Urban waste management inequities in New York city: a case study on spatial patterns of controlled and uncontrolled street waste and their associated factors

Comprehensive urban waste management systems, addressing municipal waste collection and construction waste disposal, are essential for maintaining livable and sustainable cities. Understanding the spatial distribution patterns of controlled and uncontrolled waste, along with their underlying environmental and socioeconomic determinants, is essential for developing more effective urban waste management strategies. However, comprehensive analysis of street-level waste distribution and its relationship with socioeconomic factors remains limited, particularly regarding environmental justice implications. This study developed a computer vision approach to detect controlled and uncontrolled waste in New York City and analyzed their spatial distribution patterns to examine associations with urban environmental and socioeconomic characteristics. We employed Swin Transformer architecture for automated waste detection from street-view imagery. Spatial analysis, logistic regression, and interpretable machine learning using SHAP (SHapley Additive exPlanations) were applied to analyze 43 variables across environmental and socioeconomic factors. Results revealed contrasting distribution profiles where controlled waste concentrated in high-density, well-developed areas with higher education levels, while uncontrolled waste exhibited dual marginalization in urban peripheries and socioeconomically disadvantaged communities. Hispanic populations showed 14.9 % higher odds of uncontrolled waste exposure (OR = 1.149, p = 0.032), confirming environmental justice concerns. This research provides the first comprehensive quantitative evidence of street-level waste inequality, revealing significant spatial and social disparities that support targeted policy interventions through data-driven hotspot identification for vulnerable communities in urban waste management systems.