Space and motion : data based rules of public space pedestrian motion

Abstract

The understanding of space relies on motion, as we experience space by crossing it. While in motion we sense the environment in time, interacting with space. The vision of this thesis is to incorporate people's motion into architecture design process, enabled by technology. Simulation tools that introduce human motion into the design process in early stages are rare to nonexistent. Available tools are typically used for deterministically visualizing figures and simulating pedestrians with the goal of analyzing emergency exits or egress. Such simulations are built without consideration for non-goal oriented interaction with space; this presents a gap for design. Additionally, simulations are generally governed by assumptions regarding people's motion behavior or by analogous models such as collision avoidance methods. However, the use of data from people can elucidate spatial behavior. Advancements in depth camera sensors and computer vision algorithms have eased the task of tracking human movements to millimetric precision. This thesis proposes two main ideas: creating statistics from people's motion data for grounding simulations and measuring such motion in relation to space, developing a Space- Motion Metric. This metric takes pedestrian motion and spatial features as input, seeks actions composed by speed, time, gestures, direction, shape and scale. The actions are elaborated as Space-Motion Rules through substantial data analysis. The non-prescriptive combination of the rules generates a non-deterministic behavior focused on design. This research maps, quantifies, and formulates pedestrian motion correlation with space and questions the role of data for projecting what space could be.