Representations and Data Models
The R/M cluster is focused on research into the conceptual modelling and representation of space, spatial features and phenomena, and in providing a spatial view onto various kinds of physical and abstract information objects in order to leverage the potential power of a 'spatial perspective'. Overcoming the long debate on ‘raster vs. vector’ in GIS generic concepts for spatial data handling have been developed over recent years, it is particularly important to mention the work of Goodchild et al. (2007) who hypothesize that representations of the real world interms of discrete objects better satisfies human understanding.
This research cluster focuses on physical world patterns and processes when investigating discretizations of various phenomena. While scales vary greatly between e.g. microrelief landforms derived from high resolution terrestrial and airborne LiDAR-data (R/M-1, R/M-2) to 3D structure tectonics of mountain belts (R/M-4) the real challenge is the variation between data affluence in the first examples to sparsely sampled data in the latter case. Scale also has multiple meanings relevant to GI Science. In cartography, scale refers to size onthe map relative to size in the world—small-scale maps show large regions. Map scale interacts with geometry of the world and requires map generalization. In the physical sciences, such as geomorphology tackled in this research cluster, the termscale is used to indicate the size, extent, or characteristic length for physical processes (Mark 2003). Interactions between size, shape, and function will be further explored systematically with the help of ‘newer’ technologies such as LiDAR or field geophysics. Using the metaphor as scale as a ‘window of perception’ (Marceau 1999), cognitive aspects of scale have been highlighted (Fabrikant 2001) and need to be incorporated in the GISciencecurriculum and to the research agenda. Particular new research questions (Blaschke 2010, Hay & Blaschke 2010) are focussing on multiscalar analyses and transfers (Bittner &Reitsma 2003, Burnett & Blaschke 2003).
All four research projects – though they start from different angles or research problems - have a common feature that the phenomena under investigation cannot be handledat a single scale of observation (Marceau 1999). Hence, we need to deal with nestedmodels (Strahler et al. 1986) to account for different levels of the organization of physical processes. In the empirical work the cluster investigates innovative approaches which exploit the concept of Local Variance and develop automated tools to objectively identify the most suitable range of scale parameters (Drăguţ et al. 2010) while in R/-3 geostatistical methods are applied for the 2D, 2D+t and 3D modelling of qualitative and quantitative parameters including geostatistical simulation, multiple point geostatistics and – for process modelling - inverse approaches like particle swarm optimization (PSO) which open new perspectives for developing more reliable scenarios from sparse input data.
The meta-dimension of 'space' completes the traditional 'subject-centric' and 'temporal' perspectives (see in-depth descriptions of research projects R/M-1 and R/M-2), allowing novel insights across many disciplines. There are several areas of overlap and synergies with the research cluster T/P since – naturally – data models and representations need to accommodate the spatio-temporal aspect. For the sake of simplicity and structuring the DK GIScience research program we examine the data modelmore from the context of form andthen the specificities of processes in the T/P cluster. For this R/M cluster a central aspect is the dichotomy ofdiscrete-object versus continuous-field representations. The T/P cluster thus needs to deal with formalizing ontologies for GIScience (Fonseca et al. 2002, Agarwal, 2005, Bittner et al. 2009) but will not fully embrace a philosophical – yet important – discussion on ontologies.
A special focus will be the OBIA approach (Blaschke 2010) which will specifically be investigated in R/M-1, R/M-2 and R/M-3for physical phenomena originating from Earth Science research. For example, landforms can be seeneither as a collection of non-overlapping, space-exhausting, discrete areas or as functionswhich mape.g. membership likelihoods to nominal variables. What is the scientific research problem in common with e.g. the 3D MP-geostatistical simulation of the sediment inventory in complex aquifers and associated ground water modelling (hydraulic properties, contaminant dispersion) as tackled in R/M-3? In order to restrict the research to a realistic amount of work the R/M cluster will mainly work in physio-geographical domains and will only partially investigate social data as such but will collaborate with and contribute to the other two clusters especially with the geostatistical expertise, as well as in the educational program.
Overarching research questions for PhD topics addressed in this cluster include:
Agarwal, P., 2005. Ontological considerations in GIScience. International Journal of Geographical Information Science 19, 501–536.
Bittner, T., Donnelly, M., Smith, B. 2009.A Spatio-Temporal Ontology for Geographic Information Integration. International Journal of Geographical Information Science 23(6): 765-798.
Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS International Journal of Photogrammetry and Remote Sensing 65 (1), 2-16.
Burnett, C. and Blaschke, T., 2003. A multi-scale segmentation / object relationship modelling methodology for landscape analysis. Ecological Modelling 168(3), 233-249.
Dragut, L; Tiede, D; Levick, SR, 2010.ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int. Journal of Geographical Inf. Science 24 (6), 859-871.
Fabrikant, S. I. 2001. Evaluating the Usability of the Scale Metaphor for Querying Semantic Spaces, Spatial Information Theory: Foundations of Geographic Information Science. Conference on Spatial Information Theory, Berlin, pp. 156-171.
Fonseca, F., Egenhofer, M., Agouris, P., Camara, G., 2002.Using Ontologies for Integrated Geographic Information Systems.Transactions in GIS 6, 231–257.
Goodchild, M.F., M. Yuan, T.J. Cova 2007.Towards a general theory of geographic representation in GIS. International Journal of Geographical Information Science 21(3): 239–260.
Hay, G.J. and Blaschke, T., 2010. Foreword Special Issue: Geographic Object-Based Image Analysis (GEOBIA). Photogrammetric Engineering and Remote Sensing 76 (2), 121-122.
Marceau, D., 1999. The scale issue in the social and natural sciences. Canadian Journal of Remote Sensing 25 (4), 347–356.Mark, D.M., 2003. Geographic Information Science: defining the field. In: Duckham, M., Goodchild, M.F., Worboys, M. (eds.), Foundations of Geographic Information Science. London: Taylor & Francis. pp. 3–18.