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The log-log linear regression model of the area and population of the urban areas of the states of the U.S.
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In many countries of the world governments are unable to accurately track the true magnitude of economic activity due to the large number of transactions upon which taxes are not paid. It is particularly easy to avoid paying taxes on cash transactions and on remittances transferred from outside of the country. In some cases the so called ldquoinfor...
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Nighttime light images record the brightness of the Earth surface, indicating the scope and intensity of human activities. However, there are few studies on the long-term changes in global nighttime lights. In this paper, the authors constructed a long time series (1992~2017) nighttime light dataset combining the Defense Meteorological Satellites P...
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... Over the past few years, there has been a persistent use of spatial big data and emerging methods to analyze urban issues. For instance, multiple studies report the use of enterprise distribution data and night lighting data to examine the spatial development characteristics of cultural industries and the changes in urban spatial patterns [53,54]. Furthermore, the application of multivariate data, such as satellite remote sensing data (including remote sensing imagery, traffic dynamics, and heat maps), has also been documented as a means to assess the traffic pattern and congestion status in urban areas [55]. ...
The present study investigates the dynamic evolution characteristics of urban spatial morphology by analyzing real road network data from 2000, 2010, and 2020, along with nighttime lighting data employing spatial analysis methods and spatial syntax models. Accordingly, two separate dimensions of urban morphology: internal and external, are covered. First, the integration and synergy of interior morphology features are analyzed using spatial syntactic modeling. Subsequently, the spatial compactness, fractal dimension, and level of center of gravity shift of the city are assessed by combining the nighttime lighting data with the earlier dataset. This analysis facilitated the deep exploration of the spatiotemporal evolution of the city’s external morphology. Building upon this foundation, the interaction between the "internal and external" domains was analyzed further. The main findings of the study reveal a synchronous pattern of urban expansion throughout the evolution of urban spatial morphology. Furthermore, the urban form was observed to undergo a progressive transformation, transitioning from a "single core" morphology to a "primary and secondary double core" morphology. Over time, this development progressed and evolved into a "belt-like multi-core" structure. Additionally, the coupling characteristics further validate the relationship between the structure of the road network and the urban morphology in river valley-type cities. In particular, accessibility of dense and horizontally distributed transportation network was found to significantly influence the spatial development of these cities. As observed, the findings provides valuable insights into understanding the characteristics of internal and external associations regarding urban spatial patterns.
... Payments go toward a country's Gross National Income (GNI), which is comprised of the GDP plus net revenues from employee compensation and foreign property income. The money that foreign migrants send to their home nations is known as remittances (Ghosh et al., 2009). To measure the association between the GNI and the NTL at the city scale, we sum all the lit pixels of the NTL, where "lit pixel" means a radiance value equal to or greater than 1 nWcm -2 sr -1 . ...
For mapping and monitoring socioeconomic activities in cities, night-time lights (NTL) satellite sensor images are used widely, measuring the light intensity during the night. However, the main challenge to mapping human activities in cities using such NTL satellite sensor images is their coarse spatial resolution. To address this drawback, spatial downscaling of satellite nocturnal images is a plausible solution. However, common approaches for spatial downscaling employ spatially stationary models that may not be optimal where the data are spatially heterogeneous. In this research, a geostatistical model termed Random Forest area-to-point regression Kriging (RFATPK) was employed to disaggregate coarse spatial scale VIIRS NTL images (450 m) to a fine spatial scale (100 m). The RF predicts at a coarse resolution from fine spatial resolution variables, such as a Population raster. ATPK then downscales the coarse residuals from the RF prediction. In numerical experiments, RFATPK was compared with three benchmark techniques, including the simple Allocation of pixel values from the coarse resolution NTL data, Machine Learning with Splines and Geographically Weighted Regression. The downscaled results were validated using fine resolution LuoJia 1-01 satellite sensor imagery. RFATPK produced more accurate disaggregated images than the three benchmark approaches, with mean root mean square errors (RMSE) for the year 2018 of 13.89 and 6.74 nWcm − 2 sr − 1 , for Mumbai and New Delhi, respectively. Also, the property of perfect coherence, measured by the Correlation Coefficient, was preserved consistently when applying RFATPK and was almost 1 for all years. The applicability of the disaggregated NTL data to monitor socioeconomic activities at the within-city scale against the reference NTL was illustrated by utilizing them as a proxy for the Gross National Income (GNI) per capita and the Night Light Development Index. The GNI estimation from the down-scaled NTL outperformed the coarse resolution NTL when examining their coefficients of determination, with R 2 of 0.67 and 0.47 for the GNI estimation using the fine and coarse resolution NTL data, respectively. For the Night Light Development Index (NLDI), the results of the index were compared by measuring their correlation with the Human Development Index (HDI). The NLDI from the downscaled NTL outperformed the coarse resolution NTL when measuring the correlation with the HDI, with Pearson's correlation coefficients of − 0.48 and − 0.35 for the NLDI using the fine and coarse resolution NTL data, respectively, for New Delhi. The outcomes indicate that RFATPK provides more accurate predictions than the three benchmark techniques and the downscaled NTL data are more suitable for fine scale socioeconomic applications, as demonstrated by the NLDI and GNI. This research, thus, shows that the RFATPK solution for NTL disaggregation can facilitate data enhancement for fine-scale sub-national applications in social sciences and can be generalized worldwide by including other cities as well as other applications.
... The two commonly used NTL data are the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NTL data and Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) NTL data. Some studies have demonstrated that there are statistically significant correlations between NTL intensity and human activities at the different scales [3][4][5][6][7]. Therefore, the DMSP/OLS and NPP/VIIRS NTL data are generally associated with socioeconomic topics [8], including gross domestic product [3,9,10], extracting urban built-up area [11,12], estimating population [13][14][15][16], house vacancy [17], poverty [18,19], urbanization [20], and electricity consumption [21][22][23]. ...
Nighttime light (NTL) intensity is highly associated with the unique footprint of human activities, reflecting the development of socioeconomic and urbanization. Therefore, better understanding of the relationship between NTL intensity and human activities can help extend the applications of NTL remote sensing data. Different from the global effect of human activities on NTL intensity discussed in previous studies, we focused more attention to the local effect caused by the spatial heterogeneity of human activities with the support of the multiscale geographically weighted regression (MGWR) model in this study. In particular, the Suomi National Polar Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) NTL data within Chongqing, China were taken as example, and the point of interest (POI) data and road network data were adopted to characterize the intensity of human activity type. Our results show that there is significant spatial variation in the effect of human activities to the NTL intensity, since the accuracy of fitted MGWR (adj.R2: 0.86 and 0.87 in 2018 and 2020, respectively; AICc: 4844.63 and 4623.27 in 2018 and 2020, respectively) is better than that of both the traditional ordinary least squares (OLS) model and the geographically weighted regression (GWR) model. Moreover, we found that almost all human activity features show strong spatial heterogeneity and their contribution to NTL intensity varies widely across different regions. For instance, the contribution of road network density is more homogeneous, while residential areas have an obviously heterogeneous distribution which is associated with house vacancy. In addition, the contributions of the commercial event and business also have a significant spatial heterogeneity distribution, but show a distinct decrement when facing the COVID-19 pandemic. Our study successfully explores the relationship between NTL intensity and human activity features considering the spatial heterogeneity, which aims to provide further insights into the future applications of NTL data.
... Because nighttime lights have been shown to be a good proxy for economic activity (Henderson et al., 2012;Donaldson and Storeygard, 2016;Ghosh et al., 2009) and because the spatial concentration of economic activity is as- Figure 2. Population source data, Bangkok and surrounding areas, Thailand, 2015. Note that the dark blue indicates ocean, and gray boundaries indicate first-order administrative boundaries. ...
The accurate estimation of population living in the low-elevation coastal zone (LECZ) – and at heightened risk from sea level rise – is critically
important for policymakers and risk managers worldwide. This
characterization of potential exposure depends on robust
representations not only of coastal elevation and spatial population data but also
of settlements along the urban–rural continuum. The empirical basis for LECZ
estimation has improved considerably in the 13 years since it was first
estimated that 10 % of the world's population – and an even greater share
of the urban population – lived in the LECZ (McGranahan et al., 2007a). Those
estimates were constrained in several ways, not only most notably by a single
10 m LECZ but also by a dichotomous urban–rural proxy and population
from a single source. This paper updates those initial estimates with newer,
improved inputs and provides a range of estimates, along with sensitivity
analyses that reveal the importance of understanding the strengths and
weaknesses of the underlying data. We estimate that between 750 million and
nearly 1.1 billion persons globally, in 2015, live in the ≤ 10 m LECZ,
with the variation depending on the elevation and population data sources
used. The variations are considerably greater at more disaggregated levels,
when finer elevation bands (e.g., the ≤ 5 m LECZ) or differing
delineations between urban, quasi-urban and rural populations are
considered. Despite these variations, there is general agreement that the
LECZ is disproportionately home to urban dwellers and that the urban
population in the LECZ has grown more than urban areas outside the LECZ
since 1990. We describe the main results across these new elevation,
population and urban-proxy data sources in order to guide future research
and improvements to characterizing risk in low-elevation coastal zones (https://doi.org/10.7927/d1x1-d702, CIESIN and CIDR, 2021).
... Because night-time lights have been shown to be a good proxy for economic activity (Henderson et al., 2012;Donaldson and Storeygard, 2016;Ghosh et al., 2009) and because the spatial concentration of economic activity is associated with urban location, night-time lights data products continue to be a valuable data source as an urban proxy (Hu et al., 2020 DMSP-OLS, it represents relative changes in brightness of the DMSP-OLS sensor, and is constrained to lit areas based on the 2015 VIIRS data (Small et al., 2018a). Like some of the data sets used here, dLIGHT is a new data product and has not been used extensively with other data sets indicating the spatial extents (and change thereof) of urban areas. ...
The accurate estimation of population living in the Low Elevation Coastal Zone (LECZ), and at heightened risk from sea level rise, is critically important for policy makers and risk managers worldwide. This characterization of potential exposure depends not only on robust representations of coastal elevation and spatial population data, but also of settlements along the urban-rural continuum. The empirical basis for LECZ estimation has improved considerably in the 13 years since it was first estimated that 10 % of the world’s population, and an even greater share of the urban population, lived in the LECZ (McGranahan et al., 2007). Those estimates were constrained in several ways, most notably by a single 10-meter LECZ, but also by a dichotomous urban-rural proxy and population from a single source. This paper updates those initial estimates with newer, improved inputs and provides a range of estimates, along with sensitivity analyses that reveal the importance of understanding the strengths and weaknesses of the underlying data. We estimate that between 750 million to nearly 1.1 billion persons globally, in 2015, live in the ≤ 10 m LECZ, with the variation depending on the elevation and population data sources used. The variations are considerably greater at more disaggregated levels, when finer elevation bands (e.g. the ≤ 5 m LECZ) or differing delineations between urban, quasi-urban and rural populations are considered. Despite these variations, there is general agreement that the LECZ is disproportionately home to urban dwellers, and that the urban population in the LECZ has grown more than urban areas outside the LECZ since 1990. We describe the main results across these new elevation, population, and urban proxy data sources in order to guide future research and improvements to characterizing risk in low elevation coastal zones. DOI: assigned upon completion of data peer-review.
... 2) The results of the comparison between absolute error and relative error show that the LBLoR is the least accurate in the calculation of radiometric intercalibration, which is due to the characteristic of the logarithmic regression model. This model needs to pass the fixed point, taking (15) as an example, as a fixed point (1, K0), which is a difficult criterion to meet in the actual intercalibration relation. In addition, the logarithmic regression is usually the inverse of the exponential regression; thus, if the intercalibration model conforms to the exponential regression, it will not conform to the (inverse) logarithmic regression. ...
The further scientific applications of DMSP-OLS night-time light (NTL) imagery have been being limited by the accuracy, automation and speed of radiometric intercalibration. In order to solve the aforementioned problems, this paper is the first to propose a new least-median-of-squares (LMedS)-based power regression (LBPR) for automatically radiometric intercalibration, and investigate the reasons of the optimal model of radiometric intercalibration, especially those based on the Taylor expansion and probability principle. NTL data in six different global regions, from 1998 to 2007, were used as the test datasets. When the five kinds of LMedS-based radiometric intercalibration models (i.e., linear, quadratic, power, exponential and logarithmic regression) are synthetically compared in absolute accuracy (adjusted RMSE) and running speed, it is concluded that the LBPR, which has the highest accuracy and preferable running speed, is recommended as the optimal method, which can also be used as a reference for other types of imagery pre-processing.
... Income and expenditure measures have been used by Alderslade et al. (2006) to measure shadow economy in the USA and by Fortin et al. (2010) for the Province of Quebec (Canada). Indirect non-monetary measures based on electricity consumption in different states of the USA have been used by Heath and Jones (2013), and the method of spatial patterns of nighttime imagery has been used for the states of Mexico (Ghosh et al. 2009) and for the states and union territories of India (Ghosh et al. 2010). • Model approach. ...
The aim of this paper is to determine shadow economy on regional level in connection with quality of life. The paper focuses on shadow economy’s regional differences in two countries: the Russian Federation and the Ukraine. The research methods are factor analysis and MIMIC modeling. Using these methods, we conducted a factor analysis of the relationships of quality and standard of living in regions of Russian Federation for the years 2002–2013 and in Ukraine for the years 2004–2013 in the same indices of 17 sections. The factors most common for the two countries are the demographic factor and the criminogenic factor, but the significant differences are in the standard of living monetary support factor. In Russia, monetary income and formal employment have shown a weak correlation, while in the Ukraine they are closely related. The demographic indices for the quality of life are significantly affected by the ethno-socio-cultural factors; therefore, their application in the analysis of correlations between the various manifestations of shadow economy is quite limited. Finally, the fact that the indices for the standard and quality of living can be regularly explained by the influence of various factors shows that there is a gap between the economic and societal components in the functioning of the two states. Using results of factor analysis, the MIMIC model was developed. The results point out that the level of shadow economy in Russian Federation varies from 48 to 62% from GRP at considered regions during the years 2000–2013.
... Night-time lights are unlikely to provide added value as a proxy in countries with good statistical systems, due to the high measurement error as compared to national inventories[38,47]. Sutton et al.[48]agree, though conclude that night-time lights still provide useful insights into estimating informal economic activity; Ghosh et al.[49]go one step further, assessing the informal sector in an empirical case study of Mexico. The work of Shi et al.[44]and Jean et al.[50]provide excellent recent examples of the value obtained when combining these proxy approaches with the increased resolution of VIIRS and with machine learning, respectively. ...
We use a parallelized spatial analytics platform to process the twenty-one year totality of the longest-running time series of night-time lights data—the Defense Meteorological Satellite Program (DMSP) dataset—surpassing the narrower scope of prior studies to assess changes in area lit of countries globally. Doing so allows a retrospective look at the global, long-term relationships between night-time lights and a series of socio-economic indicators. We find the strongest correlations with electricity consumption, CO2 emissions, and GDP, followed by population, CH4 emissions, N2O emissions, poverty (inverse) and F-gas emissions. Relating area lit to electricity consumption shows that while a basic linear model provides a good statistical fit, regional and temporal trends are found to have a significant impact.
... Since the 1980s, night-time lights data have been effectively used to detect human presence and activities at global and regional scales [37][38][39][40][41]. These data have served to monitor and estimate energy consumption [36,37,42,43], economic activity [42,[44][45][46], population density [39,47,48], and most recently to develop a human development index [49]. Time series of NTL have also been successfully applied to analyze the trends of urban expansion and urban dynamics at different spatial scales (e.g., national, regional, and global) [41,[50][51][52]. ...
By 2050, 90% of the population in Latin America will live in cities, but there is a lack of up-to-date spatial information about the urban extent and patterns of urbanization in cities of this region. In this study, we analyzed population growth, urban density and urbanization dynamics between 1992 and 2009 in the major cities of Bolivia, Colombia, Ecuador and Perú using Google Earth and DMSP/OLS night-time lights imagery. We used Google Earth to map the urban extent, and time series of night-time lights to analyze spatial patterns of urban development. The dominant urban development patterns were: high-density compact in Bogotá , Cali, Guayaquil, and Medellí n; high-density expansive growth in La Paz/El Alto; low-density expansive in Quito and Santa Cruz; and a mix of high-density compact and suburban growth in Lima. Urban growth occurred largely along the periphery of cities, influenced by the local landscape and by demographic and socioeconomic factors such as immigration and housing prices. Urban density in Colombia (>20,000 per/km 2) was among the highest in the world. Future growth in the region will probably be characterized by densification and slow urban expansion. This study also validates the utility of Google Earth and night-time lights for monitoring urbanization.
... For example, Elvidge et al. (1997b) showed a relationship between spatial distribution of light intensity with business activity or electricity consumption. Likewise, Dobson et al. (2000a) showed a relationship with population distribution and Ghosh et al. (2009) showed with GDP. In addition there are some studies to estimate present state of urban areas using DMSP/OLS images. ...
The main objective of this research is to show how much can be monitored various human activities using night light images by the DMSP/OLS from NOAA/NGDC. In Japan, various human activities can be monitored easily without satellite images because we can use many kinds of detailed spatial dataset and statistics. On the other hand detailed spatial data are not developed adequately especially in developing countries. Night light images by the DMSP/OLS can help to monitor them in such countries.
Therefore we discuss how to use night light images of the DMSP/OLS for this objective in Tohoku region, Japan. Human activates were explained by 3 factors, i.e. road distribution, accumulation of buildings and dynamic population. These data and light images of the DMSP/OLS were resampled into the same aggregate unit and compared with a light intensity of the DMSP/OLS. In addition it is shown which factor of human activates explains the light intensity more clear than other factors by multiple regression analyses using all factors. Results of multiple regression analyses show that impacts by road distribution are strong in urban and suburban areas and impacts by building are strong in rural areas. Impacts by dynamic population are weak in all areas.
Finally estimated images of light intensities were developed using results of multiple regression analysis and they were compared with the actual image of light intensity. The compared result shows that tendency of spatial distribution of the light intensity by the estimated result agrees rather well with tendency by the DMSP/OLS.
