Mapping Implicit Bias in Urban Sidewalk Interactions

Authors: Ofurhe Igbinedion*, University of California - Davis
Topics: Urban Geography, Transportation Geography, Geographic Information Science and Systems
Keywords: digital geographies, urban, inequality, gis, implicit bias, walkability
Session Type: Paper
Day: 4/13/2018
Start / End Time: 8:00 AM / 9:40 AM
Room: Iberville, Marriott, River Tower Elevators, 4th Floor
Presentation File: No File Uploaded


Urban planners have sought to design walkable cities that promote interactions between citizens since Jane Jacobs wrote about ‘eyes on the street’ in the 1960s. But who are people interacting with and are all these interactions positive? This study creates a novel dataset of geocoded sidewalk interactions. Researchers will visualize this data set to show patterns of interactions, potentially uncovering demographic groups who regularly experience negative interactions. Additionally, participants have the opportunity to see their own interaction patterns, providing insight into their implicit and explicit biases. Participants in the study record information about all the sidewalk interactions they perceive using a smartphone app and receive a weekly email detailing the interactions they have recorded and with a map and some descriptive analysis. Only by understanding and confronting biased actions can people begin to unlearn behaviors that lead to urban injustice and inequality. Implicit biases can drive racist, classist and misogynistic behaviors, these visualizations will help participants and planners to understand how pervasive these attitudes and actions are the city. In addition to the novel dataset, the methodology will further urban geography mapping methods by adding the recursive element of people reacting to their own biases, and perhaps making explicit what was previously implicit.

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