Shades of Green: A Visual Approach to Assessing Environmental Inequality in Cleveland, Ohio - Taraneh Meshkani (Urban Science, 2025)
This study explores environmental inequality through a novel quantitative approach, analyzing urban vegetation in two contrasting neighborhoods in Cleveland, Ohio—East Cleveland and Shaker Heights. Utilizing computer vision and Google Street View image analysis techniques, this research quantifies the distribution, quality, and visual characteristics of tree canopy and grass. This study addresses the following question: how can visual data illuminate environmental inequalities in urban landscapes? The findings reveal significant disparities between the two neighborhoods, not only in tree canopy coverage but also in the visual characteristics and density of vegetation. Shaker Heights displays more abundant, healthier, and better-maintained greenery compared to East Cleveland. These results highlight the intersection of socio-economic factors and urban vegetation quality, suggesting that both the quantity and visual attributes of greenery serve as indicators of neighborhood wealth and environmental investment. This study demonstrates the effectiveness of computer vision techniques in accessing and analyzing visual data to represent environmental inequalities. It advocates for targeted urban planning interventions to promote equitable green space policies, considering both the quantity and quality of urban vegetation.