Context Sensitive Transformation of Geographic Information

This research is concerned with theoretical and methodological aspects of geographic information transformation between different user contexts. In this dissertation I present theories and methodological approaches that enable a context sensititve use and reuse of geographic data in geographic information systems. A primary motive for the reported research is that the patrons interested in answering environmental questions have increased in number and been diversified during the last 10-15 years. The interest from international, national and regional authorities together with multinational and national corporations embrace a range of spatial and temporal scales from global to local, and from many-year/-decade perspectives to real time applications. These differences in spatial and temporal detail will be expressed as rather different questions towards existing data. It is expected that geographic information systems will be able to integrate a large number of diverse data to answer current and future geographic questions and support spatial decision processes. However, there are still important deficiencies in contemporary theories and methods for geographic information integration Literature studies and preliminary experiments suggested that any transformation between different users’ contexts would change either the thematic, spatial or temporal detail, and the result would include some amount of semantic uncertainty. Consequently, the reported experiments are separated into studies of change in either spatial or thematic detail. The scope concerned with thematic detatil searched for approaches to represent indiscernibility between categories, and the scope concerned with spatial detail studied semantic effects caused by changing spatial granularity. The findings make several contributions to the current knowledge about transforming geographic information between users’ contexts. When changing the categorical resolution of a geographic dataset, it is possible to represent cases of indiscernibility using novel methods of rough classification described in the thesis. The use of rough classification methods together with manual landscape interpretations made it possible to evaluate semantic uncertainty in geographic data. Such evaluations of spatially aggregated geographic data sets show both predictable and non-predictable effects. and these effects may vary for different environmental variables. Development of methods that integrate crisp, fuzzy and rough data enables spatial decision support systems to consider various aspects of semantic uncertainty. By explicitly representing crisp, fuzzy and rough relations between datasets, a deeper semantic meaning is given to geographic databasses. The explicit representation of semantic relations is called a Geographic Concept Topology and is held as a viable tool for context transformation and full integration of geographic datasets