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Abstract

The issue of line simplification is one of the fundamental problems of generalisation of geographical information, and the proper parameterisation of simplification algorithms is essential for the correctness and cartographic quality of the results. The authors of this study have attempted to apply computational intelligence methods in order to create a cartographic knowledge base that would allow for non-standard parameterisation of WEA (Weighted Effective Area) simplification algorithm. The aim of the conducted research was to obtain two independent methods of non-linear weighting of multi-dimensional regression function that determines the “importance” of specific points on the line and their comparison to each other. The first proposed approach consisted in the preparation of a set of cartographically correct examples constituting a basis for teaching a neural network, while the other one consisted in defining inference rules using fuzzy logic. The obtained results demonstrate that both methods have great potential, although the proposed solutions require detailed parameterisation taking into account the specificity of geometric variety of the source data.
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Abstract

In this paper, the analysis of carbon footprint values for children’s footwear was conducted. This group of products is characterized by similar small mass and diversity in the used materials. The carbon footprint is an environmental indicator, which is used to measure the total sets of greenhouse gas (GHG) emissions into the atmosphere caused by a product throughout its entire lifecycle. The complexity of carbon footprint calculation methodology is caused by multistage production process. The probability of emission greenhouse gases exists at each of these stages. Moreover, a large variety of footwear materials – both synthetic and natural, give the possibility of the emission of a lot of waste, sewage and gases, which can be dangerous to the environment. The diversity of materials could be the source of problems with the description of their origins, which make carbon footprint calculations difficult, especially in cases of complex supply chains. In this paper, with use of life cycle assessment, the carbon footprint was calculated for 4 children’s footwear types (one with an open upper and three with full uppers). The life cycles of the product were divided into 8 stages: raw materials extraction (stage 1), production of input materials (stage 2), footwear components manufacture (stage 3), footwear manufacture (stage 4), primary packaging manufacture (stage 5), footwear distribution to customers (stage 6), use phase (stage 7) and product’s end of life (stage 8). On these grounds, it was possible to point out the life cycle stages, where the optimization activities can be implemented in order to reduce greenhouse gases emissions. The obtained results showed that the most intensive corrective actions should be focused on the following stages: 3 (the higher emissivity), 4 and 8.
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Abstract

The wide access to source data, published by numerous websites, results in situation, when information acquisition is not a problem any more. The real problem is how to transform information in the useful knowledge . Cartographic method of research, dealing with spatial data, has been serving this purpose for many years. Nowadays, it allows conducting analyses at the high complexity level, thanks to the intense development in IT technologies, The vast majority of analytic methods utilizing the so-called data mining and data enrichment techniques, however, concerns non-spatial data. According to the Authors, utilizing those techniques in spatial data analysis (including analysis based on statistical data with spatial reference), would allow the evolution of the Spatial Information Infrastructure (SII) into the Spatial Knowledge Infrastructure (SKI). The SKI development would benefit from the existence of statistical geoportal. Its proposed functionality, consisting of data analysis as well as visualization, is outlined in the article. The examples of geostatistical analyses (ANOVA and the regression model considering the spatial neighborhood), possible to implement in such portal and allowing to produce the “cartographic added value”, are also presented here
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