 14(4), 825; https://doi.org/10.3390/rs14040825 - 10 Feb 2022

Cited by 4 | Viewed by 2044

Abstract

Artificial nighttime lights, emitted from residential, industrial, commercial and entertainment areas, and captured by satellites, have proven to be a reliable proxy for on-ground human activities [...] Full article

(This article belongs to the Special Issue Nighttime Lights as a Proxy for Economic Performance of Regions)

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17 pages, 872 KiB

Open AccessArticle

Estimating Local Inequality from Nighttime Lights

by Nils B. Weidmann and Gerlinde Theunissen

Remote Sens. 2021, 13(22), 4624; https://doi.org/10.3390/rs13224624 - 17 Nov 2021

Cited by 20 | Viewed by 5419

Abstract

Economic inequality at the local level has been shown to be an important predictor of people’s political perceptions and preferences. However, research on these questions is hampered by the fact that local inequality is difficult to measure and systematic data collections are rare, [...] Read more.

Economic inequality at the local level has been shown to be an important predictor of people’s political perceptions and preferences. However, research on these questions is hampered by the fact that local inequality is difficult to measure and systematic data collections are rare, in particular in countries of the Global South. We propose a new measure of local inequality derived from nighttime light (NTL) emissions data. Our measure corresponds to the local inequality in per capita nighttime light emissions, using VIIRS-derived nighttime light emissions data and spatial population data from WorldPop. We validate our estimates using local inequality estimates from the Demographic and Health Surveys (DHS) for a sample of African countries. Our results show that nightlight-based inequality estimates correspond well to those derived from survey data, and that the relationship is not due to structural factors such as differences between urban and rural regions. We also present predictive results, where we approximate the (survey-based) level of local inequality with our nighttime light indicator. This illustrates how our approach can be used for new cases where no other data are available. Full article

(This article belongs to the Special Issue Nighttime Lights as a Proxy for Economic Performance of Regions)

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14 pages, 1116 KiB

Open AccessArticle

Lifting of International Sanctions and the Shadow Economy in Iran—A View from Outer Space

by Mohammad Reza Farzanegan and Sven Fischer

Remote Sens. 2021, 13(22), 4620; https://doi.org/10.3390/rs13224620 - 17 Nov 2021

Cited by 12 | Viewed by 4510

Abstract

With the implementation of the Joint Comprehensive Plan of Action (JCPOA) in 2016, Iran experienced a short period without international sanctions which resulted in an annual increase in the gross domestic product (GDP) in the following two years. However, it was not just [...] Read more.

With the implementation of the Joint Comprehensive Plan of Action (JCPOA) in 2016, Iran experienced a short period without international sanctions which resulted in an annual increase in the gross domestic product (GDP) in the following two years. However, it was not just the formal economy that was affected by the sanctions. Previous studies have shown that sanctions can negatively affect the shadow (or informal) economy and may even have a larger impact on the informal economy than on the formal economy. Nighttime lights (NTL) data allow us to study shadow economy activities that are not reported in the official GDP. This study uses a panel of NTL (the DMSP/OLS and VIIRS/DNB harmonized dataset) from 1992 to 2018 for 31 Iranian provinces to investigate the association between the lifting of sanctions and the growth of the shadow economy. The empirical results suggest an increase in shadow economy activity with the lifting of sanctions while controlling for other drivers of informal activities. Full article

(This article belongs to the Special Issue Nighttime Lights as a Proxy for Economic Performance of Regions)

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29 pages, 10961 KiB

Open AccessArticle

Delineating Functional Urban Areas Using a Multi-Step Analysis of Artificial Light-at-Night Data

by Nataliya Rybnikova, Boris A. Portnov, Igal Charney and Sviatoslav Rybnikov

Remote Sens. 2021, 13(18), 3714; https://doi.org/10.3390/rs13183714 - 17 Sep 2021

Cited by 8 | Viewed by 3286

Abstract

A functional urban area (FUA) is a geographic entity that consists of a densely inhabited city and a less densely populated commuting zone, both highly integrated through labor markets. The delineation of FUAs is important for comparative urban studies and it is commonly [...] Read more.

A functional urban area (FUA) is a geographic entity that consists of a densely inhabited city and a less densely populated commuting zone, both highly integrated through labor markets. The delineation of FUAs is important for comparative urban studies and it is commonly performed using census data and data on commuting flows. However, at the national scale, censuses and commuting surveys are performed at low frequency, and, on the global scale, consistent and comparable data are difficult to obtain overall. In this paper, we suggest and test a novel approach based on artificial light at night (ALAN) satellite data to delineate FUAs. As ALAN is emitted by illumination of thoroughfare roads, frequented by commuters, and by buildings surrounding roads, ALAN data can be used, as we hypothesize, for the identification of FUAs. However, as individual FUAs differ by their ALAN emissions, different ALAN thresholds are needed to delineate different FUAs, even those in the same country. To determine such differential thresholds, we use a multi-step approach. First, we analyze the ALAN flux distribution and determine the most frequent ALAN value observed in each FUA. Next, we adjust this value for the FUA’s compactness, and run regressions, in which the estimated ALAN threshold is the dependent variable. In these models, we use several readily available, or easy-to-calculate, characteristics of FUA cores, such as latitude, proximity to the nearest major city, population density, and population density gradient, as predictors. At the next step, we use the estimated models to define optimal ALAN thresholds for individual FUAs, and then compare the boundaries of FUAs, estimated by modelling, with commuting-based delineations. To measure the degree of correspondence between the commuting-based and model-predicted FUAs’ boundaries, we use the Jaccard index, which compares the size of the intersection with the size of the union of each pair of delineations. We apply the proposed approach to two European countries—France and Spain—which host 82 and 72 FUAs, respectively. As our analysis shows, ALAN thresholds, estimated by modelling, fit FUAs’ commuting boundaries with an accuracy of up to 75–100%, being, on the average, higher for large and densely-populated FUAs, than for small, low-density ones. We validate the estimated models by applying them to another European country—Austria—which demonstrates the prediction accuracy of 47–57%, depending on the model type used. Full article

(This article belongs to the Special Issue Nighttime Lights as a Proxy for Economic Performance of Regions)

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19 pages, 2333 KiB

Open AccessArticle

Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

by John Gibson and Geua Boe-Gibson

Remote Sens. 2021, 13(14), 2741; https://doi.org/10.3390/rs13142741 - 12 Jul 2021

Cited by 39 | Viewed by 6392

Abstract

Nighttime lights (NTL) are a popular type of data for evaluating economic performance of regions and economic impacts of various shocks and interventions. Several validation studies use traditional statistics on economic activity like national or regional gross domestic product (GDP) as a benchmark [...] Read more.

Nighttime lights (NTL) are a popular type of data for evaluating economic performance of regions and economic impacts of various shocks and interventions. Several validation studies use traditional statistics on economic activity like national or regional gross domestic product (GDP) as a benchmark to evaluate the usefulness of NTL data. Many of these studies rely on dated and imprecise Defense Meteorological Satellite Program (DMSP) data and use aggregated units such as nation-states or the first sub-national level. However, applied researchers who draw support from validation studies to justify their use of NTL data as a proxy for economic activity increasingly focus on smaller and lower level spatial units. This study uses a 2001–19 time-series of GDP for over 3100 U.S. counties as a benchmark to examine the performance of the recently released version 2 VIIRS nighttime lights (V.2 VNL) products as proxies for local economic activity. Contrasts were made between cross-sectional predictions for GDP differences between areas and time-series predictions of GDP changes within areas. Disaggregated GDP data for various industries were used to examine the types of economic activity best proxied by NTL data. Comparisons were also made with the predictive performance of earlier NTL data products and at different levels of spatial aggregation. Full article

(This article belongs to the Special Issue Nighttime Lights as a Proxy for Economic Performance of Regions)

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20 pages, 6172 KiB

Open AccessArticle

The Modified Normalized Urban Area Composite Index: A Satelliate-Derived High-Resolution Index for Extracting Urban Areas

by Feng Li, Xiaoyang Liu, Shunbao Liao and Peng Jia

Remote Sens. 2021, 13(12), 2350; https://doi.org/10.3390/rs13122350 - 16 Jun 2021

Cited by 14 | Viewed by 3918

Abstract

The accurate and efficient extraction of urban areas is of great significance for better understanding of urban sprawl, built environment, economic activities, and population distribution. Night-Time Light (NTL) data have been widely used to extract urban areas. However, most of the existing NTL [...] Read more.

The accurate and efficient extraction of urban areas is of great significance for better understanding of urban sprawl, built environment, economic activities, and population distribution. Night-Time Light (NTL) data have been widely used to extract urban areas. However, most of the existing NTL indexes are incapable of identifying non-luminous built-up areas. The high-resolution NTL imagery derived from the Luojia 1-01 satellite, with low saturation and the blooming effect, can be used to map urban areas at a finer scale. A new urban spectral index, named the Modified Normalized Urban Areas Composite Index (MNUACI), improved upon the existing Normalized Urban Areas Composite Index (NUACI), was proposed in this study, which integrated the Human Settlement Index (HSI) generated from Luojia 1-01 NTL data, the Normalized Difference Vegetation Index (NDVI) from Landsat 8 imagery, and the Modified Normalized Difference Water Index (MNDWI). Our results indicated that MNUACI improved the spatial variability and differentiation of urban components by eliminating the NTL blooming effect and increasing the variation of the nighttime luminosity. Compared to urban area classification from Landsat 8 data, the MNUACI yielded better accuracy than NTL, NUACI, HSI, and the EVI-Adjusted NTL Index (EANTLI) alone. Furthermore, the quadratic polynomial regression analysis showed the model based on MNUACI had the best R² and Root-Mean Square Error (RMSE) compared with NTL, NUACI, HSI, and EANTLI in terms of estimation of impervious surface area. It is concluded that MNUACI could improve the identification of urban areas and non-luminous built-up areas with better accuracy. Full article

(This article belongs to the Special Issue Nighttime Lights as a Proxy for Economic Performance of Regions)

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15 pages, 2664 KiB

Open AccessArticle

Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference around Chinese Mainland via Attention-Augmented CNN from Daytime Satellite Imagery

by Haoyu Liu, Xianwen He, Yanbing Bai, Xing Liu, Yilin Wu, Yanyun Zhao and Hanfang Yang

Remote Sens. 2021, 13(11), 2067; https://doi.org/10.3390/rs13112067 - 24 May 2021

Cited by 25 | Viewed by 5690

Abstract

The official method of collecting county-level GDP values in the Chinese Mainland relies mainly on administrative reporting data and suffers from high costs of time, money, and human labor. To date, a series of studies have been conducted to generate fine-grained maps of [...] Read more.

The official method of collecting county-level GDP values in the Chinese Mainland relies mainly on administrative reporting data and suffers from high costs of time, money, and human labor. To date, a series of studies have been conducted to generate fine-grained maps of socioeconomic indicators from the easily accessed remote sensing data and achieved satisfactory results. This paper proposes a transfer learning framework that regards nightlight intensities as a proxy of economic activity degrees to estimate county-level GDP around the Chinese Mainland. In the framework, paired daytime satellite images and nightlight intensity levels were applied to train a VGG-16 architecture, and the output features at a specific layer, after dimensional reduction and statistics calculation, were fed into a simple regressor to estimate county-level GDP. We trained the model with data of 2017 and utilized it to predict county-level GDP of 2018, achieving an R-squared of 0.71. Furthermore, the results of gradient visualization confirmed the validity of the proposed framework qualitatively. To the best of our knowledge, this is the first time that county-level GDP values around the Chinese Mainland have been estimated from both daytime and nighttime remote sensing data relying on attention-augmented CNN. We believe that our work will shed light on both the evolution of fine-grained socioeconomic surveys and the application of remote sensing data in economic research. Full article

(This article belongs to the Special Issue Nighttime Lights as a Proxy for Economic Performance of Regions)

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Full article ">Figure 1
The brief procedures of collecting and matching data. There are three steps. (1) Determine the interval between adjacent image centers (2 km in this paper) and calculate all the center coordinates across the Chinese Mainland. (2) Scratch a daytime satellite image covering an area of 1 km2 centered on each coordinate. (3) Select an area of 2.5 km2 centered on each coordinate and sum up the nighttime light intensities. The sum of nightlight intensities is then classified into 3 degrees, addressed as nightlight intensity level in this paper. The nightlight intensity level serves as the label for the daytime satellite image centered on the same coordinate. Full article ">Figure 2
Instances of daytime satellite imagery with different corresponding nightlight intensity levels in 2016. From top to bottom: images with low-level, medium-level, and high-level nighttime light intensity. Full article ">Figure 3
The GDP distribution map of the Chinese Mainland in 2018 (some values along with the boundaries of county-level units are missing), and an example of matching center coordinates and county boundaries. Blue crosses denote center coordinates that fall into the boundary of Liping County, while red points denote centers that do not. Full article ">Figure 4
The structure of our method. Our method operates in three steps. (1) An attention-augmented VGG-16 network pre-trained on ImageNet [<a href="#B38-remotesensing-13-02067" class="html-bibr">38</a>] is tuned to predict nighttime light intensity levels from daytime satellite images. The middle blocks of the network are taken out as the feature extractor after transfer learning (the pre-trained VGG-16 network) and supervised training (nightlight intensity degrees as a proxy of socioeconomic indicators). (2) Reduce the dimensions of output features via PCA. (3) Calculate the embedded spatial statistical characteristics and apply regression models to predict the logarithm of county-level GDP. Full article ">Figure 5
The prediction error map of county-level GDP in 2018. White areas in the map represent regions where data are missing. Due to the large area of the Chinese Mainland, there are a few regions where images are either missing or of poor quality (Hainan Island, for instance). Nevertheless, the number of counties covered by the images we gained is enough for this study. Full article ">Figure 6
Results of different regression methods measured by R-squared. Full article ">Figure 7
Gradient visualization with guided backpropagation of VGG-A. The first row shows the input satellite imagery samples and the second row shows the corresponding visualization results. Larger gradient values result in higher brightness in the results. Full article ">

15 pages, 4694 KiB

Open AccessArticle

Comparing Luojia 1-01 and VIIRS Nighttime Light Data in Detecting Urban Spatial Structure Using a Threshold-Based Kernel Density Estimation

by Yuping Wang and Zehao Shen

Remote Sens. 2021, 13(8), 1574; https://doi.org/10.3390/rs13081574 - 19 Apr 2021

Cited by 22 | Viewed by 4461

Abstract

Nighttime light (NTL) data are increasingly used in urban studies and urban planning owing to their strong connection with human activities, although the detection capacity is limited by the spatial resolution of older data. In the present study, we comparedthe results of extractions [...] Read more.

Nighttime light (NTL) data are increasingly used in urban studies and urban planning owing to their strong connection with human activities, although the detection capacity is limited by the spatial resolution of older data. In the present study, we comparedthe results of extractions of urban built-up areas using data obtained from the first professional NTL satellite Luojia 1-01 with a resolution of 130 m and the Visible Infrared Imaging Radiometer Suite (VIIRS). We applied an analyzing framework combing kernel density estimation (KDE) under different search radii and threshold-based extraction to detect the boundary and spatial structure of urban areas. The results showed that: (1) Benefiting from a higher spatial resolution, Luojia 1-01 data was more sensitive in detecting new emerging urban built-up areas, thus better reflected the spatial structure of urban system, and can achieve a higher extraction accuracy than that of VIIRS data; (2) Combining with a proper threshold, KDE improves the extraction accuracy of NTL data by making use of the spatial autocorrelation of nighttime light, thus better detects the scale of the spatial pattern of urban built-up areas; (3) A proper searching radius for KDE is critical for achieving the optimal result, which was 1000 m for Luojia 1-01 and 1600 m for VIIRS in this study. Our findings indicate the usefulness of the KDE method in applying the upcoming high-resolution NTL data such as Luojia 1-01 data in urban spatial analysis and planning. Full article

(This article belongs to the Special Issue Nighttime Lights as a Proxy for Economic Performance of Regions)

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19 pages, 17802 KiB

Open AccessArticle

Intra-Urban Scaling Properties Examined by Automatically Extracted City Hotspots from Street Data and Nighttime Light Imagery

by Ding Ma, Renzhong Guo, Ying Jing, Ye Zheng, Zhigang Zhao and Jiahao Yang

Remote Sens. 2021, 13(7), 1322; https://doi.org/10.3390/rs13071322 - 30 Mar 2021

Cited by 14 | Viewed by 3776

Abstract

A country can be well-comprehended through its core cities. Similarly, we can learn about a city from its hotspots, as they manifest the concentration of urban infrastructures and human activities. Following this philosophy, this paper studies the intra-urban form and function from a [...] Read more.

A country can be well-comprehended through its core cities. Similarly, we can learn about a city from its hotspots, as they manifest the concentration of urban infrastructures and human activities. Following this philosophy, this paper studies the intra-urban form and function from a complexity science perspective by exploring the power law distribution of hotspot sizes and related socio-economic attributes. To detect hotspots, we rely on spatial clustering of geospatial big data sets, including street data from OpenStreetMap platform and nighttime light (NTL) data from the visible infrared imaging radiometer suite (VIIRS) imagery. Unlike conventional spatial units, which are imposed by governments or authorities (such as census block), the delineation of hotspots is done in a totally bottom-up manner and, more importantly, can help us examine precisely the scaling pattern of urban morphological and functional aspects. This results in two types of urban hotspots—street-based and NTL-based hotspots—being generated across 20 major cities in China. We find that Zipf’s law of hotspot sizes (both types) holds remarkably well for each city, as do the city-size distributions at the country level, indicating a statistically self-similar structure of geographic space. We further find that the urban scaling law can be effectively detected when using NTL-based hotspots as basic units. Furthermore, the comparison between two types of hotspots enables us to gain in-depth insights of urban planning and urban economic development. Full article

(This article belongs to the Special Issue Nighttime Lights as a Proxy for Economic Performance of Regions)

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14 pages, 642 KiB

Open AccessArticle

Can Nighttime Satellite Imagery Inform Our Understanding of Education Inequality?

by Bingxin Qi, Xuantong Wang and Paul Sutton

Remote Sens. 2021, 13(5), 843; https://doi.org/10.3390/rs13050843 - 24 Feb 2021

Cited by 7 | Viewed by 3096

Abstract

Education is a human right, and equal access to education is important for achieving sustainable development. Measuring socioeconomic development, especially the changes to education inequality, can help educators, practitioners, and policymakers with decision- and policy-making. This article presents an approach that combines population [...] Read more.

Education is a human right, and equal access to education is important for achieving sustainable development. Measuring socioeconomic development, especially the changes to education inequality, can help educators, practitioners, and policymakers with decision- and policy-making. This article presents an approach that combines population distribution, human settlements, and nighttime light (NTL) data to assess and explore development and education inequality trajectories at national levels across multiple time periods using latent growth models (LGMs). Results show that countries and regions with initially low human development levels tend to have higher levels of associated education inequality and uneven distribution of urban population. Additionally, the initial status of human development can be used to explain the linear growth rate of education inequality, but the association between trajectories becomes less significant as time increases. Full article

(This article belongs to the Special Issue Nighttime Lights as a Proxy for Economic Performance of Regions)

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16 pages, 3409 KiB

Open AccessArticle

Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China

by Dan Lu, Yahui Wang, Qingyuan Yang, Kangchuan Su, Haozhe Zhang and Yuanqing Li

Remote Sens. 2021, 13(2), 284; https://doi.org/10.3390/rs13020284 - 15 Jan 2021

Cited by 37 | Viewed by 4142

Abstract

The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial [...] Read more.

The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial optimization and the effective supply of public services in the mountainous areas. Here, we determined the spatiotemporal evolution of population in the Chongqing municipality of China from 2000–2018 by employing multi-period spatial distribution data, including nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). There was a power function relationship between the two datasets at the pixel scale, with a mean relative error of NTL integration of 8.19%, 4.78% less than achieved by a previous study at the provincial scale. The spatial simulations of population distribution achieved a mean relative error of 26.98%, improved the simulation accuracy for mountainous population by nearly 20% and confirmed the feasibility of this method in Chongqing. During the study period, the spatial distribution of Chongqing’s population has increased in the west and decreased in the east, while also increased in low-altitude areas and decreased in medium-high altitude areas. Population agglomeration was common in all of districts and counties and the population density of central urban areas and its surrounding areas significantly increased, while that of non-urban areas such as northeast Chongqing significantly decreased. Full article

(This article belongs to the Special Issue Nighttime Lights as a Proxy for Economic Performance of Regions)

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2 pages, 1274 KiB

Open AccessErratum

Erratum: Liu et al. Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference around Chinese Mainland via Attention-Augmented CNN from Daytime Satellite Imagery. Remote Sens. 2021, 13, 2067

by Haoyu Liu, Xianwen He, Yanbing Bai, Xing Liu, Yilin Wu, Yanyun Zhao and Hanfang Yang

Remote Sens. 2021, 13(17), 3360; https://doi.org/10.3390/rs13173360 - 25 Aug 2021

Cited by 1 | Viewed by 1799

Abstract

The authors wish to make the following corrections to this paper [...] Full article

(This article belongs to the Special Issue Nighttime Lights as a Proxy for Economic Performance of Regions)

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