# A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution
## 2 Exposure Data Management in Support of Disaster Risk Management
### Multi-Representation of Spatial Objects  
A multi-representation database stores different views of the same physical objects or phenomena and links them with each other, which leads to differences in the semantics and in the geometry of the objects [23]. The main reasons for introducing a multi-representation into a data model include how such information can be analyzed with respect to information provided in another representation, such as another scale or resolution. Moreover, linking different representations allows for propagating updates between, for example, different map scales [24]. A data model that supports multiple representations of spatial objects can also be defined as a multi-resolution or multi-scale database.  
Multi-resolution models mainly evolved in response to requirements imposed by web mapping applications that deal with auto-generalization between different zoom levels [25]. CityGML [26], for example, provides a comprehensive data model to represent 3D city models under consideration of  
Figure 1: Possible types of changes of a spatio-temporal object. Modified after [11].  
five different detail levels. Two general cases of multi-resolution datasets can be distinguished. First, datasets at different resolutions are generated independently and need to be linked in the database by matching procedures. Second, lower resolution representations are derived from higher resolution data by generalization functions and their link is established by the generalization process. In the context of disaster risk management, multi-resolution is of high relevance, mainly due to the fact that exposed assets and/or damages are often described by data from different sources and at different scales. In previous work of the authors [27], for example, point- and path-wise visual screenings of single buildings are used in combination with large area remote sensing observations to derive a multi-scale exposure model. Due to the fact that exposure models often combine datasets from different sources at different scales, particular emphasis was given to implement independent matching procedures.  
[PERSON] et al. (2004) [23] describe three possible structures for matching different resolutions in the database: attribute-based structure, additional attribute, and additional linking tables. In an attribute-based structure, everything is stored in one dataset and additional attributes are used to describe the different appearances. Another option is to link the datasets by using additional attributes that store only identifiers (IDs) that refer to the corresponding objects at the lower resolutions. One attribute per resolution level would be required for this option and the different resolution objects could be stored either within the same table or in different tables. The third option is based on the additional attribute structure but stores the resolution IDs in a separate table. To minimize the complexity of Structured Query Language (SQL) queries and the number of database accesses, the additional attribute linking structure was selected, and attributes are added for each resolution level in the same table.