This repository contains all source code developed and data collected during the bachelor thesis.
View App
Spatio-temporal research results are usually published in a static format, for example, as PDF. Herethe results are not directly linked to their spatial reference. Therefore, it is difficult for the user to un-derstand these results. To improve the user’s understanding, we link these results with the real world.To archive this, we use the raising concept of Augmented Reality, where it is possible to integrate theresults into the view of the user and to display the results on site. The results are often calculatedout of a specified dataset. To ensure the data used for the application indicates the same result pre-sented in the article, the outcome must be reproducible. The goal is to combine reproducibility andAugmented Reality to convey spatio-temporal results. We answered the research question about howto create an Augmented Reality application out of a reproducible article. Therefore, we performeda literature research and developed a concept which provides a guideline and explains the importantsteps. Starting with extracting the data used to calculate the results. Designing the app and decidingwhich types of visualization and devices fit best for the result and implementing the application. Toshow the feasibility of the concept, we created an application to convey the results of one scientific ar-ticle. This application was evaluated with an expert user study, with the goal to indicate whether theapplication is understandable and easy to use. Furthermore, the general interest in using AugmentedReality applications to inspect spatio-temporal results got researched.The results of our research show that it is possible to convey spatio-temporal results through Aug-mented Reality. The results are displayed understandable. Overall, Augmented Reality is an in-teresting approach to display results out of scientific articles which should be depended in furtherresearch.
The article "Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables is displayed. It was written by Laura Goulier, Bastian Paas, Laura Ehrnsperger, and Otto Klemm. It was published in the International Journal of Environmental Research and Public Health 17, no. 6 (January 2020) page number 2025. The paper is accessible under: https://doi.org/10.3390/ijerph17062025
This repository contains all data and sourcecode which was used during the thesis:
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dataExtraction contains source code used to reproduce and extract the results out of the paper
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dataExtraction/Paper contians all data and source code written and collected by the authors of the paper display. The data are also accessible under: https://osf.io/rjw8d/
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studyEvalition contains the results of the study and source code to analyze these. The results are also accessible as Exectuable Research Compendium under: https://o2r.uni-muenster.de/#/erc/6N8OQ
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public contains all CSS and JavaScript files that were used for the Application PapAR.
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index.html is the main HTML-File for the developed application.
- JQuery
- jQuery-csv
- A-Frame
- AR.js
- Metro 4
- aframe-particle-system-component
- D3
- node.js for development
- Express for development
The app is accessible and useable under njaku01.github.io.
To get a local copy up and running follow these simple steps.
Install npm
- npm
npm install npm@latest -g- Clone the repo
git clone https://github.com/NJaku01/NJaku01.github.io.git- Install NPM packages
npm install- Start the application
npm start- Go to the application under https://localhost:3000
Nick Jakuschona - n_jaku01@wwu.de
Project Link: https://github.com/NJaku01/NJaku01.github.io