Geographic information systems (GISs) are a highly influential tool in today's society, and are used in a growing number of applications, including planning, engineering, land management,and environmental study. As the field of GISs continues to expand, it is very important to observe and account for the error that is unavoidable in computerized maps. Currently, both statistical and non-statistical models are available to do so, although there is very little implementation of these methods. In this dissertation, I have focused on improving the methods available for analyzing error in GIS vector data. In particular, I am incorporating Bayesian methodology into the currently popular G-band error model through the inclusion of a prior distribution on point locations. This has the advantage of working well with a small number of points, and being able to synthesize information from multiple sources. I have also calculated the boundary of the confidence region explicitly, which has not been done before, and this will aid in the eventual inclusion of these methods in GIS software. Finally, I have included a statistical point deletion algorithm, designed for use in situations where map precision has surpassed map accuracy. It is very similar to the Douglas-Peucker algorithm, and can be used in a general line simplification situation, but has the advantage that it works with the error information that is already known about a map rather than adding unknown error. These contributions will make it more realistic for GIS users to implement techniques for error analysis. / Ph. D.

Data filtering is the disposal of unnecessary data in a data set, to save resources such as server capacity and bandwidth. The method is used to reduce the amount of stored data and thereby prevent valuable resources from processing insignificant information.The purpose of this thesis is to find algorithms for data filtering and to find out which algorithm gives the best effect in embedded devices with resource limitations. This means that the algorithm needs to be resource efficient in terms of memory usage and performance, while saving enough data points to avoid modification or loss of information. After an algorithm has been found it will also be implemented to fit the Exqbe system.The study has been done by researching previously done studies in line simplification algorithms and their applications. A comparison between several well-known and studied algorithms has been done to find which suits this thesis problem best.The comparison between the different line simplification algorithms resulted in an implementation of an extended version of the Ramer-Douglas-Peucker algorithm. The algorithm has been optimized and a new filter has been implemented in addition to the algorithm. / Datafiltrering är att ta bort onödig data i en datamängd, för att spara resurser såsom serverkapacitet och bandbredd. Metoden används för att minska mängden lagrad data och därmed förhindra att värdefulla resurser används för att bearbeta obetydlig information. Syftet med denna tes är att hitta algoritmer för datafiltrering och att undersöka vilken algoritm som ger bäst resultat i inbyggda system med resursbegränsningar. Det innebär att algoritmen bör vara resurseffektiv vad gäller minnesanvändning och prestanda, men spara tillräckligt många datapunkter för att inte modifiera eller förlora information. Efter att en algoritm har hittats kommer den även att implementeras för att passa Exqbe-systemet. Studien är genomförd genom att studera tidigare gjorda studier om datafiltreringsalgoritmer och dess applikationer. Jämförelser mellan flera välkända algoritmer har utförts för att hitta vilken som passar denna tes bäst. Jämförelsen mellan de olika filtreringsalgoritmerna resulterade i en implementation av en utökad version av Ramer-Douglas-Peucker-algoritmen. Algoritmen har optimerats och ett nytt filter har implementerats utöver algoritmen.

Simplification of contour lines in flat areas The diploma thesis is focused on the cartographic generalization of contour lines derived from dense point clouds in flat territories, where the original contour lines tend to oscillate. The main aim is to propose, develop and test a new algorithm for the contour simplification preserving the given vertical error and reflecting the cartographic rules. Three methods designed for large scale maps (1 : 10 000 and larger) are presented: the weighted average, modified Douglas-Peucker and potential-based approach. The most promising method is based on the repeated simplification of contour line segments by calculating the generalization potential of its vertices. The algorithm is implemented in Python 2.7 with the use of Arcpy library was tested on DMR5G data, the simplified contour lines were compared with the result created by a professional cartographer. Achieved results are presented on attached topographic maps. Keywords: contours, cartographic generalization, digital cartography, vertical buffer, smoothing, GIS

Topology optimization has seen rapid developments in its field with algorithms getting better and faster all the time. These new algorithms help reduce the lead time from concept development to a finished product. Simulation and post-processing of geometry are one of the major developmental costs. Post-processing of this geometry also takes up a lot of time and is dependent on the quality of the geometry output from the solver to make the product ready for rapid prototyping or final production. The work done in this thesis deals with the post-processing of the results obtained from topology optimization algorithms which output the result as a 2D image. A suitable methodology is discussed where this image is processed and converted into a CAD geometry all while minimizing deviation in geometry, compliance and volume fraction. Further on, a validation of the designs is performed to measure the extracted geometry's deviation from the post-processed result. The workflow is coded using MATLAB and uses an image-based post-processing approach. The proposed workflow is tested on several numerical examples to assess the performance, limitations and numerical instabilities. The code written for the entire workflow is included as an appendix and can be downloaded from the website:https://github.com/M87K452b/postprocessing-topopt.