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Large-scale informal recycling networks often emerge among developing economies in response to the challenges of modern urban waste accumulation. South Korea, despite its highly industrialized, developed economy, still maintains an extensive informal recycling sector made up of networks of local junk shops and individual waste pickers. As cities’ large data sources have become more widely available, the use of urban informatics in sustainable smart waste management has become more widespread. In this paper, we use geographic information system (GIS) analysis in order to uncover patterns within Korea’s informal recycling system, looking at the relationship between population demographics, waste levels, and urban planning with the prevalence of junk shops across Korea. We then interviewed junk shop owners, urban planning researchers, and government officials in order to better understand the factors that led to the coexistence of the country’s informal and formal systems of waste management and how junk shops have changed their operations over time in response to recent developments in cities’ urban fabrics. We conclude by giving suggestions for how the usage of urban informatics could increase the efficiency and sustainability of the country’s waste management systems, while also discussing the possible pitfalls of using such existing datasets for future policy decisions.
Jaehong Lee; Hans Han; Jong-Yoon Park; David Lee. Urban Informatics in Sustainable Waste Management: A Spatial Analysis of Korea’s Informal Recycling Networks. Sustainability 2021, 13, 3076 .
AMA StyleJaehong Lee, Hans Han, Jong-Yoon Park, David Lee. Urban Informatics in Sustainable Waste Management: A Spatial Analysis of Korea’s Informal Recycling Networks. Sustainability. 2021; 13 (6):3076.
Chicago/Turabian StyleJaehong Lee; Hans Han; Jong-Yoon Park; David Lee. 2021. "Urban Informatics in Sustainable Waste Management: A Spatial Analysis of Korea’s Informal Recycling Networks." Sustainability 13, no. 6: 3076.
Though the technological advancement of smart city infrastructure has significantly improved urban pedestrians’ health and safety, there remains a large number of road traffic accident victims, making it a pressing current transportation concern. In particular, unsignalized crosswalks present a major threat to pedestrians, but we lack dense behavioral data to understand the risks they face. In this study, we propose a new model for potential pedestrian risky event (PPRE) analysis, using video footage gathered by road security cameras already installed at such crossings. Our system automatically detects vehicles and pedestrians, calculates trajectories, and extracts frame-level behavioral features. We use k-means clustering and decision tree algorithms to classify these events into six clusters, then visualize and interpret these clusters to show how they may or may not contribute to pedestrian risk at these crosswalks. We confirmed the feasibility of the model by applying it to video footage from unsignalized crosswalks in Osan city, South Korea.
Byeongjoon Noh; Wonjun No; Jaehong Lee; David Lee. Vision-Based Potential Pedestrian Risk Analysis on Unsignalized Crosswalk Using Data Mining Techniques. Applied Sciences 2020, 10, 1057 .
AMA StyleByeongjoon Noh, Wonjun No, Jaehong Lee, David Lee. Vision-Based Potential Pedestrian Risk Analysis on Unsignalized Crosswalk Using Data Mining Techniques. Applied Sciences. 2020; 10 (3):1057.
Chicago/Turabian StyleByeongjoon Noh; Wonjun No; Jaehong Lee; David Lee. 2020. "Vision-Based Potential Pedestrian Risk Analysis on Unsignalized Crosswalk Using Data Mining Techniques." Applied Sciences 10, no. 3: 1057.