Abstract

The understanding of individual movement in a given space is important in many areas such as geography, architecture, and city planning. With recent advances in sensor and computing technology, trajectory data, series of coordinate data with timing, can be readily obtained and used to help us understand the characteristics of the space. However, the interpretation of patterns becomes difficult as the number of paths increases with the number of individuals examined. In this study, we intend to contribute to the development of a schematic landscape diagram of the space-time paths for movement analysis by firstly observing individual activities in an office and collecting trajectory data, and secondly developing a framework for the landscape diagram and analyzing the trajectory data to extract the tendency of the movement. Here, the interpretation of the patterns of trajectories becomes accurate as the number of trajectories increases. Finally, we argue that this is an effective way to understand the characteristics of the space.

Keywords

space design, user behavior, vector field, landscape diagram

COinS
 
Jul 1st, 12:00 AM

Visual Analysis of Human Behavior Based on Vector Field and Landscape Diagram

The understanding of individual movement in a given space is important in many areas such as geography, architecture, and city planning. With recent advances in sensor and computing technology, trajectory data, series of coordinate data with timing, can be readily obtained and used to help us understand the characteristics of the space. However, the interpretation of patterns becomes difficult as the number of paths increases with the number of individuals examined. In this study, we intend to contribute to the development of a schematic landscape diagram of the space-time paths for movement analysis by firstly observing individual activities in an office and collecting trajectory data, and secondly developing a framework for the landscape diagram and analyzing the trajectory data to extract the tendency of the movement. Here, the interpretation of the patterns of trajectories becomes accurate as the number of trajectories increases. Finally, we argue that this is an effective way to understand the characteristics of the space.

 

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