Poverty-Returning Risk Monitoring and Analysis of the Registered Poor Households Based on BP Neural Network and Natural Breaks: A Case Study of Yunyang District, Hubei Province
Abstract
:1. Introduction
2. Research Review
3. Materials and Methods
3.1. Technique Route and Research Analysis
3.1.1. Technique Route
3.1.2. Research Analysis
3.2. Overview of the Study Area
3.3. Data Source and Pre-Processing
- 1.
- The household labor force mean score: the ratio of the sum of labor force scores in households to the number of people in households
- 2.
- The number of people in the household
- 3.
- The mean age in household: the ratio of the total age of each household member to the total number of household members
- 4.
- The household education status mean score: the ratio of the sum of education status scores in households to the number of people in households
- 5.
- The past poverty determination mean score of the registered poor households
3.4. Indicators Selection
3.5. Methods
3.5.1. Constructing the Indicator System of the Poverty-Returning Risk Index
3.5.2. Divide the Level to Obtain Training Samples by Natural Breaks
3.5.3. BP Neural Network Construction
3.5.4. Mean Impact Value
3.5.5. Spatial Autocorrelation Model
4. Results
4.1. Overall Analysis of the Yunyang District
4.2. Analysis by Township
4.3. Analysis by Indicators
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Household Labor Force Situation | Education | The Past Poverty Determination of the Registered Poor Households | Scoring of Indicators |
---|---|---|---|
No labor force | Illiterate or semi-literate | Special hardship support for poor households | 1 |
Loss of labor | Primary School | Special hardship support households | 2 |
Weak labor or semi-labor | Junior High School | Low-income poor households | 3 |
General Labor | High School | Low-income households | 4 |
Skilled Workforce | College and above | General poor households | 5 |
- | - | General Farmers | 6 |
Labor Force Situation | Number of People in theHouseholds | Average Age in Households | Education Status | Properties of Poor Households | Per Capita Income | |
---|---|---|---|---|---|---|
Number of cases | 48,864 | 48,864 | 48,864 | 48,864 | 48,864 | 48,864 |
Skewness | −0.124 | 0.360 | 0.514 | 0.263 | −1.051 | 2.408 |
Standard error of skewness | 0.011 | 0.011 | 0.011 | 0.011 | 0.011 | 0.011 |
Kurtosis | −0.864 | −0.342 | −0.442 | 0.296 | 0.052 | 22.984 |
Kurtosis standard error | 0.022 | 0.022 | 0.022 | 0.022 | 0.022 | 0.022 |
Z-score skewness | −11.166 | 32.449 | 46.418 | 23.698 | −95.545 | 217.320 |
Z-score kurtosis | −38.983 | −15.430 | −19.965 | 13.369 | 2.363 | 1037.148 |
Influencing Factors | Correlation Coefficient | Significance Level |
---|---|---|
Mean value of labor force score within the households | −0.039 ** | <0.001 |
Number of people in the households | −0.181 ** | <0.001 |
Mean age in households | 0.163 ** | <0.001 |
Household education status mean score | 0.020 ** | <0.001 |
Past poverty determination of the registered poor households | 0.119 ** | <0.001 |
Mean Value of Labor Force Score within the Households | Number of People in the Households | Mean Age in Households | Household Education Status Mean Score | Past Poverty Determination of the Registered Poor Households | PRRI |
---|---|---|---|---|---|
0 | 0 | 1 | 1 | 1 | 1 |
0.1562 | 0.0909 | 0.5971 | 0.4444 | 0.8 | 2 |
0.4091 | 0.1818 | 0.4349 | 0.2917 | 0.6 | 3 |
0.6250 | 0.3636 | 0.2998 | 0.1389 | 0.2 | 4 |
1 | 1 | 0 | 0 | 0 | 5 |
PRRI | Meaning | Risk Levels |
---|---|---|
<2 | Extremely hard to return to poverty | low risk |
2–3 | Hard to return to poverty | |
3–4 | Moderate to return to poverty | medium risk |
4–5 | Easy to return to poverty | high |
>5 | Extremely easy to return to poverty | very high |
Township Name | Average Value | <2 | [2,3) | [3,4) | [4,5) | >=5 |
Anyang | 3.9804 | 16.638% | 17.602% | 15.381% | 17.267% | 33.067% |
Qingshan | 3.6202 | 22.199% | 20.040% | 15.218% | 16.524% | 26.017% |
Daliu | 3.6039 | 22.718% | 20.485% | 15.507% | 15.507% | 25.781% |
Baoxia | 3.5281 | 24.811% | 19.993% | 16.802% | 13.520% | 24.872% |
… | ||||||
Meipu | 2.9781 | 34.192% | 20.746% | 14.436% | 13.544% | 17.079% |
Liubei | 3.2028 | 22.579% | 31.407% | 15.866% | 13.344% | 16.802% |
Bailang | 3.1463 | 25.694% | 28.670% | 15.575% | 14.484% | 15.575% |
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Zhang, R.; He, Y.; Cui, W.; Yang, Z.; Ma, J.; Xu, H.; Feng, D. Poverty-Returning Risk Monitoring and Analysis of the Registered Poor Households Based on BP Neural Network and Natural Breaks: A Case Study of Yunyang District, Hubei Province. Sustainability 2022, 14, 5228. https://doi.org/10.3390/su14095228
Zhang R, He Y, Cui W, Yang Z, Ma J, Xu H, Feng D. Poverty-Returning Risk Monitoring and Analysis of the Registered Poor Households Based on BP Neural Network and Natural Breaks: A Case Study of Yunyang District, Hubei Province. Sustainability. 2022; 14(9):5228. https://doi.org/10.3390/su14095228
Chicago/Turabian StyleZhang, Runqiao, Yawen He, Wenkai Cui, Ziwen Yang, Jingyu Ma, Haonan Xu, and Duxian Feng. 2022. "Poverty-Returning Risk Monitoring and Analysis of the Registered Poor Households Based on BP Neural Network and Natural Breaks: A Case Study of Yunyang District, Hubei Province" Sustainability 14, no. 9: 5228. https://doi.org/10.3390/su14095228