Space and Motion: Data-based Rules of Public Space Pedestrian Motion

SMArchS Thesis, Computation, Spring 2015

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. 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 real motion data for grounding simulations and measuring such motion in relation to space to create a Space-Motion Metric. This metric takes pedestrian motion and spatial features as input.

The Space-Motion Metric seeks actions composed by time, speed, and gestures towards spatial features. 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.