An iterative tessellation-based analytical approach to the design and planning of waste management regions

Abstract

Collection and transportation of solid waste are costly for municipal budgets. This study challenges the use of existing administrative boundaries in waste management applications. By reducing the spread (standard deviation) of parameters (landfills, populated places, and roads), efficient and practical waste management regions are created. A novel alteration to the Centroidal Voronoi Tessellation (CVT) algorithm is proposed where Thiessen polygons are created using the central feature of a subset of data instead of the geometric centroid. The results applying the central feature method are compared to traditional CVT methods. Two Canadian provinces (Saskatchewan and Nova Scotia), the City of Regina, and two New York City boroughs (Manhattan and the Bronx) are investigated. Results suggest that the newly proposed tool can reduce the standard deviation of selected parameters compared to CVT. The spatial distribution of data and the geometry of the input tessellations are important factors in optimization. In Saskatchewan, reductions in parameter standard deviations ranged between 7.0 and 23.8% when comparing the two methods. In Nova Scotia, reductions in standard deviation of 9.64–13.25% were observed. In the City of Regina, wards may be more effective in planning solid waste collection compared to current solid waste collection boundaries. The standard deviation of parameters was minimized by 32.2–55.0% in New York. The proposed method may be able to efficiently create waste management regions in both cities and provinces, helping to reduce waste collection and transportation costs by ensuring an even spread of parameters in each region.