東華大學圖書館 |

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Improving Data Driven Canadian Waste Management Policy Using gis, Remote Sensing, and Other Advanced Techniques // Amy Johanna Richter.
作者:	Richter, Amy Johanna,
面頁冊數:	1 electronic resource (307 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-10, Section: B.
Contained By:	Dissertations Abstracts International83-10B.
標題:	Integer programming. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29020249
ISBN:	9798209920731

Improving Data Driven Canadian Waste Management Policy Using gis, Remote Sensing, and Other Advanced Techniques /
Richter, Amy Johanna,

Improving Data Driven Canadian Waste Management Policy Using gis, Remote Sensing, and Other Advanced Techniques /Amy Johanna Richter. - 1 electronic resource (307 pages)

Source: Dissertations Abstracts International, Volume: 83-10, Section: B.

Data-driven techniques are vital to improve the efficiency of waste management systemsin Canada and around the world. Canadians generated 935.6 kg/cap of waste in 2016 andspent $3.3 billion to manage this waste. This research aims to solve problems related towaste region optimization and landfill siting. Specifically, this research developstopologies for regional waste management systems and analyzes specific features of preexistingregionalized and non-regionalized systems to increase system efficiencies.Methods including Geographic Information Systems (GIS), remote sensing (RS), spatialstatistics, and data science are incorporated to inform data driven policies in wastemanagement.First, a novel recursive algorithm is proposed to optimize the shape of waste managementregions using ArcGIS. The outcome was a set of regions where the spread of populatedplaces, roads, and landfills were more equally spread throughout each region. Therecursive algorithm was then altered, applying the Central Feature spatial statisticsinstead of the geometric centroid as the 'seed' input in the algorithm. In some cases, thisalteration was able to further improve the ability of the algorithm to equally spreadinfrastructure within regions. Along with provinces, this method was also tested on cities,and the results were positive for larger metropolitan cities, however, limitedimprovements were observed in smaller urban centers. After proposing a new recursionalgorithm and an alteration, the shape characteristics of the regions were examined. Twonew metrics were proposed, accounting for the percent spread of landfills, populatedplaces, and roads, as well as the area of each proposed region in a tessellation. Theisoperimetric quotient and its measures of central tendency are used to further evaluateproposed tessellations. The number of sub-regions in a tessellation and the area of theresulting polygons are important factors in spatial optimization of waste managementregions. Finally, other spatial statistics are investigated in smaller regions ofSaskatchewan. Centroidal Voronoi Tessellation is generally the most efficient method.However, when the ratio of populated places to road length was low, the median centerspatial statistic was better able to optimize populated places. A trade off exists betweenthe number of sub-regions, computational efficiency, and optimized percent standarddeviation. Size of sub-regions remains important.Three novel analysis methods are also presented. First, a method for determining optimallandfill expansion locations is proposed. The method was able to determine the best areasfor landfill expansion. Independent of expert opinion, the method relies solely on vectorand 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, StandardDeviational Ellipses were used to assess costs related to waste collection in Nova Scotia.The distribution of waste facilities and road network significantly impact collection costsand a framework for geospatially dependent policies in Nova Scotia is presented. Finally,landfill design criteria are assessed and compared using data science techniques. Worddistributions and other characteristics are related to climatic and other features in eachprovince. Tables and figures are important in Canadian landfill design standards and guidelines.

English

ISBN: 9798209920731Subjects--Topical Terms:

648270
Integer programming.

Improving Data Driven Canadian Waste Management Policy Using gis, Remote Sensing, and Other Advanced Techniques /
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245 1 0 $a Improving Data Driven Canadian Waste Management Policy Using gis, Remote Sensing, and Other Advanced Techniques / $c Amy Johanna Richter.
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500 $a Source: Dissertations Abstracts International, Volume: 83-10, Section: B.
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502 $b Ph.D. $c The University of Regina (Canada) $d 2021.
520 $a Data-driven techniques are vital to improve the efficiency of waste management systemsin Canada and around the world. Canadians generated 935.6 kg/cap of waste in 2016 andspent $3.3 billion to manage this waste. This research aims to solve problems related towaste region optimization and landfill siting. Specifically, this research developstopologies for regional waste management systems and analyzes specific features of preexistingregionalized and non-regionalized systems to increase system efficiencies.Methods including Geographic Information Systems (GIS), remote sensing (RS), spatialstatistics, and data science are incorporated to inform data driven policies in wastemanagement.First, a novel recursive algorithm is proposed to optimize the shape of waste managementregions using ArcGIS. The outcome was a set of regions where the spread of populatedplaces, roads, and landfills were more equally spread throughout each region. Therecursive algorithm was then altered, applying the Central Feature spatial statisticsinstead of the geometric centroid as the 'seed' input in the algorithm. In some cases, thisalteration was able to further improve the ability of the algorithm to equally spreadinfrastructure within regions. Along with provinces, this method was also tested on cities,and the results were positive for larger metropolitan cities, however, limitedimprovements were observed in smaller urban centers. After proposing a new recursionalgorithm and an alteration, the shape characteristics of the regions were examined. Twonew metrics were proposed, accounting for the percent spread of landfills, populatedplaces, and roads, as well as the area of each proposed region in a tessellation. Theisoperimetric quotient and its measures of central tendency are used to further evaluateproposed tessellations. The number of sub-regions in a tessellation and the area of theresulting polygons are important factors in spatial optimization of waste managementregions. Finally, other spatial statistics are investigated in smaller regions ofSaskatchewan. Centroidal Voronoi Tessellation is generally the most efficient method.However, when the ratio of populated places to road length was low, the median centerspatial statistic was better able to optimize populated places. A trade off exists betweenthe number of sub-regions, computational efficiency, and optimized percent standarddeviation. Size of sub-regions remains important.Three novel analysis methods are also presented. First, a method for determining optimallandfill expansion locations is proposed. The method was able to determine the best areasfor landfill expansion. Independent of expert opinion, the method relies solely on vectorand 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, StandardDeviational Ellipses were used to assess costs related to waste collection in Nova Scotia.The distribution of waste facilities and road network significantly impact collection costsand a framework for geospatially dependent policies in Nova Scotia is presented. Finally,landfill design criteria are assessed and compared using data science techniques. Worddistributions and other characteristics are related to climatic and other features in eachprovince. Tables and figures are important in Canadian landfill design standards and guidelines.
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