China’s poverty assessment and analysis under the framework of the UN SDGs based on multisource remote sensing data

Figures & data

Figure 1. Spatial distribution of NPCs in China.

Figure 2. Datasets were used in this study.

Figure 3. Flow chart of SDG 1 evaluation and analysis of districts and counties in China.

Table 1. SDG 1 China’s district and county localization indicator system.

Global SDG 1 indicator framework	Localized SDG 1 index for China	SDG 1 China’s district and county localization indicator system
1. Proportion of the population living below the international poverty line by sex, age, employment status, and geographic location (urban/rural)	1. Proportion of population below the international poverty line 2. Incidence of poverty	1. Incidence of poverty
2. Proportion of population living below the national poverty line by sex and age
3. Proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definitions	3. Percentage of urban residents with minimum living allowance in urban populations 4. Proportion of rural residents with minimum living security in rural populations 5. Proportion of urban and rural persons with disabilities included in the minimum living allowance in the total population	2. Percentage of urban residents with minimum living allowance in urban populations 3. Proportion of rural residents with minimum living security in rural populations
4. Proportion of population covered by social protection floors/systems by sex, distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, work-based injury victims, and persons who are poor and vulnerable
5. Number of deaths, missing persons, and persons directly affected by disasters per 100,000 persons	6. Proportion of the population affected by natural disasters to the total population	4. Disposable income of urban residents 5. Disposable income of rural residents
6. Direct economic loss attributed to disasters in relation to the global Gross Domestic Product (GDP)		6. Per capita resident savings deposit balance
7. Proportion of total government spending on essential services (education, health, and social protection)

Table 2. Localized SDG 1 index weight for districts and counties.

SDG 1 China’s district and county localization indicator system	Weight	Indicator attributes
1. Incidence of poverty	0.1074	—
2. Percentage of urban residents with minimum living allowance in urban populations	0.0618	—
3. Proportion of rural residents with minimum living security in rural populations	0.1546	—
4. Disposable income of urban residents	0.1170	+
5. Disposable income of rural residents	0.3034	+
6. Per capita resident savings deposit balance	0.2558	+

Figure 4. SDG 1 evaluation value ranking of some districts and counties in China in 2014. (a) and (b) are calculated based on SDG 1 China’s district and county localization indicator system proposed in this study and by Wang et al. (2021a) research, respectively. If the rank of the district/county is lower, it means that the SDG 1 evaluation value of the district/county is lower.

Table 3. Multidimension feature factors based on geographic data.

Influencing factor	Data	Characteristic factor
Topography	SRTM DEM data	Average elevation ($V_1$)
		Average slope ($V_2$) Average undulation ($V_3$)
Land cover	Land cover data	The proportion of urban land area ($V_4$)
		Urban land area ($V_5$)
Social economy	NPP-VIIRS-like nighttime light data	Percentage of nighttime light area ($V_6)$
		Average nighttime light ($V_7)$
		Total nighttime lights ($V_8$)
		The standard deviation of nighttime lights ($V_9$)
		Maximum nighttime light ($V_{10}$)
		Range of nighttime light ($V_{11}$)
Urban accessibility	Land cover data	Average distance to city ($V_{12}$)

Table 4. Important parameters of the machine learning model.

Machine learning model	Parameter type	Parameter value
Regression tree; model III	Minimum leaf size	12
Support vector machine; model Ⅳ	Kernel function	Exponential kernel function
Gaussian process regression; model Ⅴ	Kernel function	Gaussian kernel function
Integrated tree; model Ⅵ	Minimum leaf size	8

Figure 5. VIP values of characteristic factors.

Table 5. Results of SDG 1 China’s district and county localization estimation model.

Evaluation model	R^2	RMSE
Partial least squares; model I	0.65	0.11
Partial least squares method of six feature factors; model II	0.65	0.11
Regression tree; model III	0.36	0.15
Support vector machine; model Ⅳ	0.50	0.13
Gaussian process regression; model Ⅴ	0.52	0.13
Integrated tree; model Ⅵ	0.55	0.12

Figure 6. The spatial distribution of NPCs and estimated NPCs of different models for China in 2014. (a) Distribution of NPCs. (b-f) Capacity of Model II, Model III, Model IV, Model V and Model VI to identify NPCs, respectively.

Figure 7. The spatial distributions of SDG 1 assessment values of China’s districts and counties in 2012, 2014, 2016, and 2018, respectively.

Figure 8. The changes in the SDG 1 assessment value of China’s districts and counties in 2012–2014, 2014–2016, 2016–2018, and 2012–2018, respectively.

Figure 9. The local autocorrelation results of the SDG 1 evaluation value of China’s districts and counties in 2012, 2014, 2016, and 2018, respectively.

Figure 10. The standard deviation ellipse spatial distributions of the six graded areas of the SDG 1 evaluation value of China’s districts and counties in 2012, 2014, 2016, and 2018, respectively.

Figure 11. The track change of the ellipse’s center of gravity of the six graded areas of the SDG 1 evaluation value of China’s districts and counties.

Figure 12. The spatial distribution of China’s estimated SDG poverty counties and NPCs in 2014.

Figure 13. The spatial distribution of China’s SDG poverty counties’ estimated results in 2012, 2014, 2016, and 2018, respectively.

Figure 14. The ability of the model with NDVI data to identify NPCs.

Table 6. Estimated results of related poverty monitoring studies.

Literature source	The proportion of NPCs in lowest-value areas	The proportion of Non-NPCs in lowest-value areas	The proportion of NPCs in low-value areas	The proportion of Non-NPCs in low-value areas
Yu et al. (2015)	41.28%	58.72%	36.31%	63.69%
Our study	89.32%	10.68%	42.15%	57.85%

Wang, Y., M. Wang, B. Huang, S. Li, and Y. Lin. 2021a. “Evaluation and Analysis of Poverty-Stricken Counties Under the Framework of the UN Sustainable Development Goals: A Case Study of Hunan Province, China.” Remote Sensing 13: 4778. doi:10.3390/rs13234778.

 Web of Science ®Google Scholar

Yu, B., K. Shi, Y. Hu, C. Huang, Z. Chen, and J. Wu. 2015. “Poverty Evaluation Using NPP-VIIRS Nighttime Light Composite Data at the County Level in China.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8: 1217–1229. doi:10.1109/jstars.2015.2399416.

 Web of Science ®Google Scholar

Data availability statement

The data that support the findings of this study are available in the public domain:

SRTM DEM data: (https://doi.org/10.1080/10095020.2022.2108346)

NPP-VIIRS-like nighttime light data: (https://doi.org/10.1080/10095020.2022.2108346)

Land cover data: (https://doi.org/10.1080/10095020.2022.2108346)

Socio-economic statistics data: (https://doi.org/10.1080/10095020.2022.2108346)

Consent for publication

All the coauthors consent the publication of this work.