Developing a novel proximity analysis approach for assessment of waste management cost efficiency in low population density regions. (February 2021) - Richter, Amy; Ng, Kelvin Tsun Wai; Karimi, Nima; Chang, Wonjae
Highlights: Geospatial regions are derived using standard deviational ellipses. The proposed approach is believed to be more applicable in sparsely populated areas. Relationships between vector and remote sensing data and cost indices are examined. Two new indicators are proposed for spatial analysis of waste management efficiency. Results support the use of geospatially dependent policies on waste collection. Abstract: Waste management system efficiency can be challenging to assess in regions with low population density or where data is only available at certain spatial resolutions. A novel spatial analysis approach is developed and applied in Nova Scotia, Canada to assess costs related to waste collection. Standard Deviational Ellipses are used to narrow areas of interest based on 14 administrative regions. Using these defined areas of interest, land (remote sensing) and infrastructure (vector) datasets are gathered. Pearson correlation is used to explore the relationships between the features and collection cost indicators. Two original indices are developed and analyzed: dwelling ratio (area of dwellings/total building area) and diversion-collection expenditure ratio. Results suggest that normalized difference vegetation index was positively related to collection cost while normalized difference built-up index was positively related to diversion indicators. Waste management facility distribution and road network significantly impact collection costs, and they are important in effective and efficient waste management system. Industrial indicators were also analyzed, and the number of utility towers was found to be related to collection costs. These findings suggest that geospatially dependent policies could be used to develop sustainable and economically efficient waste management systems in Nova Scotia and other low population density areas.