Research on the Early Warning Indicator System for Public Safety Risks in County Areas of the Yangtze River Delta Region during Urbanization Process

doi:10.21203/rs.3.rs-3952971/v1

Download PDF

Article

Research on the Early Warning Indicator System for Public Safety Risks in County Areas of the Yangtze River Delta Region during Urbanization Process

https://doi.org/10.21203/rs.3.rs-3952971/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 10 Jun, 2024

Read the published version in Discover Applied Sciences →

Version 1

posted

You are reading this latest preprint version

China's intense urbanization has profoundly impacted county-level societies, leading to increased diversity and contradictions, and pushing these communities towards a risk society. This is especially pronounced in the Yangtze River Delta, a region of peak urbanization. The stability and progress of these local societies are vital for the region's overall high-quality development. Yet, with ongoing urbanization, these areas face numerous challenges. To effectively tackle these issues, a robust system encompassing pre-evaluation, early warning, and emergency preparedness is essential. This study explores the relationships and structures among various risk factors using grounded theory analysis. Furthermore, it applies the Analytic Hierarchy Process (AHP) to develop a comprehensive risk early warning index system, significantly enhancing the response capabilities to public security challenges in the Yangtze River Delta's county-level societies.

Earth and environmental sciences/Environmental sciences

Earth and environmental sciences/Natural hazards

Yangtze River Delta region

Risk early warning index system

Grounded Theory

Analytic Hierarchy Process

Risk analysis

Counties represent the most integral microcosms within China’s national governance system. In the urban system of China, counties serve as pivotal junctions, bridging cities and countryside areas, and stand as principal vessels for the urbanization of the rural populace, as well as significant breakthrough points for innovative urbanization initiatives. As an essential component of China’s governance modernization, counties also play a critical role in advancing the construction of the national emergency management and control system. Presently, of the more than 2,000 counties in China, an overwhelming majority of the top hundred counties are clustered in the developed eastern coastal areas, with those in the Yangtze River Delta being particularly notable. The Yangtze River Delta also ranks among the regions with the highest level of urbanization in China, where the extensive process of urbanization has brought various degrees of change and impact to county-level societies. In each county, the weak links in different fields may interlock to create systemic risks, thereby causing the county-level societies to become risk societies with increased subject heterogeneity and a proliferation of contradictions. Hence, to address the various risk events faced by county-level societies, pre-evaluations and early warnings bear great significance, which is even more pronounced in the societies of today. This study includes an in-depth exploration of the factors influencing the security early warning systems of county-level societies within the Yangtze River Delta, and utilizes the analytic methods of grounded theory to figure out the relationships and patterns among these factors. On this foundation, a comprehensive risk early warning index system is established, offering robust support to policy formulation and risk management within the county domain. Concurrently, this contributes to a more effective response to the public security challenges faced by county-level societies in the Yangtze River Delta.

Steven Fink’s^[1] theory of the four stages of crisis and Robert Heath’s^[2] “4Rs Model Theory” have provided theoretical support to the study of urban emergency management, serving as significant guidance for the contemporary research endeavors in this field and illuminating the path for scholarly inquiry. Scholars have delved into the causes, assessment systems, early warning mechanisms, and governance of the social risks in counties from a variety of academic perspectives, with their research primarily focusing on three aspects: Firstly, the investigation into the causes of social risks at the county level. Researchers have analyzed the causes of mass incidents in county-level societies through an examination of the foundation, key, and core of political identity^[3], and explored the correlation between the influx of farmers into urban areas and the risks associated with the urbanization of counties^[4]. In addition, they have probed into the factors influencing the vulnerability of populations to high temperatures at the county level^[5], as well as the underlying logic behind social risks triggered by the entrepreneurial initiatives of returning young adults^[6]. Secondly, research on the assessment systems for county-level social risks. The studies in this area include the employment of GIS (Geographic Information System) technology for the assessment of ecological risks within counties^[7], and the use of Analytic Network Process (ANP), Probability Neural Network (PNN), DPSIR (Driver-Pressure-State-Impact-Response) Framework^[8], and machine learning algorithms^[9] for the assessment of natural disaster risks at the county level. Thirdly, exploration of the early warning systems for county-level social risks. Scholars have paid attention to the multi-dimensional aspects of county-level social risk early warning systems, including the economic, ecological, mass incident, and comprehensive factors. For instance, Li Meijuan^[10] has put forward an early warning index system for social risks based on the fact of imbalanced economic development at the county level. With the advent of China’s national strategy for a new type of urbanization, research into the prevention and control of social risks at the county level has progressively deepened, with the perspective gradually shifting towards the governance of such risks. In this context, some scholars put forward to address the county-level social risks from the perspective of city-level security risk governance^[11], and they also proposed effective measures to enhance the construction of county-level emergency management systems exemplified by the case study in the northwestern region^[12].

To sum up, scholars have explored various dimensions (models, systems, and governance) of public security risk early warning in county-level societies from diverse perspectives, providing theoretical support and reference for research in this field. However, due to various limiting factors, there is a dispersion of viewpoints in the existing literature, and a lack of systematic thinking on how to integrate the grounded theory-based research methods into the construction of early warning systems for public security risks of county-level societies. More specifically, there is a general consensus in the literature, both domestic and international, that the application of grounded theory in the study of public security risks in counties is still at the early stage. Moreover, the literature of research on public security risk early warning in county domains through open coding, axial coding, and selective coding requires to be further enriched. The in-depth analyses of public security issues within county-level societies are insufficient, especially in the integration of theoretical research methods with risk early warning systems, where related literature remains scarce.

3.1 Research Methodology

Grounded theory^[13] is a qualitative research method originally proposed by Glaser and Strauss. It is a specialized methodology for constructing theories from data^[14]. This theory encompasses three levels: the routinized techniques and strategies; the more abstract “logic of method”; and the most abstract issues related to epistemology, ontology, axiology, teleology, and validity^[15]. The research process of grounded theory is characterized by a rigorous logical structure^[16]. The purpose of this study is to explore the influencing factors of public security risks in the county-level societies of the Yangtze River Delta region in China and to establish a monitoring and early warning model based on these factors. Given that this study falls into the category of exploratory studies and currently there lacks a unified and clear understanding of public security risks in county-level societies, the grounded theory was selected as the methodological foundation of this study. Through grounded theory-based analysis of the data, the study aims to abstract and summarize the influencing factors for monitoring and early warning of public security risks in county-level societies, and thus lay the foundation for further exploration of a monitoring and early warning model for public security risks in the county-level societies of the Yangtze River Delta region.

3.2 Research Data

To ensure the comprehensiveness and integrity of the data, this study procured data from various sources and conducted cross-checking of data from multiple sources by comparing interview results, literature review findings, and policy documents. First of all, purposive random sampling was employed to select 18 scholars specializing in security studies and personnel from the county-level government departments. Through semi-structured interviews, profound insights were collected regarding the content and implementation processes of public security risk early warning efforts. These respondents were able to grasp the gist of the interview questions more accurately, so they provided comprehensive and detailed responses. Each interview took approximately one hour and the transcripts were sequentially coded from F01 to F18. Subsequently, in accordance with the specific content of the research questions and the availability of data, data collection was further conducted by examining relevant policies and literature. This included documents such as the “Guidelines for the Construction of Integrated Urban Security Risk Monitoring and Early Warning Platforms (Trial)” and municipal plans for establishing integrated urban security risk monitoring and early warning platforms in cities like Hefei, Nanjing, Chengdu, Foshan, and Shanghai. Meanwhile, a retrieval and analysis of literature samples related public security risk early warning in county-level societies was conducted, totaling 67 literature samples. During this process, 340 interview statements and 287 statements from original textual materials were collated. Of these, 15 randomly selected statements were utilized to verify theoretical saturation, while the rest were used for framework model building.

3.3 Data Analysis

3.3.1 Open Coding

Open coding is the process of reading data that has been initially sifted sentence by sentence and paragraph by paragraph, extracting core information, and then labeling, organizing, summarizing, and categorizing this information. During this process, the extracted information is assigned category names, forming preliminary coding subcategories, so that it is able to conceptualize and categorize similar information within the same category. Herein, a concept refers to a conceptual label attached to individual things, events, or phenomena; a category is a combination of concepts, which can be clustered together when referring to the same phenomenon and are governed by a more abstract, higher-level concept^[17]. Through open coding, interview records and literature materials can be conceptualized, allowing for the abstraction and generalization of information and the formation of new conceptual meanings. The subsequent process of categorization further refines and classifies these conceptual meanings, ultimately forming more complete categories. This coding process helps to clarify the main concepts in the data, enhance the ability to abstract and summarize information, and provide a clear and systematic analytical foundation for subsequent research.

Ultimately, this study obtained interview records, literature materials, and other data related to the risk assessment of technological security, totaling approximately 123,000 words. To ensure coding reliability, two researchers were assigned to independently code the textual data, thereby forming a horizontal comparison. Besides, each researcher performed dual rounds of coding on the same data to create a vertical comparison. The coding process yielded a high degree of reliability, evidenced by an inter-coder agreement rate exceeding 90%.

In order to eliminate personal preconceptions and biases as much as possible, 37 core concepts that appeared four or more times were extracted for the social security risk issues in the county domains of the Yangtze River Delta region. Through summarization, these core concepts were organized into 14 subcategories, as shown in Table 1.

Table 1

Concepts and Subcategories Formed by Open Coding
No.	Subcategory	Conceptualization
1	Risk response	The risks associated with the risk response capacity and response mechanisms. This includes: the lack of the capacity to respond to county-level epidemic risk prevention and control and the lack of systematic support for the response mechanisms.
2	Management system	The risks associated with the emergency organizational system construction. This includes: the incomplete emergency management system for major public health emergencies in counties.
3	Collaborative cooperation	The risks in public health emergency prevention and control collaboration and joint governance. This includes: the inefficiency in the coordinated governance mechanisms and collaborative prevention measures for public health emergencies at the county level.
4	Economic income	The risks of rural families relapsing into poverty and distress, the increase of costs for relocation to urban areas, and the reduction of farmers’ incomes in the urbanization process. This includes: the increased cost of farmers moving to urban areas, the increase in household expenses, and the reduction of income, which has led to some rural families relapsing into poverty and distress.
5	Social status	The risks arising from the transformation of farmers’ identities in the urbanization process. This includes: the changes in the life logic of rural families as the populace within the county moves towards the county towns, the exacerbation of “involution” of family life, psychological imbalance among the inhabitants, and the instability stemming from issues related to demolition and resettlement.
6	Employment environment	The risks caused by backward industrialization and governance capacity in the county. This includes: the lack of industrialization foundation in county towns, and the heavy reliance of county town consumption on external resource input.
7	Governance policy	The impact risk on the capabilities of local governance posed by petitions, public sentiment, and similar events. This includes: the social conflicts about petition governance due to population mobility during the process of urbanization, and the risks associated with local public opinion dynamics.
8	Public security	The risks associated with public disorder and the increasing number of criminal cases. This includes: the psychological gap between grassroots law enforcement personnel and the public, a succession of incidents that encroach on and harm civilians, and the risks to public security owing to the excessive population aggregation in a brief span of time.
9	Work effectiveness	The risks present at each stage during the management of mass incidents. This includes: the risk that the scale of the incident gets out of control within the governance process, the risk of minor conflicts escalating into major incidents, and the risk of inadequate governance of the root causes of mass incidents.
10	Public opinion	The risks posed by public opinion during the occurrence of mass incidents. This includes: the risk associated with the blockage of channels for public opinion expression and the risks stemming from the weakening of public political identification.
11	Production accident	Risks caused by various types of production accidents within the county, including the risks of casualties caused by accidents in departments such as coal mines and factories.
12	Disaster resilience	The risks of exposure to various natural disasters. This includes: the risks related to the capacity of the entity bearing the risk to withstand damages and defense against disasters, the vulnerability of the entity bearing the risk, the vulnerability of transportation infrastructure, and the vulnerability of individuals.
13	Population and housing	The risks of damage to population and housing within the county when a natural disaster strikes. This includes: the number of houses that have collapsed or sustained significant damage, the quantity of individuals requiring urgent relocation and resettlement, and the total number of people affected by the disaster.
14	Disaster causes	Causes of the vulnerability of counties in the Yangtze River Delta region to disasters. This includes: the occurrence of high-altitude long-runout landslides, substantial gully debris flows, the exposure of populations to high temperatures, rainfall that precipitates landslides, and the risks brought forth by typhoon.

3.3.2 Axial Coding

In the process of open coding, this paper has formed 14 initial categories through the analysis of textual data. However, these categories are independent of each other, lacking a hierarchical causal relationship. To establish a more logically coherent main category, axial coding was carried out, and cluster analysis was conducted on various subcategories to clarify the logical relationships between categories. This systematic process has revealed the interconnections between the subcategories. Based on the relationships and logical sequence between different categories, this paper classifies categories with similar themes into one group, eventually integrating them into four main categories with clear logical structures, which are “Public Health Risks”, “Migrant Population Risks”, “Mass Incident Risks”, and “Natural Disaster Risks”, as shown in Table 2.

Table 2

The Correspondence between Main Categories and Subcategories
Public health risks	Risk response	The capacity of county-level societies to respond to public health risks
	Management system	Whether the management systems of county-level societies to respond to health risks are sound and efficient
	Collaborative cooperation	The collaborative ability of various functional departments in responding to public health risks
Migrant population risks	Economic income	The income level of farmers after relocating in county towns
	Social status	The status and sense of belonging of farmers after relocating in county towns
	Employment environment	The employment opportunities for farmers after relocating in county towns
	Governance policy	The governance ability of county-level societies to deal with the relocation of farmers in county towns
	Public security	The level of public security after the population influx into the county towns
Mass incident risks	Work effectiveness	The effectiveness of county-level government departments in responding to mass incidents
	Public opinion	The public sentiment in county-level societies during mass incidents
	Production accident	The number of casualties caused by various types of production accidents within the counties
Natural disaster risks	Disaster resilience	The ability of county-level societies to resist the risks of natural disasters
	Population and housing	The number of people and houses affected by natural disasters within the counties
	Disaster causes	The types and causes of natural disasters in counties

3.3.3 Selective Coding

Selective coding is the refinement and integration of axial coding, which entails identifying the core category from the main categories, exploring the logical relationships between the main categories and subcategories, and ultimately being able to clearly describe the phenomenon. The purpose is to further reveal the connections between the core and main categories. This process that derives the conceptualized and cluster-generalized main categories and subcategories through the analysis of textual materials is used for the study of phenomena and issues. Selective coding connects the main categories through their intrinsic logical relationships, refining the categories to form an analytical framework. The core category determined in this paper is “Public Security Risks in the County-Level Societies of the Yangtze River Delta Region in China”, which consists of four core categories: Public Health Risks, Migrant Population Risks, Mass Incident Risks, and Natural Disaster Risks, as well as 14 subcategories. Based on the grounded theory-based analysis of textual materials, the framework for evaluating the public security risks in the county-level societies of the Yangtze River Delta region in China is ultimately formed, as shown in Fig. 1.

3.3.4 Saturation Test

After the theory construction is completed, it is necessary to test the saturation of the theory, which is the signal for when to stop theoretical sampling^[18]. The test of theoretical saturation specifically refers to the continued exploration of the characteristics of categories without acquiring additional data, serving as the basis for deciding whether to cease sampling^[19]. If the analysis of newly acquired data does not yield any new theories or categories, then the theory is considered to be approaching saturation. The role of testing for theoretical saturation lies in the expansion of research materials and the refinement of theoretical construction. In this paper, coding analysis was conducted on the reserved original data of 15 case studies, and no new categories were discovered, which validates the theory effectively. Therefore, it can be concluded that the mechanism model constructed in this study is saturated. Consequently, a final set of 14 risk early warning indexes was formed, covering four aspects: “Public Health Risks”, “Migrant Population Risks”, “Mass Incident Risks”, and “Natural Disaster Risks”.

4.1 Method for Allocating Weights to Early Warning Indexes

Given that the factors influencing China’s technological security are diverse, and these factors are interrelated and mutually constraining, they exhibit characteristics of complexity, dynamism, and uncertainty. Therefore, the use of purely quantitative methods to conduct a comprehensive assessment of technological security has limited explanatory power. Additionally, the factors influencing technological security have strong randomness, so relying solely on quantitative methods may lead to a significant discrepancy between the evaluation results and the actual situation. In view of the above circumstances, this study chooses to use the Analytic Hierarchy Process (AHP), which combines quantitative and qualitative methods, to determine the variable weighting and operational logic of the technological security comprehensive evaluation and warning in China. This method can deal with the complex relationships between various factors in a more comprehensive manner. It not only makes full use of the information from quantitative data but also combines expert experience and qualitative analysis to improve the comprehensiveness and accuracy of the evaluation. This integrated approach helps to more comprehensively understand and warn of the risks to China’s technological security.

AHP is a quantitative analysis method used to address multi-criteria decision-making problems. It breaks down a complex problem into manageable modules by structuring it into a hierarchy, consisting of the goal, the criteria, and the alternatives. By comparing and inducing, AHP can combine subjective judgment with objective data to obtain the relative weights of various factors, providing a scientific basis for decision-making^[20].

The main idea of using AHP method to allocate weights to indexes in this paper is: Utilizing Saaty’s 1–9 scale method^[21], to compare and score the importance of indexes within the same level two by two, and then construct the corresponding judgment matrix based on the scores obtained. Subsequently, conduct a consistency check of the judgment matrix. If it does not meet the consistency requirements, the judgment matrix needs to be reconstructed until the consistency requirements are satisfied. The specific steps are as follows.

4.1.1 Judgment Matrix Construction

The first step is to invite an expert panel to score the weights of the criteria layer and the index layer in accordance with the actual situation of early warning for public safety risks in county-level societies. The 1–9 scale method is used to determine the scores. For a given layer, when comparing the importance of the i-th element relative to the j-th element with respect to a factor of the previous layer, the quantified relative importance is represented by ${a}_{ij}$. If there are n elements involved in the comparison, the final Judgment Matrix A will be obtained as follows, with the corresponding elements being ${a}_{ij}$.

$$A=\left[\begin{array}{cccc}{a}_{11}& {a}_{12}& \cdots & {a}_{1n}\\ {a}_{21}& {a}_{22}& \cdots & {a}_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ {a}_{n1}& {a}_{n2}& \cdots & {a}_{nn}\end{array}\right]$$

The Matrix A has the following characteristics:

(1) ${a}_{ij}$represents the importance of index i compared to index j.

(2) When i = j, the two indexes are the same, and therefore equally important, denoted as 1, which means the main diagonal elements are 1.

(3) ${a}_{ij}>0$and satisfies the condition ${a}_{ij}\times {a}_{ji}=1$.

4.1.2 Calculation of Single-Layer Weight Ranking and Consistency Check

The calculation of single-layer weight ranking is based on the established judgment matrix to calculate the weights representing the order of importance of relevant indexes at the current layer relative to a certain index at the previous layer. The calculation involves normalizing all elements in the Judgment Matrix A and then determining the value of ${\stackrel{-}{a}}_{ij}$ using Formula 1.

${\stackrel{-}{a}}_{ij}=\frac{{a}_{ij}}{{\sum }_{k=1}^{n}{a}_{kj}}$ 1

After normalizing the matrix, sum up the columns in the same row to obtain ${\stackrel{-}{w}}_{i}$ using Formula 2.

${\stackrel{-}{w}}_{i}=\sum _{j=1}^{n}{\stackrel{-}{a}}_{ij}$ 2

Divide the sum of the index vectors by n to get the weight vector, that is, ${w}_{i}=\frac{\stackrel{-}{{w}_{i}}}{n}$, and determine the largest characteristic root${ \lambda }_{max}$ using Formula 3.

${\lambda }_{max}=\frac{1}{n}\sum _{i=1}^{n}\frac{{\left(Aw\right)}_{i}}{{w}_{i}}$ 3

Perform a consistency check on the data to evaluate the rationality and reliability of the judgment matrix, ensuring that the decision-making results are reliable and credible. The Consistency Index (CI) is obtained using Formula 4.

$CI=\frac{{\lambda }_{max}-n}{n-1}$ 4

CI = 0 indicates perfect consistency, and the larger the CI, the less consistent it is.

Finally, solve for the CR value using Formula 5 to determine if its consistency is acceptable.

$CR=\frac{CI}{RI}$ 5

RI refers to the Random Consistency Index, obtained from a value table. When CR equals 0, the matrix has perfect consistency; when CR is less than 0, the matrix has satisfactory consistency; when CR is greater than 0.1, the matrix lacks consistency and should be adjusted.

4.1.3 Calculation of the Total Hierarchical Ranking of Weights

The calculation of the weights representing the relative importance of all factors at a certain layer with respect to the top layer is known as the total hierarchical ranking. To achieve the final objective of the AHP, the composite weights of the indexes at each layer with respect to the goal are calculated sequentially from the bottom up.

4.2 Determination of the Weights of the Early Warning Index System

4.2.1 Data Source

The data for determining the weights of early warning indexes for the public security risks of county-level societies in Yangtze River Delta region using the AHP method was obtained from expert consultations. The selected experts scored the questionnaire designed based on the public security risk early warning indexes of a certain county society in the Yangtze River Delta region, and then these data were collected and organized.

4.2.2 Construction of Judgment Matrix

To reasonably evaluate the factors of public security risk early warning in a certain county society of the Yangtze River Delta region, 8 experts were invited, including 3 government department personnel, 3 university researchers, and 2 security assessment agency managers. These experts possess a high theoretical level and rich practical experience in the management of public security risk early warning at the county level. Therefore, their scores for the relative impact of the public security risk early warning indexes in the said county society can ensure representative assessment results.

The expert review method was used. The highest and lowest values were eliminated, and then the average score was calculated to determine the value of the assessment factors. Pairwise comparisons are made between factors at the same layer to construct the Judgment Matrix A for the goal layer.

$$A=\left[\begin{array}{cccc}1& 3& 5& 2\\ \frac{1}{3}& 1& 2& \frac{1}{2}\\ \frac{1}{5}& \frac{1}{2}& 1& \frac{1}{4}\\ \frac{1}{2}& 2& 4& 1\end{array}\right]$$

The importance scores between the indexes at the index layer were compared using the same method to construct the Comparative Judgment Matrices${ B}_{1}-{B}_{4}$.

${B}_{1}=\left[\begin{array}{ccc}1& \frac{1}{2}& 3\\ 2& 1& 5\\ \frac{1}{3}& \frac{1}{5}& 1\end{array}\right]$ ${B}_{2}=\left[\begin{array}{ccccc}1& \frac{1}{3}& 3& 4& 2\\ 3& 1& 4& 5& 6\\ \frac{1}{3}& \frac{1}{4}& 1& 2& \frac{1}{2}\\ \frac{1}{4}& \frac{1}{5}& \frac{1}{2}& 1& \frac{1}{3}\\ \frac{1}{2}& \frac{1}{6}& 2& 3& 1\end{array}\right]$

${B}_{3}=\left[\begin{array}{ccc}1& \frac{1}{3}& 2\\ 3& 1& 5\\ \frac{1}{2}& \frac{1}{5}& 1\end{array}\right]$ ${B}_{4}=\left[\begin{array}{ccc}1& 2& 3\\ \frac{1}{2}& 1& 2\\ \frac{1}{3}& \frac{1}{2}& 1\end{array}\right]$

4.2.3 Determination of Weights and Consistency Check

Considering the large amount of expert subjective experience involved in establishing the judgment matrix, a consistency check is required to eliminate obvious logical errors due to subjectivity. The weights of each judgment matrix were calculated using Formulas 1 and 2, and then the consistency check was conducted using Formulas 3, 4, and 5. The weight vectors of each matrix and the results of the consistency check are shown in Table 3.

Table 3

Weight Vector Table
Judgment Matrix	w₁	w₂	w₃	w₄	w₅	${\lambda }_{max}$	CR
A	0.4758	0.1544	0.0813	0.2884	-	4.0211	0.0079
B₁	0.3092	0.5813	0.1096	-	-	3.0037	0.0032
B₂	0.2247	0.4855	0.0946	0.06	0.1352	5.2047	0.0457
B₃	0.2299	0.6479	0.1222	-	-	3.0037	0.0032
B₄	0.5390	0.2973	0.1638	-	-	3.0092	0.0079

4.2.4 Overall Weight Ranking

The final weights of the public security and health risk early warning index system for county-level societies in the Yangtze River Delta region were obtained by multiplying the weight values of the corresponding criteria and indexes, as shown in Table 4.

Table 4

Weighted Overall Ranking Table
A	B₁	B₂	B₃	B₄	Overall Weight Ranking
A	0.4758	0.1544	0.0813	0.2884	Overall Weight Ranking
b₁₁	0.3092	-	-	-	0.1471
b₁₂	0.5813	-	-	-	0.2766
b₁₃	0.1096	-	-	-	0.0521
b₂₁	-	0.2247	-	-	0.0347
b₂₂	-	0.4855	-	-	0.0750
b₂₃	-	0.0946	-	-	0.0146
b₂₄	-	0.0600	-	-	0.0093
b₂₅		0.1352			0.0209
b₃₁	-	-	0.2299	-	0.0187
b₃₂	-	-	0.6479	-	0.0527
b₃₃	-	-	0.1222	-	0.0099
b₄₁	-	-	-	0.5390	0.1554
b₄₂	-	-	-	0.2973	0.0857
b₄₃	-	-	-	0.1638	0.0472

According to the data in Table 4, it can be seen that within the dimension of public security and health risks, the factor with the highest weight is the management system, with a weight value of 0.2766, followed by the risk response factor, and finally the collaborative cooperation factor. This indicates that urbanization has a significant impact on the management system, risk response, and collaborative cooperation factors within the dimension of public security and health risks in county-level societies in the Yangtze River Delta region, and is more likely to trigger social risks.

Within the dimension of migrant population risks, the factor with the highest weight is the social status of the migrant population, with a weight value of 0.0750, followed by the economic income of the migrant population, the public security situation of the county, the employment environment of the county, and the governance policies for the migrant population. This indicates that during the process of urbanization, the return of migrant workers to their hometowns and the integration of farmers into county towns are most sensitive to changes in their own social status and economic income. These two factors are also more likely to trigger the occurrence of social risks at the county level.

Within the dimension of the mass incident risks, the factor with the greatest weight is the public opinion following the occurrence of mass incidents, with a weight value of 0.0527, followed by work effectiveness and production accident factors. This suggests that urbanization has a significant impact on the public opinion factor within the dimension of the mass incident risks in the counties of the Yangtze River Delta region, making it more prone to social risks.

Within the dimension of natural disaster risks, the factor with the highest weight is disaster resilience, with a weight value of 0.1554, followed by the types of disasters affecting the county and the number of affected people and houses within the county. This indicates that in the process of response to natural disasters by the counties in the Yangtze River Delta, disaster resilience has the most significant impact on the risk of natural disasters.

The public security risk early warning index weights of county areas in the Yangtze River Delta region are an important part of the risk early warning index system and a crucial link in the risk early warning mechanism during the urbanization process in these county areas. Based on semi-structured interviews and literature review, this paper analyzes the risk early warning factors using grounded theory with three levels of coding. In total, four primary risk early warning indexes—public health risk, migrant population risk, mass incident risk, natural disaster risk—and fourteen secondary risk early warning indexes are summarized. In the end, the weights of each index were determined using the AHP method. The research results show:

During the urbanization process, among the four primary risk early warning indexes (public health risks, migrant population risks, mass incident risks, natural disaster risks), the weight of public health risks is the highest, followed by the natural disaster risks.

Among the fourteen secondary risk early warning indexes, the factor of the public health management system under the public health risk dimension has the highest weight and the most significant impact on society; within the migrant population risk dimension, the social status of the migrant population has the largest weight; in the dimension of mass incident risks, the public opinions following the occurrence of mass incidents carries the highest weight; and in the dimension of natural disaster risks, the factor with the greatest weight is disaster resilience.

For these early warning indexes with relatively high weights, the administrative personnel of counties should pay sufficient attention and take measures to prevent them from triggering social risks.

Funding Statement

This research was supported by the Soft Science Program Project of the Ministry of Public Security (Project No. 2021LL55), the Special Fundamental Research Funds for the Central Universities (Project No. LGZD202204), and the Pre-research Project of the Nanjing Police University (Research on the Effectiveness Evaluation and Improvement of the Early Warning Mechanism for Social Security Risks in Cities in the Yangtze River Economic Belt). The author hereby expresses the gratitude for them.

Author Contribution Statement

As the sole author of this research paper, I am responsible for all aspects of the research project. This includes, but is not limited to: conceptualization and design of the study, collection and analysis of data, interpretation of the results, and the writing and revision of the manuscript. I independently conducted the literature review to ensure the research is accurately positioned within the existing body of knowledge. I also independently selected and applied the research methods to ensure the study's validity and reliability. Furthermore, I was responsible for the visualization of data to clearly communicate research findings, as well as all related ethical review and permission processes. Lastly, I handled the final editing and proofreading of the manuscript to ensure the submission meets the journal’s quality standards and formatting requirements.

Author Contribution

Yin ke wrote the whole manuscript text, and reviewed the manuscript

Fink. S. (1986). Crisis Management: Planning for the Invisible. New York: American Management Association.
Heath. R. (2001). Crisis Management. Beijing: China Citic Press.
Nie, J., & Li, Y. Q. (2010). The Causes and Prevention of Group Incidents: From the Perspective of Political Identity. Studies in socialism, 04, 83–87.
He, X. F. (2021). The Risks of Farmers Entering Cities and County Urbanization. Social Development Research, 8(03), 11–20 + 242.
Huang, X. J., & Qi, M. Y. (2021). Population Vulnerability Assessment and Spatial Differentiation in Xi'an under High Temperature Impact. Geographical Research, 40(06), 1684–1700.
Luo, S. (2023). Life Orientation: The Practical Logic and Risk of Returning Youth Entrepreneurship in the County. Contemporary Youth Research, 02, 1–13 + 84.
Cui, Y. L., Gao, X., & Dong, B. (2021). Landscape ecological risk assessment of county. Journal of Zhejiang A&F University, 38(03), 541–551.
Zhao, W. L., Liu, X. H., & Bao, B. F. (2019). Eco-Security Assessment of Changsha-Zhuzhou-Xiangtan Urban Agglomeration at County Level. Economic Geography, 39(08), 200–206.
Yang, X., Zhang, L. S. &, Wang, T. (2022). Risk Assessment for Typhoon Economic Losses in County-Based Units Using Machine Learning. Journal of Tropical Meteorology, 38(05), 651–661.
Li, M. J. (2013). Design Warning Indicator System of County Economic Development Imbalance and Defined Early Warning Criteria. Resource Development ༆Market, 29(06), 590–594.
Huang, X. H. (2020). From County Social Governance to City Social Governance. Journal of the Fujian Provincial Party School of the Communist Party of China, 04, 4–13.
Guan, H. G., & Zhang, Y. Q. (2021). Dilemmas in the System Construction of Emergency Response Management at County Level and Countermeasures Analysis. Journal of Northwest University for Nationalities, 05, 148–156.
Glaser, B. G., & Strauss, A. L. (1999). The discovery of grounded theory: Strategies for qualitative research. New York: Routledge.
Corbin, J. (2016). Grounded theory. The Journal of Positive Psychology, 12(3), 301–302.
Bakker, J. I. H. (2019). Grounded theory methodology and grounded theory method: Introduction to the special issue. Sociological Focus, 52(2), 91–106.
Cheng, X. M. (1999). The ideas and methods of grounded theory. Educational research and experimentation, 04, 58–63 + 73.
Strauss, A., & Corbin, J. (1997). Introduction to Qualitative Research. Taibei: Juliu Book Company.
Guetterman, T. C., Babchuk, W. A., Smith, M. C. H., & Stevens, J. (2017). Contemporary approaches to mixed methods-grounded theory research: A field-based analysis. Journal of Mixed Methods Research, 13(2), 179–195.
Fassinger, R. E. (2005). Paradigms, praxis, problems and promise: grounded theory in counseling psychology research. Journal of counseling psychology, 2, 156–166
Saaty, T. L. (1988). What Is The Analytic Hierarchy Process. Berlin: Springer.
Mathivathanan, D., & Govindan, K. (2017). Exploring the impact of dynamic capabilities on sustainable supply chain firm's performance using Grey-Analytical Hierarchy Process. Journal of Cleaner Production, 147, 637–653

No competing interests reported.

Download PDF

Journal Publication

published 10 Jun, 2024

Read the published version in Discover Applied Sciences →

Version 1

posted

You are reading this latest preprint version

Research on the Early Warning Indicator System for Public Safety Risks in County Areas of the Yangtze River Delta Region during Urbanization Process

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Literature Review

3. Analysis of Public Security Risk Factors in County-Level Societies Based on Grounded Theory

3.1 Research Methodology

3.2 Research Data

3.3 Data Analysis

3.3.1 Open Coding

3.3.2 Axial Coding

3.3.3 Selective Coding

3.3.4 Saturation Test

4. Monitoring and Early Warning Index System for Public Security Risks in County-Level Societies

4.1 Method for Allocating Weights to Early Warning Indexes

4.1.1 Judgment Matrix Construction

4.1.2 Calculation of Single-Layer Weight Ranking and Consistency Check

4.1.3 Calculation of the Total Hierarchical Ranking of Weights

4.2 Determination of the Weights of the Early Warning Index System

4.2.1 Data Source

4.2.2 Construction of Judgment Matrix

4.2.3 Determination of Weights and Consistency Check

4.2.4 Overall Weight Ranking

5. Conclusions

Declarations

Funding Statement

Author Contribution Statement

Author Contribution

References

Additional Declarations

Status:

Journal Publication

Version 1