roposi- tioning) possibly combined with heading information (compass heading), possibly combined with micropositioning (low-G accelerometers), probably combined with time, possibly com- bined with temperature or other environmental data (light level or wind speed). Even simple cell ID can add substantial metadata value, for example images can be tagged and/or archived and/or searched and/or shared by cell ID location. This is an area of substantial present research, for example by the Helsinki University of Technology2 under the generic description of 'automatic metadata creation'. The research highlights the problems of manual metadata. For example, when people take a picture it is boring and usually difficult to manually annotate the image. There will generally be a lack of consistency in manual notation that will make standardised archiving and retrieval more problematic. In comparison, automated spatial and temporal metadata can be standardised and unam- biguous. We know where the user is and what the time is. Areas of potential additional value are identified. These are based, respectively, on 'inference algorithms' also known as 'disam- biguation algorithms', 'similarity processing algorithms' also known as 'guessing algorithms' and 'sharing algorithms' also known simply as 'group distribution lists'. 10.4.3 Mobile Metadata and Geospatial Value For example, if it is known that a user has just emerged from Westminster Tube Station (macroposition), has turned right and then left into Parliament Square and has then turned to face south (compass heading) and up (micropositioning) and then takes a picture, you know 2 http://www.cs.hut.fi/~rsarvas/publications/Sarvas_MetadataCreationSystem.pdf Information-Centric Software that the user is taking a picture of Big Ben. Theoretically this could be double checked against the typical image statistics of Big Ben. This is the basis of geotagging and smart tagging familiar to us in present mapping systems. Smart tagging was standardised in Windows XP and has become progr