## 2 Technology and Design
### Filtering and Relevance Approaches  
Another, conceptually different but very useful approach to handle level of detail and reduce bandwidth consumption is to filter the data based on the location or the usage context, and thus its relevance to the user. Essentially we can discard the data that is not relevant for the user _perceptually_ (e.g., [34, 44, 54]), and/or _contextually_ (e.g., [55, 56, 57, 58]). This kind of approach often needs to be \"personalized\"; e.g., for certain scenarios we need to know where the user is, where he or she is looking, or for what task the geographic visualization is needed. This way we can create an adaptive visualization for the person and his/her contextual and perceptual state, respecting the cognitive limitations as well as possibly avoiding \"bandwidth overuse\" when providing an online service. Filtering can be applied on a database level or on a service level. A request to a geographic database can include for instance standard query language (SQL) clauses that filter the amount of data according to attribute or spatial criteria. Most mapping services follow standards introduced by the Open Geospatial Consortium (OGC) [59] within a framework for geoservices, named Open Web Services (OWS). The most relevant specifications are Web Map Service (WMS) and Web Feature Service (WFS). WMS is a portrayal service and specifically aimed at serving maps based on user requirements encoded in the parameters of a _GetMap_ request. Maps from a WMS are served as raster images. WFS is a data service aimed at delivering the actual geospatial features as vector data instead of serving symbolized maps. Features are accessed by a _GetFeature_ request that can incorporate filter expressions based on the _Filter Encoding Implementation Specification_[59]. Although WFS coupled with filter expressions are in general more flexible and powerful to filter geographic information, WMS also  
Figure 3: A conceptual representation of _distance LOD_: when the object is distant “enough” from the viewer, we can use a very low LOD and this should be perceptually indistinguishable for the viewer. allows to select layers that will be incorporated in the served map and therefore also offers a somewhat limited means to filter map content. Filtering based on the context of use has been widely investigated in the last decade (e.g., [60, 61, 62]). Mainly the mobile usage profits from filtering. In the mobile case context is predominantly treated as spatial context, _i.e._, the location of the mobile user. As a consequence so-called location-based services (LBS) have been developed. LBS filter content based on the location of a mobile user. Only information that is in a certain spatial proximity to the user are transmitted and presented to the user. Although LBS are useful tools and are capable of filtering geographic information, they are restricted to the spatial dimension. More recent approaches look into a more general approach to the problem of information overload in mobile mapping services. [PERSON] and [PERSON] (2007) [63] propose additional filters for mobile geographic information beyond a binary, spatial filter used in LBS: spatial and temporal proximity, prediction of future locations based on speed and direction of movement, as well as the visibility of objects. A more recent approach that extends the idea of LBS and the filters proposed by [PERSON] and [PERSON] (2007) [63] is the concept of geographic relevance (GR). It has been introduced by [PERSON] (2005, 2007) [55, 64] and [PERSON] (2007) [65]. GR is an expression of the relationship between geographic information objects and the context of usage. This context defines a set of criteria for the geographic relevance of objects, such as spatio-temporal, proximity, co-location, clusters, etc.