Potential of Night-Time Lights to Measure Regional Inequality
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data Collection
2.2.1. DMSP Night-Time Light Data
2.2.2. NPP-VIIRS Night-Time Light Imagery
2.2.3. Demographic and Economic Data
3. Methodology
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Data | Data Description | Year |
---|---|---|
DMSP-OLS NTL | annual product–stable light composite | 1992, 2008, 2012, 2013 |
DMSP-OLS NTL | annual product–average light composite | 1992, 2008, 2012, 2013 |
NPP-VIIRS DNB | annual product–“vcm-orm-ntl” | 2016 |
NPP-VIIRS DNB | monthly product–“vcmslcfg” | 2014, 2018 (April, May, June, July, August, September) |
Mean | Standard Deviation | ||||||||
GDP per capita | Local tax income | Net migration rate | Population | GDP per capita | Local tax income | Net migration rate | Population | ||
1992 | * | * | −2.0 | 550,638 | 1992 | * | * | 3.4 | 309,804.9 |
2008 | 10,935.7 | 751.1 | −0.2 | 536,718.3 | 2008 | 4887.1 | 457.9 | 5.4 | 308,976.7 |
2012 | 12,173.8 | 802.7 | −0.4 | 533,377.7 | 2012 | 5102.5 | 447.9 | 5.8 | 308,994.3 |
2013 | 12,226.2 | 883.1 | −0.5 | 532,377.4 | 2013 | 5278 | 451.7 | 5.7 | 308,117.2 |
2014 | 12,690.5 | 908.9 | −0.4 | 530,946 | 2014 | 5585.5 | 469.8 | 6.1 | 304,972.3 |
2016 | 14,540.5 | 1068.2 | −1.0 | 529,430 | 2016 | 6363.7 | 520.2 | 6.5 | 305,148 |
2018 | * | 1200.8 | −1.8 | 528,038.2 | 2018 | * | 554.5 | 5.7 | 309,051.3 |
Minimum | Maximum | ||||||||
GDP per capita | Local tax income | Net migration rate | Population | GDP per capita | Local tax income | Net migration rate | Population | ||
1992 | * | * | −9.4 | 235,196 | 1992 | * | * | 6.9 | 2,191,176 |
2008 | 5900 | 340.4 | −5.8 | 232,279 | 2008 | 33,000 | 2724 | 31.4 | 2,158,816 |
2012 | 7000 | 389.0 | −6.1 | 230,600 | 2012 | 36,400 | 2739.7 | 34.4 | 2,151,758 |
2013 | 6800 | 438.9 | −7.0 | 230,226 | 2013 | 37,700 | 2768.5 | 31.8 | 2,140,816 |
2014 | 7000 | 436.4 | −4.9 | 229,563 | 2014 | 39,400 | 2861.5 | 35.2 | 2,110,752 |
2016 | 8100 | 519.3 | −6.4 | 228,492 | 2016 | 45,600 | 3131.2 | 37.7 | 2,102,675 |
2018 | * | 559.3 | −10.3 | 226,879 | 2018 | * | 3402.6 | 29.1 | 2,121,794 |
Year | Altitude | X_Long | Y_Lat | Local Tax Income | GDP /Capita | Positive Net Migration Rate | Negative Net Migration Rate | Population |
---|---|---|---|---|---|---|---|---|
1992 | 0.105 | −0.162 | 0.120 | ** | −0.747 * | −0.131 | −0.277 | −0.669 * |
2008 | 0.088 | −0.085 | 0.247 | −0.864 * | −0.840 * | −0.743 * | −0.044 | −0.666 * |
2012 | 0.102 | −0.055 | 0.222 | −0.862 * | −0.840 * | −0.748 * | 0.308 | −0.637 * |
2013 | 0.093 | −0.077 | 0.170 | −0.858 * | −0.824 * | −0.560 * | 0.306 | −0.670 * |
2014 | 0.288 | −0.120 | 0.470 * | −0.827 * | −0.797 * | −0.689 * | 0.205 | −0.553 * |
2016 | 0.304 | −0.118 | 0.474 * | −0.831 * | −0.806 * | −0.741 * | 0.110 | −0.573 * |
Year | Variable | Unstandardized Coefficients | Standardized Coefficients |
---|---|---|---|
2016 | (Constant) | 0.783 | |
GDP per capita | −0.00000755 | −0.450 | |
Altitude | 0.00005078 | 0.127 | |
X_Long | −0.00000009 | −0.121 | |
Y_Lat | 0.00000023 | 0.273 | |
Local tax income | −0.00008564 | −0.418 | |
Net migration rate | −0.00000516 | −0.384 | |
Population | −0.00000011 | −0.311 |
Model | Metric 1 | 2008 | 2012 | 2013 | 2014 | 2016 | 2018 |
---|---|---|---|---|---|---|---|
Linear | R2 | 0.746 | 0.743 | 0.736 | 0.683 | 0.691 | 0.617 |
Adjusted R2 | 0.740 | 0.737 | 0.729 | 0.675 | 0.683 | 0.607 | |
RMSE | 233,512 | 229,753 | 235,001 | 267,680 | 292,820 | 347,545 | |
AIC | 460,022 | 458,659 | 460,556 | 471,493 | 479,034 | 493,426 | |
Polynomial | R2 | 0.856 | 0.885 | 0.845 | 0.762 | 0.746 | 0.682 |
RMSE | 178,054 | 155,534 | 182,044 | 235,052 | 268,758 | 320,801 | |
AIC | 440,183 | 428,824 | 442,044 | 463,511 | 474,768 | 489,636 | |
Exponential | R2 | 0.839 | 0.870 | 0.829 | 0.752 | 0.739 | 0.677 |
RMSE | 188,374 | 165,788 | 191,394 | 239,688 | 272,340 | 323,133 | |
AIC | 444,915 | 434,187 | 446,252 | 465,152 | 475,880 | 490,245 |
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Ivan, K.; Holobâcă, I.-H.; Benedek, J.; Török, I. Potential of Night-Time Lights to Measure Regional Inequality. Remote Sens. 2020, 12, 33. https://doi.org/10.3390/rs12010033
Ivan K, Holobâcă I-H, Benedek J, Török I. Potential of Night-Time Lights to Measure Regional Inequality. Remote Sensing. 2020; 12(1):33. https://doi.org/10.3390/rs12010033
Chicago/Turabian StyleIvan, Kinga, Iulian-Horia Holobâcă, József Benedek, and Ibolya Török. 2020. "Potential of Night-Time Lights to Measure Regional Inequality" Remote Sensing 12, no. 1: 33. https://doi.org/10.3390/rs12010033