Measuring the visual pedestrian qualities of urban streets through crowdsourcing

Abstract

We experience our neighborhoods and cities through our streets. An essential question is what street features contribute to a superior pedestrian experience? Further, can interacting with urban streetscapes improve cognitive functioning, as interacting with nature does? To answer these questions, large urban street perception databases are enormously helpful. Toward this end, we propose a crowdsourcing method for measuring the pedestrian qualities of streets – preference, imageability, complexity, enclosure, human scale, transparency, and order, all of which are important design dimensions for pedestrian experience. We obtained 556 street images from Google Street View by sampling two sidewalk images from 278 geo-coordinates in Chicago. For each dimension, over 58 (SD=2.5) Amazon Mechanical Turk workers (469 in total) completed an image rating task for the 556 images. In each trial, participants were shown 12 images in a 4x3 grid and asked to choose four images that they evaluate highly on that dimension. The probability of selecting each image for a given dimension across participants was used to quantify how much that image represented that dimension. To test the inter-rater reliability, we randomly split participants into two groups 2000 times and calculated rank-correlations between the measures from each group. We found that the split-half correlations were high for walkability (0.86±0.01; M±SD), preference (0.83±0.02), imageability (0.78±0.02), complexity (0.79±0.02), enclosure (0.80±0.02), and transparency (0.88±0.01) and modest for human scale (0.45±0.05) and order (0.57±0.05). To test whether the measures are internally consistent, we randomly split two images from the same geo-coordinate into two bins (of 278 images) 2000 times, calculated rank-correlations, and found modest to low correlation values, range: [0.06, 0.37]. These results suggest that our method can be used to efficiently measure pedestrian street qualities from non-experts, but with some limitations, to build a large database for studying street features affecting urban street pedestrian experiences.