Aswin Indraprastha

Elaboration Model for Mapping Architectural Space

Abstract The key objective of this study is to develop an experimental computational method for mapping architectural space, and further validate the method using several case studies. The result will offer possibilities for quantitative design analysis, particularly on spatial quality influenced by architectural elements. Our proposed method for this computation consists of two phases: determination of L-shaped enclosed spaces and axial lines to establish an enclosed space relative to a circulation space, and determination of subdivided enclosed spaces using territorial lines. The concept underlying this method is that architectural space is composed of subdivided enclosed spaces each of which has distinct physical properties influenced by different elements. Therefore, it is possible to develop mapping for the further qualitative evaluation of architectural space.

Computing Level of Privacy in a Virtual Environment

This paper presents a computational model designed to analyze architectural space and to a develop privacy level using two methods: visual distance method and viewing angle method as those are primary cognitive mechanism to experience architectural space. We suggest that the result will offer possibilities for quantitative design analysis of privacy that influenced by architectural elements. The proposed method consists of two stages: determination of subdivided enclosed spaces and measuring privacy level on each subdivided enclosed space. Previous models showed that spatial quality is related to the visual distance to the architectural elements. In addition to distance variables, our model includes viewing angle variables to determine area and the direction angle from any observation point to the architectural elements on the boundary of an interior space. The case study evaluation is able to rank the quality of design of interior spaces with respect to their privacy level.

Approximate Enclosed Space Using Virtual Agent

In agent-based pedestrian model, steering movement is driven by position of attractor or goal, and a graph of their relations. Our work studied on constructing relationship between spatial cognition and enclosed space using virtual agent. Instead of focusing on location-based goals, we are investigating enclosed space as primary factor for locomotion. Our contribution on the identification of enclosure enhances the artificial model of spatial cognition. This is significant for the development of agent-based simulation with spatial cognition to determine and to measure space in architectural design model. We present our approach using three stages of methods. First, we constructed object detection algorithm on agent line of sight. Second, by decomposing detected objects as set of points, we analyzed their attributes and properties to define center of enclosed space. Third, as point of enclosed spaces determined, we classified them into L-shaped space and U-shaped space using simple arithmetic algorithm. Finally, computational results of points represent goals for navigational purpose.