MapFish. A WebGIS 2.0 Framework: toolkit to build rich client WebGIS

MapFish is an open-source development framework for building webmapping applications. MapFish is based on the OpenLayers API and the Geo extension of Ext library, and extends the Pylons general-purpose web development framework with geo-specific functionnalities. This presentation first describes what the MapFish development framework provides and how it can help developers implement rich web-mapping applications. It then demonstrates through real web-mapping realizations what can be achieved using MapFish : Geo Business Intelligence applications, 2D/3D data visualization, on/off line data edition, advanced vectorial print functionnalities, advanced administration suite to build WebGIS applications from scratch, etc. In particular, the web-mapping application for the UN Refugee Agency (UNHCR) and a Regional Spatial Data Infrastructure will be demonstrated

Application of compositional data analysis to geochemical data of marine sediments

In an earlier investigation (Burger et al., 2000) five sediment cores near the Rodrigues Triple Junction in the Indian Ocean were studied applying classical statistical methods (fuzzy c-means clustering, linear mixing model, principal component analysis) for the extraction of endmembers and evaluating the spatial and temporal variation of geochemical signals. Three main factors of sedimentation were expected by the marine geologists: a volcano-genetic, a hydro-hydrothermal and an ultra-basic factor. The display of fuzzy membership values and/or factor scores versus depth provided consistent results for two factors only; the ultra-basic component could not be identified. The reason for this may be that only traditional statistical methods were applied, i.e. the untransformed components were used and the cosine-theta coefficient as similarity measure. During the last decade considerable progress in compositional data analysis was made and many case studies were published using new tools for exploratory analysis of these data. Therefore it makes sense to check if the application of suitable data transformations, reduction of the D-part simplex to two or three factors and visual interpretation of the factor scores would lead to a revision of earlier results and to answers to open questions . In this paper we follow the lines of a paper of R. Tolosana- Delgado et al. (2005) starting with a problem-oriented interpretation of the biplot scattergram, extracting compositional factors, ilr-transformation of the components and visualization of the factor scores in a spatial context: The compositional factors will be plotted versus depth (time) of the core samples in order to facilitate the identification of the expected sources of the sedimentary process. Kew words: compositional data analysis, biplot, deep sea sediments