# A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution
## 2 Exposure Data Management in Support of Disaster Risk Management
### Spatio-Temporal Databases  
Spatio-temporal databases are characterized by both spatial and temporal semantics and deal with real-world applications in which spatial changes occur over time. A data model must, therefore, allow for spatial, temporal, and spatio-temporal queries to be performed. Changes over time can apply to the geometry, topology, or attribute characteristics of an object, leading to eight possible change type scenarios [11], as depicted in Figure 1. Earlier work in the direction of spatio-temporal database design was characterized by separate research in either the spatial [12] or temporal [13] domain. Since the emerging of combined spatio-temporal databases, numerous approaches have been proposed for data modeling, and reviews have categorized and compared the existing work. [PERSON] (1992) [14] was one of the first authors who specifically addressed the temporal domain in Geographic InformationSystems (GIS). Different approaches to spatio-temporal data modelling have been proposed since then, including object-oriented data modelling [15] or cell tuple-based data models, as proposed for example in [16]. Also relational models have been proposed to handle spatio-temporal data such as in [17; 18]. Comprehensive literature reviews on the topic can be found, amongst others, in [19; 20; 21]. Relational database models are generally considered to be less efficient than object-oriented approaches when dealing with very large data volumes and semi-structured data. However, due to its simplicity, its ability to deal with transactions, its extensive join capabilities, and the fact that it is a well-known and standard data modelling approach, a relational approach is followed in this study.  
In spatio-temporal databases, different time representations are possible and the decision of what to timestamp or on which level may vary. Timestamping an entire geographical object (e.g., a building as a whole), for example, may be a valuable choice if storage capacity is limited, but it does not allow for a description of the temporal variation of its individual attributes (e.g., number of storeys) over its lifespan. Timestamping at the object primitive level (records) takes more storage capacity but allows for a more detailed description of the temporal variability within objects with a finer granularity. In this context, bi-temporal data models that support transaction time (or registration time) and valid time (or real world time) are flexible and allow object identity to be represented in a way that is suitable for the envisaged application. When modelling changes are made to an exposed building stock, information updates (new information becomes available as part of database updates) need to be distinguishable from real-world changes (actual modifications to buildings) in order to identify and track the object identity, meaning the lifespan of an object. This becomes particularly important when crowd-sourced data acquisition is involved [22]. Therefore, a bi-temporal data model with timestamping at the most detailed object primitive level is selected. Changes to the building stock are recorded in discrete transactions, as is the case in cadastral land register systems.