No description available.

Geoscience data sharing and processing is very advanced in terms of surface data. Subsurface data sharing has not received the attention that surface data sharing has received and so there are fewer applications or software packages which focus on it. This research is funded by the NSF EarthCube GEO Domain program in an effort to develop a continental-scale repository of 3D subsurface data to facilitate the sharing of complex 3D data and to enable the development of geoprocessing tools and workflows that operate on that data. The work in this thesis is a small part of the EarthCube project with two parts. The first part is to research current tools for 3D subsurface data visualization, specifically virtual globes, and to recommend one for use in the development of the EarthCube project. The second part is to develop an online prototype visualization platform for the EarthCube project referred to as the "Digital Crust" using the recommended virtual globe. Additional work was done with the Digital Crust to develop geoprocessing tools to show the ability for the Digital Crust to work with a data repository. These tools convert geoscience data file types, and interpolate soil cross-sections from borehole log data.

This paper aims to explore the perceptual recognition of topographical building patterns from real-world OpenStreetMap data on virtual globes. An implementation was developed in which all geographical and contextual information was layered and, for the purpose of this study, what solely remained were building patterns as viewed from above. This was developed as a module for the planetarium visualization software Uniview. The aim was to determine how cities with different building patterns were perceived by participants in terms of size, scale, and building density. This was measured as the comparative difference between city pairs, that is, how much they differed in the percentage of the area that they covered. Two quantitative studies were conducted, one smaller controlled study with 19 participants and one larger online crowd-sourced study with 72 participants. The results show that participants are generally able to discern building patterns when the comparative difference is greater than a certain critical threshold. This critical threshold was determined to be at approximately 0.5% for both studies and for accuracy levels above 60%. Thus it was concluded that below this critical threshold users should be provided with visual feedback or other means of identifiers in order to allow for definite recognition, depending on what kind of information a certain type of visualization is trying to convey. / Den här rapporten avser att utforska den perpetuella igenkänningen av topografiska byggnadsmönster genom att använda geografisk data från OpenStreetMap som avbildas på virtuella sfärer. En implementation utvecklades där geografisk data samt kontextuell information ordnades i överlappande lager som filtrerades, och där endast byggnadsmönster sett från ovan kvarstod. Denna modul utvecklades för Uniview som är en mjukvara för visualisering i planetarier. Målet var att avgöra hur deltagare uppfattade städer med olika byggnadsmönster med hänsyn till storlek, skala, samt byggnadsdensitet. Detta mättes genom den procentuella skillnaden mellan städer, dvs. skillnaden i procent för varje stads geografiska utsträckning. Två kvantitativa studier utfördes, en mindre kontrollerad studie med 19 deltagare samt en större nätbaserad studie med 72 deltagare. Resultatet visar att deltagare generellt kunde bedöma den procentuella skillnaden i byggnadsmönster upp till en viss kritisk gräns. Denna kritiska gräns fastställdes till runt 0.5% för båda studier och för noggrannhetsnivåer över 60%. Slutsatsen från detta är att användare bör ges visuella indikatorer för nivåer under denna kritiska gräns för att säkerställa definitiv igenkänning beroende på vilken information som skall förmedlas i en viss typ av visualisering.