Abstract

Worker safety during construction is widely accepted, but the selection of safe sites for a building is generally not considered. Safe site selection (SSS) largely depends upon compiling, analyzing, and refining the information of an area where a building is likely to be located. The locational and topographical aspects of an area located in hilly regions play a major role in SSS, but are generally neglected in traditional and CAD-based systems used for site selection. Architects and engineers select a site based on their judgment, knowledge, and experience, but issues related to site safety are generally ignored. This study reviewed the existing literature on site selection techniques, building codes, and approaches of existing standards to identify various aspects crucial for SSS in hilly regions. A questionnaire survey was conducted to identify various aspects that construction professionals consider critical for SSS. This study explored the application of geographic information systems (GIS) in modeling the locational and topographical aspects to identify areas of suitability. A GIS-based methodology for locating a safe site that satisfies various spatial safety aspects was developed.

Abstract

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.