Diploma Thesis Mario Härtwig

Dienstebasierte raum-zeitliche Aggregation von Geodaten

Abstract

Growing amounts of data on space- and time-varying phenomena require efficient methods for extracting the information contained. The statistical spatio-temporal aggregation is such a method. By the condensation of data it eliminates details by which relations become recognizable. Aggregated geodata can be used on account of its increased explanatory power, for example, in the context of the decision making. But also in other use cases the statistical aggregation is an often used functionality, which is why it belongs to the basic functions in geoinformation systems (GIS). In geosciences, however, GIS are increasingly replaced by service-oriented spatial data infractructures that allow a common use of geodata and make it available to a wide range of users. Therefore, and due to the variety of uses, functionalities for the statistical aggregation in a SDI would be characterized by a high level of utilization. In order to enable the services-based processing of spatial data, the Open Geospatial Consortium (OGC) specifies the Web Processing Service (WPS). A principle in distributed architectures is that services operate as independently as possible and handle requests automatically. Thus, structured, machine-readable information about the meaning of the data being processed are essential for an adequate data analysis. Semantic interoperability is therefore one of the main tasks of research in the field of geoinformatics. In this thesis a service for the statistical aggregation of spatio-temporal data is described conceptually. The service should be able to handle requests automatically without requiring any further user interaction. Necessary semantic information for an automated statistical aggregation are formally described. Consequently, possibilities to provide these information within a SDI are discussed. The subsequent description of the prototypal implementation shows that the presented concept can in principle be technically realised. The application context of the described implementation is the research project GLUES (Global Assessment of Land Use Dynamics, Greenhouse Gas Emissions and Ecosystem Services).