Capturing impressions of pedestrian landscapes used for healing purposes with decision tree learning

A customized version of decision tree machine learning algorithm rules for designing good pedestrian landscapes for health purposes were extracted from the grass roots surveys. The data indicated that variables influencing the decision to walk for health purposes in the study area included weather, sound, water, light and edge of space. The analytical model derived from the discipline of artificial intelligence facilitated examining a subset of variables and manipulating of individual or group of these variables to better understand how the built environment affected decisions to walk for different purposes. This collaboration was our first phase in developing intelligent tools for designers that provided site-specific user-specific data to the planner or designer of pedestrian space.