Distribution Characteristics and Influencing Factors of the National Comprehensive Disaster-Reduction Demonstration Community in China

Abstract

Establishing the National Comprehensive Disaster-Reduction Demonstration Community (NCDDC) is crucial for enhancing comprehensive disaster risk reduction at the grassroots level in China. Studying the distribution characteristics and influencing factors of NCDDCs can guide future NCDDC layout optimization and related policy adjustments. Using the standard deviation ellipse, nearest neighbor index, kernel density, spatial autocorrelation, and Geodetector, we analyzed the spatiotemporal distribution characteristics of NCDDCs in China from 2008 to 2021 and detected their influencing factors. The findings are as follows: (1) NCDDCs exhibit an uneven distribution at different scales, including spatial, urban–rural, and county scales. (2) The spatial distribution of NCDDCs mainly follows a northwest–southeast pattern during 2008–2014 and shows a northeast–southwest trend after 2014. (3) The positive spatial correlation and spatial agglomeration of NCDDCs increase annually. (4) NCDDCs show a concentrated and contiguous distribution pattern in 2021, based on “core density zone–ring-core decreasing area–ring-core expansion group–Ɔ-shaped area–belt-shaped area”. (5) The main factors affecting the NCDDC distribution are hospital density, road density, GDP density, and population density, with factors’ interactions exhibiting bilinear and nonlinear enhancement effects. This study reveals the NCDDC spatiotemporal distribution characteristics and its influence mechanism, providing a scientific basis for future NCDDC layout optimization and related policy adjustments.

1. Introduction

China is highly susceptible to disaster events [1,2]. Recent statistics reveal that in 2022, a multitude of natural disasters affected 112 million individuals in China, resulting in 554 deaths and missing individuals and causing direct economic losses of CNY 238.65 billion [3]. In recent decades, China has experienced a rise in the frequency and intensity of natural disasters [4,5]. This has a severe impact on the lives of people and society’s sustainable development. At present, there is increasing concern about reducing disaster risk and enhancing disaster response capacity.

The community, referred to as a village in rural China, is the smallest social unit impacted by and responding to disasters [6]. It plays a pivotal role at the forefront of disaster prevention and mitigation efforts [7], as both the subject afflicted by disasters and the focal point for post-disaster reconstruction efforts [4,8]. The concept of a “disaster-resistant community” was initially introduced in the United States in 1994, emphasizing the community as the fundamental unit for disaster prevention and mitigation efforts [8]. Since then, numerous international slogans and initiatives have underscored the enhancement of community disaster resilience as the primary objective of disaster prevention and mitigation. For instance, the World Conference on Disaster Reduction 1999 management forum proposed that communities should be recognized as the fundamental unit of disaster reduction [9]. The United Nations, on the International Day for Disaster Reduction in 2001, advocated for the development of community-based strategies for disaster reduction [7]. In 2005, the World Conference on Disaster Reduction recommended the establishment of disaster prevention and mitigation mechanisms at the community level and the improvement of community emergency response capacities [10]. The Sendai Framework for Disaster Risk Reduction (SFDRR) 2015 explicitly outlined various priorities and tasks to establish sustainable and resilient communities [11,12]. Currently, the community-based disaster prevention and mitigation model has gained global recognition [9,11,13,14,15]. Many countries have already initiated efforts to establish disaster-resistant communities, such as the disaster-resistant community in the United States [8] and the disaster-safe welfare community in Japan [16].

In 2007, China initiated the establishment of the National Comprehensive Disaster-Reduction Demonstration Community (NCDDC). According to the established standard of the NCDDCs and the relevant literature, an NCDDC is defined as a community, both urban and rural, that integrates the promotion of emergency knowledge, the establishment of emergency facilities, and the enhancement of residents’ self-rescue and mutual aid capabilities [14,17,18]. As the model for community disaster reduction, the NCDDC plays a leading, demonstrative, and radiating role in enhancing China’s comprehensive disaster reduction capacity at the grassroots level. In 2008, the Chinese government released the first and second batches of NCDDCs, designating a total of 284 NCDDCs. The total number of NCDDCs had exceeded 15,000 by the end of 2022.

The existing studies on community disaster prevention and mitigation are valuable and contribute significantly to enriching the research content and theoretical system of NCDDC. However, it is evident that existing studies mainly focus on public participation [18,19], community resilience [20,21,22,23,24], community vulnerability [25,26,27,28], community preparedness in disasters [29,30], community disaster risk assessment [9,31,32], and community disaster mitigation capacity assessment [33,34]. For example, Ting Que et al. [18] argued that public participation in community-organized disaster risk reduction activities is important for improving disaster risk reduction capacity. Shaikh Abdullah Al Rifat and Weibo Liu [22] developed a composite community disaster resilience index from six dimensions affecting disaster response to assess the disaster resilience of the United States’ coastal communities. Gerges et al. [23] constructed a community resilience index from transport, energy, health, and socio-economic perspectives. Nicolás C. Bronfman et al. [24] analyzed community resilience in Chile at six levels, i.e., society, resources, economy, institutions, infrastructure, and environment. Jejal Reddy Bathi and Himangshu S. Das [25] assessed the storm surge and flood hazard vulnerability of three coastal communities in Mississippi in terms of social vulnerability, economic vulnerability, and climatic vulnerability. Zhuoran Shan et al. [31] used the analytic hierarchy process and geography-weighted regression (GWR) model to evaluate the community’s high-temperature disaster risk in Wuhan from the aspects of danger, sensitivity, and vulnerability. Xiaoxiao Wang et al. [34] used the analytic hierarchy process to measure the community’s disaster reduction ability in ethnic minority aggregation areas. Overall, many achievements have been made in community disaster prevention and mitigation, which will facilitate the establishment and layout of NCDDCs. Factors such as community disaster preparedness, community disaster vulnerability, community resilience, etc., are all worthwhile considerations in the establishment and layout of NCDDCs. In practice, a community, whether established by the local government or itself, can apply to become an NCDDC by meeting a set of standards [14]. NCDDCs and non-NCDDCs usually have significant differences in disaster prevention and mitigation construction, with NCDDCs relying on resources and policy support to have a stronger advantage in disaster prevention and mitigation construction. In other words, compared with common communities, NCDDCs have a higher disaster prevention and mitigation capacity. Hence, the spatial distribution of NCDDCs also reflects regional differences in disaster prevention and mitigation capacity to some extent.

Studying the distribution characteristics and influencing factors of NCDDCs is of great value. This study not only visually reveals the disparities in the distribution of NCDDCs and regional disaster prevention and mitigation capacity but also contributes to optimizing the layout of NCDDCs and serves as a scientific reference for implementing differentiated development measures. At present, however, there are few studies on the distribution characteristics and influencing factors of NCDDCs. In limited studies, Zhou et al. [35] employed ArcGIS to examine the spatiotemporal characteristics of the NCDDC distribution in China from 2008 to 2011 at the county scale. Wu et al. [14,36] used spatial autocorrelation, hotspot analysis, standard deviational ellipse, and Spearman correlation to study the spatial differentiation and influencing factors of NCDDCs in 2017 at the county scale. Their findings showed that NCDDCs have provided important demonstration and mitigation benefits during disaster events. Li et al. [37] investigated the spatial distribution and influencing factors of NCDDCs in the Beijing–Tianjin–Hebei region using the kernel density, nearest neighbor index, geographic centralization index, Gini coefficient, imbalance index, and Geodetector. Ma et al. [38] examined the evolutionary characteristics of NCDDCs in China from 2008 to 2020 at various administrative unit scales and determined the impact mechanisms using Pearson correlation analysis.

Despite the fact that some results have been obtained from existing studies on the distribution characteristics and influencing factors of NCDDCs, there are still pertinent issues worth discussing. Firstly, most studies focused on administrative units as the research subject instead of NCDDCs represented as point elements, which may lead to a failure in capturing the more detailed spatial distribution characteristics of NCDDCs and introduce bias in the NCDDC layout optimization and related policy adjustments. Secondly, relatively few studies have been conducted at the national level, especially on spatiotemporal distribution characteristics. This, to a certain extent, weakened the capacity for spatiotemporal comparisons and understanding of NCDDCs across expansive regions, as well as the reference value for formulating NCDDC-related policies at the national level. Thirdly, most studies did not quantify the impact of influencing factors on the NCDDC distribution, particularly the interaction among factors.

In view of this, various spatial analysis methods, including the nearest neighbor index, standard deviation ellipse, kernel density, and spatial autocorrelation, were used to examine the spatiotemporal distribution characteristics of NCDDCs from 2008 to 2021. This will contribute to grasping the past and present development levels of NCDDCs across different regions in China and identifying the lagging areas of NCDDC establishment. Moreover, statistical analysis and Geodetector were employed to detect the effects of single-factor and two-factor interactions on the NCDDC distribution in China in a qualitative and quantitative way. This will contribute to optimizing the spatial structure of NCDDCs according to local conditions and providing spatially differentiated decisions for NCDDC development.

This paper is structured in subsequent sections as follows: Section 2 provides a comprehensive description of data sources and research methods. Section 3 presents the spatiotemporal distribution characteristics of NCDDCs, along with a qualitative and quantitative analysis of the influencing factors. Section 4 explores the disparities in NCDDC distribution at the urban–rural scale, the relationships between NCDDCs and influencing factors, our study’s advantages and limitations, and provides recommendations for future NCDDC development. Section 5 concludes our research findings.

2. Materials and Methods

2.1. Data Sources

The data for the cumulative 15 batches of NCDDCs in China from 2008 to 2021 were collected from the official websites of China’s Ministry of Emergency Management, National Disaster Reduction Center, and Ministry of Civil Affairs. After eliminating duplicates, we obtained a total of 15,030 NCDDCs. The geographical coordinates of the NCDDCs were sourced from Gaode Maps, and their locations are depicted in Figure 1.

Combining the relevant literature [36,37,38] and data availability, we selected nine potential influencing factors to discuss the spatial distribution of NCDDCs in five dimensions: disaster, medical services, government agency accessibility, transportation, and socioeconomics. The higher the population density, the higher the exposure and the greater the potential losses caused by a disaster event [39]. Gross domestic product (GDP) density serves as an indicator of the local capacity to facilitate NCDDC establishment. High-density medical services can increase community disaster and social resilience and reduce casualties [40]. The distances to local government and fire stations reflect the government’s organizational capacity and firefighting accessibility, respectively. They have a direct impact on emergency response efficiency. Road density affects the evacuation of people during disasters, which helps improve resilience [41]. In China, the natural disasters that pose the greatest threat to people and property are earthquakes and floods [42]. Therefore, we only discuss the impact of earthquakes and floods on the spatial distribution of NCDDCs. The seismic peak ground acceleration (PGA) is a very important factor in engineering seismology and reflects the earthquake-associated hazard [43,44]. It is very difficult to accurately determine the national flood hazard. Generally, floods have an extensive geographical impact. The comprehensive assessment of river and coastal flood risks, as rendered by the Aqueduct Water Risk Atlas, offers free and accessible data to obtain both the geographical expanse and the degree of flood risk across China’s regions. River flood risk and coastal flood risk are combined indicators of hazards, exposure, and vulnerability. It is pertinent to acknowledge that an intersection exists between flood risk and other indicators such as population and economy. However, the population and economy indicators used in this study are 1 km × 1 km raster data, which contributes to an understanding of the local population and economy. As flood risk reflects larger regional flood characteristics, population and economic indicators can be considered as a further complement to flood risk. Additional descriptions of these influences are provided in

Table 1. Descriptions of influencing factors.

Influencing Factors	Descriptions	Sources
Road density	It includes China’s primary, secondary, tertiary, and fourth roads.	The National Earth System Science Data Center (http://www.geodata.cn, accessed on 5 June 2022).
Hospital density	It includes China’s secondary and tertiary hospitals up to May 2023.	Provincial health commission in China.
Distance to local government	It includes county/district, city, and provincial governments.	The Gaode Maps (https://www.amap.com, accessed on 5 May 2023)
Distance to fire station	It includes fourth level (county/district level) and above fire station.
Population density	The data with a spatial resolution of 1 km in 2019.	The Resource and Environmental Science Data Center of the Chinese Academy of Sciences (http://www.resdc.cn, accessed on 5 May 2023).
Gross Domestic Product (GDP) density
Seismic peak ground acceleration (PGA)	It reflects the earthquake-associated hazard of China.	The Seismic Ground Motion Parameters Zonation Map of China (GB18306-2015) [45] (https://www.gb18306.net, accessed on 5 May 2023)
Coastal flood risk	It reflects regional flood risk caused by storm surge.	The Aqueduct’s Water Risk Atlas Map (https://www.wri.org, accessed on 5 May 2023)
Riverine flood risk	It reflects regional flood risk caused by river overflow.

.

2.2. Methods

2.2.1. Standard Deviation Ellipse

The standard deviation ellipse is often used to reveal the spatiotemporal evolutionary characteristics of geographic phenomena [46,47,48,49]. It mainly contains the center, long axis, short axis, and rotation angle. These parameters are calculated as shown below.

$$CenterX=\sqrt{\frac{{\sum }_{i=1}^n{(x_i-\overline{X})}^2}{n}}, CenterY=\sqrt{\frac{{\sum }_{i=1}^n{(y_i-\overline{Y})}^2}{n}}$$

(1)

$$\mathit{tan}\theta =\frac{\left({\sum }_{i=1}^n{\tilde{x}}_i^2-{\sum }_{i=1}^n{\tilde{y}}_i^2\right)+\sqrt{{({\sum }_{i=1}^n{\tilde{x}}_i^2-{\sum }_{i=1}^n{\tilde{y}}_i^2)}^2+4{\left({\sum }_{i=1}^n{\tilde{x}}_i{\tilde{y}}_i\right)}^2}}{2{\sum }_{i=1}^n\widetilde{x_i}\widetilde{y_i}}$$

(2)

$$XstdDist=\sqrt{2}\sqrt{\frac{{\sum }_{i=1}^n{({\tilde{x}}_i\cos \theta -{\tilde{y}}_i\sin \theta )}^2}{n}}, YstdDist=\sqrt{2}\sqrt{\frac{{\sum }_{i=1}^n{({\tilde{x}}_i\sin \theta -{\tilde{y}}_i\cos \theta )}^2}{n}}$$

(3)

where ($CenterX$, $CenterY$) denotes the center point of the ellipse; ($\overline{X}$, $\overline{Y}$) denotes the arithmetic mean center; ($x_i$, $y_i$) denotes the coordinates of the element; (${\tilde{x}}_i$, ${\tilde{y}}_i$) denotes the deviation between the mean center and ($x_i$, $y_i$); $XstdDist$ and $YstdDist$ denote the length of the X and Y axes, respectively; $\theta$ denotes the rotation angle, which is the main trend direction of the element distribution.

2.2.2. Spatial Autocorrelation

Spatial autocorrelation is a method utilized to assess the extent of a significant correlation between the attribute values of an element and those of its neighboring elements [50,51]. It includes both global and local spatial autocorrelation.

The overall spatial correlation degree of NCDDCs in China can be quantified using Moran’s $I$.

$$I=\frac{n}{\sum _{i=1}^n\sum _{j=1}^n{w}_{ij}}\frac{\sum _{i=1}^n\sum _{j=1}^n{w}_{ij}(x_i-\overline{x})(x_j-\overline{x})}{\sum _{i=1}^n{(x_i-\overline{x})}^2}$$

(4)

where $n$ denotes the county number; $x_i$ ($x_j$) denotes the NCDDC number in county $i$($j$); $\overline{x}$ denotes the average NCDDC number across all counties; $w_{ij}$ denotes the spatial weight matrix between county $i$ and $j$; Moran’s $I$ varies between −1 and 1; $I$ > 0 denotes positive spatial correlation, while $I$ < 1 denotes negative spatial correlation; as $I$ is close to 0, it denotes a random distribution.

Local spatial autocorrelation analysis can identify spatial correlation and variability between a given spatial unit and its neighboring units [52], which can be detected by the LISA tool of Geoda.

$${LISA}_i=\frac{(x_i-\overline{x})}{S_i^2}\sum _{j=1,j\ne i}^n{w}_{ij}(x_i-\overline{x})$$

(5)

where ${LISA}_i$ denotes the local Moran’s $I$ of the county $i$; $S_i^2$ denotes the variance of the NCDDC number in the county; other symbols are the same as above.

2.2.3. Nearest Neighbor Index

The nearest neighbor index can determine the proximity degree and distribution type of point elements in space [49,53]. It is calculated as:

$$R=\frac{r_i}{r_E},r_E=\frac{1}{2}\sqrt{n/S}$$

(6)

where $r_i$ and $r_E$ denote the actual and theoretical nearest neighbor distances, respectively; $S$ denotes the study area; $n$ denotes the NCDDC number; $R$ denotes the nearest neighbor index of NCDDCs; $R$ > 1, $R$ = 1, and $R$ < 1 denote dispersed, random, and agglomerated distributions, respectively.

2.2.4. Kernel Density

Kernel density is a non-parametric method used to estimate surface density [54], which can visualize the specific locations and degrees of point element aggregation. In this study, kernel density was employed to measure the spatial aggregation characteristics of NCDDCs. The equation for kernel density is as follows:

$$f(x)=\frac{1}{nh}\sum _{i=1}^{n}K\left(\frac{x-x_i}{h}\right)$$

(7)

where $f\left(x\right)$ denotes the kernel density estimate at location $x$; $K\left(\frac{x-x_i}{h}\right)$ denotes the kernel function; $h$ denotes the bandwidth; $x-x_i$ denotes the distance from $x$ to $x_i$; the larger the kernel density value, the higher the aggregation level of NCDDC.

2.2.5. Geodetector

Geodetector is mainly used for detecting the spatial heterogeneity of geographical elements and analyzing their influencing factors [55,56]. The method can quantify the impact of single factors and interactions between factors on geographical phenomena [57]. In this study, we used the Geodetector’s factor and interaction detection to detect the effects of influencing factors on the NCDDC distribution.

The factor detection of Geodetector provides a quantitative estimate of a single factor’s impact on the NCDDC distribution. It is calculated as:

$$q=1-\frac{\sum _{h=1}^L{N}_h{\sigma }_h^2}{N{\sigma }^2}$$

(8)

where $q$ denotes the impact level of a factor on the NCDDC distribution, and its range is [0, 1]; the larger of $q$, the stronger the impact on the NCDDC distribution; $L$ denotes the number of layers divided by an explanatory variable; $N_h$ and $N$ denotes the NCDDC number in layer $h$ and the entire area, respectively; ${\sigma }_h^2$ and ${\sigma }^2$ denotes the variance of the NCDDC kernel density value in layer $h$ and the entire area, respectively.

Interaction detection identifies interactions between different factors; it assesses whether two factors enhance or weaken the explanatory power of the NCDDC distribution or whether these factors have an independent impact on the NCDDC distribution. The types of interactions are listed in

Table 2. Types of interaction between two factors.

Description	Type of Interaction
$q\left(X_1\cap X_2\right)<Min(q\left(X_1\right),q\left(X_2\right))$	Nonlinear weakening
$Min\left(q\left(X_1\right),q\left(X_2\right)\right)<q\left(X_1\cap X_2\right)<Max\left(q\left(X_1\right),q\left(X_2\right)\right)$	Univariate weakening
$Max\left(q\left(X_1\right),q\left(X_2\right)\right)<q\left(X_1\cap X_2\right)<q\left(X_1\right)+q\left(X_2\right)$	Bivariate enhancement
$q\left(X_1\cap X_2\right)>q\left(X_1\right)+q\left(X_2\right)$	Nonlinear enhancement
$q\left(X_1\cap X_2\right)=q\left(X_1\right)+q\left(X_2\right)$	Independent

.

3. Results

3.1. Evolution of County Distribution

Figure 2 shows that NCDDCs experienced the highest annual growth rate in 2008–2011. During this period, the number of counties without NCDDCs decreased from 2593 to 1575. Subsequently, from 2011 to 2018, there was an accelerated development of NCDDCs, with approximately 1200–1500 new NCDDCs being established each year. Thus, the number of counties without NCDDCs decreased from 1575 in 2011 to 435 in 2018. This positive trend can be attributed to the implementation of the National Comprehensive Disaster Prevention and Mitigation Plan (2011–2015) and the National Comprehensive Disaster Prevention and Mitigation Plan (2016–2020), both of which aimed to establish 5000 NCDDCs. However, from 2011 to 2018, there was an increase in the standard deviation of NCDDC numbers within counties, rising from 1.83 to 4.33. This indicates an imbalance in NCDDC development at the county scale. Despite the increase of 2592 NCDDCs between 2018 and 2021, the number of counties without NCDDCs decreased by only 100. Moreover, the standard deviation of NCDDC numbers in counties further increased to 5.23, exacerbating the critical imbalance at the county scale. In 2021, the numbers of counties with NCDDC numbers in the range of 0–5, 5–10, 10–20, 20–50, and >50 were 1658, 775, 319, 88, and 8, respectively. There is a “pyramid” distribution in the number of counties with varying NCDDC number scales. This indicates that most counties have a limited number of NCDDCs, and as the scale of NCDDCs increases, the number of counties decreases.

3.2. Evolution of Gravity Center and Distribution Direction

Figure 3 depicts the gravity centers and standard deviation ellipses of the NCDDCs in China from 2008 to 2021, while

Table 3. The parameters of standard deviation ellipses of NCDDC in China from 2008 to 2021.

Year	CenterX (°E)	CenterY (°N)	XstdDist (km)	YstdDist (km)	Rotation (°)	Move Distance (km)
2008	112.52	35.81	1182.20	1053.59	94.84	-
2009	113.03	34.75	1131.52	1076.87	91.60	126.82
2010	113.28	33.76	1033.62	1105.03	9.33	115.95
2011	113.43	33.50	1042.37	1110.85	172.19	34.12
2012	113.60	33.54	1035.32	1101.35	176.52	15.76
2013	113.68	33.53	1024.80	1096.48	178.08	7.33
2014	113.79	33.53	1016.97	1092.28	179.80	9.26
2015	113.86	33.48	1005.31	1082.38	2.34	8.26
2016	113.95	33.41	988.14	1076.63	6.93	9.68
2017	114.03	33.37	970.57	1074.73	10.02	7.54
2018	114.07	33.33	959.49	1070.84	11.80	4.64
2019	114.13	33.30	957.94	1073.25	12.54	5.86
2020	114.12	33.24	956.33	1071.93	12.41	6.39
2021	114.10	33.19	957.12	1069.50	12.36	5.29

provides the corresponding parameter values.

Generally, the XStdDist (long axis) gradually decreased with time, from 1182.20 km in 2008 to 957.12 km in 2021. The YstdDist (short axis) first increased and then decreased with time, fluctuating within the range of 1050–1110 km. These findings indicate a clear spatial agglomeration of NCDDCs in China, with the degree of agglomeration growing with time. At each characteristic time point, the gravity center of the NCDDCs was located between 33.19° N and 35.81° N and 112.52° E and 116.60° E, all to the southeast of the geometric center of China (103° E, 36° N). This indicates that the Eastern and Southern regions have a higher concentration of NCDDCs than the Western and Northern regions in China.

Specifically, from 2008 to 2014, the spatial distribution of NCDDCs in China mainly followed a northwest–southeast trend, except in 2010, when an apparent northeast–southwest trend emerged. The gravity centers of NCDDCs were located in Jincheng in 2008, Zhengzhou in 2009, and Pingdingshan in 2010. After 2010, Zhumadian became the stable location for the NCDDCs’ gravity centers. The gravity centers of NCDDCs moved drastically to the southeast from 2008 to 2010, with movements of 126.82 km and 115.95 km, respectively. During this period, the rotation angle of the ellipses decreased from 94.84° in 2008 to 9.33° in 2010. This is due to the fact that in 2008, China’s NCDDCs were in the primary stage, and most provinces had poor NCDDC development, with Xinjiang, Zhejiang, and Shandong taking the lead. In 2010, Beijing, Hubei, Guangdong, and Zhejiang experienced significant NCDDC development, with each province having more than 90 NCDDCs. From 2011 to 2014, the rotation angle of the ellipses increased from 172.19° to 179.80°, which reveals that the NCDDCs’ northwest–southeast spatial distribution is continuously weakening, gradually tending to the south–north direction. The gravity centers of the NCDDCs first moved to the northeast and then to the east, with the latter move attributed to the development of NCDDCs in the Eastern coastal provinces. From 2015 to 2019, the rotation angle first increased and then decreased, eventually stabilizing in the range of 12–12.5°. This means that the spatial distribution of NCDDCs in China showed a clear northeast–southwest trend after 2014 that intensified with time and eventually reached a stable pattern. During this period, the gravity centers of NCDDCs moved marginally. In terms of the movement direction of the gravity centers, between 2014 and 2019, they moved to the southeast, while from 2019 to 2021, their movement direction changed to the southwest.

3.3. Evolution of Spatial Correlation

Table 4. Global Moran’s $I$ of NCDDCs in China.

Year	$\mathbf{Moran}’\mathbf{s} \mathit{I}$	$\mathit{Z}$ Score	$\mathit{p}$ Value	Year	$\mathbf{Moran}’\mathbf{s} \mathit{I}$	$\mathit{Z}$ Score	$\mathit{p}$ Value
2008	0.583	14.861	0.000	2015	0.815	20.927	0.000
2009	0.651	16.582	0.000	2016	0.840	21.568	0.000
2010	0.742	19.032	0.000	2017	0.844	21.658	0.000
2011	0.758	19.523	0.000	2018	0.842	21.597	0.000
2012	0.766	19.747	0.000	2019	0.849	21.783	0.000
2013	0.801	20.646	0.000	2020	0.858	22.019	0.000
2014	0.815	20.958	0.000	2021	0.865	22.188	0.000

lists the Moran’s $I$ of NCDDCs in China from 2008 to 2021. The results demonstrate that the Moran’s $I$ values of NCDDCs were consistently positive and statistically significant ($p$ < 0.05) at the 1% level. This indicates that there is a strong positive spatial correlation among NCDDCs across counties from 2008 to 2021, and the NCDDC development has a positive space spillover effect; that is, the counties with a greater number of NCDDCs are inclined to be situated adjacent to each other, while the counties with a lower number of NCDDCs also exhibit such adjacency. On the other hand, both the Moran’s $I$ values and $Z$ scores gradually increased with time, indicating a strengthening spatial correlation of NCDDCs, accompanied by an increasing level of statistical significance.

To reflect the local spatial agglomeration heterogeneity of NCDDCs in various counties, we selected NCDDCs from the years 2008, 2013, 2018, and 2021 to draw LISA clustering maps (Figure 4). The LISA analysis results show five distribution types: High–High (HH), Low–Low (LL), Low–High (LH), High–Low (HL), and Not Significant. In general, the local spatial aggregation patterns of NCDDCs in 2013, 2018, and 2021 are similar.

In 2008, the HH distribution was observed in a total of 61 counties, with a significant cluster of such counties in Beijing. In later years, the distribution of HH counties became more concentrated and stable. Specifically, HH counties formed three core clusters with large coverage areas in the Beijing–Tianjin, Pearl River Delta, and Yangtze River Delta regions, which are the leading regions for NCDDC development in China. Furthermore, some sub-clusters of HH counties occurred at the junction of Hunan and Jiangxi, along with some dispersed micro-clusters in Xiamen, Dalian, Qingdao, Urumqi, and Liuzhou. These counties and their neighboring counties have a large number of NCDDCs and belong to an area with high NCDDC homogeneity. Fortunately, the coverage of HH clusters gradually expanded from 2013 to 2021, with the number of counties belonging to this type increasing from 289 in 2013 to 356 in 2021.

Most counties belonged to the LL distribution in 2008, which was mainly located in the provincial boundary areas. As depicted in Figure 4, the clustering characteristic of LL county distribution has strengthened with time, forming a concentrated and contiguous area. This area mainly includes Southern Hebei, Northern Henan, Northern Inner Mongolia, Southern Ningxia, Shanxi, Shaanxi, Western Sichuan, Tibet, and Yunnan. The LL counties, as well as their surrounding counties, have a low number of NCDDCs. Unfortunately, the number of LL counties increased from 558 in 2008 to 861 in 2021.

HL counties demonstrate an unstable and scattered pattern across most provinces in China. These counties are typically located adjacent to LL clusters. HL counties exhibit a significant disparity in the number of NCDDCs compared to neighboring counties. Despite their relatively high number of NCDDCs, the development of surrounding counties is not stimulated. As a result, the spatial layout is characterized by a “polarization type”, with a high number of NCDDCs in the center and a low number in the surrounding areas. HL county is often rich in NCDDCs in a particular area and is referred to as a “raised inequality county”. However, the number of such counties decreased from 118 in 2013 to 98 in 2021.

The LH distribution comprised a total of 219 counties in 2008, which exhibited three distinct clusters of HH counties in Beijing, Western Inner Mongolia, and Xinjiang. After 2008, the distribution of LH counties became stable, and they exhibited proximity to the clusters of HH counties. The NCDDC number in LH counties is significantly lower than that in surrounding counties, forming a negatively correlated distribution pattern with a small number in the center and a large number on the periphery in space, which we refer to as “depressed inequality counties”. However, the number of LH counties increased from 96 in 2013 to 141 in 2021 due to the influence of the HH cluster.

3.4. Evolution of Spatial Agglomeration

The results of the NCDDCs’ nearest neighbor index for each year are shown in

Table 5. The nearest neighbor index of NCDDC in China from 2008 to 2021.

Year	Average Observation Distance (km)	Expected Mean Distance (km)	Nearest Neighbor Index	z-Score	$\mathit{p}$ Value	Pattern
2008	61.114	90.016	0.679	−10.352	0.000	agglomeration
2009	30.468	58.046	0.525	−23.753	0.000	agglomeration
2010	16.315	38.544	0.423	−43.424	0.000	agglomeration
2011	10.922	28.541	0.383	−62.770	0.000	agglomeration
2012	8.779	23.709	0.370	−77.080	0.000	agglomeration
2013	7.773	20.678	0.376	−87.590	0.000	agglomeration
2014	6.829	18.550	0.368	−98.850	0.000	agglomeration
2015	6.067	16.889	0.359	−110.102	0.000	agglomeration
2016	5.421	15.556	0.348	−121.545	0.000	agglomeration
2017	5.003	14.482	0.345	−131.171	0.000	agglomeration
2018	4.603	13.602	0.338	−141.150	0.000	agglomeration
2019	4.428	13.103	0.338	−146.639	0.000	agglomeration
2020	4.306	12.644	0.341	−151.349	0.000	agglomeration
2021	4.235	12.374	0.342	−154.266	0.000	agglomeration

. From 2008 to 2021, all $p$ values were less than 0.01, indicating that they passed the 1% significance test. In each year, the nearest neighbor index of NCDDCs was less than 1, showing agglomeration in the NCDDC distribution. Meanwhile, the nearest neighbor index of NCDDCs decreased gradually, indicating that the agglomeration of NCDDCs in China is gradually increasing.

To visually illustrate the evolutionary characteristics of NCDDC distribution in space, we calculated the kernel density of NCDDCs for 2008, 2013, 2018, and 2021. After conducting multiple experiments, we determined a search radius of 110 km, which provided the best smoothing effect of the NCDDC kernel density distribution map. The results of the NCDDC kernel density were classified into five groups using the natural interval method: low-value, lower-value, medium-value, higher-value, and high-value zones (Figure 5).

Overall, NCDDCs formed a single-center core density zone, a double-center sub-core density zone, and nine single-center sub-core density zones in 2008. The single-center core density zone, with Beijing and Tianjin at its core, formed a hierarchical ring-core decreasing area as it spread and decreased to the surrounding regions. The double-center sub-core density zone cores were in Ningbo and the junction area of prefecture-level cities in Southern Jiangsu (including Changzhou, Taizhou, Suzhou, Wuxi, and Zhenjiang). This zone radiated outward, forming a sub-ring-core expansion group. The single-center sub-core density zones tended to form regional peaks in provincial capitals such as Baoji, Changchun, Changsha, Qingdao, Shenyang, Shijiazhuang, Wuhan, Yinchuan, and the main area of Chongqing. These zones gradually decreased in density as they spread to the surrounding regions, forming mini-sub-ring-core decreasing areas. Moreover, the limited number of designated NCDDCs in 2008 resulted in distinct and separate clusters with lower densities in many regions.

In 2013, there was a significant increase in NCDDC density in the Pearl River Delta and Yangtze River Delta regions. Specifically, in the Pearl River Delta region, a single-center core density zone emerged with Foshan, Guangzhou, Shenzhen, and Zhongshan as its core. This zone spread and decreased towards the surrounding regions, forming a hierarchical ring-core decreasing area. Similarly, in the Yangtze River Delta region, a single-center core density zone developed with Shanghai as its core, radiating outwards to Anhui, Jiangsu, and Zhejiang, forming a ring-core expansion group. However, the number of sub-core density zones decreased significantly in 2013, with these disappearing zones typically transforming into lower-density aggregation areas. Furthermore, the sub-core density zones in Changsha, Chongqing, and Wuhan experienced a significant reduction in coverage. Notably, a large cluster pattern emerged in 2013 as medium- and lower-density zones became interconnected in a faceted manner.

In 2018, the sub-core density zone centered around the main area of Chongqing disappeared, while two new sub-core density zones emerged. The first one was located in Dalian and had limited coverage. This zone spread and decreased to the surrounding regions, forming a mini-sub-ring-core decreasing area. Another new sub-core density zone emerged in Xiamen, radiating northeastward and forming a belt-shaped sub-extension group. Moreover, the sub-core density zone centered around Wuhan formed a Ɔ-shaped sub-extension group with Nanchang and Xinyu. The coverage area of the core density zones in the Beijing–Tianjin and Pearl River Delta regions underwent minor changes. Both the high and medium-density zones expanded in the ring-core expansion group centered around Shanghai. Specifically, the high-density zone extended to Nanjing, forming the largest high-density agglomeration in China. The medium-density zone expanded northward towards Jinan-Qingdao. Notably, a distinct Ɔ-shaped medium-density agglomeration emerged in Northern China, connecting Taiyuan, Shijiazhuang, and Zhengzhou.

The distribution characteristics of NCDDC density in 2021 were similar to those in 2018. A notable difference was the extension of the high-density zone in the ring-core extension group centered around Shanghai, stretching from central Anhui to Shandong. This extension resulted in an island phenomenon of low-density agglomeration at the junction of Bengbu, Chuzhou, Suizhou, Huai’an, and Suqian. Furthermore, the sub-core density zone centered around Dalian disappeared and transformed into a medium-density aggregation.

In summary, the NCDDC distribution was characterized by a dispersal effect in earlier periods and a combination of agglomeration and diffusion effects in later periods. In 2013, a distribution pattern with three core density zones emerged. In 2018, the NCDDCs followed a concentrated and contiguous distribution pattern in space, based on “core density zone–ring-core decreasing area–ring-core expansion group–Ɔ-shaped area–belt-shaped area”. This distribution pattern was further strengthened from 2018 to 2021.

3.5. Influencing Factors of NCDDC Distribution

Due to limitations in data collection, we used Geodetector to examine the impact of influencing factors on the distribution of NCDDCs in 2021. The detection results of single factors on the NCDDC distribution are presented in

Table 6. Detection results of single factor on the NCDDC distribution.

Influencing Factors	$\mathit{q}$-Statistic	$\mathit{p}$-Value	Influencing Factors	$\mathit{q}$-Statistic	$\mathit{p}$-Value
distance to local government (X1)	0.0694	0.0000	road density (X6)	0.4847	0.0000
distance to fire station (X2)	0.0661	0.0000	PGA (X7)	0.1315	0.0000
GDP density (X3)	0.4480	0.0000	coastal flood risk (X8)	0.1115	0.0000
population density (X4)	0.3696	0.0000	riverine flood risk (X9)	0.0608	0.0000
hospital density (X5)	0.5300	0.0000

. All factors passed the significance test, with $p$-values below 0.01. The factors are ranked according to their influence as follows: X5 (hospital density) > X6 (road density) > X3 (GDP density) > X4 (population density) > X7 (PGA) > X8 (coastal flood risk) > X1 (distance to local government) > X2 (distance to fire station) > X9 (riverine flood risk).

3.5.1. Disaster

Figure 6 shows the spatial overlap between NCDDCs and PGA, riverine flood risk, and coastal flood risk, while

Table 7. Number of NCDDCs in different ranges of PGA, riverine flood risk, and coastal flood risk.

PGA	Number	Percentage	Riverine Flood Risk	Number	Percentage	Coastal Flood Risk	Number	Percentage
0.05 g	6224	41.41%	Low	989	6.58%	Low	10,757	71.57%
0.1 g	5092	33.88%	Low–Medium	4345	28.91%	Low–Medium	481	3.20%
0.15 g	1422	9.46%	Medium–High	5475	36.43%	Medium–High	1103	7.34%
0.2 g	2113	14.06%	High	3302	21.97%	High	1041	6.93%
0.3 g	171	1.14%	Extremely High	919	6.11%	Extremely High	1648	10.96%
0.4 g	8	0.05%

presents the number of NCDDCs in different ranges of PGA, riverine flood risk, and coastal flood risk.

PGA is a critical indicator in engineering seismology, with higher values indicating a greater level of the earthquake-associated hazard [43,44]. Among the various disaster factors, PGA exhibits the strongest explanatory power of 13.15% on the NCDDC distribution in China. Statistics reveal that the number of NCDDCs with PGAs of 0.05 g, 0.1 g, 0.15 g, 0.2 g, 0.3 g, and 0.4 g is 6224 (41.41%), 5092 (33.88%), 1422 (9.46%), 2113 (14.06%), 171 (1.14%), and 8 (0.05%), respectively. More than 75% of NCDDCs are located in areas with PGAs of 0.05 g and 0.1 g. It can be concluded that the number of NCDDCs decreases as PGA increases, indicating a negative correlation between both of them.

Among all factors, river flood risk exhibits the lowest explanatory power in the NCDDC distribution, with an explanatory power of 6.08%. Table 7 reveals an inverted U-shaped relationship between the number of NCDDCs and river flood risk, in which the number of NCDDCs first increases and then decreases as the river flood risk increases. Furthermore, over 1/3 of NCDDCs are situated in areas with moderate–high river flood risk, while more than 2/3 of NCDDCs are located in areas with low–moderate and moderate–high river flood risks.

According to Geodetector analysis, coastal flood risk has an explanatory power of 11.15% in the NCDDC distribution. Figure 6 depicts that most areas in China have low coastal flood risk. Therefore, our analysis focuses on the NCDDC distribution characteristics, excluding the low coastal flood areas. Table 7 shows that there are 481 (3.20%), 1103 (7.34%), 1041 (6.93%), and 1648 (10.96%) NCDDCs located in areas with low–moderate, moderate–high, high, and extremely high coastal flood risk, respectively. Thus, we can conclude that there is a positive correlation between NCDDC number and coastal flood risk, indicating that a higher coastal flood risk corresponds to a greater number of NCDDCs.

In general, a mismatch exists between the distribution of current NCDDCs and the disaster factor in the majority of areas in China. The expected relationship between having more NCDDCs distributed in higher PGA and flood-risk areas is not observed. However, coastal areas of China constitute an exception, demonstrating an alignment between NCDDC distribution and disaster factors. Nevertheless, it is essential to note that this alignment is limited solely to coastal areas and the coastal flood risk.

3.5.2. Medical Resource

Hospital density demonstrates the highest explanatory power of 53.00% on the NCDDC distribution. The kernel density of hospitals was calculated using ArcGIS 10.5. We assigned weights of 985.94 and 252.86 to secondary and tertiary hospitals, respectively, based on the average number of beds in secondary and tertiary hospitals reported in the 2021 Statistical Communique on the Development of China’s Health Undertakings [58]. Finally, the quintile method was used to divide hospital density results into five levels: low, lower, medium, higher, and high density (Figure 7).

The mean hospital density of 15,030 NCDDCs is 10.86 beds/km², which belongs to the high-density level. The number of NCDDCs located in low, lower, medium, higher, and high-density areas is 6530, 2065, 2847, and 9522, accounting for 0.44%, 3.53%, 13.74%, 18.94%, and 63.35%, respectively. Obviously, most NCDDCs (82.30%) are distributed in areas with a high hospital density. Moreover, there is a positive correlation between the NCDDC number and hospital density, meaning that as the hospital density increases, so does the number of NCDDCs.

3.5.3. Government Agency

The distances to local government and fire stations serve as indicators of government organizational capacity and firefighting accessibility, respectively. They have a direct impact on emergency response efficiency and disaster resilience. However, their influence on the NCDDC distribution in China is relatively weak, with an explanatory power of 6.94% and 6.61%, respectively. Using ArcGIS 10.5, we calculated the Euclidean distances from NCDDCs to local government and fire stations separately and overlaid them (Figure 8 and Figure 9). Due to the limited number of NCDDCs located far from these facilities, we selected NCDDCs within a Euclidean distance of less than 20 km for plotting the histograms. The histogram in Figure 8 represents 92.57% of the NCDDCs, while the histogram in Figure 9 represents 93.67% of the NCDDCs.

As can be seen in Figure 8 and Figure 9, as the distance to the local government and fire station increases, the number of NCDDCs first increases and then decreases. The distances of the turning points in Figure 8 and Figure 9 are 0.945 km and 1.5 km, respectively. On the left side of the turning point, the NCDDC number increases sharply with increasing distance from the local government and the fire station. Conversely, the NCDDC number decreases exponentially. Actually, there are 6875 (45.74%), 3168 (21.08%), 1857 (12.36%), 1205 (8.02%), 808 (5.38%), 770 (5.12%), 319 (2.12%), and 28 (0.19%) NCDDCs located in areas with distances of 0–2.5, 2.5–5, 5–10, 10–15, 15–20, 20–30, 30–60, and >60 km from the local government, respectively. Similarly, 6194 (41.21%), 4033 (26.83%), 2087 (13.89%), 1031 (6.86%), 688 (4.58%), 686 (4.56%), 292 (1.94%), and 19 (0.13%) NCDDCs are located in areas with the same distance range from the fire station. Obviously, more than 2/3 of the NCDDCs are located within 5 km of the local government and fire station. This shows that NCDDCs tend to be distributed near the local government and fire station. The establishment of current NCDDCs is strongly influenced by the radiating effects of the local government and the fire station.

3.5.4. Transportation

Road density plays a critical role in determining the convenience and accessibility of regional transportation [59]. It ranks second in terms of its impact on the NCDDC distribution in China, with an explanatory power of 48.47%. The line density of major roads in China was calculated using ArcGIS 10.5, and the resulting values were classified into five levels using the quintile method: low, lower, medium, higher, and high density (Figure 10).

The number of NCDDCs demonstrates an inverted U-shaped relationship with road density, as depicted in Figure 10. The turning point has a road density of 0.2065 km/km², and the mean road density among the 15,030 NCDDCs is 0.2216 km/km², both falling within the high road density range. Our findings indicate an exponential increase in the number of NCDDCs as road density increases on the left side of the turning point. Conversely, on the right side of the turning point, an increase in road density corresponds to a decrease in the NCDDC distribution. Specifically, there are 9 (0.06%), 165 (1.10%), 1467 (9.76%), 3482 (23.17%), and 9907 (65.91%) NCDDCs located within the road density ranges of 0.0000–0.0197, 0.0198–0.0544, 0.0545–0.1335, 0.1336–0.1896, and 0.1897–0.4253 km/km², respectively. Clearly, nearly 90% of NCDDCs are situated in areas characterized by high road density. The above results indicate that convenient transportation contributes to the establishment of NCDDCs.

3.5.5. Socioeconomic

The impacts of GDP density and population density on the NCDDC distribution in China rank third and fourth, with an explanatory power of 44.80% and 36.96%, respectively. Concentrations of population and property in local areas are associated with higher vulnerability to disasters [60]. GDP density and population density reflect the aggregation of population and property in a given area, respectively, and were used to analyze the interaction between NCDDC distribution and socioeconomics (Figure 11).

Figure 11 shows that NCDDCs are mainly located in economically developed provinces of China, such as Guangdong (1438, 9.57%), Zhejiang (1111, 7.39%), and Jiangsu (946, 6.29%). With nearly 1/4 of China’s NCDDCs, these regions can provide significant financial support for the establishment and development of NCDDCs. Furthermore, the median and mean GDP density values among the 15,030 NCDDCs are 6500 and 45,009 10⁴ CNY/km², respectively, significantly exceeding the mean GDP density value (1011 10⁴ CNY/km²) in the study area. Statistically, there are 2783 (18.52%), 1601 (10.65%), 1083 (7.21%), 1353 (9.00%), 1717 (11.42%), 3816 (25.39%), 1139 (7.58%), and 1538 (10.23%). NCDDCs are located in areas with a GDP density of <1011, 1011–2022, 2022–3033, 3033–5000, 5000–10,000, 10,000–50,000, 50,000–100,000, and > 100,000 10⁴ CNY/km², respectively. When considering areas with a GDP density higher than two times the mean GDP density of the study area (2022 10⁴ CNY/km²) as developed areas, the number of NCDDCs in these areas reaches 10,646, accounting for 70.83% of the total. Similarly, if areas with a GDP density exceeding three times the mean GDP density of the study area (3033 10⁴ CNY/km²) are considered developed areas, the number of NCDDCs in these areas is 9563, accounting for 63.63% of the total. These findings indicate that NCDDCs are mainly concentrated in areas with developed economies in China.

The mean population density in the study area is 148 persons/km², significantly lower than the median (1007 persons/km²) and mean (3190 persons/km²) population density of all NCDDCs. Using the mean population density in the study area as a reference, the NCDDC distribution is as follows: 1526 (10.15%), 1731 (11.52%), 1203 (8.00%), 3016 (20.07%), 4953 (32.95%), 1391 (9.25%), and 1210 (8.05%) NCDDCs are located in areas with a population density of <148, 148–296, 296–444, 444–1000, 1000–5000, 5000–10,000, and >10,000 persons/km², respectively. These findings show that most NCDDCs are located in areas with a population density exceeding 1000 persons/km², totaling 10,570 NCDDCs, accounting for over 70% of the total NCDDCs. Therefore, it can be inferred that NCDDCs in China tend to be established in densely populated areas. Furthermore, the distribution of NCDDCs in China closely aligns with the Heihe–Tengchong line of population distribution, where 9.2% of NCDDCs to the west are more scattered, while 90.8% of NCDDCs to the east demonstrate relatively concentrated patterns.

3.5.6. Interaction Effects of Different Factors

Table 8. Interaction detection results of influencing factors on the distribution of NCDDC in China.

	X1	X2	X3	X4	X5	X6	X7	X8	X9
X1	0.0694
X2	0.1527 **	0.0661
X3	0.5561 **	0.5428 **	0.4480
X4	0.5037 **	0.4720 **	0.5837 *	0.3696
X5	0.6373 **	0.6067 **	0.6396 *	0.6240 *	0.5300
X6	0.5429 *	0.5344 *	0.7256 *	0.7133 *	0.7553 *	0.4847
X7	0.2022 **	0.2026 **	0.5173 *	0.4706 *	0.5805 *	0.6214 **	0.1315
X8	0.1783 *	0.1701 *	0.5287 *	0.4791 *	0.6599 **	0.6292 **	0.2569 **	0.1115
X9	0.1438 **	0.1413 **	0.5350 **	0.4749 **	0.6122 **	0.6244 **	0.2856 **	0.2292 **	0.0608

Notes: * represents bilinear enhancement, ** represents nonlinear enhancement.

shows the two-factor interaction effect on the NCDDC distribution. The interaction relationships between the two influencing factors show a nonlinear and bilinear enhancement without a weakening or independent relationship. There are 21 nonlinear enhancement relationship combinations: (1) distance to local government and distance to fire station, GDP density, population density, hospital density, PGA, and river flood risk; (2) distance to fire station and GDP density, population density, hospital density, PGA, and river flood risk; (3) GDP density and river flood risk; (4) population density and river flood risk; (5) hospital density and riverine flood risk and coastal flood risk; (6) road density and PGA, riverine flood risk, and coastal flood risk; (7) PGA and riverine flood risk and coastal flood risk; (8) river flood risk and coastal flood risk. The combined effect of these factors after their interaction surpasses the sum explanatory power of the individual factors. The explanatory power of the remaining 15 combined factors, when considered in conjunction and accounting for their interaction, exceeds their individual explanatory power when acting alone. However, after interaction, the explanatory power is less than the sum of the explanatory powers of the two factors.

The first three combinations of interaction test results are as follows: road density ∩ hospital density (0.7553), road density ∩ GDP density (0.7256), and road density ∩ population density (0.7133). These results suggest that the NCDDC distribution in China is mainly influenced by hospital density, road density, GDP density, and population density. These factors play a crucial role in promoting the NCDDC establishment. Although the explanatory power of individual disaster factors is weak, significant enhancement is observed when considering the interactions with socioeconomic and road density factors. Overall, the NCDDC distribution in China is the outcome of a combination of multiple factors.

4. Discussion

4.1. Urban–Rural Differences of NCDDCs in China

Using the urban–rural division codes provided by the National Bureau of Statistics of China (http://www.stats.gov.cn/sj/tjbz/tjyqhdmhcxhfdm/2022/index.html, accessed on 10 April 2023), and following the accompanying compilation rules (http://www.stats.gov.cn/sj/tjbz/gjtjbz/202302/t20230213_1902741.html, accessed on 10 April 2023), we divided NCDDCs into seven groups and counted the proportion of new NCDDCs with different types per year (

Table 9. Proportion of new NCDDCs with different types per year in China from 2008 to 2021.

Year	The Urban–Rural Division Code
	111	112	121	122	123	210	220
2008	81.34%	2.82%	10.92%	0.70%	1.06%	0.00%	3.17%
2009	77.94%	3.76%	12.28%	1.00%	0.75%	0.25%	4.01%
2010	72.52%	3.46%	15.82%	2.31%	1.39%	0.12%	4.39%
2011	64.73%	4.15%	19.91%	2.82%	1.57%	0.39%	6.43%
2012	58.55%	4.89%	23.80%	4.18%	1.34%	0.24%	7.01%
2013	55.28%	3.42%	26.16%	4.50%	1.32%	0.78%	8.54%
2014	52.83%	4.75%	23.89%	6.43%	1.45%	0.84%	9.80%
2015	50.65%	6.09%	24.57%	5.94%	1.52%	0.43%	10.80%
2016	51.60%	4.16%	25.73%	5.20%	1.04%	0.49%	11.79%
2017	51.88%	5.54%	22.56%	4.78%	1.09%	0.75%	13.40%
2018	48.87%	5.26%	24.44%	6.14%	0.82%	1.16%	13.31%
2019	48.70%	6.11%	23.63%	5.70%	1.14%	0.73%	13.99%
2020	46.52%	5.45%	23.01%	4.74%	1.41%	0.81%	18.06%
2021	46.38%	6.13%	21.86%	6.13%	0.94%	1.73%	16.82%
Total	55.12%	4.84%	22.72%	4.76%	1.24%	0.65%	10.67%

Notes: 111, 112, 121, 122, 123, 210, and 220 represent the downtown area, urban–rural fringe area, town center, town–township fringe area, special zone, rural center, and NCDDC, respectively. The first digit of these three-digit codes distinguishes between urban and rural communities, with 1 indicating an urban community and 2 a rural community.

). As shown in Table 9, the proportion of NCDDCs with code 111 decreased annually from 2008 to 2021, decreasing below 50% after 2017. The proportion of NCDDCs in codes 112 and 121 experienced an initial increase, then a decrease. In contrast, the proportion of NCDDCs in codes 210 and 220 increased annually. Such findings suggest that the establishment of NCDDCs has gradually tilted towards rural areas with time.

In 2020, China had a total of 652,097 communities (villages). The proportions of communities (villages) coded as 111, 112, 121, 122, 123, 210, and 220 were 10.58%, 4.39%, 7.60%, 8.92%, 0.92%, 2.21%, and 65.39%, respectively. These proportions deviated significantly from the proportions of annual new and cumulatively designated NCDDCs, indicating that the establishment of NCDDCs is not solely determined by the number of existing communities (villages) of different types. According to the 7th census of China in 2020, there were 90,199,000 people living in towns and cities, accounting for 63.89% of the total population, and 509,790,000 people living in villages, accounting for 36.11%. Based on these figures, we can conclude that the average population sizes of communities in urban and rural areas are approximately 4300 and 1200, respectively. Therefore, it can be calculated that in 2021, the populations in urban and rural NCDDCs were 56,888,100 and 1,967,400, respectively. This ratio differs significantly from the current urban and rural population distribution in China, suggesting that the establishment of NCDDCs is not solely determined by China’s current urban–rural population, potentially signifying urban–rural differences in NCDDC distribution.

To better understand urban–rural differences in NCDDC distribution in China, we conducted a tally of the number and population of communities and NCDDCs in urban and rural areas in each province and computed the corresponding ratios in 2021 (

Table 10. Urban–rural differences of NCDDCs in China and each province in 2021.

Province	A1	A2	B1	B2	C1	C2	A1/B1	A2/B2	A2B1/A1B2
Tianjin	3090	196	2363	1	4.69	702.33	1.31	196.00	149.89
Qinghai	1022	168	3630	4	1.01	150.50	0.28	42.00	149.18
Inner Mongolia	3738	256	10,636	7	1.26	131.05	0.35	36.57	104.06
Xinjiang	4371	417	10,323	10	1.52	149.43	0.42	41.70	98.48
Gansu	3516	343	13,980	14	0.90	87.79	0.25	24.50	97.41
Jilin	3258	397	8363	12	1.40	118.55	0.39	33.08	84.92
Shanghai	5544	249	21,892	15	0.91	59.48	0.25	16.60	65.55
Shaanxi	7061	353	12,781	10	1.98	126.49	0.55	35.30	63.90
Ningxia	897	140	1914	6	1.68	83.61	0.47	23.33	49.79
Tibet	311	23	5125	9	0.22	9.16	0.06	2.56	42.11
Hebei	16,456	488	36,784	29	1.60	60.30	0.45	16.83	37.61
Yunnan	3161	232	11,292	25	1.00	33.25	0.28	9.28	33.15
Sichuan	12,354	658	40,261	66	1.10	35.72	0.31	9.97	32.49
Hubei	8824	644	19,503	52	1.62	44.38	0.45	12.38	27.37
Guangxi	4044	301	12,295	48	1.18	22.47	0.33	6.27	19.07
Anhui	6228	524	11,807	55	1.89	34.14	0.53	9.53	18.06
Heilongjiang	4726	407	9150	45	1.85	32.41	0.52	9.04	17.51
Liaoning	6753	491	9482	45	2.55	39.10	0.71	10.91	15.32
Henan	14,811	465	36,944	76	1.44	21.92	0.40	6.12	15.26
Fujian	5555	454	11,276	66	1.77	24.65	0.49	6.88	13.96
Hunan	9717	569	19,560	86	1.78	23.71	0.50	6.62	13.32
Shandong	23,857	699	53,055	123	1.61	20.36	0.45	5.68	12.64
Jiangsu	12,382	888	9574	58	4.63	54.86	1.29	15.31	11.84
Guizhou	5497	257	12,044	57	1.64	16.16	0.46	4.51	9.88
Beijing	4462	547	2700	37	5.92	52.98	1.65	14.78	8.95
Guangdong	11,716	1239	14,631	199	2.87	22.31	0.80	6.23	7.78
Hainan	1018	76	2289	29	1.59	9.39	0.44	2.62	5.89
Chongqing	3427	199	7681	82	1.60	8.70	0.45	2.43	5.44
Jiangxi	7667	426	13,725	141	2.00	10.83	0.56	3.02	5.41
Zhejiang	105,89	853	14,932	258	2.54	11.85	0.71	3.31	4.66
Shanghai	5286	370	767	36	24.70	36.83	6.89	10.28	1.49
Total	88,601	13,329	232,099	1701	1.72	28.08	0.48	7.84	16.34

Notes: A1 and A2 represent the number of urban communities and NCDDCs, respectively; B1 and B2 represent the number of rural communities and NCDDCs, respectively; C1 represents the ratio of urban to rural population; and C2 represents the ratio of population located in urban NCDDCs to population located in rural NCDDCs.

). In Table 10, the notation A2B1/A1B2 represents the ratio of the NCDDC number ratio in urban and rural areas to the community number ratio in urban and rural areas. This ratio serves as a metric to quantify the urban–rural differences in NCDDC distribution, which we refer to as the “urban–rural difference index”. For the number and population of urban and rural NCDDCs, as shown in Table 10, the respective ratios exceed the current proportions of urban and rural populations, as well as the proportions of urban and rural communities, in each province and the whole country. The urban–rural difference index for the entire country is 16.31, indicating that NCDDCs show a clustered distribution pattern in urban areas. Furthermore, notable regional disparities emerge in the urban–rural difference index of NCDDCs. This index surpasses the national urban–rural difference index in 17 provinces, where urban NCDDCs number 5796 and rural NCDDCs number 408. The top five provinces exhibiting the most pronounced rural–urban differences in NCDDC distribution are, in descending order, Tianjin, Qinghai, Inner Mongolia, Xinjiang, and Gansu. These provinces present an urban–rural difference index exceeding 90, with nearly 10% NCDDCs (1380 urban NCDDCs, 36 rural NCDDCs). It is noteworthy that, except for Tianjin, all these provinces belong to the underdeveloped provinces of northwest China. Conversely, 14 provinces have an urban–rural difference index lower than the national urban–rural difference index. In these regions, the number of NCDDCs in urban areas reaches 8826, compared with 1293 in rural areas. Shanghai has the lowest urban–rural difference index and the most balanced rural–urban distribution of NCDDCs. Nevertheless, it is essential to acknowledge that, despite its status as one of China’s most developed regions, Shanghai’s proportion of NCDDCs only comprises a modest 2.70% of the total. In summary, the NCDDC distribution in China is uneven at the urban–rural scale.

4.2. Distribution Characteristics of NCDDCs in China

China has a vast area with diverse population distribution, economic development, and disaster risk [61]. This diversity leads to significant regional differences in the NCDDC distribution. The number of counties without NCDDCs significantly decreased from 2008 to 2021. However, there is an increasing standard deviation in the number of NCDDCs among counties, indicating a considerable imbalance in NCDDC development at the county level. The distribution of counties with different numbers of NCDDCs follows a “pyramid” pattern in 2021. Moreover, there is an annual increase in the positive spatial correlation of NCDDCs at the county scale. The “neighboring reliance” phenomenon and positive space spillover effect of NCDDCs have been formed, which contribute to the development of NCDDCs in the whole region. In recent years, the gravity center of NCDDCs has started moving west, indicating a tilt in the establishment of NCDDCs in China to the Western regions. The nearest neighbor index reveals an increasing spatial aggregation of NCDDCs in local areas, indicating a continuous improvement in disaster prevention and mitigation capabilities in specific regions. Initially, the NCDDC distribution was characterized by dispersion, followed by the coexistence of aggregation and diffusion effects. The NCDDCs in space will show a concentrated and contiguous distribution pattern in 2021, based on “core density zone–ring-core decreasing area–ring-core expansion group–Ɔ-shaped area–belt-shaped area”. This distribution pattern aligns with the objective of the NCDDC project in China; that is, the NCDDC plays a leading, demonstrative, and radiating role in enhancing China’s comprehensive disaster reduction capacity at the grassroots level. However, the effectiveness of this leading, demonstrative, and radiating role varies significantly among different regions.

4.3. Relationship between the NCDDCs and Influencing Factors

Multiple factors influence the NCDDC distribution in China. The hospital density has the strongest influence on the NCDDC distribution. Our study reveals a strong concentration of NCDDCs in regions with high hospital density, and there is a positive correlation between the NCDDC number and hospital density, which is in line with the general law. Hospitals play a fundamental role in enhancing disaster resilience by minimizing community losses and providing essential treatment for injuries during emergency responses to disasters [62]. However, hospitals may experience a significant surge in patients during a disaster, surpassing their capacity [23]. Offering high-density medical care can bolster social resilience in the face of disasters, as the presence of doctors, beds, and nurses is vital to mitigating casualties [40]. Medical resources in China are mainly concentrated in economically developed areas characterized by high population density and substantial medical needs. In contrast, Western China faces a scarcity of medical resources, leading to insufficient medical services for numerous communities and a relatively limited presence of NCDDCs.

Currently, NCDDCs are mainly located in regions with a high population density and GDP density. In general, population density and GDP density serve as crucial indicators for assessing disaster vulnerability. A disaster event’s severity is typically related to the number of people affected by it. In fact, evacuating affected people in densely populated areas during a disaster event is extremely difficult [41]. The establishment of NCDDCs aims to improve residents’ awareness and self-help capabilities in disaster preparedness and mitigation [14]. Therefore, establishing NCDDCs in densely populated areas is of paramount importance for ensuring regional security. The concentration of NCDDCs in areas with a developed economy is easily understandable. Firstly, economically developed regions can provide sufficient financial support for establishing NCDDCs. Secondly, as the regional economy advances and residents’ income levels rise, the demand for a favorable living environment, particularly a safe community, increases. Considering both the economy and population, sparsely populated and economically underdeveloped areas have fewer casualties and less property damage, resulting in fewer NCDDCs. Conversely, higher population density and GDP density increase the potential loss from disaster events, requiring more NCDDCs.

The relationship between NCDDC distribution and disaster factors is diverse. The NCDDC number demonstrates a positive correlation with coastal flooding risk in areas other than low coastal flood regions. However, a negative correlation is observed between the NCDDC number and the PGA. Most NCDDCs are located in areas with low–moderate and moderate–high river flood risks. This finding contradicts the general understanding that areas with higher disaster hazard and risk should have more NCDDCs. Our study shows that the individual impact of disaster factors on the NCDDC distribution is relatively weak, consistent with previous research [37,38]. However, their explanatory power on the NCDDC distribution significantly increases when disaster factors interact with hospital density, GDP density, population density, and road density. In other words, the establishment of NCDDCs is directly influenced by the interaction of disaster factors with other factors. At present, China is experiencing a rise in both the frequency and intensity of disaster events [4,5]. Unlike the developed Eastern regions with numerous NCDDCs, underdeveloped regions such as Central and Western China face a higher combined disaster hazard and risk [42,61]. Meanwhile, NCDDCs have been proven to provide significant mitigation benefits during disaster events [14,36]. Therefore, it is imperative to promote the establishment of NCDDCs in areas with high disaster risks to mitigate the losses caused by disaster events.

Local government and fire stations serve as critical emergency service facilities, playing a vital role in providing relief assistance during disasters. Despite the limited explanatory power of distance from local government and fire stations on the NCDDC distribution, the number of NCDDCs first increases and then decreases as the distance to the local government and fire station increases. Most NCDDCs are located near local government and fire stations. This suggests that the radiating effect of local government and fire stations strongly influences the establishment of current NCDDCs. Moreover, increasing the distance between communities and these facilities hampers the speed of relief efforts and raises the level of danger [63]. However, in rural areas, most communities are located far from these facilities, leading to challenges in promptly accessing their services.

Roads are an important lifeline for disaster-affected populations. As shown in Figure 10, the Central–Eastern part of China has a high road density and a high number of NCDDCs. However, in alpine regions in the northeast and highland desert regions in the northwest, such as Heilongjiang, Qinghai, and Tibet, transport infrastructure is heavily hindered by topographical and climatic conditions. Thus, road density in these areas is low, which greatly limits the establishment and development of NCDDCs [14]. Notably, road density influences the accessibility of emergency facilities to a certain extent. On the one hand, higher road density improves accessibility to hospitals, fire stations, and the government. On the other hand, during disaster events, roads may experience hidden damages that lead to blockages and disruptions [64], such as building collapses during earthquakes or roads being flooded. A higher road density ensures community mobility and connectivity, enabling access to critical destinations like hospitals and facilitating the evacuation of residents in high-hazard areas for disaster-affected populations.

4.4. Planning and Policy Recommendations

Based on the comprehensive analysis of NCDDC distribution characteristics and their relationship with relevant influencing factors, we propose the following targeted recommendations. These recommendations aim to guide the optimization of the NCDDC layout and promote the sustainable development of society.

4.4.1. Establishing Rural-Type NCDDCs

At present, NCDDCs are extremely unevenly distributed on an urban–rural scale in China. As discussed in Section 4.1, most NCDDCs are located in urban areas, with relatively few NCDDCs in rural areas. Despite the increasing number of NCDDCs in rural areas, there remains a substantial disparity between the population served by urban NCDDCs and the population served by rural NCDDCs, with proportions significantly higher than those of the urban and rural populations in the provinces and the whole country. Most provinces have a high urban–rural difference index of NCDDCs. In fact, rural areas face more challenges than urban areas, including inadequate infrastructure, poor emergency management capacity, and high disaster risk [65]. Research shows that rural areas account for over 80% of human casualties and economic losses in China [65]. Furthermore, the central region of China has shown particular vulnerability to disasters over the past decade [66]. This region is characterized by a significant concentration of rural populations [67], leading to more severe impacts of disasters on the rural population [38]. Therefore, it is necessary to strengthen comprehensive disaster mitigation capacity in rural areas through the establishment of rural-type NCDDCs. Specifically, priority should be given to establishing NCDDCs in areas with a significant clustering of villages. In areas with dispersed villages, the establishment of NCDDCs can be phased to achieve a “leading by point” effect. Moreover, the establishment of NCDDCs in rural areas can be seamlessly integrated with China’s rural revitalization strategy by taking into account building shelters, improving transportation conditions, and enhancing the capacity of primary medical services.

4.4.2. Developing a Cross-Administrative NCDDC Layout

The emergence of disaster events is usually cross-administrative [68], indicating that the layout of NCDDCs solely in specific administrative districts is no longer suited for the reality of frequent cross-administrative disasters. Our study identifies a significant mismatch between the distribution of existing NCDDCs and the associated disaster factor, with a limited number of NCDDCs located in high PGA and flood-risk areas. Therefore, the layout of NCDDCs should be developed based on the impact coverage of disasters, ensuring that the scale of NCDDCs corresponds to the associated disaster risks, and promoting the development of a regional disaster prevention and mitigation system based on NCDDCs. Specifically, it is recommended to establish clusters of NCDDCs in small flood-prone watersheds to enhance the disaster prevention and mitigation capacity of the entire watershed area. Similarly, in earthquake-prone regions, it is important to establish NCDDCs in a balanced manner, taking into account population distribution characteristics, to enhance disaster prevention and mitigation capabilities. Furthermore, based on the findings of China’s national comprehensive natural disaster risk survey, NCDDCs should be established evenly in areas with a high overall risk of natural disasters.

4.4.3. Strengthening the Radiating Effect of Regional NCDDCs

From 2008 to 2021, with Shanghai as the core, NCDDCs in the Yangtze River Delta radiated to Anhui, Jiangsu, Shandong, and Zhejiang, forming a concentrated and contiguous ring-core expansion cluster group. The radiating effect of this expansion group is highly evident. However, NCDDCs in the Beijing–Tianjin and Pearl River Delta regions formed significant ring-core decreasing areas. Despite being economically developed areas in China, these regions have a limited radiating effect in terms of NCDDCs. Areas with such characteristics have a higher capacity for disaster prevention and mitigation, forming a clear gap with the surrounding areas. Therefore, it is necessary to adjust the NCDDC layout in these areas. Specifically, these areas should leverage their economic and policy advantages to establish NCDDCs in the surrounding areas, thereby enhancing their radiating effect. This strategy can improve the rationality of the NCDDC layout by transforming the NCDDC distribution from agglomeration to diffusion type. Moreover, the identification of sub-core zones in most parts of China is challenging, preventing NCDDCs from playing a leading, demonstrative, and radiating role in their local areas. In the future, more efforts should be made to establish NCDDCs in some provincial capitals and economically strong cities to form sub-core zones and promote their radiating effect in these areas.

4.4.4. Implementing Flexible and Differentiated Policies

The spatial distribution of NCDDCs exhibits diverse morphological characteristics influenced by various complex socioeconomic, infrastructural, and disaster factors. China’s Western region has a relatively low number of NCDDCs, with 58 out of 74 counties in Tibet lacking NCDDCs. Compared to the Central–Eastern region, the Western region of China faces numerous challenges, including an underdeveloped economy, a scattered population, limited medical resources, inadequate road infrastructure, and high disaster risk [14,38]. Achieving harmonious and synchronized NCDDC development between Western and Central–Eastern China is very difficult. Therefore, the Western region should receive policy support and financial assistance to achieve full NCDDC coverage. Specifically, supportive policies should be implemented in areas with a high population density and high disaster hazard in the Western region. Moreover, setting up special financial funds and reducing the entry threshold for NCDDC establishment should be considered to increase the number of NCDDCs in the Western region.

4.5. Advantages, Limitations, and Future Work

This study contributes in several ways compared to previous studies. Specifically, it focuses on studying NCDDCs represented as point elements, providing more detailed information about the distribution characteristics of NCDDCs. The spatiotemporal differentiation and patterns of NCDDCs in China can be better investigated using the standard deviation ellipse, spatial autocorrelation, nearest neighbor index, and kernel density. Statistical analysis and Geodetector are employed to explore the influencing factors of NCDDCs in China in a qualitative and quantitative way, contributing to the systematic summarization of the NCDDCs’ impact mechanisms. Unlike focusing only on building NCDDCs, this study explores the complexity and diversity of NCDDC distribution. It contributes to optimizing the layout of NCDDCs and serves as a scientific reference for implementing differentiated development measures. Given the diverse regional conditions and developmental disparities, we provide a series of targeted recommendations to guide the optimization of the NCDDC layout and promote the sustainable development of society. Moreover, this study can provide an important reference for studies on NCDDCs in other countries or regions. The reason is that the purpose of establishing disaster-resistant communities is not only to enhance their individual disaster prevention and mitigation capabilities but also to enhance their regional disaster prevention and mitigation capabilities through their strategic implementation.

This study has several limitations that require further improvement. First, the selection of disaster factors focused solely on natural disasters, potentially underestimating the impact of disaster factors on NCDDC distribution. Future research should consider the impact of sudden disaster events, including accidental disasters, public health events, and social security events [35]. Second, the establishment of NCDDCs is significantly influenced by the policy regime, requiring further refinement in future studies. Given China’s vast size and diverse levels of economic development and disaster hazard [61], conducting additional comprehensive regional studies is crucial to exploring the distribution characteristics of NCDDCs in-depth and proposing region-specific optimizations for NCDDC layout.

5. Conclusions

We conducted an analysis of the spatiotemporal distribution characteristics and influencing factors of 15,030 NCDDCs in China using the standard deviation ellipse, spatial autocorrelation, nearest neighbor index, kernel density, and Geodetector. The main findings are summarized as follows.

From 2008 to 2021, although the proportion of NCDDCs located in rural areas increased with time, the proportion of the population benefiting from urban NCDDC areas and those from rural NCDDCs remained much higher than the current proportion of urban and rural populations in the provinces and the whole country. There is an increasing standard deviation in the number of NCDDCs among counties, indicating a considerable imbalance in NCDDC development at the county scale. The distribution of counties with varying numbers of NCDDCs exhibits a “pyramid” pattern in 2021.

There is an annual increase in the positive spatial correlation of NCDDCs at the county scale. The “neighboring reliance” phenomenon and positive space spillover effect of NCDDCs have been formed. The HH-type distribution, characterized by concentrated clustering, is mainly observed in the Beijing–Tianjin, Pearl River Delta, and Yangtze River Delta regions. The LH cluster area, located adjacent to the HH cluster area, experiences an increase in the number of counties due to the radiation effect from the HH cluster area. Most counties belong to the LL-type distribution, exhibiting a contiguous spatial pattern. HL clusters display an unstable distribution and are scattered across several provinces in China, often adjacent to LL clusters.

The gravity center of NCDDCs has almost always moved to the east and south. In recent years, the gravity center of NCDDCs has started moving west. The spatial distribution of NCDDCs mainly follows a northwest–southeast pattern during 2008–2014 and shows a northeast–southwest trend after 2014. The spatial agglomeration of NCDDCs in China increases annually. Initially, the distribution of NCDDCs was characterized by dispersion, followed by the coexistence of aggregation and diffusion effects. The spatial distribution of NCDDCs exhibits a concentrated and contiguous pattern in 2021, based on “core density zone–ring-core decreasing area–ring-core expansion group–Ɔ-shaped area–belt-shaped area”.

The NCDDC distribution is influenced by various factors, with bilinear and nonlinear enhancements in factors’ interactions. Among all factors, hospital density and road density demonstrate the highest explanatory power in the NCDDC distribution. The NCDDC number is positively correlated with hospital density. There is a significant inverted U-shaped relationship between the number of NCDDCs and road density. Most NCDDCs are situated in areas with a high road density. GDP density and population density rank third and fourth in terms of their explanatory power in the NCDDC distribution, respectively. NCDDCs are mainly located in areas with high GDP density and high population density, particularly concentrated in the Eastern regions beyond the Heihe–Tengchong line. Although the impact of disaster factors on the NCDDC distribution is relatively weak, their explanatory power significantly increases when they interact with other factors. Distances to local government and fire stations have a limited impact on the NCDDC distribution, but they have a strong radiating effect on the establishment of current NCDDCs. The NCDDC number first increases and then decreases as the distance to the local government and fire station increases; most NCDDCs are concentrated near these facilities.