This master thesis report presents the development of a geographical visualization system using taxi data. The system uses a large data base from a taxi company that have previously never used the data for visualization purposes. The taxi company requested a system that processes the data on a server on demand and visualizes it on a web client using heat map visualization as a primary visualization technique. The web client was supposed to be easy to use, provide deeper knowledge about the business of a taxi company and at the same time kept interactive with low latency for data requests. A big part of the thesis focuses on techniques for decimating an original data set to a smaller representational data set to be used for heat map visualization and sent to a web client from a server. The project continues by optimizing the system to keep latency to a minimum and finally developing a web client to explore the data. The result is a system with promising latency that is easy to use for exploring data and gaining a deeper knowledge about a taxi business.

A denoising autoencoder is a type of neural network which excels at removingnoise from noisy input data. In this project, a denoising autoencoder isoptimized for removing noise from mobile positioning data. The mobilepositioning data with noise is generated specifically for this project. In orderto generate realistic noise, a study in how real world noise looks like is carriedout. The project aims to answer the question: can a denoising autoencoderbe used to remove noise from mobile positioning data? The results showthat using this method can effectively cut the noise in half. In this reportit is mainly analyzed how the amount of hidden layers and respective sizesaffected the performance. It was concluded that the most optimal design forthe autoencoder was a single hidden layer model with multiple more nodes inthe hidden layer than the input and output layer. / En brusreducerande autoencoder är ett sorts neuralt nätverk som är specialiserat för att ta bort brus från indata. I detta projekt optimeras en brusreducerande autoencoder för att ta bort brus från mobilpositioneringsdata. Till projektet skapades helt ny mobilpositioneringsdata med realistiskt brus. Detta gjordes genom att studera hur verkligt brus ser ut och skapa ett program som efterliknar detta. Projektets syfte var att undersöka om en brusreducerande autoencoder kan användas för att ta bort brus från mobilpositioneringsdata. Resultaten visar att metoden kan ta bort ungefär hälften av bruset. I rapporten undersöks och analyseras även hur antalet dolda lager och antalet noder i dessa lager påverkade mängden brus som autoencodern lyckades ta bort. Från de gjorda testerna drogs slutsatsen att den mest optimala designen var en enkel design med ett enda dolt lager som hade betydligt fler noder än input- och outputlagren.