Published in Computers, Environment and Urban Systems, this study by Assistant Professor Urban Spatial Analytics Xiaojiang Li and Doctoral Student Shengao Yi (MUSA'23), along with international collaborators, addresses a critical but overlooked issue in urban analytics: how seasonal variations in street view imagery can significantly skew urban environmental assessments. Using over 262,000 images from 40 cities worldwide, the researchers developed a systematic framework to quantify seasonal bias in urban form indicators like the Green View Index (GVI). Their findings reveal substantial bias, with an average error of 54% for GVI across cities, particularly pronounced in areas with distinct seasonal changes. The study demonstrates that this bias strongly correlates with geographic location—cities with lower rainfall and temperatures showing greater seasonal variation—and can lead to significant analytical errors in practical applications like urban functional zoning (with clustering accuracy dropping to 0.35).
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