# A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution
## 2 Exposure Data Management in Support of Disaster Risk Management
### Information Life-Cycle Management  
The concept of a life cycle implies that information elements (in the following referred to as objects) have a definable point of creation and a similar point of destruction or disposal. Life cycle development focuses on the change of the information itself and on the change of its value over time. Value transformation occurs when information is created, updated, or disposed. Information value is often defined in financial terms as the amount a decision maker would be willing to invest for information prior to making a decision. However, the types of information value can be of different natures, reaching from financial to operational to intrinsic. [PERSON] (2005) [35], for example, presents an information valuation approach that quantifies the value of a given piece of information in an automated manner based on an analysis of its usage over time. In the context of building exposure data management, the information value is closely linked to information quality, wherein map and thematic accuracy are widely used proxies to value the information. Map accuracy can be distinguished into location and classification accuracy. Location accuracy answers the question if the objects that are mapped and the data sources themselves are located on the earth and to one another. Classification accuracy quantifies if a material on the earth or a condition is mapped correctly [36]. The thematic accuracy is less well constrained and can be approached from different directions. A possible way to quantify thematic accuracy is to assign a degree of belief, as is described in detail by [PERSON] (2006) [37]. In the context of this study, the information value is associated with map accuracy for spatial attributes and with thematic accuracy (here defined as degree of belief) for thematic attributes.  
In database models, the information life cycle indicates the history of objects that are stored in a database both in terms of their real-world evolution and in terms of the database transactions referring to them. This means that a database model should be able to keep track of and archive the real-world changes of an object (from creation to destruction). At the same time, it should be able to register any modifications to an object at database level by logging transactions that are linked to a specific object. In order to do this, a bi-temporal database model is implemented in this study as described above.  
Information collected from different sources is, moreover, subject to uncertainties that not only vary between sources but also over time. Therefore, life-cycle management should be uncertainty aware. The integration and use of spatio-temporal information affected by uncertainty is a topic gaining attention since when Geographical Information Systems (GIS) started becoming widespread. For instance, [PERSON] (2007) [38] describes an object-oriented meta-data error database for capturing quality information and temporal information, information about sources of data, processing steps, and cartographic generalizations on the datasets. [PERSON] et al. (2006) [39] use hierarchical and hybrid Bayesian networks to integrate information among multiple agencies. [PERSON] et al. (2007) [40] introduce integration algorithms for vector and raster data sets in federated databases. In previous work, the authors used Bayesian networks to integrate proxy information extracted from different image sources to derive posterior probability distributions of structural vulnerability classes for buildings [41].