# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## Data and Metadata Representation and Storage
### Geospatial and Environmental Data Modeling and Storage  
The representation of geospatial data has been a topic to which much attention was paid in the areas of GIS and spatial data management [6; 7]. Two main data modeling approaches were proposed. A first approach considers space as populated by objects or entities. Such geospatial entities may have both conventional (alphanumerical) and geospatial properties, which define their location and shape. Geospatial properties are represented with geometric approximations based on vectors of 2D or 3D pairs of coordinates (vector representations). The second approach represents directly the properties of each point of space, by considering different kinds of regular or irregular discrete spatial grids. Given that the above approaches fit better different types of geospatial datasets, both are currently supported by the data modeling framework defined by OGC and adopted by ISO. The reference model defined by OGC in [8] (also ISO 19101) provides metamodel artifacts to represent geospatial entities (called features in OGC notation), entity types (feature types) and entity properties (attributes). Rules for the creation of application models following the above reference framework are given in the ISO 19109 standard. Figure 3a depicts the geometric representation and the values of the conventional properties of four features of a feature type named \"FeatureType1\". Properties of space are represented with coverages in the OGC modeling framework [10] (also ISO 19123). A coverage is a special type of feature whose property values vary along its spatial, temporal or spatio-temporal extension. More precisely, a coverage is defined as a collection of functions (one for each conventional property) with a common domain (spatial, temporal or spatio-temporal) and with ranges of conventional data types. Different types of coverages, depending on the representation of its domain are supported by the above standard. Discrete Coverages has a domain composed of discrete zones where the values of the properties are constant. An example of a Discrete Spatial Coverage with four zones is depicted in Figure 3b. On the other hand, the progressive variation of a property (such as temperature, elevation above sea level, etc.) along space, time or both is represented with a continuous coverage. Five different types of continuous spatial coverages are considered, depending on the discrete approximation used for its continuous domain, namely, Thiessen polygon coverages, triangulated irregular network (TIN) coverages, square grid coverages, hexagonal grid coverages and curve segment coverages (its domain is a line feature). By far, the most common continuous representation used for coverages is the one based on square grids, which are called raster coverages. Figure 3c illustrates a raster coverage whose domain is composed of two spatial dimensions and a temporal dimension. Recalling the above discussion on the two data modeling approaches, the same spatial phenomena may be represented either as a collection of features or as a discrete coverage. This is the case for example of a discrete coverage of the soil type, which may also be represented as a collection of features (soil type zones). Beyond that, depending on the properties of interest, a municipality may be represented either as a feature with a property representing its total population at a given moment, or as a continuous coverage that records the continuous variation of its population density along its spatial extension. The OGC model that defines the representations for geospatial properties of Features [9] (also ISO 19107) contains vector-based geometric and topological primitives, which may be combined to construct complex structures. Geometric primitives include points, lines, polygons, and 3D solids. Homogeneous and heterogeneous collections of primitives, called aggregates, are also supported. Geometric complex geometries are collections of primitives, but contrary to the aggregates they form a single connected geometry.