In 1800, only approximately 2% of the world’s population lived in cities, whereas today, about 54% of the world’s population reside in cities, which is projected to rise to over 67% by 2050 [1
]. Most of these individuals live in the developing world, in places such as China. Over the past three decades, China become increasingly urbanized and its urbanization rate has soared from 19.39% in 1980 to 53.73% in 2013 [3
]. According to the Demographic World Urban Areas Report (12th Annual Edition, 2016), Shanghai, Beijing, and Guangzhou-Foshan are among the top twenty largest cities in the world. Shanghai has become the eighth largest city (with a population of 22.7 million people), Beijing is ranked as the eleventh largest in the world (with a population of 20.4 million), and Guangzhou-Foshan is the thirteenth largest city (with a population of 18.8 million). Such rapid urbanization has and will continue to strengthen the connection and cooperation among cities. Thus, around Shanghai, Beijing, and Guangzhou-Foshan, three giant city agglomerations have developed separately: the Yangtze River Delta Region, the Beijing-Tianjin-Hebei Region, and the Pearl River Delta Region. However, the emergence of such high-density city agglomerations in a developing country has incurred social stratification and inequality, and increased the exposure of urban residents to adverse environmental conditions such as natural hazards, climate change, environmental pollutions, and so on [3
]. As a result, local residents are more vulnerable both physically and socially. As one of the defining components of risk, identifying vulnerability has become an important foundation that supports urban risk management. Furthermore, integrating the connections between cities into a vulnerability assessment is extremely essential in making cities safe and resilient in the context of rapid urbanization.
This paper concentrates on urban social vulnerability and investigates a new conceptual framework for assessing social vulnerability based on network theory. The paper is organized as follows. Section 2
reviews the literature on social vulnerability including concepts, evolution, and assessment. Section 3
presents the methodology of urban social vulnerability based on the new conceptual framework from a network perspective, using a case study of the Yangtze River Delta region in China. Section 4
consists of the study results including the vulnerability profile of each city in the study areas through GIS mapping and a discussion of the results. Section 5
provides our final conclusions and recommendations.
2. Literature Review: Conceptual Basis and Evolution of Vulnerability
More than 40 years ago, the concept of vulnerability emerged within the geography and natural hazards research field. At that time, most studies emphasized disaster exposure risk and linked vulnerability to the amount of damage caused by a particular hazard from a technical or engineering sciences perspective [5
]; however, since the 1980s, scholars have been reluctant to take this perspective. From a social science viewpoint, researchers have embraced the theory that vulnerability is a state that exists in a system before it encounters a hazard and emphasized that the negative impacts of disasters could be magnified by certain social factors, including poverty, low levels of education, poor public infrastructure, and social services [7
]. Methodologies that take vulnerability as a starting point for risk reduction and apply demographic data to assess vulnerability were regarded and signified a paradigm shift from natural sciences to a social sciences perspective in the standard interpretation of natural disasters [9
]. To date, vulnerability research has covered different fields including climate studies, security studies, engineering, geography, political ecology, and disaster risk management [14
]. To do so, vulnerability has been divided into biophysical and social aspects, both of which help scholars clarify the circumstances that put people and places at risk, and the conditions that reduce responsiveness to environmental threats [17
As one important aspect of vulnerability, social vulnerability refers to the predisposition and inner state of individuals, organizations, or societies that affect the way they withstand adverse impacts from disruptive events such as natural hazards, climate change, or other dangerous incidences [18
]. In essence, social vulnerability is a by-product of social stratification and social inequalities among different communities and different places from the built environment [21
]. It has roots in the various characteristics of people including socio-economic status, demographics, and risk perception [24
]. Cutter et al. provided generally accepted factors that influence social vulnerability: (1) frail and physically limited individuals (e.g., the elderly, children, special needs people, and even females); (2) the type and density of infrastructure and lifelines; (3) building stock; (4) limited access to public resources (e.g., knowledge, information and technology); (5) insufficient access to service resources (e.g., education and medicine); (6) lack of access to political power and social capital; and, (7) beliefs and customs [22
]. Owing to its “mediating role”, social vulnerability is not only registered by exposure to hazards alone, but also resides in the local sensitivity to external stress and the capacity of the system to prepare, cope, and recover from damage [5
]. Therefore, social vulnerability typically includes three basic components (or dimensions): exposure, sensitivity, and adaptability [28
]. The United Nation Office for Disaster Risk Reduction (UNISDR) defines exposure as “the people, property, systems, or other elements present in hazard zones that are thereby subject to potential losses”, and can be biophysical or social [30
]. Sensitivity reflects the degree to which a given community or system is affected by climatic stresses [31
]. Adaptability is defined as “the ability of a system or individuals to respond, adjust, and cope with negative impacts of climate change and natural hazards [8
Scholars have proposed multiple conceptual frameworks to assess social vulnerability [33
], among which, there are four famous frameworks: the Pressure-and-Release (PAR) framework, the Hazards-of-Place (HOP) framework, the Exposure-Sensitivity-Resilience (ESR) framework, and the Bogardi-Birkmann-Cardona (BBC) framework. The PAR framework shows the progression of vulnerability with three social components: root causes, dynamic pressures, and unsafe conditions; however, it does not consider exposure in the definition of vulnerability [35
]. The Hazards of Place (HOP) framework proposes the idea of “place” to bridge the gap between biophysical vulnerability produced by dangerous geographic context and social vulnerability created by social fabric [36
]. The Exposure-Sensitivity-Resilience framework describes the complexity and interactions involved in vulnerability analysis. In this model, vulnerability emerges in a specific place, whereas, it is influenced by a human-environment system at broader scales such as regional and global levels [20
]. The BBC framework indicates that vulnerability is a dynamic process and consists of two elements: exposed and vulnerable elements, and coping capacity. In this process, vulnerability is hidden in the environmental, social, and economic key spheres, which ultimately results in three risks: environmental risk, social risk, and economic risk [12
]. In these conceptual frameworks, a geographic area (or place) is taken as a whole and complex system, where each component affects or is affected by other components, just like a systems view is taken in most logistic and supply chain management studies [38
]; however, all of the interactions analyzed in vulnerability studies are mostly limited inside of the system border, that is to say, the interactions and connections between the systems are neglected.
Under these frameworks, a variety of tools and methods, such as integrated assessment models, household surveys, and indicator approaches have been used to measure social vulnerability [6
]. Despite ongoing debates on the viability of measuring social vulnerability [41
], a methodology of aggregating related indicators to produce a composite index of social vulnerability (SVI) has gained general acceptance and is one of the leading tools for quantifying social vulnerability [42
]. This methodology has been successfully applied in various contexts described in References [21
]. It is fairly robust and strongly supported for identifying and monitoring social vulnerability over space.
4. Results and Discussion
These sixteen cities of in the YRD region in the order of highest connectivity to lowest were Shanghai, Suzhou, Nanjing, Hangzhou, Ningbo, Wuxi, Changzhou, Jiaxing, Yangzhou, Nantong, Taizhou (Z), Zhenjiang, Zhoushan, Shaoxing, Taizhou (J), and Huzhou. More details on the connectivity are shown in Table 2
(note: “Flow-in” means the total searches from all the other 15 cities to city i
in the Baidu Index. “Flow-out” means the total searches from city i
to all the other 15 cities. “Itself” means the total searches from city i
to itself. “Connectivity” is the sum of “Flow-in” and “Flow-out”).
According to Table 2
, there were five types of connectivity structure: (1) “Flow-out” > “Itself” > “Flow-in”, for example, Shanghai; (2) “Flow-out” > “Flow-in”> “Itself”, e.g., Suzhou; (3) “Flow-in” > “Flow-out” > “Itself”, from Nanjing to Zhoushan; and (4) “Flow-out” > “Flow-in” > “Itself” (Shaoxing, Taizhou and Huzhou). By considering the general characters of the cities described in Table 1
, several conclusions were drawn from the results in Table 2
: (1) smaller or less-developed cities had fewer searches from other cities, focused less on themselves, and were more interested in other cities, that is, a city with less inner vitality was used to following others; (2) As the scale or economy of the city increased, more searches from other cities and more focus was placed on itself, which meant that the city began to affect others and be more active; and, (3) as the city or economy grew further, searches from this city to other cities increased again, and even searches about itself were larger than searches from the other cities, which demonstrated that a city has become more open, active, and is capable of learning from others.
To illustrate the spatial distribution of connectivity, we mapped the results with QGIS and are shown in Figure 5
(note: the circle’s area and color represents the connectivity of the city, and the width and color of line represents the strength of connections between two cities) [63
According to Table 2
and Figure 5
, the connectivity of Shanghai was the highest in the region and all of the connections between Shanghai and another city belong to Level 1 (the red lines), or Level 2 (the orange lines). This made Shanghai the most important node of this information network in the YRD region. Considering the total connection strength, Suzhou, Nanjing, and Hangzhou had leading roles on their local information connection. Aside from the relatively weak connection between Nanjing and Hangzhou, the connectivity between Shanghai, Suzhou, Nanjing, and Hangzhou were stronger than any other city pairs. Hence, they can be considered as four basic pillars carrying the whole information network and constitute the red backbone of the network, therefore were recognized as Level 1. The second group was composed of Yangzhou, Nantong, Changzhou, Wuxi, Jiaxing, Shaoxing, and Ningbo. Coupled with the top four, they constituted the Level 2 skeleton of the network which is presented in orange. The third group included Taizhou (J), Zhenjiang, Huzhou, Zhoushan, and Taizhou (Z). Connecting with the first and the second group, they formed the Level 3 framework of network rendered in yellow.
Although all of these connections in the information network are immaterial, they actually correspond to physical connections of the material network, such as (1) the transportation network where Shanghai, Nanjing, and Hangzhou are three key transport hubs for rail or highway networks, and Suzhou, Wuxi, and Changzhou are important sub-hubs of transportation networks; and, (2) urban hierarchy. As a direct-controlled municipality of China, Shanghai is the largest city in the region and a global financial hub. Thus, it is on the top tier of the urban hierarchy. Being the capital city of the province, both Nanjing and Hangzhou are classified as sub-provincial cities and sub-centers in urban hierarchy. Suzhou, the second largest city in Jiangsu province and a major economic center in China, was another sub-center of the YRD region. The reality of the material networks supported our results of the immaterial network.
4.2. SVInode and SVIurban
Mapped with QGIS 2.14.10, the spatial distribution of SVInode
is illustrated in Figure 6
. The detailed information about the differences between SVInode
are shown in Table 3
The condition of traditional social vulnerability (SVInode
) for cities in the YRD region can be classified in three parts: (1) The lowest three are Hangzhou (1.09), Nanjing (1.06), and Shanghai (0.88); (2) these were followed by Zhoushan, Suzhou, Wuxi, Ningbo, Zhenjiang, Changzhou, Nantong, Yangzhou, Shaoxing, and Taizhou (J), with SVI from 1.61 to 2.34; and, (3) the top three high values of SVInode
were located in Taizhou (Z) (2.76), Jiaxing (2.60), and Huzhou (2.48), which all belong to Zhejiang Province. Either the top three or the bottom three just corresponded to the urbanization ranking in Table 1
. The average value in Jiangsu Province as less than in Zhejiang Province, which showed that the condition of traditional social vulnerability in Jiangsu Province was better than Zhejiang Province. The distribution of new social vulnerability (SVIurban
) showed that once these sixteen cities were connected with each other by information flows, their social vulnerability changed obviously. SVIurban
in some cities was reduced such as in Yangzhou, Suzhou, Taizhou (Z), whereas, Taizhou (J) increased its social vulnerability distinctly. By examining their changes more clearly, we normalized SVInode
, and these are demonstrated in Figure 7
In Figure 7
, both the SVInode
of Shanghai were the minimum in our study area, so these two values became zero after normalization. In most cities, the SVIurban
was far less than the SVInode
. It indicated that connecting in the information network helped most of the cities mitigate their social vulnerability. However, two cities, Taizhou in Jiangsu Province and Huzhou in Zhejiang Province, had an SVIurban
higher than their SVInode
. This showed that their social vulnerability could not be reduced for the connecting network. To explore the relationship between SVI and connectivity, we examined the variation of SVInode
, and linked it to the hierarchy of cities. According to Figure 5
, there were five hierarchies of connectivity between the cities: (1) Hierarchy 1, where connectivity was from 188,039 to 340,907, of which only Shanghai belonged to this type; (2) Hierarchy 2, where connectivity was from 116,923 to 188,039, and Suzhou, Hangzhou, and Nanjing belonged to this type; (3) Hierarchy 3, where connectivity was from 71,370 to 116,923, and eight cities (Ningbo, Wuxi, Jiaxing, Changzhou, Yangzhou, Taizhou (Z), Nantong, and Zhenjiang) belonged this type; (4) Hierarchy 4, with connectivity from 34,425 to 71,370 and Zhoushan, Shaoxing and Taizhou (J) belonged to this type; and (5) Hierarchy 5, where connectivity was below 34,425, and only Huzhou was classified as this type. Furthermore, the variation between SVInode
(normalized value) was calculated and all the results are shown in Table 3
First of all, the city in Hierarchy 1 (Shanghai) kept its minimum value for either SVInode or SVIurban, so no variation occurred. For Hierarchy 2, though the SVInode of these three cities were already sufficiently low, the SVIurban still obviously decreased because their connectivity was strong enough. Eight cities belonged to Hierarchy 3, which meant that their connectivity was weaker than cities in Hierarchy 2. At the same time, the mean values of SVInode and SVIurban were both higher than ones in Hierarchy 2 so they have decreased their social vulnerability more so than cities in Hierarchy 2, therefore, these eight cities benefited most from the network. As for the three cities belonging to Hierarchy 4, the situation was the reverse. The mean value of the SVInode of Hierarchy 4 was less than Hierarchy 3, while the mean value of SVIurban was much more than that of Hierarchy 3. The variation in Hierarchy 4 was slight. The city of Taizhou (J) even increased its social vulnerability after connecting in the network. Therefore, Hierarchy 4 did not benefit much from the network. The last city in Hierarchy 5 increased its social vulnerability most when adding connectivity in the social vulnerability assessment. Based on the results of our case study, a conclusion was drawn that network connectivity influenced social vulnerability. If connectivity was strong enough, it could help cities mitigate their traditional social vulnerability, otherwise, a loose connection in the network aggregated their traditional social vulnerability.
5. Discussion and Conclusions
In modern conceptions, cities are regarded as residential places with various attributes and also as nodes in a variety of networks run together. Network analysis can offer new insights into social vulnerability assessments. Based on the theory of city networks, this paper proposed a new conceptual framework to evaluate social vulnerability in city agglomerations. In detail, it measured social vulnerability in three parts: (1) with the attribute data, the SVInode was calculated using the PPC model based on the real-coded genetic algorithm implemented in Python; (2) with the relational data retrieved from the Baidu Index, a new dimension of social vulnerability (connectivity in networks) was measured; and, (3) based on SVInode and SVIconnectivity, a new integrated social vulnerability of a city (SVIurban) was evaluated. This method was applied in the Yangtze River Delta region in China.
Our findings are summarized as follows. First, regarding the traditional social vulnerability (SVInode), the top three highest values of SVInode were located in Taizhou (Z), Jiaxing, and Huzhou. All of these are cities in the Zhejiang Province. The bottom three were Hangzhou, Nanjing, and Shanghai. Thus, the condition of traditional social vulnerability of Jiangsu Province is better than Zhejiang Province. Second, on connectivity. The connectivity of Shanghai was the highest in the region, which made Shanghai the most important node of the information network in the YRD region. Aside from the relatively weak connection between Nanjing and Hangzhou, the connectivity between Shanghai, Suzhou, Nanjing, and Hangzhou were stronger than that any other city pairs, therefore making them the four basic pillars carrying the whole information network. The second group as composed of Yangzhou, Nantong, Changzhou, Wuxi, Jiaxing, Shaoxing, and Ningbo. Coupled with the top four, they constituted the Level 2 skeleton of network. Third, concerning integrated social vulnerability (SVIurban), the social vulnerability of cities in different hierarchies behaved differently. For Hierarchy 2, the SVIurban obviously decreased because their connectivity was sufficiently strong. The connectivity of cities in Hierarchy 3 was weaker than that of cities in Hierarchy 2, who had decreased their social vulnerability the most. The situation is the reverse for cities in Hierarchy 4 where the variation between SVInode and SVIurban in was slight. The city of Taizhou (J) even increased its social vulnerability after connecting to the network. Huzhou in Hierarchy 5 also increased its social vulnerability the most when adding connectivity in the social vulnerability assessment. Based on the results of our case study, a conclusion was drawn that network connectivity influenced social vulnerability. If only connectivity was strong enough, it could help cities mitigate their traditional social vulnerability, otherwise, a loose connection in the network aggregated their traditional social vulnerability. Hence, the latter should be the main emphasis of future urban risk management.
Our study also had limitations despite yielding interesting explorative results. First, our conceptual framework was simple and only provided a primary theoretical analysis of social vulnerability and connectivity. This should be further developed to reflect their complicated relationships in reality. Second, this paper only focused on the informational connection among cities, and would be better if physical connections of material networks, such as transports networks, logistics networks, or electricity network were added. Third, data availability is the biggest challenge in social vulnerability assessment in China. On one hand, it is time consuming to collect data; however, it was still difficult for us to obtain all of the required indicators even if much time was spent on data collection. As data limited our study, we only selected 16 cities and 19 indicators to measure social vulnerability in networks and to explore the relationship between social vulnerability and connectivity. Moving forward, our aim is to collect more samples and indicators in future works.