Typology-Driven Urban Community Resilience Assessment in China: Spatial Disparities and Smart Transformation Roadmaps

Abstract

Building community resilience is essential for ensuring that communities can not only survive but also thrive in the face of various challenges and uncertainties. However, existing research has deficiencies in the comprehensive evaluation framework and systematic analysis of different types of urban communities within high-density Chinese cities. This study constructed a comprehensive urban community resilience assessment system (UCRA) that covers four dimensions: environmental, service, social, and governance resilience. In a case study of the Chinese megacity of Tianjin, urban communities were categorized into three physical types and three regional categories. The UCRA contained 40 detailed indicators, and the weighting of indicators was was determined using a mixed approach combining the AHP and entropy methods. The findings revealed that tower apartments in urban Chinese communities demonstrated relatively high resilience, whereas older residential complexes exhibited the lowest resilience performance. Furthermore, central urban communities generally displayed high resilience, in contrast to peripheral urban areas, where low levels of resilience were often discovered. Building upon these findings, this study discusses the characteristics and challenges associated with the resilience of various community types. By establishing a theoretical basis for creating intelligent assessment and monitoring systems, we advocate for targeted community development strategies, thereby promoting smart transformation of community resilience.

1. Introduction

1.1. Community Resilience: Concept and Inclusions

The concept of community resilience (CR) is rooted in interdisciplinary studies and acts as an enhancement and complement to earlier community theories [1]. Norris et al. defined community resilience as a process linking a set of adaptive capacities to a positive trajectory of functioning and adaptation in constituent populations after a disturbance [2]. The focus on developing resilient communities has grown significantly as communities are fundamental components of urban areas. In 2002, the International Council for Local Environmental Initiatives (ICLEI) introduced ‘resilience’ at the United Nations World Summit on Sustainable Development [3]. The Second and Third World Conferences on Disaster Reduction, held in 2005 and 2015, engaged in discussions on urban resilience [4,5]. The United Nations 2030 Agenda for Sustainable Development released in 2015 put forward specific goals related to resilience [6], such as ‘accelerating the construction of resilient infrastructure’ and ‘building more inclusive, safe, and resilient cities and settlements’. A community serves as the fundamental unit of a city, acting as the primary hub for residents’ daily activities, the basic unit for risk prevention, a crucial part of the urban disaster prevention system, and a key entry point for developing resilient cities [7,8].

Community resilience plays a crucial role in mitigating the impacts of disasters and risks. It encompasses the ability of communities to withstand, adapt to, and recover from various shocks and stresses. Building resilient communities depends not only on physical infrastructure, such as strengthened building structures, but also on comprehensive spatial organization, including a well-thought-out transportation network and emergency evacuation system [9]. Fostering resilient communities requires establishing a complete and varied system of public services. Advanced distribution facilities can enhance residents’ quality of life and boost the community’s capacity to adapt to and manage disasters, thus elevating resilience. The resilience of social and governance systems is also crucial, as demonstrated by the community’s ability to remain stable under stress and its capacity to adapt, learn, and develop [10]. Additionally, the trend in developing community resilience involves integrating diverse groups and encouraging their participation in community governance to strengthen community vitality and cohesion [11,12]. In Singapore, representatives of community organizations are pivotal in fostering community resilience by motivating residents to engage in local affairs. To enhance public involvement in the development of community organizations at various stages, a set of evaluation tools [13] has been developed to assess connections and unity among community members on a community scale.

The essential traits of a community generally encompass a specific population size, clearly defined geographic limits, a particular scale of facilities, unique cultural attributes, and distinct organizational types. Developing community spaces requires strategies to enhance environmental quality, thereby significantly impacting the social lives of residents [14]. Scholars have proposed multidimensional recommendations for enhancing community resilience, including improvements in the spatial layout, facilities, environment, governance, and capital [15,16,17]. Sun et al. conducted resilience studies on aging neighborhoods and found issues such as poor environments and low redundancy [18]. However, most studies on community resilience tend to focus on a specific community type or risk [19,20], with limited research on comparisons across different types or integrated studies of regional characteristics. In addition to these shared trends, variations in the openness and social composition of different urban areas require further research into differentiated governance strategies, precise assessments of risks and capacities in target areas, and refined governance measures based on tiered and classified spatial evaluations. Recent urban renewal initiatives and renovation of old communities have greatly promoted the optimization and upgrading of existing built environments [21]. By comparing socio-spatial structural features in multiple community patterns and grasping their organizational forms and key nodes, it is valuable to provide direct references for areas undergoing new development while also offering valuable experience for the transformation and renewal of existing urban areas.

1.2. Community Resilience Assessments

Community resilience is frequently characterized as complex, contextual, and multifaceted, making it difficult to define, identify, and implement. The field of community resilience assessment is currently undergoing a period of diversification and rapid development, with various perspectives and methodologies emerging. Numerous researchers have delved deeply into developing indicator systems to measure community resilience (

Table 1. International Studies on Community Resilience Assessments.

Literature	Primary Dimensions	Focus
Norris, F. H.; Stevens, S. P.; Pfefferbaum, B.; Wyche, K. F. (2008) [2]	Economic Development, Social Capital, Information & Communication, Community Competence	Proposes the 4-Dimension-20-Indicator Community Resilience Scale (CD-RISC); emphasizes the integration of pre-disaster capacity and post-disaster adaptation processes.
Bruneau, M.; Chang, S. E.; Eguchi, R. T. et al. (2003) [22]	Technical, Organizational, Social, Economic	Engineering-oriented resilience framework focusing on infrastructure performance loss-recovery curves under seismic risk.
Cutter, S. L.; Burton, C. G.; Emrich, C. T. (2010) [23]	Social Resilience, Economic Resilience, Institutional Resilience, Infrastructure Resilience, Community Capital Resilience	Serves as baseline indicators for evaluating the effectiveness of resilience projects, policies, and interventions; highlights spatial dimensions.
Sutley, E. J.; van de Lindt, J. W.; Peek, L. (2017) [24]	Environmental Resilience, Institutional Resilience, Individual Resilience	Community-based seismic resilience, linking socioeconomic characteristics with engineering building systems.
Revell, P.; Henderson, C. (2019) [25]	Economic, Governance, Social, Environmental, Cultural	Validates the “Community Resilience Index (CRI)” across eight EU case studies; emphasizes multi-scale governance and cultural identity.
Shen, L.; Tian, Y.; Du, Y. (2021) [26]	Construction Resilience, Social Resilience, Economic Resilience, Organizational Resilience	Targets China’s old residential areas; emphasizes spatial construction and governance dimensions; proposes diversified retrofitting strategies to enhance resilience.
Wu, Y.; Zuo, P.; Peng, C.; Zhu, X. (2023) [27]	Economic Resilience, Social Resilience, Facility Resilience, Institutional Resilience, Spatial Resilience, Community Life-Circle Support Resilience	Merges and adapts dimensions within China’s urban community planning context, integrating impacts from public health emergencies.
Dong X.; Cai J. (2024) [28]	Institutional resilience, organizational resilience, technology resilience, physical resilience	Institutional, structural, technical and physical factors can enhance or weaken governance performance; a complex heterogeneous connection between the intervention of community social capital and multiple resilience indicators.

).

Norris’s model [2] was a pioneering and comprehensive framework, primarily encompassing six aspects: structural, social, economic, political, cultural, and environmental resilience. The PEOPLES framework [29], developed in 2010, aimed to identify the distinct resilience characteristics of communities and address potential responses. It consists of seven major dimensions: population and demographics, environmental ecosystems, organized governmental services, physical infrastructure, lifestyle and community competence, economic development, and social-cultural capital. Some scholars have developed resilience models specifically for communities in China [26,27,28]. These models incorporate similar dimensions that have been adjusted and combined while also introducing several dimensions and indicators unique to Chinese characteristics. For example, in a framework proposed by Wu et al. [27], the demographic dimension that reflects the characteristics of community residents, the capital dimension that reflects resident relationships and social participation, were incorporated into social resilience. The institutional and organizational dimensions that reflect the emergency prevention capabilities of the community were also merged into governance resilience. While global discussions on the fundamental inclusion of community resilience differ, they consistently highlight common elements: the environment, society, buildings and infrastructure, and organizational systems.

In practical applications, research institutions and social organizations across various countries have developed their own technical routes. The primary focus of the Baseline Resilience Indicators for Communities (BRIC) is on assessing the resilience of county-level community units in the US [30]. Its evaluation system encompasses six major categories: society, economy, community capital, institutions, infrastructure, and the environment, utilizing 49 variables for a comprehensive assessment. These variables were derived from open-source datasets provided by the federal government, primarily targeting county-level territorial analyses. The international organization GOAL began resilience assessments in 2009, and launched the Analysis of Community Resilience to Disasters (ARC-D) tool in 2016 [31]. Later, it was widely applied in America and many Caribbean countries. Its dashboard indicators include: basic population data, local governance groups, community-level planning, natural and physical environment, vulnerable groups, major shocks and stresses, and analysis of their interactions to “risk scenarios.” The assessment results can be shared with stakeholders, with multiple language versions available based on the country of implementation. In Canada, the Community Disaster Resilience Planning (CDRP) initiative was launched in 2010 to assist various communities in assessing their resilience levels [32]. The programme consisted of three essential sessions. The first session involved gathering data on key community aspects, such as spaces, economy, and population, and creating visual maps. The second session focused on calculating hazard risk analysis, hazard resilience index, and community resilience index using specific indicators. In the third session, priority strategies were identified based on the community resilience level, potential hazards, and risk ratings. This leads to the development of short- or long-term action plans, such as the formation of volunteer fire brigades and implementing emergency response plans.

Urban communities exhibit distinct socio-spatial diversity as they are social groups formed by people living in a particular geographic area [33]. Hong et al., found clear disparities in terms of resilience capacities across neighborhoods in the US [34]. Community-level research on context-specific factors of community resilience and variations between urbanized and fringe areas can provide effective decision support and design spatially targeted interventions [35]. Resilience in the literature review arises from the differences among widely dispersed communities within a city region, where geographic and ecological factors often differ significantly in terms of resources, capacities, and organizations [36]. These factors also significantly influence a community’s ability to handle challenges. Disparities in infrastructure damage and recovery capabilities are evident among communities with diverse socioeconomic backgrounds [37]. Discussions on building and infrastructure resilience can gain from understanding place-based social vulnerabilities [38]. Consequently, grasping community typology is crucial for ensuring the accuracy of resilience interventions. Developing community typology allows for the examination of current characteristics, resilient performance, and potential for community improvement [39].

1.3. Characteristics of Chinese Urban Communities

China’s urban communities exhibit a unique structural diversity shaped by rapid urbanization, historical development, and socioeconomic differences. Significant spatial and demographic variations were observed. Megacities such as Beijing and Shanghai have high-density central districts, suburban new towns, and peripheral areas. These differ in infrastructure, service accessibility, and population composition, directly impacting resilience to shocks like natural disasters or public health crises. Urban and community development in China during the rapid urbanization phase has often neglected the significance of building resilience, leading to a lack of systems for managing natural disasters and social risks at the community level.

The current urban community patterns are highly complex and variable [40]. However, insufficient attention has been paid to the socio-spatial differentiation of urban communities [41,42]. Over the past few decades, urban communities in China have evolved in various spatial forms. Numerous residential complexes began to emerge in the mid-1990s [43]. After 2000, the restructuring of originally publicly owned housing took place, and by 2010, their numbers had declined significantly [44]. Owing to the long-lasting and irreversible nature of urban development, the spatial configurations of many existing urban areas cannot be fundamentally altered in the short term. Now, the focus has shifted to redeveloping existing communities, as the central city has fully entered the era of existing stock.

Consequently, governance-focused improvements aimed at improving and restoring the current environment have become a primary focus of recent urban development initiatives. Many urban communities in China are undergoing environmental enhancements and service upgrades. Community planning and management is evolving from traditional, experience-based actions to a big data-supported, refined model [45]. Looking ahead, the focus on resilience-driven community renewal has been elevated to a national agenda. The outline of the 2035 long-term vision planning of the Chinese central government sets forth resilient city construction, renovation of aging neighborhoods, enhancement of urban disaster prevention capabilities, improved governance, and strengthening of megacities [46]. In 2023, China’s national housing department began mandating pilot projects to enhance community resilience.

The nexus between community resilience and community type, especially within the Chinese context, is still a less-discussed topic but of great importance. At present, systematic analyses of different types of communities remain underdeveloped, limiting our understanding of how various community characteristics influence resilience outcomes. This research gap prevents the formulation of tailored strategies to enhance resilience in diverse urban settings, potentially leading to ineffective interventions and resource allocation at the community level when facing disasters or new challenges.

From a typology perspective, the uniqueness of Chinese communities compared to those overseas is primarily evident in several aspects. Spatially, Chinese urban areas adhere to a high-density development model, where aging residential neighborhoods exist alongside gated commercial housing estates, leading to a significant shortage of physical space redundancy. In contrast, developed nations such as those in Europe and North America have much lower-density communities and generally do not follow such classification models. In terms of governance, there is a focus on co-governance among three key entities: resident committees, homeowner associations, and property management companies, whereas many Western countries tend to rely more on NGOs and neighborhood self-organizations. China places importance on organizational integration capacity. Economically, many foreign models consider factors like ‘insurance coverage rate’ while Chinese communities depend more on the ‘mobilization capacity of resident committees’. To transform the concept of ‘resilient communities’ into a measurable, comparable, and actionable governance tool, it is essential to develop a novel indicator system that integrates highly demanding resilience frameworks. Such a system would effectively support the increasing efforts in risk management and renewal of urban communities in China. The BRIC tool is mainly based on counties/census areas in the United States, while surveys on Chinese communities need to be detailed to the “community building” ledger to reflect the urban population concentration difference.

When Western frameworks are applied to assess Chinese communities, issues of applicability invariably arise. In China, high-density urban areas face numerous challenges in resilience when confronted with risks such as natural disasters, infrastructure failures, and public health threats. These challenges stem from urban factors, such as population concentration, dense building structures, limited spatial resources, and complex mixed-use functions. Fundamentally, they result from the confluence of ‘spatial constraints, resource pressure, and social complexity’ within the urban Chinese environment. Despite the current advancements in research, the development of practical CR assessment methods and the analysis of CR variations across different community types remain limited. Many current studies in China [18,26,47] still largely focus solely on a single type of community or countermeasures against a single disaster, while more recent research, spurred by the COVID-19 pandemic, emphasizes addressing public health emergencies [27,48]. Few studies have developed comprehensive CR assessment tools that are applicable to multiple community types. This study aims to utilize a new assessment result to provide a localized paradigm for Chinese urban communities, offering a framework with comparability to advance precision community-level policies towards resilience improvement, as required by national-level policies. The purpose of this research also includes comparing community resilience across the various types and spatial distributions of communities based on the unique characteristics in urban China. It aids in enhancing the comprehension of community resilience from a typological standpoint and in developing specific optimization strategies to tackle identified challenges in various communities.

2. Materials and Methods

2.1. The Case Study Area

We conducted a case study in Tianjin, a megacity in northern China that serves as a significant economic and industrial hub (Figure 1). With its strategic location along the Bohai Gulf, Tianjin has experienced rapid urbanization and economic growth over the past few decades. The area of the Tianjin metropolitan is 11,916.85 square kilometers and the population is approximately 13.63 million. Its average regional population density is 1144 capita per square kilometer. The city of Tianjin covers approximately 178.72 square kilometers and has a population of 4.1 million. Notably, over 30.3% of the metropolitan population resides within the city zone, underscoring the high-density development characteristic of urban communities. The central downtown district, with an area of roughly 10.00 square kilometers, boasts a population density exceeding 35,000 individuals per square kilometer.

The city’s residential population is spread across numerous urban and suburban counties, encompassing a diverse range of communities, from extremely crowded central downtown neighborhoods to more spacious suburban developments. Thousands of communities within Tianjin represent a microcosm of China’s urban development. These communities vary greatly in terms of their socioeconomic status, infrastructure, and cultural characteristics. Some are old neighborhoods with traditional features, whereas others are modern high-rise complexes catering to the city’s growing middle class. This diversity makes Tianjin an ideal location for studying community resilience dynamics and the challenges associated with urbanization.

Based on a survey in Tianjin, mainstream urban communities can be classified into three major categories:

(1)

Old residential complex (X1): These areas typically consist of housing built before the 2000s, with most constructed between the 1970s and the 1990s. Some originated from public housing projects funded by the government or work units in the previous century. Compared to complexes developed after the housing reform, these are significantly older. They mainly feature traditional multi-story buildings, are generally large in scale, and have smaller floor areas.

(2)

Tower block apartments (X2): Predominantly constructed in the 1990s, these complexes emerged from gradual changes in national housing policies and the rise of high-rise residential buildings in major cities following the reform and opening-up. Unlike contemporary high-rise buildings, tower apartments usually consist of one or a few high-rise blocks. Although the overall land use is not extensive, it contains numerous housing units. They are situated on independent plots with entrances facing the street and are often located in central urban areas.

(3)

Commodity housing estate (X3): These are residential communities developed and sold through formal channels since housing reforms. Built more recently, they incorporated advanced planning concepts, focusing mainly on medium and high-rise buildings, with relatively high floor area ratios and comprehensive supporting facilities.

Owing to their specialized nature, residential areas linked to industrial zones and shanty town villages were excluded from this study. This is because these areas lack universality and exhibit diverse shaping dynamics. Additionally, these communities exhibit clear resilience weaknesses when compared to the aforementioned three groups.

We also divided these communities into central, sub-central, peripheral three groups according to their location inside the city regions (Figure 2). The classification of communities within city regions into central, subcentral, and peripheral groups provides a nuanced framework for understanding changes in community resilience at the city scale.

(1)

Central urban communities (Y1) typically occupy the core of the city, often encompassing the central business district, major cultural institutions, and historical landmarks. These areas are characterized by high population density, diverse economic activities, and well-developed infrastructure.

(2)

Subcentral urban communities (Y2) form a transitional zone between the core and outskirts, often featuring a mix of residential and commercial areas with varying degrees of urban development and population density.

(3)

Peripheral urban communities (Y3), situated at the outer edges of the city, exhibit distinct characteristics compared to their central and subcentral counterparts. These areas may include suburban developments, industrial zones, or even with some semi-rural landscapes that transit into urban spaces. Peripheral communities often experience rapid growth and transformation as cities expand outward, leading to challenges in infrastructure development, service provision, and maintaining a balance between the urban and natural environments.

This tripartite division allows for a more comprehensive analysis of urban dynamics, including patterns of growth, socioeconomic disparities, and the distribution of resources and opportunities across different parts of the city region. Our site includes comprehensive local coverage across 30 communities with a multiyear collection period and covers the above types, which provides a robust foundation for statistical inference.

2.2. Development of Community Resilience Framework and Indicators

This study presents an assessment framework, developed following a literature review, that combines both the physical and social attributes of a community to evaluate its resilience development (Figure 3). The framework is structured into four dimensions: environmental, service, social, and governance resilience. In the outset of this research, we identified these four dimensions and 48 indicators through a literature review, and we sought feedback from experts with diverse backgrounds, including those from universities, government departments, and planning and design institutions. Our four-dimensional framework achieved an agreement rate of over 90%, with some experts suggesting additional potential indicators beyond our initial provisions. Ultimately, the significance of these indicators was ranked through an expert group survey, resulting in 40 consistent indicators being recognized by more than two-thirds of the participants. Certain indicators, such as the availability of elevators and parking spaces, were deemed less important, while indicators that are impractical to obtain, such as the number of individuals with negative social records and drug users, were also removed.

Each module contains specific indicators. Environmental resilience refers to a community’s inherent physical traits, which are categorized into three areas: environmental foundations, road transportation, and disaster prevention and rescue. The environmental foundation assesses the characteristics of the living environment by understanding the building density, floor area ratio, and size of residential units to evaluate the development of the basic residential environment. Road transportation emphasizes connectivity aspects, including the convenience of entry and exit, internal accessibility, and road network completeness. Disaster prevention and rescue focuses on spatially built elements that have a direct impact on community resilience, such as the accessibility of open spaces, the reach of logistics distribution networks, and the proximity to emergency to rescue points. The resilience of public service facilities pertains to various urban service assurances, particularly the ability of public facilities within the community’s 15-min life circle to withstand sudden public health events. This study focuses on two main categories of public facilities: basic living support facilities and quality enhancement facilities. Basic living support facilities include daily shopping, public transportation, medical facilities, green spaces, and plazas, while quality enhancement facilities consist of cultural and leisure spaces, fitness and sports areas, and research and educational institutions. In addition to specialized evaluations of service facility resilience, the overall diversity of facilities and compliance rates within the life circle are also important assessment criteria, providing a comprehensive evaluation of facility resilience from an overall facility perspective.

To assess social resilience, the socioeconomic traits of the primary population within a community were scrutinized, with an emphasis on health capacity, economic capacity, and population stability. Health capacity is evaluated by examining the age and health status indicators of residents, and economic capacity is measured using metrics such as income index, property and rental prices, and property management fees, as communities with high property prices and management fees have a relatively more robust infrastructure. According to existing studies [10,37], stability is determined by factors such as the proportion of non-local residents, willingness to move, duration of residence, and rental rates. Social resilience indicators can be assessed using a combination of various resources. These may include property-related online data, such as property and rental prices and property management fees, official community census and statistics, like the proportion of the elderly and migrant populations, and questionnaire surveys that gather information on age, health, income level, and length of stay.

The resilience of community governance consists of three elements: governance service system, social trust, and community interaction. Community governance involves government entities, social organizations, residents, and other stakeholders. Community governance resilience indicates the capability of community institutional governance and the quality of neighborhood relations, encompassing three aspects and related indicators: service systems, social trust, and community interaction. A comprehensive and collaborative organizational structure is essential for effectively developing resilient communities and reflects a community’s ability to withstand and recover from adversities. The key governance system includes satisfaction with property services, community management, and the completeness of community institutions. Social trust involves mutual trust between residents, trust in property management, and trust in community initiatives. Community interaction encompasses neighborly care, degree of neighborhood engagement, and participation in public benefit activities. Indicators of community governance are largely subjective and can be assessed using a questionnaire survey.

2.3. Data Collection

Our exploration of urban community resilience involved mixed data collection approaches (

Table 2. Community Resilience Framework: Indicators and Measurements.

Environmental Resilience	Measure	Social Resilience	Measure
X1-Land Plot Ratio	Official Statistics	X21-Proportion of Elderly Population	Official Statistics
X2-Building Layout Density	Official Statistics	X22-Proportion of Migrant Population	Official Statistics
X3-Internal Floor Area of Dwellings	Online Property Statistics	X23-Health Index	Questionnaire
X4-Entrances and Exits	Spatial Statistics	X24-Income Index	Official Statistics
X5-Road Integration Degree	Space Syntax	X25-Rental Index	Online Property Statistics
X6-Internal Accessibility	ArcGIS analysis	X26-Housing Price Index	Online Property Statistics
X7-Spatial Accessibility	ArcGIS analysis	X27-Property Management Fee Index	Online Property Statistics
X8-Green Space Ratio	Official Statistics	X28-Willingness to Relocate	Questionnaire
X9-Logistics Distribution Network	ArcGIS analysis	X29-Length of Residence	Questionnaire
X10-Emergency Rescue Network	ArcGIS analysis	X30-Homeowner Living Rate	Official Statistics
Service Resilience	Measure	Governance Resilience	Measure
X11-Accessibility of Medical Facilities	ArcGIS analysis	X31-Satisfaction with Property Services	Questionnaire
X12-Accessibility of Daily Shopping Facilities	ArcGIS analysis	X32-Satisfaction with Community Management	Questionnaire
X13-Accessibility of Public Transport	ArcGIS analysis	X33-Completeness of Community Institutions	Questionnaire
X14-Accessibility of Cultural Exhibition Facilities	ArcGIS analysis	X34-Satisfaction with Social Organizations	Questionnaire
X15-Accessibility of Sports and Fitness Facilities	ArcGIS analysis	X35-Mutual Trust among Residents	Questionnaire
X16-Accessibility of Parks and Squares	ArcGIS analysis	X36-Trust in Property Work	Questionnaire
X17-Accessibility of Scientific Research and Education Facilities	ArcGIS analysis	X37-Trust in Community Work	Questionnaire
X18-Accessibility of Entertainment and Leisure Facilities	ArcGIS analysis	X38-Degree of Mutual Care among Neighbors	Questionnaire
X19-Functional Mix Degree of Plots	Spatial Statistics	X39-Degree of Neighborly Interaction	Questionnaire
X20-Compliance Rate of Facilities in the 15 min Life Circle	Spatial Statistics	X40-Participation in Public Benefit Activities	Questionnaire

). For objective measurements, most of the data were derived from on-site surveys and spatial mapping. Partial data were derived from big data collected from real estate agency websites, which included comprehensive housing details such as floor area and pricing information. The others were sourced from map data providers, such as Gaode map and satellite navigation, offering Point of Interest (POI) datasets, and road network datasets. Spatial analysis, which is mainly focused on evaluating the resilience of the built environment, creates an urban spatial database to extract and compute indices such as building layout density, green space ratio, and land plot ratio. The land plot ratio and building layout density have their resilience levels determined based on existing research [49,50] and are converted into subjective measures of relative levels using scoring methods. For instance, a low plot ratio may lead to inefficient community service and interaction intensity, whereas extremely high ratio causes congestion and risks. Thus, we assessed the two factors using a scoring method, and the optimal resilience peaks at a moderate range. Road integration and permeability were assessed using spatial syntax on a GIS platform, whereas indicators such as internal accessibility and spatial accessibility were derived through GIS network analysis techniques. The evaluation of public facility resilience focuses on determining the service capacities of various public facilities. This study employed GIS network analysis methods to calculate the POI along with road network datasets. The 15-min community life circle is then used to define the road network-based boundaries of a community’s service accessibility. The number of accessible facilities was measured and statistically calculated for each facility. In addition to key facilities, two additional indicators—overall facility compliance rate and facility mix degree within each 15 min living circle—are included to reflect the completeness and diversity of accessible services in the community. For various indicators specific to community social statistics and structure, such as the proportion of elderly residents and migrant population, data were obtained from population statistics provided by neighborhood committees, and information on rent levels, housing prices, and property management fees were captured and downloaded from housing listing websites.

Subjective data were obtained from a large questionnaire survey. The survey was conducted in typical communities across Tianjin between 2018 and 2020. A stratified sample distribution was applied and a quota table for questionnaire distribution was established based on the current population proportion in major city areas, covering all the major types of neighborhoods. For each community selected, a number of households were selected for face-to-face interviews. A total of 965 valid responses were collected from the 30 selected urban communities. The distribution of these responses across communities and their geographic spread are detailed in Supplementary Table S1.

Respondents completed the questionnaire item by item, according to the survey questions. The survey consisted of six sections: I. basic family details and, II. housing and relocations, III. public service facilities and their usage, including healthcare and education, IV. community services, involvement, and satisfaction with community life, V. employment, and the work of key family members, and VI. Family income and spending. The survey gathered data on community residents’ health levels, income levels, willingness to relocate, and duration of residence. It also assessed most indicators of the governance resilience layer. Initial data were collected using a Likert 5-point scale and then uniformly normalized. The reliability and validity of the survey results were assessed using the Cronbach’s α coefficient, Bartlett’s sphericity measure, and the KMO test. The SPSS version 23 program results indicated that Cronbach’s α exceeded 0.8, the KMO statistic was greater than 0.7, and Bartlett’s sphericity test value was significant (p < 0.05). Therefore, the questionnaire data passed both the reliability and validity tests.

2.4. The Weighing Methods

Given the substantial numerical disparities between the results of the different indicators, the absolute values of these measurements lack comparability. To address this issue and enable comparison across a wide range of measurement results, the min-max standardization and a comprehensive weighing method were applied. We employed two methods for calculating weights: the Analytic Hierarchy Process (AHP) and Entropy Weight Method. The AHP method involves experts assigning scores to the weights of indicators. This approach uses multilevel pairwise comparisons to evaluate the significance of each indicator, thereby determining the relative weights among different levels of indicators in the community resilience assessment. Between 2024 and 2025, we collected data from 11 experts who contributed to the urban community resilience indicator system. We developed questionnaires that conducted pairwise comparisons at each hierarchical level using a three-level system design, thereby determining the relative importance of the indicators. The measurement scale was divided into nine levels, with values of 9, 7, 5, 3, and 1 corresponding to absolutely important, extremely important, relatively important, slightly important, and equally important, respectively, while 8, 6, 4, and 2 indicate degrees of importance falling between adjacent levels. The reverse answers were coded from −2 to −9 in a similar way. To prevent inconsistencies in the judgment matrices due to subjective expert opinions that do not align with common sense, it is necessary to conduct a consistency check for each judgment matrix to ensure the rationality of pairwise comparisons. The consistency index (CI) and consistency ratio (CR) are calculated:

$$\mathrm{C}\mathrm{I}={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}$$

(1)

$$\mathrm{C}\mathrm{R}={\displaystyle \frac{CI}{RI}}$$

(2)

In general, the closer the value is to 0, the greater the consistency in the judgment matrix. If the CR result of the judgment matrix exceeds 0.1, experts must adjust their responses to enhance accuracy. Ultimately, all the expert scores successfully passed the consistency tests.

The entropy weighting method leverages the objectivity of data to assess the importance of indicators, making it particularly suitable for extensive multi-indicator community resilience datasets [51]. Its benefits include eliminating subjective biases, accurately reflecting indicators’ discriminative capabilities, and supporting scientific evaluation and decision making [52]. This method efficiently manages large datasets and identifies indicators that most significantly influence resilience. It is crucial to avoid the biases introduced by human factors, particularly when dealing with extensive data or intricate indicator systems. In our study, this systematic mathematical approach began by normalizing the original data to eliminate the effects of differences in the dimensions and magnitudes. For positive indicators (higher values indicate better performance, e.g., green space ratio), standardization was based on Equation (3).

$${\mathrm{x}}_{\mathrm{i}\mathrm{j}}^{\prime }={\displaystyle \frac{{\mathrm{x}}_{\mathrm{i}\mathrm{j}}-\mathrm{m}\mathrm{i}\mathrm{n}\left(\mathrm{x}\mathrm{j}\right)}{\mathrm{m}\mathrm{a}\mathrm{x}\left(\mathrm{x}\mathrm{j}\right)-\mathrm{m}\mathrm{i}\mathrm{n}\left(\mathrm{x}\mathrm{j}\right)}}$$

(3)

where ${\mathrm{x}}_{\mathrm{i}\mathrm{j}}^{\prime }$ is the jth index value of the ith community. Min (xj) and Max (xj) are the minimum and maximum values of the jth index, respectively.

For negative indicators (lower values indicate better performance, e.g., willingness to relocate), standardization was based on Equation (4).

$${\mathrm{x}}_{\mathrm{i}\mathrm{j}}^{\prime }={\displaystyle \frac{\mathrm{m}\mathrm{a}\mathrm{x}\left(\mathrm{x}\mathrm{j}\right)-{\mathrm{x}}_{\mathrm{i}\mathrm{j}}}{\mathrm{m}\mathrm{a}\mathrm{x}\left(\mathrm{x}\mathrm{j}\right)-\mathrm{m}\mathrm{i}\mathrm{n}\left(\mathrm{x}\mathrm{j}\right)}}$$

(4)

The proportion of each standardized value within its indicator column was then computed according to Equation (5).

$${\mathrm{p}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{x}}_{\mathrm{i}\mathrm{j}}^{\prime }}{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{x}}_{\mathrm{i}\mathrm{j}}^{\prime }}}$$

(5)

where n is the number of samples (e.g., n = 30 in this study) and p_ij represents the proportion of the jth indicator value for the ith sample.

The third step was to calculate the indicator entropy based on Equation (6), which measures the uncertainty of the indicator information; lower entropy indicates higher information richness.

$${\mathrm{e}}_{\mathrm{j}}={\displaystyle \frac{1}{-\mathrm{l}\mathrm{n}\left(\mathrm{n}\right)}}{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{p}}_{\mathrm{i}\mathrm{j}}\mathrm{l}\mathrm{n}\left({\mathrm{p}}_{\mathrm{i}\mathrm{j}}\right)$$

(6)

The fourth step was to calculate information utility value based on Equation (7).

$${\mathrm{d}}_{\mathrm{j}}=1-{\mathrm{e}}_{\mathrm{j}}$$

(7)

The information utility value reflects an indicator’s discriminative power (inverse entropy). A higher dj indicates a stronger discriminative ability.

The final step was to normalize to obtain the indicator weights ${:\mathrm{w}}_{\mathrm{j}}$ based on Equation (8).

$${\mathrm{w}}_{\mathrm{j}}={\displaystyle \frac{{\mathrm{d}}_{\mathrm{j}}}{\sum _{\mathrm{j}=1}^{\mathrm{m}}{\mathrm{d}}_{\mathrm{j}}}}$$

(8)

where m is the number of indicators.

The entropy weighting process is a mathematical approach grounded in information entropy, concentrating on the indicator values themselves, and assessing the degree of dispersion among the indicators to determine their weights in the overall evaluation. While this method can mitigate human subjectivity, it may overlook the actual significance of certain indicators. In contrast, the AHP weighting process is a multicriteria decision analysis that is straightforward to understand and apply. It aids decision-makers in ranking or comparing multiple indicators or factors, but it depends on experts’ subjective judgment, which can influence the accuracy of the assigned weights. As a result, this study integrated both methods to derive comprehensive indicator weights, aiming to address the limitations of each. This circumvents the potential issues associated with using a single method to determine weights and enhance the scientific validity of weight assignments. The final resilience index was treated as the average of the two weighting indices. Here, the weights reflect the extent to which indicators distinguish sample differences, with a high weight making it crucial for resilience differentiation. The data were normalized and summed to derive the resilience measurement outcomes for the various indicators. Subsequently, the average of each indicator was computed to achieve a comprehensive resilience assessment for the different types of urban communities in Tianjin.

3. Results

3.1. The Descriptive Statistics of the Surveyed Community Cases

Figure 4 presents a diverse timeline of community development, clearly illustrating the ongoing growth of urban communities within the city. Figure 5a shows the distribution of the total number of households across communities, revealing that most communities consist of 500 to 2999 households. Notably, there were 10 communities with 1000 to 1999 households and 7 with 2000 to 2999, indicating that medium-sized household populations are prevalent. In contrast, there were fewer communities with very small (<500) or very large (≥4000) household counts. Figure 5b displays the distribution of the total number of buildings in these communities. The most common were communities with 10 to 19 buildings (10 communities), followed by those with fewer than 10 buildings (8 communities). The distribution decreases for higher building counts, with only a few communities having 30 to 39, 40 to 49, or 50 or more buildings. The land plot ratios ranged from 1 to 3.5, with most falling between 1.5 and 2.0. On average, these communities had a greenspace ratio of 30%, with the highest being 45% and the lowest at 10%.

3.2. The Weights of Indicators and the Overall Performance Score

Table 3. Indicators and weights of Community Resilience Assessment.

Environmental Resilience	AHP Weight	Entropy Weight	Social Resilience	AHP Weight	Entropy Weight
X1-Land Plot Ratio	0.0606	0.008	X21-Proportion of Elderly Population	0.0252	0.017
X2-Building Layout Density	0.0294	0.009	X22-Proportion of Migrant Population	0.0690	0.01
X3-Internal Floor Area of Dwellings	0.0350	0.04	X23-Health Index	0.0179	0.012
X4-Entrances and Exits	0.0294	0.049	X24-Income Index	0.0158	0.031
X5-Road Integration Degree	0.0156	0.018	X25-Rental Index	0.0202	0.029
X6-Internal Accessibility	0.0138	0.016	X26-Housing Price Index	0.0111	0.037
X7-Spatial Accessibility	0.0276	0.01	X27-Property Management Fee Index	0.0324	0.059
X8-Green Space Ratio	0.0269	0.012	X28-Willingness to Relocate	0.0264	0.005
X9-Logistics Distribution Network	0.0167	0.049	X29-Length of Residence	0.0293	0.014
X10-Emergency Rescue Network	0.0356	0.012	X30-Homeowner Living Rate	0.0225	0.011
Service Resilience	AHP Weight	Entropy Weight	Governance Resilience	AHP Weight	Entropy Weight
X11-Accessibility of Medical Facilities	0.0254	0.021	X31-Satisfaction with Property Services	0.0444	0.02
X12-Accessibility of Daily Shopping Facilities	0.0397	0.06	X32-Satisfaction with Community Management	0.0190	0.016
X13-Accessibility of Public Transport	0.0347	0.019	X33-Completeness of Community Institutions	0.0172	0.019
X14-Accessibility of Cultural Exhibition Facilities	0.0101	0.052	X34-Satisfaction with Social Organizations	0.0130	0.008
X15-Accessibility of Sports and Fitness Facilities	0.0123	0.054	X35-Mutual Trust among Residents	0.0243	0.016
X16-Accessibility of Parks and Squares	0.0139	0.06	X36-Trust in Property Work	0.0172	0.01
X17-Accessibility of Scientific Research and Education Facilities	0.0127	0.037	X37-Trust in Community Work	0.0163	0.014
X18-Accessibility of Entertainment and Leisure Facilities	0.0096	0.063	X38-Degree of Mutual Care among Neighbors	0.0276	0.021
X19-Functional Mix Degree of Plots	0.0415	0.014	X39-Degree of Neighborly Interaction	0.0173	0.014
X20-Compliance Rate of Facilities in the 15 min Life Circle	0.0280	0.024	X40-Participation in Public Benefit Activities	0.0151	0.01

illustrates the weighting outcome, which served as the final resilience community assessment index in the empirical study conducted in Tianjin. The most significant factors, as determined by the weighting ranking, include the accessibility of entertainment and leisure facilities, property management fees, logistics distribution networks, and entrances and exits, all of which are closely linked to residents’ daily lives. The overall scores for all the surveyed communities were calculated after integrating the weighted sum of the four-dimensional resilience measurements. According to the overall community resilience assessment results (Figure 6), most communities scored between 0.3 and 0.5. The lowest score was recorded for Metallurgy Lane, an older residential area in the subcentral region, whereas the highest score was achieved by Yitian Garden, a commodity housing estate community located in the central area.

3.3. Comparing Three Types of Communities by Resilience Dimensions

3.3.1. Environmental Resilience

For the community resilience performance within the four key dimensions, we first compared each indicator without considering their weights. Figure 7 presents the performance of Environmental Resilience across the three types of communities, with an average score of 0.663. Notably, the old residential complexes exhibited the lowest resilience in their built environments, with an average score of approximately 0.595. The weakest performance was primarily attributed to the lowest scores in the internal floor area of dwellings (x1 = 0.348), entrances and exits (x1 = 0.145), and internal accessibility (x1 = 0.500). As a central metropolitan area in China, Tianjin’s older residential complexes and tower block apartments are predominantly located in the city center. In these neighborhoods, indicators such as average residential area, green space ratio, and building density, which contribute to comfortable and less dense living environments, are generally lacking. Commodity housing development emerged later as a product of the market economy. In these areas, the quality of living spaces and the environment have become a significant concern for both developers and consumers. However, our survey indicated that they still did not achieve the highest level of resilience in built environments. Commodity housing types have a clear advantage in aspects such as the internal floor area of dwellings, building density, entrances and exits, and greenspace ratio. Nevertheless, they face significant challenges related to significantly higher plot ratios owing to the adoption of high-density development patterns, as well as weaker logistics distribution and emergency rescue networks. In contrast, the other high-density pattern, the tower apartment group, achieved the highest overall results, benefiting from advantages in spatial connectivity, internal accessibility, logistics distribution, and emergency rescue networks. This is attributed to their unique spatial design, which allows residents easy access from buildings to streets, resulting in significantly reduced evacuation time during disasters.

3.3.2. Service Resilience

The results of the community service resilience assessment are shown in Figure 8. The average score was 0.389 across all communities. The order of service resilience from highest to lowest is as follows: tower apartment, new commodity housing, and old residential complex. Tower apartments, owing to their earlier construction, typically have compact block layouts, denser road networks, and enhanced walkability, providing a notable advantage in the availability of service facilities in urban centers. This is particularly evident in their excellent accessibility to services, such as medical facilities (x2 = 0.787), educational institutions (x2 = 0.581), entertainment venues (x3 = 0.5), and public transportation (x2 = 0.412), all of which are crucial for daily living. The scores for new commodity housing and older residential areas are similar because the availability of public facilities is closely linked to the level of construction and development in various urban areas. In commodity housing estates, high resilience is mainly seen in the compliance rate of life circle facilities (x3 = 0.578) and the degree of functional mix (x3 = 0.667). Conversely, in older residential communities, the accessibility of parks, open spaces, and sports and fitness facilities is significantly below average (x1 = 0.128, 0.181), failing to meet the general requirements of the new 15-min life circle plan (x1 = 0.390).

3.3.3. Social Resilience

Figure 9 shows the results of the assessment of the social resilience of communities. The average score, without weighting, across the ten indicators was 0.56, with the lowest scores found in older residential areas (x1 = 0.43). Regarding community health resilience, tower apartments and aging neighborhoods have a higher concentration of elderly residents, resulting in a relatively low health index. In terms of economic resilience, commodity housing and tower apartments performed better on indicators such as property management fees (x3 = 0.45, x2 = 0.38), rent (x3 = 0.41, x2 = 0.98), and housing prices (x3 = 0.32, x2 = 0.44), indicating that residents in these areas have greater economic strength and resilience to financial challenges. By contrast, older residential complexes have lower economic levels. Regarding community stability, tower apartment communities had the smallest proportion of migrants and the lowest willingness to relocate, reflecting strong residential stability. The length of residence indicator was surprising, as older residential complexes scored the lowest in our survey. Residents in commodity housing reported longer stays than those in older residential complexes, suggesting that urban mobility and property aging may lead to the displacement of original residents. Overall, the weakest social performance of older residential complexes is characterized by low socioeconomic status, the lowest health levels, and the highest willingness to relocate, all of which impact community resilience.

3.3.4. Governance Resilience

The outcomes of the governance resilience assessment are shown in Figure 10. The average scores of the indicators show minimal variation between old tower apartment communities (x2 = 0.52) and commodity housing (x3 = 0.56), yet older communities exhibit a notably higher level of governance resilience (x1 = 0.62). From a management standpoint, residents of tower apartments and commodity housing reported greater satisfaction with community property services, systems, social organizations, and social work. Conversely, those living in older residential complexes expressed the least satisfaction with property services (x31 = 0.42), completeness of community institutions (x33 = 0.56), and trust in property workers (x36 = 0.55), highlighting a significant disparity compared to the other two community types. However, residents of old residential complexes scored higher on all other indicators, such as stronger mutual trust among residents (x34 = 0.70) and active participation in public benefit activities (x40 = 0.70), reflecting a strong sense of mutual trust and care among neighbors, which indicates robust community self-organization. In contrast, tower apartments and new commodity housing communities experience weaker neighborly interactions and mutual trust, with relatively low organizational capacity.

According to the assessment results with the weight (Figure 11), the resilience levels of the three primary community types were outlined as follows:

(1)

Tower apartments: This community type exhibits the highest overall resilience (X = 0.511). Constructed mainly between the 1990s and the 2000s, these buildings were predominantly located at the heart of Tianjin. Compared to the other two community types, tower apartments generally show strong resilience in both service and social aspects. Their resilience is bolstered by the optimal utilization of nearby resources and excellent access to surrounding public services, fulfilling residents’ daily requirements. Consequently, their overall resilience was relatively robust. However, many of these structures are old and require extensive renovation and upgrades, making them a key target for future community renewal and governance efforts. The governance system is also weaker in areas such as social organizations, neighborly interactions, and public engagement.

(2)

Old residential complexes: These communities have a low overall resilience level (X = 0.424). While community governance resilience is relatively high, the resilience of the built environment and public service facilities is low. These communities are often marked by significant aging, primarily consisting of workplace compounds with large land coverage, enclosed layouts, and a notable lack of multifunctional facilities. Additionally, these communities typically experience high rental rates, strong population mobility, and relatively high willingness among residents to relocate. Nonetheless, their overall cohesion and sense of community belonging remained fairly strong. Future resilience improvements should focus on enhancing infrastructure and updating the spatial environment. Improving the interactive relationships among various neighborhood life circle spaces is essential.

(3)

Commodity housing estates: These communities generally have a moderate level of overall resilience (X = 0.488), particularly in terms of environmental and governance resilience. These communities feature newly planned spatial layouts, well-equipped infrastructure, and professional property management. Nevertheless, because of their vast size and relatively distant location, access to facilities is limited. For these communities, future enhancements should focus on boosting public-service offerings and improving accessibility. It is crucial to optimize the availability of daily service facilities, expand the reach of convenience stores through both online and offline channels, and strengthen express delivery and logistics capabilities at the entry and exit points.

3.4. Regional Differentiation

Since the tower department type is exclusive to the central area, our primary comparison focused on old residential complexes and commodity housing estates, both of which are evenly distributed across the central, sub-central, and peripheral areas of the Tianjin city. Figure 12 illustrates the resilience assessment outcomes for the old residential complexes by region. The average resilience scores across different categories vary from 0.402 to 0.542, with the central old residential group showing the highest overall resilience, while the sub-central group had the lowest. The central group consistently surpassed the other groups in all the aspects of resilience (Figure 13a–d). The primary advantage lies in the logistics distribution and emergency rescue networks, which bolster its physical environment. Notably, service resilience is exceptionally high in the central area, where the compliance rate of the 15-min life circle and accessibility to various facilities are also excellent. This highlights the differences in public service provisions across various urban zones. Clear differences are also found between the subcentral and peripheral groups in old residential complexes. Regarding service resilience, the peripheral group scores were lower at 0.071 compared to the sub-central group’s 0.091, indicating relatively better service access in the sub-central areas. Additionally, the sub-central group had a slight advantage in social resilience, with a score of 0.100 compared to the peripheral group’s score of 0.091, suggesting stronger social networks and community involvement in sub-central regions. In terms of environmental resilience, the peripheral group scored higher (0.130) than the sub-central group (0.114), indicating more favorable environmental conditions in peripheral areas. For governance resilience, the peripheral group also leads with a score of 0.117 compared to the sub-central group’s score of 0.097, reflecting more effective governance practices in early built communities in the peripheral region.

Figure 14 illustrates the results of the resilience evaluation of commodity housing types across the various city regions. The average resilience scores for each dimension range from 0.089 to 0.240. The central group exhibited the highest resilience (Y1 = 0.695), whereas the sub-central group exhibited the lowest resilience (Y2 = 0.443). Generally, the central group communities demonstrate superior resilience performance in most dimensions (Figure 15a–d), particularly service resilience. The higher-level performance is also characterized by high property fee and income-related economic indices. In contrast, the subcentral and peripheral regions had comparably much lower resilience scores. Although the subcenter group performs relatively well in service resilience compared with the sub-central group, its weakest performance in environmental, social, and governance places it at the lowest level overall among the three groups. The central region leads in the service resilience dimension, with a score of 0.240, indicating that commodity housing benefits from a broad and high-quality range of services. This suggests that communities in sub-central areas are affected by factors such as land scarcity and environmental pressures, leading to reduced built environmental resilience. Subcentral and peripheral zone communities face significant deficiencies in their service resilience. Additionally, the subcentral zone experiences greater population mobility, lower community social trust, weaker community cohesion and interaction, and an incomplete community governance system, resulting in relatively lower social and governance resilience.

4. Discussion

4.1. Exploring Resilience Enhancement Strategies Towards Targeted Measures

Recently, there has been a global trend of considerable activity in facilitating the resilience of community by incorporating intelligent assessment techniques and developing initiatives driven by specific actions. The uneven distribution of urban residential spaces is a common feature of cities worldwide [53]. In China, the transformation of housing into a market commodity in the late 1990s led to a rapid increase in the construction of new urban neighborhoods from 2000 onwards. This shift resulted in the decline of traditional social structures, with market economy principles now primarily connecting people [54]. A significant critique of the literature is that swift urban development and residential displacement have dismantled traditional urban community structures and weakened informal social connections despite improvements in living conditions and urban infrastructure [55,56]. Additionally, there is notable dissatisfaction, with growing social apathy towards neighborhood governance [57]. For community development, it is crucial to uphold trust and norms of reciprocity across communities in both spatial and temporal contexts. Furthermore, it is evident that communities exhibit lower economic and stabilizing capacities, resulting in reduced intrinsic resilience, which aligns with scholarly findings.

Table 4. A Comparison of the Community Resilience Frameworks between UCRA in this Study and other Well-known Tools.

	Analysis of Resilience of Communities to Disasters (ARC-D)	Baseline Resilience Indicators for Communities (BRIC)	Community Disaster Resilience Planning (CDRP)	Urban Community Resilience Assessment (UCRA)
Region/Organization	International Humanitarian Emergency Agency	Research Institute from University of South Carolina	Justice Institute of British Columbia, Canada	University and research institution joint research Team
Objective	Identify community disaster-resistance capacity	Monitor county-level resilience to natural disasters	Provide a planning framework for community disaster response	Detect gaps through assessment and carry out targeted interventions to enhance community resilience
Content	1. Build community database 2. Evaluate the level of community disaster response	1. Use resilience indicators to compare county-level resilience 2. Conduct a community resilience assessment every five years to understand the improvement of community resilience	1. Provide community feature information and boundaries to assist in collecting community information 2. Develop community hazard risk analysis tools to calculate community disaster resilience indices.	1. Integrate community basic information data, spatial basic data, survey questionnaires and other multi-source data ledger 2. Visualize the shortcomings of community infrastructure, services, and governance 3. Clarify the direction of action for enhancing community resilience
Key dimensions & Indicators	Education, health, economy, society (30 indicators)	Human well-being, economy, community capital, institutions, infrastructure, environment (50 indicators)	Community resources, disaster management (265 indicators)	Environment, service, social and governance (40 indicators)
Application Areas	South America, North America, Africa	U.S. mainland, Alaska, Hawaii	Communities in Canada	Urban neighborhoods within built-up areas of Chinese cities
Technology-Policy Integration	Bottom-up policy support for local governments	Collect annual data to guide public-health emergency planners	Government-funded project actively engaging social capital (Vancouver Foundation)	Co-governance model aimed at renewal and governance enhancement

is a comparison between this study and existing community resilience assessment tools. The existing tools were largely built for North American counties, Canadian small towns, or Latin American rural settings. This UCRA is tailored to the realities of contemporary Chinese urban neighborhoods. It expands to 40 fine-grained indicators grouped into four tailored dimensions that reflect a Chinese neighborhood typology, which is calibrated for built-up urban districts in China, accounting for high-density redevelopment. The framework moves to context-sensitive, data-rich, and intervention-ready assessment and can guide targeted interventions for upgrading neighborhood resilience.

Communities that lack essential living facilities can be accurately identified by assessing and evaluating service resilience levels. For example, the overall completeness of the 15-min life circle and service coverage still need to be enhanced in older neighborhoods. Some older residential areas, due to their later construction, lack space for logistics and rescue operations and cannot offer high-quality property services to residents. Newly built commodity communities are typically larger and have stricter internal management but often lack sufficient facilities, particularly in education and healthcare. Most communities in central Tianjin show strong service resilience, whereas those outside central areas have significant deficiencies that have considerably weakened community resilience. This can be improved by enhancing 15-min life circle facilities through online services and advanced internet platform development. Governance weaknesses are evident in most subcentral communities, and can be addressed through targeted renewal and enhancement with strategically paired projects in recent urban renewals. Following the assessment of community resilience, it is vital to identify the core vulnerabilities of each neighborhood and establish a comprehensive baseline database. To enhance community resilience, the suggested strategies include improving the guidance and service roles of facilities, optimizing the capacity and functions of public spaces, and strengthening mutual support and social resilience. These initiatives aim to create a vibrant, diverse, and comprehensive living environment that encourages walking and fosters social interactions among residents.

4.2. Promotion Systems and Implementation Pathways

Numerous community developments in Chinese cities have faced criticism for adhering to inflexible, technically driven standards that overlook the importance of sustainable, long-term management [58]. As noted by Wates and Knevitt [59], neighborhoods that endow the greatest satisfaction to their inhabitants are those shaped and managed by the residents themselves. Traditional communities in China often have fixed functions, are outdated, difficult to replace, and occupy large areas, resulting in low environmental utilization and reduced layout flexibility. Community regeneration plans and designs must address both daily functional needs and disaster emergency requirements, with improvements in site selection, quantity, form, distribution, and connectivity to better respond to disasters. Comprehensive planning should identify critical facilities and protection targets under specific conditions, enhance the flexibility of existing spaces, and consider adaptive models. Optimizing community public facility resilience should prioritize improving medical facility allocation and strengthening primary healthcare response capabilities. Upgrading community sports venues and fitness facilities is crucial to provide residents with exercise opportunities during community closures.

Urban communities in China need to build resilient spatial-social entities and upgrade and refine community functions, social organizations, and disaster response systems. When facilities become outdated or malfunction, backup modules can be swapped swiftly, ensuring that infrastructure updates are both efficient and convenient. In the future planning and configuration stage, a new modular design can offer the benefit of combining facility modules to meet the diverse needs of groups, creating multifunctional spaces for activities, such as healing, reading, resting, viewing, playing, and contemplation. This strategy not only fulfills residents’ material and safety needs, but also fosters interaction among community members, promotes recreation, and enhances social engagement and the vibrancy of community governance.

Drawing on international experience and taking into account real-world conditions, the future construction of resilient communities should also incorporate the following development goals:

(1)

Protect vulnerable groups and promote social equity and justice

Regarding vulnerable groups, there is often a significant disparity in how shelter communities respond to risks; low-income communities and vulnerable groups generally have higher morbidity rates. For example, in Houston’s Neighborhood Resilience Planning (NRP), co-production facilitated community-driven adaptation strategies, focusing on building capacity, enhancing local governance, and addressing power dynamics to promote equitable action [60]. This work encourages fostering collaboration, empowerment, and equitable governance. The Barcelona Resilience Network brings together local entities, mutual aid groups, and public and private institutions with the goal of supporting victims of major incidents, such as providing shelter for the homeless during freezing winter nights [61]. In addition, many cities approach this from an environmental justice perspective, ensuring that community policies and actions avoid unfair distributions, so as to prevent and reduce environmental pollution and risks in low-income or disadvantaged residential areas.

(2)

Respond to changing risks and strengthen infrastructure

In terms of climate change, it is important to enhance and upgrade infrastructure to increase adaptability and resilience against extreme weather events (such as floods, storms, and high temperatures). Enhancing the flexibility and redundancy of energy systems can ensure stable energy supply during extreme weather events. Upgrading infrastructure, such as buildings and green roofs, can not only reduce the direct impact of disasters, but also contribute to the long-term sustainable development of communities. For instance, New York City provides detailed analyses and risk assessments at the community, building, infrastructure, and coastline levels [62]. Growing populations, aging infrastructure, evolving economies, and intensifying inequalities continue to challenge the adaptability of communities.

(3)

Address social needs and create economic and employment opportunities

As localized units, communities often rely on specific local policy documents for implementation in alignment with regional planning and development policies, thereby promoting the rollout of local economic plans. Resilience engineering can be treated as a new type of infrastructure, bringing with it dedicated funding schemes. Thus, economic planning for community resilience can be organically integrated with urban renewal projects and redevelopment of impoverished areas. For example, in Houston, the city is divided into 88 “Super Neighborhoods,” utilizing a community-centered approach to tailor resilience plans to each community [63]. By integrating government and social resources to achieve synergy, institutions such as the Houston Land Bank and the Community Land Trust Fund collaborate to invest in resilient community development. The implementation of public–private partnership (PPP) models can help disperse fiscal pressure on the government and attract more investment in community infrastructure and social services.

4.3. Establishing a Resilience Monitoring Platform Towards Smart Governance

The digital development of resilient communities is an inevitable trend. Many cities encourage communities to actively practice digitization pathways, contributing smart digital capabilities to build more resilient communities. Integrating regular assessment methods to enhance community resilience capacity has a significant practical value. Through the rational use of digital technology, not only can a community’s self-recovery and adaptability be enhanced, but it can also drive innovation in governance tools for communities. Currently, many cities worldwide have established a citywide electronic community survey record database on GIS or similar platforms [64,65], but the development of targeted integrated application systems is insufficient. The efficiency of data utilization among these communities was low. In particular, the capability to use big data and artificial intelligence for pandemic tracking and timely monitoring is lacking. Therefore, there is an urgent need to build a unified, open, shared, and intelligent comprehensive community information platform citywide.

Based on the current situation, various community infrastructure data can be integrated, incorporating population, land use, buildings, facilities, and internet data. This platform integrates functions such as comprehensive data aggregation, monitoring and disaster preparation, simulation, and intelligent decision making across emergency management fields. A series of corresponding recommendations must be adopted to realize the digital development of resilient communities. For instance, it increases investment in digitalization and strengthens the construction of digital infrastructure, providing the community with stable and efficient information networks and digital service interfaces. Cultivate digital talent and improve community members’ digital literacy and skill levels. However, the digitalization of resilient communities faces many challenges. Digital security, data privacy protection, and the digital divide are all important issues that need to be addressed in the future. It is important to formulate and improve relevant laws and policies to ensure compliance and effectiveness of digital technology in community risk governance. Cross-sector and cross-field cooperation and data sharing should be strengthened to jointly promote the digital development of resilient communities.

From a developmental perspective, community demands have evolved with the continuous advancement of the socioeconomic landscape and new supply technologies. Advancing a resilient community will necessitate the adoption of sophisticated management and flexible planning, which demand comprehensive research and ongoing contributions. Resources and services must be channeled to local communities and streets to accelerate the development of resilient communities. Responding to people’s increasing aspirations for a better life and leveraging smart information technology, initiatives are underway to address the shortcomings of resilient community construction and effectively optimize the allocation of resources within communities. To develop pathways for implementing resilient community policies, the main modes include: (1) Promotion: Increasing awareness and implementation effectiveness of resilient community policies through media, internet, and meetings. (2) Training: Offering training to those responsible for policy implementation to improve understanding and execution capabilities. (3) Monitoring: Setting up mechanisms for regular evaluations of the resilient community policy implementation process. It is also notable that non-digital preparations are still of great importance in addition to the evolution of smart governance. While digital tools promise resource optimization, empirical evidence shows that digital exclusion can widen resilience gaps. This is especially evident in communities with a large number of elderly and low-income residents. Relying solely on digital or online methods can be risky. Offline redundancy (e.g., community loudspeakers and largely printed evacuation maps) in response to emergencies must be considered. These offline tools ensure that vital information reaches all members of a community, including those who may not have access to or familiarity with digital devices.

Additionally, this effort helps raise public awareness and participation in community resilience, thereby fostering united and cohesive community groups. Governance systems should play a more significant role in improving the resilience of established urban communities. How government departments, communities, residents, and third-party service entities can establish effective mechanisms centered on community governance and develop coordinated tools and approaches is also a major challenge in policy implementation. This paper provides a useful reference for conducting such work.

Community resilience assessments and enhancement strategies involve a complex analytical process, and the strategic moving forward steps generally include the following aspects. First, by reviewing relevant materials, statistical data, questionnaires, and other methods, data on a community’s demographics, economy, society, environment, etc., must be integrated to analyze the situation of the community. Second, a community’s relative strengths and weaknesses must be effectively identified. Strengths may include abundant service resources, sufficient rescue conditions, and a spirit of mutual assistance and cooperation among residents; while weaknesses include outdated infrastructure, incomplete services, and deficiencies in social governance and organizational management systems.

5. Conclusions

In this study, we developed a novel framework to assess urban community resilience in the Chinese context, encompassing four dimensions: environmental, service, social, and governance resilience. By categorizing communities according to their construction patterns and dividing them into three geographical layers, we introduced a new perspective linking community resilience with typology. Utilizing the APH and entropy methods to weigh data collected from typical urban communities in a large Chinese city, Tianjin, we discovered that tower apartments exhibited the highest resilience, while older residential complexes showed the lowest resilience. Communities located in central urban areas generally demonstrate greater resilience than those in peripheral urban regions. Our study also proposes strategies for enhancing resilience across community types and regions, underscoring the importance of establishing resilient action systems and implementing differentiated pathways. By consistently observing and improving resilience, communities can bolster their ability to effectively respond to future challenges.

6. Limitations

This study has some limitations, such as sample size and data restrictions, which can be addressed in future research to further refine the assessment system and enhance the generalizability of the findings. This research was based on findings from Tianjin, China. In the future, we plan to include more cities and comparative cases, which will help verify the robustness of our findings and allow us to examine the differences between cities. Longitudinal studies can be conducted to better understand the temporal evolution of community resilience. China’s current situation differs from that of many countries that have equal development in urban and rural areas. This study focuses on Chinese urban communities within developed urban areas, so flexibility will need to be considered when applying its findings in other non-urban areas. This may lead to different constitutions of community land and property rights, as well as social organizations, which vary greatly in China. Greater consideration should be given to factors such as socioeconomic conditions, local community circumstances, and traditional local perceptions when work is undertaken in broader regions. In future studies, we plan to develop more dynamic indicators to monitor the sustainable learning and growing abilities of communities. The digital inclusion index can also be discussed with communities regarding the potential of training in confronting the digital divide for seniors and low-income groups.