Construction of the CAS-Based Resilience Evaluation System for Old Communities
Resilience in the context of disaster management and urban planning has undergone significant evolution over the years. Initially defined as engineering resilience, this concept emphasized the rapid return to equilibrium after disruption. As understanding deepened, ecological resilience was recognized, acknowledging multiple states of equilibrium and ongoing evolution. The current perspective, evolutionary resilience, focuses on continuous adaptation and innovation. Within urban old communities, resilience is about the ability to withstand disturbances while maintaining core functions, self-organization, and adaptability to change. This study interprets disaster resilience for aging urban neighborhoods as their capacity to adjust, adapt, learn, and innovate, ensuring the maintenance of essential functions and effective recovery from disasters. Identifying resilience structures and assessing their current state is crucial in creating a comprehensive framework for these communities.
Complex Adaptive System (CAS) Theory in Community Resilience
The Complex Adaptive System (CAS) theory, introduced by John Holland in 1994, provides a framework for understanding systemic evolution through interactions among adaptive agents. In community contexts, these agents include residents, organizations, and infrastructures that interact dynamically to maintain stability amid disasters. These interactions, characterized by nonlinear, coordinated, and evolving dynamics, facilitate learning, adaptation, and improvement. Our study employs a CAS-based model tailored for old urban communities, offering a theoretical and practical structure to enhance resilience effectively.
The model conceptualizes these communities as consisting of several layers: the outer ring represents major risks they face, the middle ring highlights six key resilience dimensions, and at the core are individuals and groups interacting through dynamic feedback loops. These visual elements illustrate the complex, interconnected nature of disaster resilience in aging urban environments.
Community Members as Adaptive Agents
In the model, community members are fundamental adaptive agents. Their resources and attributes—ranging from material and economic to cultural and structural—form the resilience resource system of the community. Dynamism, learning ability, adaptability, and innovation drive the enhancement of this system. Traditional disaster risk reduction approaches in China often focus on broad urban-scale analyses or isolated resilience aspects, missing the nuanced micro-scale interactions within community systems. The CAS model for old communities suggests that disaster resilience emerges from the synergy of resources facilitated by adaptive agents. This study evaluates resilience across six dimensions—built infrastructure, ecological environment, member composition, economic conditions, organizational and institutional frameworks, and cultural awareness.
Utilizing Cloud Model Theory for Resilience Evaluation
Evaluating the resilience level of old communities is inherently a qualitative, fuzzy process rather than a clear-cut classification. Traditional methods fall short in providing continuous, comprehensive assessments. Recognizing this gap, cloud model theory—refined by experts led by Academician Li Deyi—provides a method to express linguistic values through numerical characteristics (Ex, En, He) to bridge qualitative judgments and quantitative data.
– **Expectation (Ex):** Represents the expected level of distribution within domain space.
– **Entropy (En):** Reflects the granularity of qualitative concepts, indicating uncertainty and range.
– **Hyper-entropy (He):** Measures changes in uncertainty, reflecting the cloud’s thickness.
Combining forward and reverse cloud generators enables effective conversion between qualitative and quantitative data, considering their randomness and fuzziness.
The Role of Entropy Weight Method
Given that numerous indicators influence community resilience, assigning weights proportionate to their contributions is essential. The entropy weight method, favored for its objectivity, evaluates information in each indicator to assign weights. Higher entropy indicates more uncertainty and lesser weight, ensuring scientific accuracy.
Steps include:
1. **Normalization of Data:** Eliminating scale differences using min-max normalization.
2. **Entropy Calculation:** Computing entropy for each indicator.
3. **Determining Weights:** Weights are assigned inversely proportional to entropy values.
Incorporating Fuzzy Cognitive Map (FCM)
Fuzzy Cognitive Map (FCM), developed by Professor Bart Kosko, models complex causal relationships using weighted graphs underpinned by fuzzy logic. It’s particularly effective for dynamically representing multi-party systemic influences, crucial for predicting developmental trends and identifying key factors in urban old communities.
The model iterates initial data inputs to simulate future states, with nodes and relationships determining each evaluation. FCM addresses uncertainties and non-linear relationships dynamically.
Enhancing FCM with DEMATEL Method
Traditional FCMs often overlook indirect relationships among indicators. The DEMATEL method refines this by assessing direct influences through expert input, supplemented by cloud model evaluations, and then defuzzifying to represent uncertainty effectively. This systematizes the evaluation process, offering a robust analysis of the resilience dynamics.
Application in Urban Old Communities
Urban resilience research heavily focuses on contemporary built environments, often neglecting older urban communities. These areas, marked by aging infrastructure and distinct demographic compositions, face unique challenges. Our study, conducted in Taiyuan—a central city in China—assesses 56 old communities. Criteria such as building age, structure, spatial planning, and infrastructure inform our selection process. By employing the CAS framework and advanced methods like the cloud model and FCM, we aim to enhance disaster resilience in these crucial communities. This approach is not only practical but imperative for sustainable urban management in rapidly developing regions.
The study’s methodology and participant engagement adhere strictly to ethical guidelines, ensuring data privacy and integrity, while the analysis, aided by statistical tools, ensures reliability and precision in evaluating community resilience. Ultimately, these efforts provide valuable insights for policymakers and urban planners to bolster the resilience of aging urban landscapes effectively.
