The implementation of data cube concept to the multidimensional data of social vulnerability assessment: An example of Special Region of Yogyakarta, Indonesia

Social vulnerability is a key component of disaster risk assessment because it reflects the capacity of populations to anticipate, cope with, and recover from hazards. However, vulnerability is inherently multidimensional, involving spatial, temporal, and socio-demographic factors that are often analyzed separately in conventional approaches. This limitation makes it difficult to integrate multiple indicators across different spatial and temporal scales. This study proposes a multidimensional data cube framework to systematically organize and analyze social vulnerability data. The framework integrates three dimensions: spatial units, time, and social indicators. Using the Special Region of Yogyakarta, Indonesia, as a case study, vulnerability data are structured according to hierarchical administrative levels and socio-demographic indicators such as gender, age, employment, education, and poverty. The results of this study developed a prototype data cube for social vulnerability assessment and implemented it in a database with three dimensions: spatial, indicator, and time. The spatial dimension includes four hierarchical levels (village to province), the indicator dimension consists of 11 entities based on criteria, and the time dimension contains multi-year data. Data retrieval is performed through aggregation. Spatial aggregation moves from lower to higher administrative levels, affecting scaling and generalization. Indicator aggregation combines lower-level indicators (e.g., male and female population) into higher-level ones (total population), allowing cross-checking between criteria. Temporal data is not aggregated but analyzed over time to observe trends and support predictions. • A multidimensional data cube framework is proposed for social vulnerability assessment. • Spatial, temporal, and socio-demographic indicators are integrated in a unified structure. • Hierarchical cube operations enable multi-scale aggregation and flexible data analysis. • The framework improves efficiency and consistency in managing large demographic datasets.