







































 Humanities and Social Science Research; Vol. 8, No. 1; 2025 

ISSN 2576-3024   E-ISSN 2576-3032 

https://doi.org/10.30560/hssr.v8n1p53 

 53 Published by IDEAS SPREAD 

 

Measurement of County Relative Poverty, Spatial and Temporal 

Evolution and Risk Evaluation of Return to Poverty in Shaanxi 

Province Under the Perspective of Rural Revitalization 

Ruixi Wu1 

1 Xi'an International Studies University business school, China 

Correspondence: Ruixi Wu, Xi'an International Studies University business school, China. 

 

Received: January 15, 2025; Accepted: February 10, 2025; Published: February 12, 2025 

 

The project "Measurement of Relative Poverty, Spatio-temporal Evolution, and Risk Assessment of Returning to 

Poverty in Counties of Shaanxi Province from the Perspective of Rural Revitalization" (Project No.: 

S201410724089) was supported by the Innovation and Entrepreneurship Training Program for College Students 

of Xi'an International Studies University. 

 

Abstract 

Against the background of rural revitalization, this study makes an in-depth analysis of the measure, evolution and 

the risk of returning to poverty in the counties of counties in Shaanxi Province. By combining quantitative and 

qualitative methods, a multi-dimensional evaluation index system of relative poverty was constructed, and spatial 

autocorrelation model and geographical weighted regression model were used to analyze the measurement and 

spatiotemporal evolution of relative poverty in 78 counties (cities) in Shaanxi Province between 2014 and 2023. 

Furthermore, this study identified key influencing factors for the risk of returning to poverty and proposed targeted 

anti-poverty strategies. The results of this study show that the county relative poverty in Shaanxi province showed 

significant spatial agglomeration characteristics and obvious regional differences. Meanwhile, this study also 

found that factors such as education level, health conditions and industrial development had an important influence 

on the risk of returning to poverty. Based on this, this research puts forward policy suggestions such as 

strengthening education and medical investment and promoting industrial diversification development, in order to 

provide scientific basis and decision support for realizing the strategic goal of rural revitalization in Shaanxi 

Province. 

Keywords: rural revitalization, relative poverty, time and space evolution, risk of returning to poverty, Shaanxi 

Province 

1. Introduction 

With the acceleration of globalization and the rapid development of society and economy, poverty is still an 

important factor restricting the development of many countries and regions. Especially in China, despite the battle 

against poverty in recent years, relative poverty still exists, and its complexity and diversity pose new challenges 

to the implementation of the rural revitalization strategy. As an important province in western China, its county 

economic development is unbalanced, and the poverty problem is particularly prominent. Therefore, it is of great 

significance to deeply study the measure of county relative poverty, time and space evolution and the risk of 

returning to poverty in Shaanxi Province to formulate scientific and reasonable anti-poverty strategies and promote 

rural revitalization. 

This study aims to quantitatively analyze the relative poverty of counties in Shaanxi Province by using the spatial 

autocorrelation model and geographical weighted regression model, and reveal the characteristics of spatial and 

temporal evolution and the key influencing factors of the risk of returning to poverty. At the same time, according 

to the actual situation of Shaanxi Province, targeted anti-poverty strategies and policy suggestions are put forward 

to provide scientific basis for the government decision-making. 

Relative poverty, as a concept relative to the social average, emphasizes the relative backwardness of individuals 

or families in their social and economic status. Scholars at home and abroad have studied this extensively and 

developed various measurement methods, such as income ratio method, Martin method and Sen index, etc. These 

methods have their own advantages and disadvantages, but the common goal is to more accurately reflect the 



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 54 Published by IDEAS SPREAD 

 

actual economic situation and social welfare level of individuals or families. As a major decision and deployment 

for China to solve the problems of "agriculture, rural areas and farmers" and realize agriculture and rural 

modernization, the rural revitalization strategy has become the focus of scholars in recent years. They believe that 

rural revitalization will not only help solve the current poverty problem, but also fundamentally prevent the 

occurrence of returning to poverty by promoting the development of thriving industries, ecological livability, local 

customs and civilization, effective governance and a rich life. However, different regions face different challenges 

and opportunities when implementing the rural revitalization strategy, and they need to formulate anti-poverty 

strategies according to local conditions. Spatial analysis techniques have important applications in poverty research. 

By using GIS (geographic information system) and ESDA (spatial statistics), scholars can reveal the spatial 

distribution characteristics, agglomeration effects and causes of poverty phenomenon, identify high-risk areas of 

poverty, and provide scientific basis for the government to formulate precise anti-poverty policies. In recent years, 

more and more studies have begun to focus on the spatial heterogeneity and dynamic changes of relative poverty, 

providing new perspectives for the deep understanding of poverty problems. The return to poverty refers to the 

situation that the poverty-stricken households fall into poverty again due to various reasons, and its risks seriously 

affect the consolidation of the achievements of poverty alleviation and the implementation of the rural 

revitalization strategy.  

2. Theoretical Analysis and Research Hypothesis 

2.1 The Concept of Relative Poverty 

Relative poverty emphasizes the relative backwardness of individuals or families in their social and economic 

status, rather than just an income level below a certain absolute standard. The key to this concept is its relativity, 

in terms that the poverty state is relative to the social average. Therefore, accurately measuring relative poverty 

requires a comprehensive consideration of multiple dimensions of economic and social indicators, such as income, 

education, health, quality of life, etc. Commonly used measurement methods include income ratio method, Martin 

method and Sen index, which have advantages and disadvantages, but the common goal is to more accurately 

reflect the actual economic status and level of social well-being of individuals or families. 

2.2 Theory of Spatial Analysis in Poverty Research 

Spatial analysis techniques have important applications in poverty research. GIS (GIS) and ESDA (spatial statistics) 

can reveal the spatial distribution characteristics, agglomeration effect and causes of poverty. These technologies 

can help identify high-risk areas of poverty and provide a scientific basis for the government to formulate precise 

anti-poverty policies. Spatial analysis can also help to understand the heterogeneity and dynamic change of poverty 

problems, that is, poverty conditions may vary significantly in different regions and at different times, and these 

differences may be affected by multiple factors, such as geographical location, resource endowment, policy 

environment, etc. 

2.3 Theory of the Risk of Return to Poverty and its Influencing Factors 

The return to poverty refers to the poverty-stricken households falling into poverty again due to various reasons. 

The existence of the risk of returning to poverty has seriously affected the consolidation of the achievements of 

poverty alleviation and the implementation of the rural revitalization strategy. Scholars have analyzed the 

influencing factors of the risk of returning to poverty from many perspectives, including natural disasters, market 

fluctuations, family changes, health problems and so on. These factors interweave and work together to constitute 

a complex risk network of returning to poverty. Therefore, it is particularly important to develop a comprehensive 

and systematic anti-poverty strategy, and it is necessary to comprehensively consider various potential risks, and 

take targeted measures to prevent them.Based on the above theoretical analysis, the study makes the following 

assumptions: 

Hypothesis 1: Relative poverty has significant spatial heterogeneity 

Due to the differences in economic development level, resource endowment and policy environment, it is 

reasonable to speculate that the performance of relative poverty will be different in different regions. That is, 

relative poverty has significant spatial heterogeneity, and this heterogeneity may be affected by many factors, such 

as geographical location, industrial structure, educational resources, etc. 

Hypothesis 2: The risk of returning to poverty is influenced by multiple factors and is complex 

Returning to poverty is a complex phenomenon, whose risk may be affected by a variety of factors, such as natural 

disasters, market fluctuations, family changes, health problems, etc. These factors interweave and work together 

to constitute a complex risk network of returning to poverty. Therefore, it can be assumed that the risk of returning 



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to poverty is affected by many factors and has complexity, that is, the contribution of different factors to the risk 

of return to poverty may differ, and there may be an interaction between them. 

Hypothesis 3: Spatial analysis techniques will help to identify areas at high risk of return to poverty 

Spatial analysis technology can reveal the spatial distribution characteristics, agglomeration effect and causes of 

poverty, and help to identify high-risk areas of poverty. Therefore, it can be assumed that spatial analysis 

technology is also helpful to identify high-risk areas of returning to poverty and provide a scientific basis for the 

government to formulate precise anti-poverty policies。 

3. Model Specification. Variable Declaration 

3.1 Space Autocorrelation Model 

In this study used Moran Index (Moran's I) to measure the spatial correlation of relative poverty in counties in 

Shaanxi Province. The Moran index is a commonly used spatial statistic to assess the concentration of a property 

in space. By calculating the Moran index in different years, we can reveal the distribution characteristics of relative 

poverty in space and its changing trend. 

3.2 Geographical-Weighted Regression Model 

To further analyze the factors affecting relative poverty and their spatial heterogeneity, a geographic-weighted 

regression (GWR) model was introduced in this study. The GWR model allows regression coefficients to vary 

with geographic location, thus to capture spatial differences in factors affecting relative poverty. Through the GWR 

model, variables with significant effects on relative poverty and spatial heterogeneity can be identified and used 

for the development of targeted anti-poverty strategies. 

3.3 Description of the Variables 

explained variable: 

Relative poverty index: as an explained variable, used to measure the relative poverty level of various counties in 

Shaanxi Province. The index considers multiple dimensions of economic and social indicators, such as income, 

education, health, etc. 

explanatory variable: 

Economic development level: including per capita GDP, industrial structure and other factors, used to reflect the 

economic development of the county. 

Educational resources: such as the number of schools, teacher-student ratio, etc., used to measure the level of 

educational development in the county. 

Health status: such as the number of medical facilities, the health level of residents, are used to reflect the medical 

and health conditions of the county. 

Infrastructure: The degree of improvement of infrastructure such as transportation and communication is used to 

assess the convenience of life in the county. 

Policy environment: such as the implementation of poverty alleviation policies and rural revitalization strategies, 

which are used to examine the impact of policy factors on relative poverty. 

controlled variable: 

In addition to the above explanatory variables, some control variables such as geographical location, topography 

may be introduced to reduce the bias of the model estimation. 

controlled variable: 

In addition to the above explanatory variables, some control variables, such as geographical location, topography, 

may be introduced to reduce the bias of the model estimation。 

4. Empirical Analysis 

4.1 The Index System of County Relative Poverty Evaluation in Shaanxi Province 

Poverty can be caused by many factors, such as natural conditions, social and economic conditions. Building a 

comprehensive indicator describing the regional poverty situation is a comprehensive measure of poverty in a 

region. Based on the existing literature, the comprehensive identification of poverty in counties is mainly carried 

out from multiple dimensions such as social economy and natural nature. The natural condition is the background 

natural assets of the region, which is difficult in quantification, and the level of economic development can directly 

reflect the relative poverty of the region. Therefore, this project draws on the existing research results, combines 



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the regional characteristics of Shaanxi Province, and considers the economic and social dimensions to measure the 

relative poverty degree of the study area. 

The specific indicators are as follows Table 1: 

 

Table 1. Index system of county relative poverty measurement in Shaanxi Province 

Index Significance 

Per capita GDP Reflects the level of regional economic development 

Per capita fiscal revenue Reflects the income level of the residents 

Per capita disposable income Reflects the income level of the residents 

Total output value of industry above 

designated size 

Reflects the improvement of residents' material living 

conditions 

Total retail sales of social goods Reflects the improvement of residents' material living 

conditions 

Local telephone users Reflects the people's living standards 

Number of students per 10,000 people Reflects the regional education level 

Number of hospital beds per 10,000 people Reflects the regional medical level 

 

4.2 County Relative Poverty Measures in Shaanxi Province 

In county relative poverty measure, to eliminate the data dimension and size difference and the subjectivity of 

artificial index weight, the project adopts the entropy weight method, calculate the weight of the selected index, 

specific model is as follows: first, the index of linear transformation to eliminate the influence of different 

dimension on the results, after the standardized data value domain, convenient statistics and not loss data itself 

contains information. 

x =
x −min

max − min
 

Where ɑ represents the normalized value of each indicator, x represents the value of each index, min is the 

minimum value and max is the maximum value. Next, the percentage of the first evaluated object Pij under the j 

the index is calculated. 

Pij =
bij

∑ bijm
i=1

 

Then calculate the entropy value Ej of the j th index. 

Ej = −
(1 −Wj)

(n − ∑Wj)
 

Finally, the weight of the j th index is calculated. 

wj = (1 −Wj)/(n −∑Wj) 

Where w is the weight of each indicator, n is the total number of indicators, and i is the i th index of the indicators. 

The final county relative poverty index IDI was obtained by the calculation of each index and weight values. 

IDI =∑Wj × i

n

i=1

 

4.3 The Spatial and Temporal Evolution of Relative Poverty in Counties in Shaanxi Province 

First of all, in order to explain the spatial distribution characteristics between different elements, this project 

describes and reveals the standard deviation ellipse in spatial statistics by comparing the basic parameters of ellipse 

and comparing the basic parameters of ellipse in different years. The specific model is as follows: 

SDEx = √
∑ (Xi−x )2n
i=1

n
 



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SDEy = √
∑ (Xi−y)2n
i=1

n
 

In the sum, Xi and Yi are the coordinates of the elements (xi, yi), the average of xy represents the average center 

of the element, and n is equal to the total number of elements. Then, considering the strong spatial correlation 

between different counties, combined with the geographer to bull has proposed a mutual correlation between 

anything, with the Arcgis space auto correlation tools to analyze the spatial agglomeration characteristics of county 

relative poverty, and to moran index of different spatial correlation between county relative poverty effect, the 

model is as follows: 

Moran′sI =
n

So
×
∑ ∑ Wij(Xi − x)((Xi − x)n

j
n
i

∑ (Xi − x)2n
j

 

Where Xi represents the observed value of county i; Wij is the spatial weight matrix, spatial adjacent 1, non-

adjacent 0, and 𝑠 0 is the sum of all elements of the spatial weight matrix. Global space autocorrelation analysis 

can only reflect the county relative poverty spatial pattern of agglomeration situation, is likely to cover the local 

space autocorrelation, in order to analyze the county unit relative poverty agglomeration situation, the project first 

use local moran index to analyze the local space autocorrelation, the model is as follows 

Ii =
(Xi − x)2

S2
∑ Wij(xi − x)

n

i≠j,j=1

 

Formula (9), Ii> 0, H-H (high and high), L-L (low); Ii <0, H-L (high and low), L-H (low and high). 

In this equation, S represents the distance measure between the center of gravity of county relative poverty and the 

center of gravity of various indicators, the farther the distance, the lower the overlap, and otherwise, the higher the 

overlap. At the same time, from a dynamic perspective, the project is used to analyze the change consistency of 

each index and the change of the relative poverty degree of the county relative to the previous time node to reflect 

the Angle θ of displacement. Due to the limitation of the value range θ, the other string values are used as the 

change consistency index C. The specific model is as follows: 

C = COSθ = [(∆x1∆x2) + (∆y1∆y2)]/√(∆x12 + ∆y12)(∆x22 + ∆y22) 

5.Interpretation of Result 

(1) Construction and measurement of the county relative poverty evaluation index system 

By comprehensively using the index system method and improving the existing evaluation method, we have 

systematically constructed the evaluation index system of county relative poverty from the two dimensions of 

economy and society. This system not only covers all aspects of the relative poverty of the county, but also ensures 

the scientific and rationality of the weight of each index through the entropy right method. The application of the 

entropy weight method enables us to measure the relative poverty degree of counties more objectively, which 

provides a solid data basis for the subsequent analysis. 

(2) The spatial and temporal evolution characteristics of the relative poverty in counties 

1. Evolution characteristics of the spatial pattern 

Using standardized ellipses and ArcGIS spatial statistics, we explore in depth the evolution of relative poverty in 

counties. The results show that the spatial distribution of county relative poverty in Shaanxi province shows 

significant agglomeration characteristics, and the differences between different regions are obvious. With the 

passage of time, this spatial pattern has both some stability and some subtle changes. This provides an important 

basis for identifying the high-risk areas of relative poverty. 

2. Characteristics of the spatial distribution 

Further using spatial autocorrelation analysis methods, we reveal the spatial distribution characteristics of relative 

poverty in counties. The analysis results show that there is a significant positive correlation between county relative 

poverty in space, that is, counties with similar relative poverty levels tend to gather together in space. This finding 

provides new insight into our understanding of the spatial transfer mechanisms of relative poverty. 

6. Conclusions 

Based on the county social and economic statistics of Shaanxi Province from 2000 to 2019, the space-time 

evolution characteristics of county relative poverty in Shaanxi Province were deeply analyzed by using various 



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methods and models. By constructing the evaluation index system of county relative poverty, exploring the 

characteristics of spatial and temporal evolution, and quantitatively analyzing the problem of returning to poverty, 

we draw the following main conclusions: 

1. The scientificity and effectiveness of the evaluation index system of county relative poverty 

Through the comprehensive use of the index system method and the improvement of the existing evaluation 

methods, the evaluation index system of the county relative poverty constructed from the two dimensions of 

economy and society is scientific and effective. The application of entropy weight method ensures the rationality 

of the weight of each index, enabling us to measure the relative poverty degree of counties more objectively. 

2. The evolution characteristics of the spatial pattern of relative poverty in counties 

Using standardized ellipse and ArcGIS spatial statistical methods, we found that the spatial distribution of county 

relative poverty in Shaanxi province showed significant agglomeration characteristics, with obvious differences 

between different regions. With the passage of time, this spatial pattern has both some stability and some subtle 

changes. This provides an important basis for identifying the high-risk areas of relative poverty. 

3. Risk of return to poverty and its influencing factors 

Through the cluster analysis and the construction of the risk index system, we identified the counties with the risk 

of returning to poverty, and quantitatively analyzed the influencing factors of the problem of return to poverty. 

The results showed that education level, health conditions, industrial development and other factors had an 

important impact on the risk of returning to poverty. In particular, the improvement of education level and medical 

conditions are of great significance for reducing the risk of returning to poverty. 

Countermeasures and suggestions 

Based on the above research conclusions, in order to effectively curb the occurrence of people returning to poverty, 

consolidate the achievements of poverty alleviation, and provide strong support for the implementation of the rural 

revitalization strategy, we put forward the following countermeasures and suggestions: 

1. Strengthen investment in education and medical care 

The government should further increase the investment in education and medical care in the poor areas, and 

improve the cultural quality and health level of the local residents. Specific measures include expanding schools, 

improving the treatment of teachers, improving medical facilities, and training medical staff. This will help 

improve the self-development capacity of poor areas and fundamentally reduce the risk of returning to poverty. 

2. Promote diversified industrial development 

We will encourage and support the development of diversified industries in poor areas, especially those 

characteristic industries that can boost employment and increase farmers' incomes. The government can guide 

enterprises to invest and start business in poor areas by providing tax incentives, financial support and technical 

guidance to promote the prosperity and development of the local economy. 

3. China 

We will establish a sound social security system covering both urban and rural areas to provide basic living 

allowances for low-income groups. This includes improving the minimum living allowance system, establishing 

a medical assistance system, and promoting the rural old-age insurance system. By improving the social security 

system, the poverty problem caused by disease and unemployment can be effectively alleviated. 

4. Strengthen targeted poverty alleviation and dynamic monitoring 

For different types of poor families and groups of people, personalized assistance measures have been formulated 

to implement targeted poverty alleviation. At the same time, a sound poverty monitoring system will be established 

to timely detect and respond to the risk of return to poverty. By conducting regular poverty surveys and establishing 

an early warning mechanism for returning to poverty, poverty trends can be grasped in time and provide a basis 

for policy adjustment. 

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