Identification of Relative Poverty Based on 2012–2020 NPP/VIIRS Night Light Data: In the Area Surrounding Beijing and Tianjin in China
Abstract
:1. Introduction
2. Study Area and Methodology
2.1. Study Area
2.2. Index Selection of Relative Poverty
2.3. Data Sources
2.3.1. NPP/VIIRS NTL Data
2.3.2. Social Statistics
2.4. Methodology
2.4.1. MRPI
2.4.2. Analytic Hierarchy Process
2.4.3. Entropy Weight Method
2.4.4. Fixed Effect Models
2.4.5. Error Test
3. Results
3.1. NPP/VIIRS NTL Data Correction
- (1)
- Annual synthesis of monthly NTL data
- (2)
- Reprojection and resampling
- (3)
- Cutting
- (4)
- Stability correction
- (5)
- Elimination of outliers
- (6)
- Time series correction
3.2. Results of Multidimensional Relative Poverty Index
3.3. Identification of Relative Poverty at County Level in HABT
3.4. Establishment of Multidimensional Relative Poverty Index Estimation Model
3.5. Accuracy Test of MPRI Estimation Model
3.6. Identification of Relative Poverty at the Township Scale in the HABT
4. Discussion
- (1)
- This study selected HABT as the study area, and used “free from worries over food and clothing and have access to compulsory education, basic medical services, and safe housing” as the standard for establishing MRPI. In the study of relative poverty in other regions of China, researchers can refer to this choice of indicator.
- (2)
- To conduct multidimensional relative poverty assessment based on social statistics, it may be necessary to wait for a long period until existing statistics are updated through economic censuses. Knowledge of the relative poverty in an area cannot be kept up to date over a short period of time, which limits targeted poverty alleviation work. The use of night light data can effectively identify the areas of relative poverty in a timely and effective manner. It also provides a convenient means of conducting poverty research in regions lacking social statistics.
- (3)
- This study established an MRPI estimation model at the county scale based on MRPI and NTL data. The feasibility of using NTL data to evaluate the relative poverty of counties and identify areas of relative poverty was verified. This lays the foundation for the application of night light data in the identification of relative poverty at the county scale, and provides additional ideas for the identification of relative poverty in other regions on the county or smaller scales.
- (1)
- When constructing MRPI, a five-dimension index system was constructed that takes into account the availability of statistical data. In the further study of relative poverty by government departments and scholars, more multidimensional indicators of relative poverty may be used to develop more reliable studies.
- (2)
- Due to space limitations, after providing the estimates of the MRPI at the township scale, this paper does not further discuss and monitor the relative poverty at the township scale. In the future, further research on relative poverty at the township scale can be carried out, in order to provide a smaller-scale scope of reference for the policy formulation of relevant departments, and to provide a more refined spatial reference for the prevention of a large-scale return to poverty.
- (3)
- As the scale of poverty research has been continuously narrowing and deepening, as a result of the future development of the work of the relevant departments, it will be feasible to obtain smaller-scale statistical data, which will provide an improved basis for the study of NTL data.
5. Conclusions
- (1)
- The 71 counties of HABT from 2012 to 2019 were selected as the research scope, and MRPI was established in terms of the five dimensions of economy, quality of life, education, health care, and social security. Then, 60% of the median of each dimension was selected as the relative poverty evaluation standard, and 71 counties of HABT from 2012 to 2019 were identified. The identification results show that the maximum number of relative poverty counties was 29 in 2013, and the relative poverty incidence rate was 40.85%. From 2012 to 2019, mild relative poverty was the main type in HABT, and the number of severe relative poverty counties was the least.
- (2)
- Analysis of the identified list of counties experiencing relative poverty from 2012 to 2019 shows that, from 2012 to 2019, there were 13 counties in a state of long-term relative poverty for 8 years: Boye county, Chicheng county, Chongli county, Dachang county, Fuping county, Guyuan county, Huaian county, Kangbao county, Laishui county, Mengcun county, Shangyi county, Wei county, and Yangyuan county. There were five counties in a state of long-term relative poverty for 6 years: Haixing county, Laiyuan county, Wangdu county, Xinglong county, and Zhangbei county. There are four counties in a state of long-term relative poverty for 5 years: Fengning county, Wanquan county, Yongqing county, and Zhulu county. Anxin Coun-ty, Gu’an county, Kuancheng county, and Weichang county were in a state of relative poverty for four years. Luanping county, Rongcheng county, and Xiong county were in a state of relative poverty for three years.
- (3)
- A panel regression model was established using MRPI and adjusted NPP/VIIRS ANTL data of 71 counties in the HABT region from 2012 to 2019 as the dependent and independent variables. Hausman’s test was used to determine the choice of fixed-effects model. Comparing the three fixed-effects models, it was found that the R2 of the individual–time fixed-effects model was best at 0.6573. Thus, an MRPI estimation model was obtained. From the results of the RE test, more than 50% of the counties had high accuracy for a period of 8 years, and the proportion of counties with low precision from 2012 to 2019 was less than 10%. The results of the ARE test showed that the ARE of fifty-two counties was within 25%, and the ARE of seventeen counties was between 25% and 50%. Combining the results of the RE and ARE tests, it was found that using the 2012–2019 NPP/VIIRS ANTL to estimate MRPI at the county scale passed the accuracy test. This lays a theoretical foundation for the subsequent study of the identification of relative poverty using night light data.
- (4)
- This study constructed a multi-dimensional relative poverty index calculation model, and calculated the multi-dimensional relative poverty index from 2012 to 2019. A multidimensional relative poverty index estimation model was constructed using the corrected nighttime light data and the multidimensional relative poverty index. On this basis, county-level correction was performed on the nighttime light data in Chongli District, and the estimation of the multi-dimensional relative poverty index at the township level was realized. This provides a feasible method and data reference for the identification of relative poverty at the township scale. This study provides a theoretical reference for the identification of relative poverty and a practical basis for the application of NPP/VIIRS NTL data in the study of relative poverty. It provides a feasible method for the identification and monitoring of relative poverty at the township scale. Achieving the identification of small-scale relative poverty is helpful for formulating scientific and effective poverty reduction strategies, and is of great significance for preventing a large-scale return to poverty. This study provides a spatial reference for alleviating relative poverty in the future.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
County (City) | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 |
---|---|---|---|---|---|---|---|---|---|
Anguo City | 0.1674 | 0.2182 | 0.2497 | 0.2998 | 0.4428 | 0.7807 | 1.0172 | 1.5205 | 1.8742 |
Anxin County | 0.3921 | 0.4937 | 0.5140 | 0.6041 | 0.6980 | 0.8585 | 0.9321 | 1.0655 | 1.1509 |
Bazhou City | 1.3672 | 1.9701 | 2.2171 | 3.0671 | 3.4518 | 3.5537 | 3.7595 | 3.9348 | 4.0658 |
Botou City | 0.2368 | 0.3257 | 0.3573 | 0.4680 | 0.5623 | 0.6640 | 0.7431 | 0.8463 | 0.8978 |
Boye County | 0.1494 | 0.1943 | 0.2264 | 0.2748 | 0.3462 | 0.4630 | 0.4823 | 0.5579 | 0.5840 |
Cang County | 0.4310 | 0.5753 | 0.6581 | 0.8213 | 0.9165 | 1.0619 | 1.1349 | 1.2329 | 1.3829 |
Chengde County | 0.0796 | 0.0951 | 0.1072 | 0.1538 | 0.1831 | 0.1923 | 0.2076 | 0.2304 | 0.2417 |
Chicheng County | 0.0218 | 0.0240 | 0.0249 | 0.0288 | 0.0345 | 0.0381 | 0.0439 | 0.0466 | 0.0492 |
Chongli County | 0.0363 | 0.0399 | 0.0446 | 0.0752 | 0.1056 | 0.1237 | 0.1449 | 0.1695 | 0.1811 |
Dachang County | 1.3392 | 1.8691 | 2.4094 | 4.3303 | 5.0571 | 4.8889 | 5.3144 | 5.4682 | 5.5228 |
Dacheng County | 0.3764 | 0.4855 | 0.5589 | 0.6673 | 0.7651 | 0.8349 | 0.8814 | 0.9258 | 0.9625 |
Dingxing County | 0.2069 | 0.2961 | 0.3241 | 0.4064 | 0.5309 | 0.6098 | 0.6280 | 0.6995 | 0.7764 |
Dingzhou City | 0.3730 | 0.4696 | 0.6384 | 0.9147 | 1.0941 | 1.1493 | 1.2578 | 1.3944 | 1.4782 |
Dongguang County | 0.2200 | 0.2867 | 0.3086 | 0.3980 | 0.4648 | 0.5138 | 0.5373 | 0.6392 | 0.7048 |
Fengning County | 0.0122 | 0.0174 | 0.0196 | 0.0242 | 0.0363 | 0.0487 | 0.0748 | 0.0895 | 0.0940 |
Fuping County | 0.0564 | 0.0649 | 0.0697 | 0.0857 | 0.1072 | 0.1365 | 0.1546 | 0.1909 | 0.1949 |
Gaobeidian City | 0.4602 | 0.6311 | 0.6974 | 0.9079 | 1.0510 | 1.5716 | 1.6020 | 1.7157 | 2.1909 |
Gaoyang County | 0.5315 | 0.7095 | 0.7369 | 0.9284 | 1.0070 | 1.1590 | 1.2402 | 1.3908 | 1.4612 |
Gu’an County | 0.6720 | 0.9814 | 1.1592 | 1.7491 | 1.8760 | 1.6357 | 2.1287 | 2.3000 | 2.4401 |
Guyuan County | 0.0184 | 0.0221 | 0.0250 | 0.0398 | 0.0768 | 0.0881 | 0.0924 | 0.0979 | 0.1019 |
Haixing County | 0.1936 | 0.2372 | 0.2519 | 0.2968 | 0.4125 | 0.5241 | 0.5461 | 0.5711 | 0.6014 |
Hejian City | 0.4338 | 0.5421 | 0.5609 | 0.6583 | 0.7439 | 0.9395 | 0.9591 | 1.0639 | 1.1344 |
Huai’an County | 0.1370 | 0.1545 | 0.1694 | 0.1929 | 0.2303 | 0.2372 | 0.2579 | 0.2807 | 0.2988 |
Huailai County | 0.2297 | 0.2477 | 0.2629 | 0.3539 | 0.4694 | 0.5274 | 0.5632 | 0.6502 | 0.6695 |
Huanghua City | 0.7422 | 0.9460 | 1.0565 | 1.4227 | 1.7471 | 1.9624 | 2.1021 | 2.2609 | 2.4212 |
Kangbao County | 0.0098 | 0.0123 | 0.0145 | 0.0175 | 0.0236 | 0.0264 | 0.0300 | 0.0334 | 0.0372 |
Kuancheng County | 0.2077 | 0.2499 | 0.3035 | 0.4520 | 0.4863 | 0.4794 | 0.5278 | 0.5528 | 0.5651 |
Laishui County | 0.1401 | 0.1982 | 0.2406 | 0.2787 | 0.3288 | 0.3575 | 0.3860 | 0.4117 | 0.4152 |
Laiyuan County | 0.1071 | 0.1399 | 0.1599 | 0.1994 | 0.2325 | 0.2284 | 0.2589 | 0.2785 | 0.2924 |
Laoting County | 0.7485 | 0.9613 | 1.1972 | 1.6685 | 1.7655 | 1.6307 | 1.8349 | 1.8847 | 2.0133 |
Li County | 0.3238 | 0.4391 | 0.5054 | 0.6183 | 0.7328 | 0.8368 | 0.8716 | 0.9965 | 1.0164 |
Longhua County | 0.0372 | 0.0468 | 0.0515 | 0.0661 | 0.0762 | 0.0770 | 0.0914 | 0.0973 | 0.1033 |
Luannan County | 0.5173 | 0.6705 | 0.8730 | 1.0476 | 1.1947 | 1.1619 | 1.3243 | 1.3730 | 1.4270 |
Luanping County | 0.1064 | 0.1294 | 0.1456 | 0.1792 | 0.2070 | 0.2120 | 0.2436 | 0.2679 | 0.2819 |
Luan County | 0.7970 | 0.9361 | 1.1186 | 1.4407 | 1.7710 | 1.7344 | 1.8645 | 1.9412 | 1.9886 |
Mancheng County | 0.4285 | 0.5625 | 0.6537 | 0.8352 | 0.9580 | 1.0215 | 1.1078 | 1.3815 | 1.4080 |
Mengcun County | 0.5272 | 0.6197 | 0.6384 | 0.7210 | 0.8134 | 0.9736 | 0.9923 | 1.0117 | 1.0708 |
Nanpi County | 0.1962 | 0.2780 | 0.3163 | 0.4271 | 0.4921 | 0.5672 | 0.6107 | 0.6881 | 0.7890 |
Pingquan County | 0.0763 | 0.0856 | 0.1023 | 0.2090 | 0.2454 | 0.2544 | 0.2692 | 0.2833 | 0.2974 |
Qian’an City | 1.6468 | 1.7522 | 1.8390 | 2.3232 | 2.4110 | 2.1379 | 2.5231 | 2.6936 | 2.7425 |
Qianxi County | 0.3206 | 0.3570 | 0.3742 | 0.4589 | 0.4993 | 0.4805 | 0.5530 | 0.6597 | 0.6988 |
Qing County | 0.3224 | 0.4126 | 0.4366 | 0.5102 | 0.5782 | 0.6931 | 0.7096 | 0.7469 | 0.7950 |
Qingyuan County | 0.3630 | 0.4725 | 0.5030 | 0.6429 | 0.7734 | 1.0300 | 1.0733 | 1.1628 | 1.1910 |
Quyang County | 0.1504 | 0.1952 | 0.2195 | 0.2550 | 0.3686 | 0.4566 | 0.4751 | 0.5483 | 0.6113 |
Renqiu City | 0.9509 | 1.2426 | 1.2980 | 1.8350 | 2.0062 | 2.2886 | 2.4693 | 2.6765 | 2.8404 |
Rongcheng County | 0.4159 | 0.5868 | 0.6446 | 0.8058 | 0.9186 | 1.0334 | 1.2011 | 1.4784 | 3.2714 |
Sanhe City | 1.7494 | 2.2241 | 2.4639 | 3.4282 | 3.7081 | 3.5692 | 3.8877 | 4.1110 | 4.2072 |
Shangyi County | 0.0253 | 0.0263 | 0.0306 | 0.0453 | 0.0560 | 0.0592 | 0.0690 | 0.0847 | 0.0919 |
Shunping County | 0.1310 | 0.1807 | 0.2096 | 0.2512 | 0.2982 | 0.3667 | 0.3910 | 0.4472 | 0.4576 |
Suning County | 0.5118 | 0.6858 | 0.8100 | 1.1882 | 1.2578 | 1.1861 | 1.4226 | 1.6085 | 1.7791 |
Tang County | 0.1145 | 0.1558 | 0.1811 | 0.2177 | 0.2676 | 0.3253 | 0.3449 | 0.3967 | 0.4041 |
Wangdu County | 0.1813 | 0.2287 | 0.2639 | 0.3617 | 0.4358 | 0.4970 | 0.5307 | 0.6684 | 0.6829 |
Wanquan County | 0.2334 | 0.2627 | 0.2760 | 0.3397 | 0.3764 | 0.3690 | 0.4179 | 0.4674 | 0.4881 |
Weichang County | 0.0116 | 0.0144 | 0.0170 | 0.0234 | 0.0341 | 0.0396 | 0.0424 | 0.0470 | 0.0490 |
Wen’an County | 0.5697 | 0.7874 | 0.8525 | 1.0383 | 1.1373 | 1.2163 | 1.2725 | 1.3472 | 1.4510 |
Wuqiao County | 0.1421 | 0.1605 | 0.1814 | 0.2030 | 0.2350 | 0.2956 | 0.3078 | 0.3557 | 0.4100 |
Xianghe County | 0.8936 | 1.2764 | 1.4544 | 2.5333 | 3.2387 | 3.3366 | 3.4399 | 3.6372 | 3.7330 |
Xian County | 0.2202 | 0.2870 | 0.3084 | 0.3503 | 0.3924 | 0.4235 | 0.4637 | 0.5319 | 0.5656 |
Xinglong County | 0.0487 | 0.0544 | 0.0597 | 0.0892 | 0.1027 | 0.1190 | 0.1330 | 0.1431 | 0.1496 |
Xiong County | 0.5719 | 0.8206 | 0.9141 | 1.2111 | 1.4471 | 1.5141 | 1.5826 | 1.7841 | 2.3284 |
Xushui County | 0.4586 | 0.6196 | 0.7585 | 0.9600 | 1.4613 | 1.7740 | 1.8890 | 2.2952 | 2.5431 |
Yangyuan County | 0.0777 | 0.0889 | 0.0968 | 0.1215 | 0.1443 | 0.1479 | 0.1754 | 0.1849 | 0.1886 |
Yanshan County | 0.2929 | 0.3764 | 0.4201 | 0.4895 | 0.5822 | 0.6371 | 0.6673 | 0.7103 | 0.7803 |
Yi County | 0.0641 | 0.0965 | 0.1211 | 0.1398 | 0.1817 | 0.2195 | 0.2308 | 0.2523 | 0.2565 |
Yongqing County | 0.4143 | 0.5378 | 0.5840 | 0.6822 | 0.8872 | 1.0940 | 1.1669 | 1.3244 | 1.4318 |
Yutian County | 0.4681 | 0.5478 | 0.7487 | 1.1575 | 1.2908 | 1.3088 | 1.4088 | 1.5028 | 1.5477 |
Yu County | 0.0785 | 0.0857 | 0.0945 | 0.1113 | 0.1382 | 0.1423 | 0.1562 | 0.1687 | 0.1740 |
Zhangbei County | 0.0487 | 0.0513 | 0.0570 | 0.1026 | 0.1541 | 0.1630 | 0.1732 | 0.1859 | 0.1921 |
Zhuolu County | 0.0833 | 0.1002 | 0.1150 | 0.1524 | 0.1727 | 0.1975 | 0.2095 | 0.2262 | 0.2345 |
Zhuozhou City | 0.6021 | 0.8054 | 0.9169 | 1.1499 | 1.3278 | 1.4362 | 1.5309 | 1.6434 | 1.7157 |
Zuihua City | 0.4142 | 0.4505 | 0.4654 | 0.5419 | 0.6215 | 0.6677 | 0.7254 | 0.8388 | 0.8685 |
County (City) | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
---|---|---|---|---|---|---|---|---|
Anguo City | 0.2062 | 0.2130 | 0.2228 | 0.2306 | 0.2378 | 0.2479 | 0.2430 | 0.2562 |
Anxin County | 0.1881 | 0.1942 | 0.1932 | 0.1962 | 0.2023 | 0.2047 | 0.1987 | 0.2196 |
Bazhou City | 0.2686 | 0.2824 | 0.2819 | 0.2861 | 0.3000 | 0.3104 | 0.3257 | 0.3731 |
Botou City | 0.2874 | 0.3084 | 0.2656 | 0.3009 | 0.2976 | 0.3020 | 0.2636 | 0.3021 |
Boye County | 0.1959 | 0.1795 | 0.1805 | 0.1768 | 0.1917 | 0.1966 | 0.2067 | 0.2120 |
Cang County | 0.2356 | 0.2361 | 0.2334 | 0.2525 | 0.2527 | 0.2621 | 0.2703 | 0.3090 |
Chengde County | 0.1876 | 0.2146 | 0.2125 | 0.2103 | 0.2085 | 0.2290 | 0.2314 | 0.2606 |
Chicheng County | 0.1421 | 0.1530 | 0.1570 | 0.1566 | 0.1437 | 0.1685 | 0.1630 | 0.1813 |
Chongli County | 0.1692 | 0.1640 | 0.1917 | 0.1906 | 0.2085 | 0.2165 | 0.1964 | 0.2139 |
Dachang County | 0.2586 | 0.2875 | 0.3285 | 0.3739 | 0.3778 | 0.3923 | 0.4197 | 0.4505 |
Dacheng County | 0.2117 | 0.2205 | 0.2180 | 0.2239 | 0.2271 | 0.2543 | 0.2558 | 0.2936 |
Dingxing County | 0.2104 | 0.2035 | 0.2115 | 0.2165 | 0.2260 | 0.2360 | 0.2323 | 0.2521 |
Dingzhou City | 0.2245 | 0.2623 | 0.2441 | 0.2901 | 0.2962 | 0.3255 | 0.3300 | 0.4618 |
Dongguang County | 0.2297 | 0.2329 | 0.2546 | 0.2433 | 0.2494 | 0.2704 | 0.2674 | 0.2681 |
Fengning County | 0.1628 | 0.1819 | 0.1942 | 0.2002 | 0.2013 | 0.2375 | 0.2397 | 0.2520 |
Fuping County | 0.1457 | 0.1473 | 0.1622 | 0.1644 | 0.1675 | 0.1821 | 0.1792 | 0.2139 |
Gaobeidian City | 0.2007 | 0.2139 | 0.2195 | 0.2163 | 0.2357 | 0.2529 | 0.2671 | 0.3087 |
Gaoyang County | 0.2057 | 0.2162 | 0.2286 | 0.2261 | 0.2336 | 0.2598 | 0.2692 | 0.2809 |
Gu’an County | 0.2074 | 0.2174 | 0.2474 | 0.2544 | 0.2720 | 0.2821 | 0.3271 | 0.3427 |
Guyuan County | 0.1717 | 0.1730 | 0.1782 | 0.1723 | 0.1759 | 0.1986 | 0.2021 | 0.1763 |
Haixing County | 0.1567 | 0.1693 | 0.1792 | 0.1921 | 0.1984 | 0.2208 | 0.2256 | 0.2084 |
Hejian City | 0.2159 | 0.2235 | 0.2328 | 0.2192 | 0.2428 | 0.2303 | 0.2334 | 0.2971 |
Huai’an County | 0.2279 | 0.1854 | 0.1828 | 0.1979 | 0.1762 | 0.1910 | 0.2126 | 0.2065 |
Huailai County | 0.1941 | 0.2175 | 0.2486 | 0.2296 | 0.2368 | 0.2462 | 0.2603 | 0.2804 |
Huanghua City | 0.2951 | 0.3060 | 0.3039 | 0.3015 | 0.3036 | 0.3151 | 0.3092 | 0.3400 |
Kangbao County | 0.1399 | 0.1590 | 0.1890 | 0.1703 | 0.1812 | 0.2017 | 0.1908 | 0.1952 |
Kuancheng County | 0.2317 | 0.2599 | 0.2594 | 0.2526 | 0.2624 | 0.2813 | 0.2603 | 0.2577 |
Laishui County | 0.1676 | 0.1741 | 0.1804 | 0.1945 | 0.1917 | 0.1967 | 0.2010 | 0.2240 |
Laiyuan County | 0.1748 | 0.1950 | 0.1911 | 0.1949 | 0.1997 | 0.2042 | 0.2159 | 0.2209 |
Laoting County | 0.2549 | 0.2717 | 0.2658 | 0.2875 | 0.3003 | 0.3146 | 0.3542 | 0.3826 |
Li County | 0.1878 | 0.1700 | 0.2024 | 0.2085 | 0.2158 | 0.2371 | 0.2355 | 0.2577 |
Longhua County | 0.1885 | 0.1879 | 0.2099 | 0.2016 | 0.2068 | 0.2247 | 0.2300 | 0.2498 |
Luannan County | 0.2657 | 0.2957 | 0.3021 | 0.2935 | 0.2875 | 0.2894 | 0.3120 | 0.3516 |
Luanping County | 0.1882 | 0.2055 | 0.2184 | 0.2126 | 0.2232 | 0.2285 | 0.2675 | 0.2599 |
Luan County | 0.2841 | 0.2924 | 0.3158 | 0.3125 | 0.3073 | 0.3210 | 0.3273 | 0.3799 |
Mancheng County | 0.2062 | 0.2156 | 0.2239 | 0.2240 | 0.2313 | 0.2439 | 0.2503 | 0.2731 |
Mengcun County | 0.1960 | 0.1999 | 0.2277 | 0.2215 | 0.2062 | 0.2069 | 0.2079 | 0.1873 |
Nanpi County | 0.2137 | 0.2233 | 0.2228 | 0.2261 | 0.2307 | 0.2476 | 0.2512 | 0.2418 |
Pingquan County | 0.1947 | 0.2094 | 0.2107 | 0.2097 | 0.2174 | 0.2420 | 0.2312 | 0.2515 |
Qian’an City | 0.3801 | 0.3947 | 0.3886 | 0.2510 | 0.3871 | 0.4008 | 0.4235 | 0.4900 |
Qianxi County | 0.2937 | 0.2917 | 0.2977 | 0.2904 | 0.3065 | 0.3083 | 0.3251 | 0.3547 |
Qing County | 0.2188 | 0.2258 | 0.2332 | 0.2343 | 0.2495 | 0.2483 | 0.2538 | 0.2754 |
Qingyuan County | 0.1919 | 0.1976 | 0.2039 | 0.2281 | 0.2397 | 0.2540 | 0.2562 | 0.3012 |
Quyang County | 0.1765 | 0.1772 | 0.1953 | 0.1999 | 0.2144 | 0.2332 | 0.2472 | 0.2899 |
Renqiu City | 0.2885 | 0.2918 | 0.3151 | 0.3304 | 0.3293 | 0.3382 | 0.3619 | 0.4033 |
Rongcheng County | 0.2018 | 0.2126 | 0.2167 | 0.2241 | 0.2182 | 0.2286 | 0.2305 | 0.2163 |
Sanhe City | 0.3512 | 0.3722 | 0.3776 | 0.3903 | 0.3901 | 0.3775 | 0.3884 | 0.4476 |
Shangyi County | 0.1326 | 0.1570 | 0.1660 | 0.1664 | 0.1326 | 0.1821 | 0.1618 | 0.1533 |
Shunping County | 0.1757 | 0.1793 | 0.2016 | 0.2064 | 0.2076 | 0.2229 | 0.2284 | 0.2394 |
Suning County | 0.2302 | 0.2384 | 0.2553 | 0.2531 | 0.2521 | 0.2613 | 0.2747 | 0.2785 |
Tang County | 0.1748 | 0.1754 | 0.1798 | 0.1908 | 0.2039 | 0.2164 | 0.2238 | 0.2849 |
Wangdu County | 0.2208 | 0.2403 | 0.2361 | 0.2324 | 0.2437 | 0.2510 | 0.2527 | 0.2336 |
Wanquan County | 0.1835 | 0.2098 | 0.2104 | 0.2134 | 0.2111 | 0.2155 | 0.2254 | 0.2250 |
Weichang County | 0.1610 | 0.1747 | 0.1766 | 0.1784 | 0.1918 | 0.2277 | 0.2383 | 0.2677 |
Wen’an County | 0.2325 | 0.2362 | 0.2569 | 0.2535 | 0.2510 | 0.2832 | 0.2907 | 0.3010 |
Wuqiao County | 0.2419 | 0.2505 | 0.2527 | 0.2437 | 0.2499 | 0.2595 | 0.2655 | 0.2229 |
Xianghe County | 0.2765 | 0.3026 | 0.3068 | 0.3382 | 0.3436 | 0.3477 | 0.3579 | 0.4043 |
Xian County | 0.2096 | 0.2136 | 0.2226 | 0.2225 | 0.2352 | 0.2377 | 0.2467 | 0.2819 |
Xinglong County | 0.1766 | 0.1894 | 0.1929 | 0.1932 | 0.1995 | 0.2078 | 0.2321 | 0.2441 |
Xiong County | 0.1987 | 0.1989 | 0.2241 | 0.2285 | 0.2251 | 0.2204 | 0.2056 | 0.2337 |
Xushui County | 0.2271 | 0.2369 | 0.2442 | 0.2454 | 0.2586 | 0.3060 | 0.2977 | 0.2878 |
Yangyuan County | 0.1431 | 0.1527 | 0.1664 | 0.1628 | 0.1726 | 0.1882 | 0.1868 | 0.2045 |
Yanshan County | 0.1942 | 0.2024 | 0.2066 | 0.2170 | 0.2012 | 0.2088 | 0.2028 | 0.2391 |
Yi County | 0.1772 | 0.1844 | 0.1932 | 0.2021 | 0.1999 | 0.2286 | 0.2312 | 0.2711 |
Yongqing County | 0.1925 | 0.2053 | 0.2069 | 0.2209 | 0.2296 | 0.2553 | 0.2611 | 0.2894 |
Yutian County | 0.2804 | 0.2889 | 0.2854 | 0.2707 | 0.2784 | 0.2836 | 0.2974 | 0.3392 |
Yu County | 0.1428 | 0.1567 | 0.1573 | 0.1556 | 0.1569 | 0.1781 | 0.1832 | 0.2043 |
Zhangbei County | 0.1760 | 0.1791 | 0.1863 | 0.1979 | 0.1846 | 0.2027 | 0.2098 | 0.2453 |
Zhuolu County | 0.1749 | 0.1874 | 0.1993 | 0.2028 | 0.1790 | 0.1959 | 0.1853 | 0.2144 |
Zhuozhou City | 0.2459 | 0.2544 | 0.2627 | 0.2826 | 0.2951 | 0.3111 | 0.3174 | 0.3469 |
Zuihua City | 0.2632 | 0.2748 | 0.2755 | 0.2687 | 0.2771 | 0.2766 | 0.2951 | 0.3803 |
County | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
---|---|---|---|---|---|---|---|---|
Anguo City | - | mild | mild | - | - | - | - | - |
Anxin County | mild | mild | mild | - | - | - | mild | - |
Boye County | mild | mild | mild | mild | mild | mild | mild | moderate |
Chicheng County | moderate | moderate | moderate | moderate | moderate | moderate | moderate | mild |
Chongli County | moderate | moderate | moderate | moderate | moderate | moderate | moderate | moderate |
Dachang County | mild | mild | mild | mild | mild | mild | mild | moderate |
Dongguang County | - | - | - | mild | - | - | - | - |
Fengning County | mild | mild | mild | mild | mild | - | - | - |
Fuping County | mild | mild | mild | mild | mild | mild | moderate | mild |
Guyuan County | moderate | moderate | moderate | mild | moderate | mild | mild | moderate |
Gu’an County | mild | mild | - | mild | - | mild | - | - |
Haixing County | mild | mild | mild | mild | - | - | mild | moderate |
Huai’an County | mild | mild | mild | mild | mild | mild | mild | moderate |
Huailai County | - | mild | - | - | - | - | - | - |
Kangbao County | moderate | moderate | mild | mild | mild | mild | mild | moderate |
Kuancheng County | - | - | mild | - | - | mild | mild | mild |
Laishui County | moderate | moderate | mild | mild | mild | mild | mild | moderate |
Laiyuan County | mild | moderate | mild | - | - | mild | mild | mild |
Li County | mild | mild | - | - | - | - | - | - |
Luanping County | mild | mild | - | - | - | mild | - | - |
Mengcun County | mild | mild | mild | mild | mild | mild | mild | moderate |
Rongcheng County | - | - | - | mild | - | mild | mild | |
Shangyi County | moderate | moderate | moderate | moderate | severe | moderate | severe | severe |
Suning County | - | - | - | - | - | mild | - | - |
Wanquan County | mild | mild | mild | - | mild | - | - | mild |
Wangdu County | mild | mild | mild | mild | - | - | mild | moderate |
Weichang County | mild | mild | mild | mild | - | - | - | - |
Xinglong County | mild | mild | mild | mild | mild | mild | - | - |
Yu County | mild | mild | mild | mild | mild | mild | mild | mild |
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Dimension | Indicators | Indicators Explanation |
---|---|---|
Economy | Per capita GDP | County GDP/county population |
Per capita fiscal revenue | General public budget revenue/county population | |
The employment rate | Employees/county population | |
Living quality | Per capita output of grain | Total grain output/county population |
Tap water benefit village rate | Villages benefiting from tap water/number of villages | |
Mobile Phone subscriber rate | Mobile phone subscribers/county population | |
Density of road network | Road mileage/county area | |
Education | Teaching faculty | Total number of teachers/students |
Education level | Total number of students/county population | |
Health care | Number of beds in health facilities | Number of medical beds/10,000 citizens |
Proportion of medical technicians | Medical staff/county population | |
Social security | Basic medical insurance participation rate | Number of basic medical insurance participants/county population |
Basic endowment insurance participation rate | Number of basic endowment insurance participants/county population |
Scaling | Definition |
---|---|
0.5 | equally important |
0.6 | slightly important |
0.7 | obviously important |
0.8 | much more important |
0.9 | extremely important |
The Index Name | AHP | EWM | Average Weight |
---|---|---|---|
Per capital GDP | 0.0533 | 0.1476 | 0.1425 |
Per capital fiscal revenue | 0.0613 | 0.0038 | 0.0337 |
Employment rate | 0.0454 | 0.2275 | 0.2417 |
Per capita output of grain | 0.0394 | 0.0681 | 0.0616 |
Tap water benefit village rate | 0.0461 | 0.0171 | 0.0371 |
Mobile Phone subscriber rate | 0.0615 | 0.0609 | 0.0384 |
Density of road network | 0.0580 | 0.0846 | 0.0822 |
Teaching faculty | 0.1050 | 0.0478 | 0.0590 |
Education Level | 0.0700 | 0.0299 | 0.0262 |
Number of beds in health facilities | 0.1183 | 0.0979 | 0.1043 |
Proportion of medical technicians | 0.0968 | 0.0871 | 0.1007 |
Basic medical insurance participation rate | 0.1103 | 0.0543 | 0.0294 |
Basic endowment insurance participation rate | 0.1348 | 0.0734 | 0.0431 |
Year | Economy | Living Quality | Education | Health Care | Social Security |
---|---|---|---|---|---|
2012 | 0.0330 | 0.0400 | 0.0261 | 0.0092 | 0.0141 |
2013 | 0.0346 | 0.0422 | 0.0262 | 0.0104 | 0.0171 |
2014 | 0.0348 | 0.0440 | 0.0267 | 0.0110 | 0.0170 |
2015 | 0.0354 | 0.0419 | 0.0273 | 0.0125 | 0.0167 |
2016 | 0.0363 | 0.0425 | 0.0261 | 0.0141 | 0.0162 |
2017 | 0.0358 | 0.0443 | 0.0270 | 0.0160 | 0.0186 |
2018 | 0.0369 | 0.0463 | 0.0289 | 0.0171 | 0.0176 |
2019 | 0.0382 | 0.0454 | 0.0298 | 0.0186 | 0.0260 |
Year | Number of Mild Relative Poverty Counties | Number of Moderate Relative Poverty Counties | Number of Severe Relative Poverty Counties | Number of Relative Poverty Counties | Relative Poverty Incidence |
---|---|---|---|---|---|
2012 | 21 | 7 | 0 | 28 | 39.44% |
2013 | 21 | 8 | 0 | 29 | 40.85% |
2014 | 20 | 5 | 0 | 25 | 35.21% |
2015 | 19 | 4 | 0 | 23 | 32.39% |
2016 | 13 | 4 | 1 | 18 | 25.35% |
2017 | 19 | 4 | 0 | 23 | 32.39% |
2018 | 14 | 5 | 1 | 20 | 28.17% |
2019 | 9 | 11 | 1 | 21 | 29.58% |
Model | R2 | p-Value |
---|---|---|
Individual fixed effect | 0.4309 | 0.0000 |
Time fixed effect | 0.6198 | 0.0000 |
Individual–Time fixed effect | 0.6578 | 0.0000 |
Year | Number of Counties | Precision Percentage (%) | ||||
---|---|---|---|---|---|---|
RE < 25% | RE (25~50%) | RE > 50% | High | Middle | Low | |
2012 | 56 | 13 | 2 | 0.79 | 0.18 | 0.03 |
2013 | 53 | 17 | 1 | 0.75 | 0.24 | 0.01 |
2014 | 55 | 16 | 0 | 0.77 | 0.23 | 0.00 |
2015 | 54 | 14 | 3 | 0.76 | 0.20 | 0.04 |
2016 | 51 | 17 | 3 | 0.72 | 0.23 | 0.04 |
2017 | 51 | 19 | 1 | 0.72 | 0.27 | 0.01 |
2018 | 52 | 16 | 3 | 0.73 | 0.23 | 0.04 |
2019 | 42 | 23 | 6 | 0.59 | 0.32 | 0.09 |
County | ARE | County | ARE | County | ARE | County | ARE |
---|---|---|---|---|---|---|---|
Anguo City | 0.0624 | Gu’an County | 0.0724 | Mengcun County | 0.1508 | Wen’an County | 0.2235 |
Anxin County | 0.1373 | Guyuan County | 0.3340 | Nanpi County | 0.0144 | Wuqiao County | 0.2976 |
Bazhou County | 0.1352 | Haixing County | 0.0949 | Pingquan County | 0.0392 | Xian County | 0.0955 |
Botou City | 0.1201 | Hejian City | 0.1052 | Qian’an City | 0.2563 | Xianghe County | 0.4091 |
Boye County | 0.1356 | Huai’an County | 0.1839 | Qianxi County | 0.3015 | Xinglong County | 0.2512 |
Cang County | 0.1838 | Huailai County | 0.1538 | Qing County | 0.0920 | Xiong County | 0.1869 |
Chengde County | 0.0821 | Huanghua City | 0.3133 | Qingyuan County | 0.0672 | Xushui County | 0.2413 |
Chicheng County | 0.2637 | Kangbao County | 0.5223 | Quyang County | 0.0983 | Yanshan County | 0.0581 |
Chongli County | 0.0769 | Kuancheng County | 0.2553 | Renqiu City | 0.1354 | Yangyuan County | 0.1701 |
Dachang County | 0.4424 | Laishui County | 0.1188 | Rongcheng County | 0.0841 | Yi County | 0.1314 |
Dacheng County | 0.6339 | Laiyuan County | 0.0512 | Sanhe City | 0.0875 | Yongqing County | 0.0820 |
Dingxing County | 0.0303 | Laoting County | 0.3275 | Shangyi County | 0.2486 | Yutian County | 0.3190 |
Dingzhou City | 0.2680 | Li County | 0.2552 | Shunping County | 0.0624 | Yu County | 0.4749 |
Dongguang County | 0.0545 | Longhua County | 0.0732 | Suning County | 0.0364 | Zhangbei County | 0.0689 |
Fengning County | 0.1055 | Luannan County | 0.3483 | Tang County | 0.1001 | Zhuolu County | 0.0600 |
Fuping County | 0.1684 | Luanping County | 0.0957 | Wanquan County | 0.0277 | Zhuozhou City | 0.1248 |
Gaobeidian City | 0.1663 | Luan County | 0.3651 | Wangdu County | 0.1253 | Zuihua City | 0.2256 |
Gaoyang County | 0.0327 | Mancheng County | 0.1424 | Weichang County | 0.1338 |
2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | |
---|---|---|---|---|---|---|---|---|
Saiwanzi Street | 0.28112 | 0.28112 | 0.28298 | 0.40960 | 0.43146 | 0.43097 | 0.45675 | 0.47235 |
Sitaizui Township | 0.19407 | 0.19430 | 0.19498 | 0.19597 | 0.19724 | 0.19895 | 0.20267 | 0.20908 |
Saiwanzi Township | 0.19398 | 0.19417 | 0.19460 | 0.19678 | 0.20150 | 0.20759 | 0.20970 | 0.21083 |
Gaojiaying Town | 0.19382 | 0.19443 | 0.19485 | 0.19782 | 0.19888 | 0.19912 | 0.20011 | 0.20049 |
Baiqi Township | 0.19116 | 0.19150 | 0.19153 | 0.19156 | 0.20018 | 0.20119 | 0.20127 | 0.20132 |
Shizuizi Township | 0.19090 | 0.19096 | 0.19099 | 0.19130 | 0.19148 | 0.19161 | 0.19168 | 0.19175 |
Qingsanying Township | 0.19089 | 0.19120 | 0.19152 | 0.19182 | 0.19264 | 0.19295 | 0.19316 | 0.19316 |
Shizigou Township | 0.19078 | 0.19088 | 0.19088 | 0.19094 | 0.19113 | 0.19135 | 0.19135 | 0.19136 |
Hongqiying Township | 0.19074 | 0.19075 | 0.19086 | 0.19094 | 0.19103 | 0.19105 | 0.19109 | 0.19111 |
Yimatu Township | 0.19071 | 0.19072 | 0.19072 | 0.19073 | 0.19082 | 0.19090 | 0.19090 | 0.19090 |
Shiyaozi Township | 0.19070 | 0.19070 | 0.19070 | 0.19070 | 0.19070 | 0.19070 | 0.19070 | 0.19070 |
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Liu, H.; Wang, J.; Liu, H.; Chen, Y.; Liu, X.; Guo, Y.; Huang, H. Identification of Relative Poverty Based on 2012–2020 NPP/VIIRS Night Light Data: In the Area Surrounding Beijing and Tianjin in China. Sustainability 2022, 14, 5559. https://doi.org/10.3390/su14095559
Liu H, Wang J, Liu H, Chen Y, Liu X, Guo Y, Huang H. Identification of Relative Poverty Based on 2012–2020 NPP/VIIRS Night Light Data: In the Area Surrounding Beijing and Tianjin in China. Sustainability. 2022; 14(9):5559. https://doi.org/10.3390/su14095559
Chicago/Turabian StyleLiu, Hao, Jingtao Wang, Haibin Liu, Yuzhuo Chen, Xinghan Liu, Yanlei Guo, and Hui Huang. 2022. "Identification of Relative Poverty Based on 2012–2020 NPP/VIIRS Night Light Data: In the Area Surrounding Beijing and Tianjin in China" Sustainability 14, no. 9: 5559. https://doi.org/10.3390/su14095559