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Know the variety of spatial functions incorporated in GIS, and their most usual classifications referring to information search, reclassification, overlapping, neighbourhood and distance, and connectivity.
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Argue the theoretical foundations of spatial analysis by means of GIS and adequately use the terminology of the subject. Define the most common spatial analysis functions and describe their meaning and usefulness.
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Apply theoretical knowledge to the resolution of real cases by modelling spatial problems of a geographic nature, selecting the necessary GIS functions and data models.
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Implement the problem-solving cartographic models in one of the most popular and widely used GIS programs.
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Explain the theoretical foundations necessary for DEM generation and use the basic concepts and terminology appropriately.
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Elaborates DEMs from digital topographic cartography, selecting the most suitable method for the characteristics of the data and applying the appropriate methods for the detection and/or correction of systematic and random errors.
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Apply the procedures to generate digital models derived from DEMs.
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Apply network analysis to the resolution of complex tasks using GIS and properly use the terminology of this type of analysis (arcs, nodes, flow).
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Define network analysis, identifies the types of network analysis that exist, and adequately prepare the spatial basis for this type of task.
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Appropriately use of available resources to reinforce previously acquired knowledge.
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Work well in a team, constructively criticizing the opinions of others, sharing information and knowledge with peers and seeking joint solutions.
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Identify the type of geographic phenomena whose management may require the use of network analysis and discriminate them from those for which it is not useful.
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Argue the importance of having continuous surfaces of information on significant environmental variables for use in territorial studies.
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Explain the most usual methods of spatial interpolation - inverse to distance, radial functions, trend surfaces, kriging, cokriging and regression models and apply them correctly, modifying their parameters and choosing the most appropriate one for the spatial representation of the data using ArcGIS.
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Model environmental variables from the existing statistical relationship with a set of independent variables and to elaborate, with these models, detailed cartographies from the parameters obtained in the modelling.
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Correctly apply the procedures that, based on error statistics, help to select the most appropriate cartography for the analysed variable.
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Argue the importance of the quality of the original data for the final cartographic result.
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Explain the fundamentals of the Linux operating system and the features of the open source software environment, and be able to use them at an intermediate user level.
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Explain the fundamental aspects of parametric and non-parametric statistical models and apply them to the analysis of geographic information.
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Explain and apply a standardized methodology for nonparametric data analysis.
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Describe the fundamental elements of programming in Python, ArcP and R and be able to implement small programs and modules that can be integrated into other GIS software by programming in these languages.