Improving Data Driven Canadian Waste Management Policy Using GIS, Remote Sensing, and Other Advanced Techniques

Date

2021-05

Journal Title

Journal ISSN

Volume Title

Publisher

Faculty of Graduate Studies and Research, University of Regina

Abstract

Data-driven techniques are vital to improve the efficiency of waste management systems

in Canada and around the world. Canadians generated 935.6 kg/cap of waste in 2016 and

spent $3.3 billion to manage this waste. This research aims to solve problems related to

waste region optimization and landfill siting. Specifically, this research develops

topologies for regional waste management systems and analyzes specific features of preexisting

regionalized and non-regionalized systems to increase system efficiencies.

Methods including Geographic Information Systems (GIS), remote sensing (RS), spatial

statistics, and data science are incorporated to inform data driven policies in waste

management.

First, a novel recursive algorithm is proposed to optimize the shape of waste management

regions using ArcGIS. The outcome was a set of regions where the spread of populated

places, roads, and landfills were more equally spread throughout each region. The

recursive algorithm was then altered, applying the Central Feature spatial statistics

instead of the geometric centroid as the ‘seed’ input in the algorithm. In some cases, this

alteration was able to further improve the ability of the algorithm to equally spread

infrastructure within regions. Along with provinces, this method was also tested on cities,

and the results were positive for larger metropolitan cities, however, limited

improvements were observed in smaller urban centers. After proposing a new recursion

algorithm and an alteration, the shape characteristics of the regions were examined. Two

new metrics were proposed, accounting for the percent spread of landfills, populated

places, and roads, as well as the area of each proposed region in a tessellation. The

isoperimetric quotient and its measures of central tendency are used to further evaluate

proposed tessellations. The number of sub-regions in a tessellation and the area of the

resulting polygons are important factors in spatial optimization of waste management

regions. Finally, other spatial statistics are investigated in smaller regions of

Saskatchewan. Centroidal Voronoi Tessellation is generally the most efficient method.

However, when the ratio of populated places to road length was low, the median center

spatial statistic was better able to optimize populated places. A trade off exists between

the number of sub-regions, computational efficiency, and optimized percent standard

deviation. Size of sub-regions remains important.

Three novel analysis methods are also presented. First, a method for determining optimal

landfill expansion locations is proposed. The method was able to determine the best areas

for landfill expansion. Independent of expert opinion, the method relies solely on vector

and remote sensing data; but is flexible enough to apply weighting methods if so desired.

Remote sensing and vector data can capture distinctly different aspects of the study area,

and vector data can be used as a proxy when cloud cover is present. Next, Standard

Deviational Ellipses were used to assess costs related to waste collection in Nova Scotia.

The distribution of waste facilities and road network significantly impact collection costs

and a framework for geospatially dependent policies in Nova Scotia is presented. Finally,

landfill design criteria are assessed and compared using data science techniques. Word

distributions and other characteristics are related to climatic and other features in each

province. Tables and figures are important in Canadian landfill design standards and

guidelines

Description

A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Environmental Systems Engineering, University of Regina. xvii, 287 p.

Keywords

Regionalization, Waste management, Centroidal Voronoi Tessellation, Thiessen polygons, Geographic Information Systems, Remote sensing

Citation