Estimating the Visual Complexity of Streetscapes with AI: Predicting the Spatial Agglomeration of the Creative Class

Presenter's Name(s)

Samuel Keller

Abstract

In urban areas, many factors contribute to places being attractive to humans. Some of these, like sense of place, are difficult to interpret and communicate. Integrating multiple ways of knowing may facilitate the communication of a community’s sense of place. By using GIS network modeling to measure the pedestrian accessibility of food outlets, locations significantly influencing community sense of place were identified. Panoramas and audio field recordings were captured to create interactive, multimedia representations of network hubs. Thus, aesthetic and spatial ways of knowing were integrated to produce translations of sense of place for communities in Chittenden County, VT.

Primary Faculty Mentor Name

Steven Costell

Status

Undergraduate

Student College

College of Agriculture and Life Sciences

Program/Major

Economics

Primary Research Category

Arts & Humanities

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Estimating the Visual Complexity of Streetscapes with AI: Predicting the Spatial Agglomeration of the Creative Class

In urban areas, many factors contribute to places being attractive to humans. Some of these, like sense of place, are difficult to interpret and communicate. Integrating multiple ways of knowing may facilitate the communication of a community’s sense of place. By using GIS network modeling to measure the pedestrian accessibility of food outlets, locations significantly influencing community sense of place were identified. Panoramas and audio field recordings were captured to create interactive, multimedia representations of network hubs. Thus, aesthetic and spatial ways of knowing were integrated to produce translations of sense of place for communities in Chittenden County, VT.