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Within cluster pattern identification: a new approach for optimizing recycle point distribution to support policy implementation on waste management in Malaysia

Despite the government’s policies and objectives, Malaysia lags behind in sustainable waste management techniques, particularly recycling. Bins should be located conveniently to encourage recycling and reduce waste. The current model of bin location-allocation is mostly determined by distance. Howev...

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Main Authors: Azri, Suhaibah, Ujang, Uznir, Abdullah, Nurul Shafika
Format: Article
Language:English
Published: SAGE Publications Ltd 2023
Subjects:
H Social Sciences (General)
Online Access:http://eprints.utm.my/107534/1/SuhaibahAzri2023_WithinClusterPatternIdentificationNewApproach.pdf
http://eprints.utm.my/107534/
http://dx.doi.org/10.1177/0734242X221123489
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spelling my.utm.1075342024-09-23T04:30:35Z http://eprints.utm.my/107534/ Within cluster pattern identification: a new approach for optimizing recycle point distribution to support policy implementation on waste management in Malaysia Azri, Suhaibah Ujang, Uznir Abdullah, Nurul Shafika H Social Sciences (General) Despite the government’s policies and objectives, Malaysia lags behind in sustainable waste management techniques, particularly recycling. Bins should be located conveniently to encourage recycling and reduce waste. The current model of bin location-allocation is mostly determined by distance. However, it has been identified that previous studies excluded an important factor: litter pattern identification. Litter pattern is important to identify waste generation hotspots and litter distribution. Thus, we proposed the within cluster pattern identification (WCPI) approach to optimize the recycle point distribution. WCPI gathers the information on litter distribution using geotagged images and analyses the pattern distribution. The optimal location for recycle bin can be identified by incorporating k-means clustering to the pattern distribution. Since k-means faces the non-deterministic polynomial-time-hard challenge of selecting the ideal cluster and cluster centre, WCPI used the total within-cluster sum of square on top of k-means clustering. The proposed location by WCPI is validated in terms of accessibility and suitability. Furthermore, this study provides further analysis of carbon footprint that can be reduced by simulating the data from geotagged images. The results show that 10,323.55 kg of carbon emission can be reduced if the litter is sent for recycling. Thus, we believe that locating bins at an optimal location will embark on consumer motivation to dispose of recycled waste, reduce litter and lessen the carbon footprint. At the same time, these efforts could transform Malaysia into a clean and sustainable nation that aims to achieve Agenda 2030. SAGE Publications Ltd 2023-03 Article PeerReviewed application/pdf en http://eprints.utm.my/107534/1/SuhaibahAzri2023_WithinClusterPatternIdentificationNewApproach.pdf Azri, Suhaibah and Ujang, Uznir and Abdullah, Nurul Shafika (2023) Within cluster pattern identification: a new approach for optimizing recycle point distribution to support policy implementation on waste management in Malaysia. Waste Management and Research, 41 (3). pp. 687-700. ISSN 0734-242X http://dx.doi.org/10.1177/0734242X221123489 DOI:10.1177/0734242X221123489
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic H Social Sciences (General)
spellingShingle H Social Sciences (General)
Azri, Suhaibah
Ujang, Uznir
Abdullah, Nurul Shafika
Within cluster pattern identification: a new approach for optimizing recycle point distribution to support policy implementation on waste management in Malaysia
description Despite the government’s policies and objectives, Malaysia lags behind in sustainable waste management techniques, particularly recycling. Bins should be located conveniently to encourage recycling and reduce waste. The current model of bin location-allocation is mostly determined by distance. However, it has been identified that previous studies excluded an important factor: litter pattern identification. Litter pattern is important to identify waste generation hotspots and litter distribution. Thus, we proposed the within cluster pattern identification (WCPI) approach to optimize the recycle point distribution. WCPI gathers the information on litter distribution using geotagged images and analyses the pattern distribution. The optimal location for recycle bin can be identified by incorporating k-means clustering to the pattern distribution. Since k-means faces the non-deterministic polynomial-time-hard challenge of selecting the ideal cluster and cluster centre, WCPI used the total within-cluster sum of square on top of k-means clustering. The proposed location by WCPI is validated in terms of accessibility and suitability. Furthermore, this study provides further analysis of carbon footprint that can be reduced by simulating the data from geotagged images. The results show that 10,323.55 kg of carbon emission can be reduced if the litter is sent for recycling. Thus, we believe that locating bins at an optimal location will embark on consumer motivation to dispose of recycled waste, reduce litter and lessen the carbon footprint. At the same time, these efforts could transform Malaysia into a clean and sustainable nation that aims to achieve Agenda 2030.
format Article
author Azri, Suhaibah
Ujang, Uznir
Abdullah, Nurul Shafika
author_facet Azri, Suhaibah
Ujang, Uznir
Abdullah, Nurul Shafika
author_sort Azri, Suhaibah
title Within cluster pattern identification: a new approach for optimizing recycle point distribution to support policy implementation on waste management in Malaysia
title_short Within cluster pattern identification: a new approach for optimizing recycle point distribution to support policy implementation on waste management in Malaysia
title_full Within cluster pattern identification: a new approach for optimizing recycle point distribution to support policy implementation on waste management in Malaysia
title_fullStr Within cluster pattern identification: a new approach for optimizing recycle point distribution to support policy implementation on waste management in Malaysia
title_full_unstemmed Within cluster pattern identification: a new approach for optimizing recycle point distribution to support policy implementation on waste management in Malaysia
title_sort within cluster pattern identification: a new approach for optimizing recycle point distribution to support policy implementation on waste management in malaysia
publisher SAGE Publications Ltd
publishDate 2023
url http://eprints.utm.my/107534/1/SuhaibahAzri2023_WithinClusterPatternIdentificationNewApproach.pdf
http://eprints.utm.my/107534/
http://dx.doi.org/10.1177/0734242X221123489
_version_ 1811681217226997760
score 13.223943

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