Sage Journals: Discover world-class research

Abstract

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.

Keywords

Litter recycle location allocation spatial analysis waste management clustering

Introduction

Agenda 2030 increases the resolve to pursue the journey of sustainable development more aggressively. Thus, Malaysia has aligned sustainable development goals (SDGs) principles with the 11th Malaysia Plan (2016–2020) and 12th Malaysia Plan (2021–2025), which will entrench SDGs in all facets of Malaysia’s development. It is mentioned in the SDG indicator of 12.5.1 that by 2030, substantially reduce waste generation through prevention, reduction, recycling and reuse (United Nations, 2020).

Sustainable consumption and production (SCP) were introduced into the 11th Malaysia Plan as a critical approach to achieving green growth and addressing environmental sustainability. One of the keys to achieving SCP is by increasing the recycling rate for solid waste. It was aimed that by 2020, the recycling rate is rise to 22% from 17.5% in 2016. A recent press release by the Department of Statistics Malaysia (2020) shows that Malaysia’s recycling rate in 2019 has exceeded the set target of 28.1%. However, practicing solid waste recycling in developing countries like Malaysia is still challenging compared to developed countries. Developed countries have achieved a high rate of above 50% of waste recycling. As Malaysia prepares to project itself as a developed nation, it is important to narrow the gaps and enhance the waste management situation in Malaysia to be on par with other developed countries.

Rampant litter has long been a problem globally, especially in developing countries. Heaps of trashes dumped on streets are not only unsightly, and they are dangerous too, posing health hazards to people and threats to the environment. In Malaysia, it is common to have empty water bottles, cans, plastic bags, cigarette butts and other rubbish strewn on the roadside and in public spaces. Numerous efforts and policies are implemented to prevent litter in public settings. For example, implementation of certain fines and consents, cleaning campaigns and public awareness (Cao et al., 2016), designing and locating of smart bins with sensors in city centres (Nagalingeswari and Satamraju, 2017) and promotion of antilitter campaigns (Hughes et al., 2019). However, the effectiveness of such measures can vary from one place to another.

Over the years, the litter problem has only gotten worse even though public littering is prohibited by Malaysian law and act. Malaysia is categorized as one of the most successful developing countries in Southeast Asia in terms of economic growth and development. This contributes to the factors of population migration to the urban areas. Due to this migration, Malaysia has to experience a huge transition, and it has become a challenging task to handle solid waste management. The government has introduced a policy and legislation for solid waste management. The policies are the Enactment of Solid Waste Management and Public Cleansing Act (Act 672) and Solid Waste and Public Cleansing Management Corporation (Act 673). National Solid Waste Management Policy is one of the listed plans to support SDG Keys No. 11: Make cities and human settlements inclusive, safe, resilient and sustainable (Economic Planning Unit, Malaysia, 2017). Another national plan is the Solid Waste and Public Cleansing Management Corporation Strategic Plan, 2014–2020, to support Goal 12: Ensure SCP patterns through Responsible Consumption and Production. However, the policy implementation on solid waste management looks delicate and doubtful. Landfilling is still the main solid waste disposal approach, and the recycling rate among Malaysian is low and still left behind compared to the neighbouring countries. According to the Government Activist Circular, an organization specializing in packaging and food waste research, Malaysia’s polyethylene terephthalate (PET) bottle recycling rate is very low. Only 16% of PET bottles in Malaysia are collected for recycling purposes (Chung, 2019; Jereme et al., 2014). The report also added in 2018 that about 660,000 t of PET bottles were dumped at landfills or leaked into the environment.

There are several unresolved problems related to recycling management in Malaysia. Developing countries, such as Malaysia deal with improper collection services, such as low collection coverage and irregular collection services. This has led to difficulties in tracking back the recycling rate. Another factor contributing to the low recycling rate is the unavailability of recycling facilities. According to Tiew et al. (2019), the lack of easy access to recycling facilities, such as recycle bins, recycle centres and reverse vending machine is an obstacle to the Malaysian community to practice recycling. Studies have found that recycling is time-consuming and may determine the intensity of recycling activities (Ho, 2018; Kattoua et al., 2019; Matsumoto, 2018). To overcome this issue, several researchers suggested providing convenient facilities and infrastructure to impact people’s motivation and behaviour towards recycling (Bahçelioğlu et al., 2020; Conke, 2018; Munguía-López et al., 2020; Stoeva and Alriksson, 2017; Xiao et al., 2018).

Several studies suggested that bins should be located strategically to reduce litter and promote recycling (El-Hallaq and Mosabeh, 2019; Letelier et al., 2021; Rathore et al., 2019). This approach manipulates the appearance of recycling bins in common areas to attract consumers to dispose of recycling waste. The location of bins is important to consumers. It should be easily accessed where there is a higher generation of waste because it affects the consumer’s recycling behaviour (Geertman and van Gent, 2018). More travel may be required if bins are not strategically located or more bins are required for the densely populated area (Miller et al., 2016).

Location is often considered one of the most important factors leading to the success of many systems. Ebrahimi et al. (2017) has performed a location-allocation analysis to provide a potential solution to find the optimal locations for the bin. Their study subdivided the weight of waste demand for each building entrance. Meanwhile, the weight of waste allocated to waste bins was identified. Then, the portion demand for each entrance is calculated and mapped based on its location. The analysis determined the potential areas for placing additional recycling and trash bins and resolved the lack of bin coverage.

Another study by Boskovic and Jovicic (2015) and Boskovic et al. (2016) proposed a model based on the total amount of waste generated per week for residential and commercial activities. The total generated waste had a significant influence on the optimal collection point. The author claims that significant savings can be achieved by adopting the proposed model. In the actual case study, the number of collection points may be reduced by 24%, thus reducing the number of waste bins by more than 33% and it leads to the analysis of CO₂ reduction where 4.5 t have been reduced.

A study by Rathore et al. (2019) considers different factors in their location-allocation model. The model considered several factors such as multiple types of sources, waste bins, waste types and safety and rag-picking. The study is aimed to provide efficient service to the entire targeted site. Data from an Indian municipality is used to demonstrate the effectiveness of the newly constructed model. Based on the test, the model could reduce the collection points up to 15% and reduce costs by about 25%. The overall results suggest that implementing the proposed model could reduce 25% of the carbon emission by the waste truck.

Meanwhile, Erfani et al. (2017) use the total amount of solid waste generated in each district in Mashhad, Iran, as one of the parameters or location-allocation models. The study also incorporates the number of population and vehicle routing problems for the collection point. It is mentioned in the study that, based on the results, significant improvements and savings were made through the proper application of the model. The total number of crew members was reduced from 24 in the current collection system to 14 in the improved system offered by the model (a reduction of 41.70%).

Selecting the appropriate site or location for recycle bin distribution is a complex problem. It requires an extensive evaluation process because it is challenging to develop a selection criterion that can precisely describe the preference of one location over another. Various models of bin location-allocation are developed using multiple parameters, such as walking distance and proximity to a normal waste bin. Studies conducted by Sheau-Ting et al. (2016) and Bergman (2017) concluded that the optimal distance to access the recycle bins is between 500 and 650 m. In addition, studies by Struk (2017), Digiacomo et al. (2018) and Leeabai et al. (2021) show that waste separation motivation is increased if the recycling bins are reached within walking distance. However, none of the current approaches incorporates pattern identification in their study. According to Wu et al. (2018) and Lu et al. (2020), spatial distribution patterns play an important role in various applications, such as population genetics, widespread contagious disease and many more.

Additionally, the spatial pattern was not addressed as a parameter or element in any model. The current model does not consider the actual location or the litter hotspot. In certain cases, when the visual analysis is insufficient, the spatial pattern can explain the quantification of spatial data (Scott, 2015). This is directly relevant to this research, as trash distribution must be quantified to make assumptions for subsequent analytical procedures. The spatial pattern has been widely applied in various research and application. Understanding a phenomenon’s distribution requires an understanding of its spatial pattern. For example, a recent study shows that analysis of spatial patterns identifies several factors of tourism development in the Yellow River Basin, China, such as location, terrain and cultural resources (Zhang et al., 2020). Also, the spatial pattern has been used to identify and analyse the spatial distribution of marine debris along the Thondi coast, Palk Bay, Southeast coast of India (Perumal et al., 2021). Another study also shows that spatial pattern is used to detect geographic factors related to the prevalence of COVID-19 infection (Jesri et al., 2021). The identification of spatial patterns is important since the spread of COVID-19 can affect public health. This proves that spatial pattern is important to assist our understanding of phenomena and their connection and correlation. In this study, the spatial pattern would provide insight into where litter occurs, how the litter distribution aligns with other features in that area and the potential connections and correlations. Due to this factor, it is believed that identifying litter patterns will add an advantage to the proposed algorithm for recycle bin location-allocation model. Bins that are strategically placed are believed to reach the target client and promote recycling behaviour effectively.

Another approach for location selection is by using the clustering-based location-allocation method. The clustering method will serve as the location-allocation method to determine the location of the bin and its coverage area. There is various clustering algorithm for machine learning. However, k-means can be considered the most popular clustering algorithm due to its simplicity, versatility and ease of implementation in various applications (Zhao et al., 2018). For example, Anagnostopoulos et al. (2015) adopted a k-means clustering algorithm to cluster the bins into a set of partial clusters. The algorithm aims to provide efficient solutions for waste collection problems by managing the trade-off between the immediate collection and cost.

Meanwhile, Vu and Kaddoum (2017) use a k-means clustering algorithm to make the working cluster of each garbage truck for the collection system. The system is used to monitor and predict each trash bin’s status daily. However, the authors claimed that k-means is a naive algorithm. The algorithm clusters the data into k clusters even though k is not the right number to be used. Therefore, users need to pre-determine the right number of clusters when using k-means clustering. Besides that, the k-means algorithm is facing a non-deterministic polynomial-time (NP) hard problem, which requires repetitive iteration and produces more than one cluster centre in the same group (Cohen-Addad et al., 2019; Friggstad et al., 2019). Thus, this research aims to provide a new approach to identifying the optimum location of recycling bins, specifically in Malaysia, by considering litter pattern identification. The combination of the k-means clustering algorithm and the total within-cluster sum of square (WSS) is applied in the WCPI approach to finding the optimal location. With the combination of litter pattern distribution, clustering and the total Within-Cluster-Sum-Square (WCSS), WCPI could identify the optimal location of recycle bin placement in the Iskandar Region, Malaysia.

Materials and methods

Three main areas are chosen as study areas: Kulai, Iskandar Puteri and Johor Bahru, Malaysia. These areas are chosen since it is located in Iskandar, Malaysia. Iskandar Malaysia is a visionary economic region in Johor that was established in 2006 as one of the catalyst development corridors to spur the growth of the Malaysian economy. The Low Carbon Society Blueprint for Iskandar Malaysia 2025 is a written document presenting comprehensive climate change mitigation (carbon emission reduction) and policies (low carbon society actions and subactions). The plan also described detail strategies (measures and programs) to guide the development of Iskandar Malaysia towards achieving its vision of a strong, sustainable metropolis of international standing by 2025.

In this study, three main approaches are used to identify the optimal recycle bin location. The approaches are spatial pattern, k-means data clustering and WCSS. The combination of these approaches is named within cluster pattern identification (WCPI). WCPI begins with geotag data collection. The process of WCPI is designed dedicatedly for waste management to identify optimal locations for recycling points or bin distribution. There are three phases involved in WCPI: Phase 1: Geotagging Litter Distribution, Phase 2: Litter Pattern Identification and Phase 3: Data Clustering. Figure 1 shows the whole process of WCPI. The WCPI technique is not only concerned with the location of litter but also considers the litter patterns. It is critical to understand the litter patterns in a given area (i.e. clustered, random or dispersed). As a result, the identified litter will be clustered according to the litter type, allowing for easy identification of data clustering before the litter bin’s location is determined. This way, recycling bins will be placed more systematically and accurately according to the type of litter found in a given area.

Figure 1.

Within cluster pattern identification process.

In this study, the methodology begins with geotagging the litter using smartphones in eight study areas. Then the next phase is data analysis, and the last phase is data clustering. The litter distribution is gathered around the study areas for a certain district under the administration of Kulai, Iskandar Puteri and Johor Bahru, Malaysia. Data analysis is performed using the distribution of recycling points based on the litter clusters. Clustering is used to suggest the best location of the recycle bin based on the distribution of litter and the analysis of the litter pattern in each spatial distribution of the neighbourhood. Details for each phase are explained in the following section.

Phase 1: Geotagging litter distribution and data profiling

Phase 1 started with the boundary identification for eight study areas in Johor Bharu, Malaysia is identified. The boundary areas are Bandar Baru Kangkar Pulai (BBKP), Kampung Melayu Kangkar Pulai (KMKP), Kangkar Pulai (KP), Sri Pulai Perdana 2, Taman Pulai Emas, Taman Sri Pulai (TSP), Taman Sri Pulai Perdana (TSPP) and Taman Teratai (TT). Based on the study area, each litter in the public space is captured using smartphones equipped with Assisted Global Positioning System (A-GPS). This process is known as geotagging. The combination of smartphone networks and a Global Positioning System antenna can determine and fix the phone’s location. Litter needs to be captured distinctly to acquire its location. The coordinate system used is the World Geodetic System 1984 (WGS84), with the latitude and longitude coordinates. Thus, each set of coordinates (latitude, longitude) captured represents the litter’s location. Figure 2 shows some images of litter found in the public spaces during the geotagged activity. Table 1 shows the number of litter images captured for each study area. Based on the table, the highest number of litter captured by the smartphone is 1814 for the TSP study area. The lowest number of litters is 1001 from the TT study area. The total number of litters for eight study areas is 11,945 and the average number of litters captured for each study area is 1493 litter.

Figure 2.

Litter in public spaces.

Table 1.

Numbers of litter in each Taman.

Study area	Number of litters	Number of litter rank
Bandar Baru Kangkar Pulai	1065	7
Kampung Melayu Kangkar Pulai	1484	5
Kangkar Pulai	1794	2
Sri Pulai Perdana 2	1690	3
Taman Pulai Emas	1620	4
Taman Sri Pulai	1814	1
Taman Sri Pulai Perdana	1477	6
Taman Teratai	1001	8
Total	11,945	–
Average number of litter for each study area	1493	–

Location data of the geotagged image is extracted from the image’s exchangeable image file format metadata. Litters are mapped individually and represented as a points data set. Figure 3 shows the litter point distribution for eight study areas which are BBKP, KMKP, KP, Sri Pulai Perdana and Part of Kangkar Pulai (SPPKP), Taman Pulai Emas and Taman Pulai Jaya (TPETPJ), TSP, TSPP and TT. These points are mapped onto the OpenStreetMap layer. Points are grouped based on study areas with different colours.

Figure 3.

Location of litter distribution of each study area.

The data acquired is then extracted into rows and columns. Each image is inspected for details, such as litter kind, geographic location and material composition before being exported to a spreadsheet. Based on this information, the statistic for recyclable and nonrecyclable can be projected, and material can be analysed. For future planning, this insight can be used as a benchmark for suitability for recycling, especially for areas with a high number of recyclable litter. Besides that, litter materials can be used to forecast the recycling cost. According to Genc et al. (2019), different recyclable litter materials require different recycling processes such as plastic cleaning and separation cost for plastic, scrapping for aluminium and crushed, melted and moulded for glass. The acquired data is projected in Figure 4. Figure 4(a) shows the recyclable and nonrecyclable litter profile for each study area. From the figure, more than 50% of the litter is recyclable, indicating that the recycle point distributions are required for each area. Figure 4(b) shows the projected number of different recycling materials. Figure 4 shows that most of the disposed of litter around the study area are plastic, followed by paper and glass. Based on the profile and statistic in Figure 3, it can be concluded that each study areas require a recycling point to increase recycling activity and, at the same time, reduce waste disposal.

Figure 4.

(a) Percentage of recyclable and non-recyclable litter and (b) types of litter of each study area.

Phase 2: Litter pattern identification

Understanding spatial patterns is a basic of spatial analysis. Therefore, Phase 2 of WCPI uses nearest neighbour analysis to determine the frequency of litter in one area. Nearest neighbour analysis can measure the distribution and define the distribution of points. In this study, the nearest neighbour analysis aims to show the pattern of recyclable litter, whether it is a cluster, random or dispersed. This pattern distribution will subsequently affect the number of bins located in the study areas. If the litter pattern is clustered, several group clusters can be formed in the next clustering phase. If the pattern is random, normally only one or few clusters can be formed, and a dispersed pattern can usually be associated with one group cluster. Furthermore, description of locating the recycle bin based on group cluster is explained in Phase Data Clustering.

In this study, the average nearest neighbour is used to test the statistical significance based on the size of the study area. The average nearest neighbour summary output is observed mean distance, expected mean distance, nearest neighbour ratio, z-score and p-value. The following equations explain the average nearest neighbour formulation.

The average nearest neighbour for a set of points is explained in equation (1):

ANN = \frac{D_{O}}{D_{E}},

(1)

where D_o is the observed mean distance between two features and their nearest neighbour:

D_{O} = \frac{\sum_{i = 1}^{n} d_{i}}{n},

(2)

Where D_E is the expected mean distance for the features given a random pattern.

D_{E} = 0.5 \sqrt{\frac{A}{n}},

(3)

where n corresponds to the total number of features and A is the total study area. Based on the observed mean distance and expected means distance, the average nearest neighbour ratio can be obtained to measure the average distance between neighbours in a hypothetical random distribution. If the ration value is <1, the pattern exhibits clustering. Meanwhile, if the ratio is more significant than one, the pattern is more likely to be random or dispersion. Other factors that can be considered to quantify the clustering degree are the z-score and p-value. z-Score and p-value are statistical hypotheses that indicate whether to reject the null hypothesis. Usually, the null hypothesis (Ho) is defined as ‘no cluster exists on the point distribution’. If the p-value is very small, the Ho can be rejected because it represents a very small probability that the observed pattern is based on the result of random chance.

Meanwhile, the z-score is a standard deviation. A very high or low (negative) z-score is associated with a very small p-value. The z-score for the average nearest neighbour can be calculated as described in the equation:

z = \frac{D_{O} - D_{E}}{SE},

(4)

where:

SE = \frac{0.26136}{\sqrt{\frac{n^{2}}{A}}} .

(5)

Phase 3: Data clustering

Based on the litter pattern generated from the average nearest neighbour analysis, several group clusters can be detected. Cluster analysis is used to form the group clusters. Cluster analysis is one of the most convenient methods or approaches in spatial statistical analysis. Various types of cluster analysis have been used in data analysis for various applications. One of the well-known clustering algorithms is k-means. k-Means can be considered the most popular clustering algorithm due to its simplicity. Previous studies have adopted the k-means algorithm for different applications, such as geo-marketing (Azri et al., 2016b), database organization (Azri et al., 2016a), wireless sensor framework (Azri et al., 2019) and location-allocation problem (Kim et al., 2018). However, the drawback of k-means is that it is a NP hard problem (García et al., 2018; Tîrnăucă et al., 2018; Wang et al., 2019). The NP-hard problem would produce more than one cluster centre in the same group. According to Azri et al. (2016a), this will cause repetitive data entries, leading to multipath queries, increasing the system storage or memory and leads performance degradation. The k-means clustering algorithm can be described as follows.

k-Means clustering
Input	k (number of clusters)
Input	D (a set of points)
Output	A set of k clusters
Method	Arbitrarily choose k objects from D as the centres of the initial cluster
Repeat	1. Assign each object to the cluster to which the object is the most similar based on the mean value of distance in the cluster 2. Update the clusters mean distance
Until	No change

k-Means require repeated iterations while dealing with n data points (Arthur and Vassilvitskii, 2006). The process will exponentially increase due to the bad selection of initial centroids C. This is the reason that causes the overall time complexity of k-means exponential in n. It is challenging to determine a suitable value and the number of times to repeat the algorithm execution. Furthermore, the repetition of iteration is not a good choice, especially while dealing with a huge dataset and producing numbers of group clusters (Nidheesh et al., 2017).

Thus, in this study, the elbow method of WCSS is adopted during the clustering process. Each cluster consists of measuring and comparing the distances between the data points within a cluster and its centroid. Calculating the WCSS would be an optimal way of finding out the proper number of clusters (Nainggolan et al., 2019; Regla et al., 2019). WCSS is the sum of squares of the distances of each data point to their respective centroids in both clusters. The elbow method can find the optimal value for k based on the Elbow point graph. The graph shows a set of k values for each k value against WCSS. The data were divided into numerical attributes according to several clusters based on the distance clustering algorithm by k-means Clustering. The selection of the centre values on clustering depends on the outputs of k-means.

The average WCSS is mathematically based on the points inside the average distance cluster. The number of clusters is k, the number of points in cluster r is n_r and D_r is the sum of all points in a cluster distance:

W_{k} = \sum_{r = 1}^{k} \frac{1}{n_{r}} D_{r},

(6)

where

D_{r} = \sum_{i = 1}^{n_{r} - 1} \sum_{j = 1}^{n_{r}} {‖ d_{i} - d_{j} ‖}_{2} .

(7)

Using a gap statistic approach to differentiate the k-means clustering method, identify an appropriate reference distribution and choose the number of clusters (Tibshirani et al., 2001). By plotting the number of clusters and WCSS, the number of k can be identified when the plot is starting to decrease or stagnant.

Carbon footprint estimation

One of the aims of the SDG is to reduce carbon emissions. By recycling litter and waste, landfilling can be avoided, and land resources could be saved. Later, they can be utilized for other vital purposes. Thus, in this study, the total emission of CO₂ is calculated for the collected litter. The computation is based on the assumption that all recyclable litter in Phase 1 is recycled and remanufactured. Production of new products would result in energy digestate. Thus, recycling would save natural resources and reduce carbon footprint.

It has been found that the 2000 ml PET bottles, compared to the other options, have the lowest carbon footprint for most environmental impacts, such as water use and waste disposal, in a study in the United Kingdom. The glass bottle was also considered to be the least preferred choice among manufacturers. The carbon footprint of aluminium cans and 500 ml PET bottles would be equal if the glass bottles were reused just three times. However, if PET bottles were to be recycled at a rate of 60%, the glass would have to be recycled 20 times in order to be on par. The study summarized that the total carbon footprint of a 500 ml PET bottle is 293 g of CO₂, a 750 ml glass bottle is 555 g of CO₂ and 330 ml aluminium can is 312 g of CO₂ (Amienyo et al., 2013). Meanwhile, a study from China calculates the carbon footprint emission of copying paper. Based on the study, it is found that the carbon footprint of 1000 kg of copying paper was 647.89 kg CO₂ (Yue et al., 2017). Other materials that have been found during the data collection in Phase 1 are rubber or latex and cloth. For concentrated latex production, almost 70% of the carbon footprint originates from rubber cultivation. The carbon footprint of 200 rubber gloves was approximately 42 kg of CO₂ (Usubharatana and Phungrassami, 2018). By estimating that one piece of rubber glove is 1 g, thus the carbon footprint for 1 g of latex is 0.21 kg CO₂. Meanwhile, for the cotton t-shirt, the average carbon footprint produces 0.015 kg per t-shirt (Sandin et al., 2019). Table 2 summarizes the materials of recyclable litter and the total carbon footprint per gram.

Table 2.

Type of litter and carbon footprint produced.

Type of litter	Carbon footprint (g)	References
Paper (per piece)	647	Yue et al. (2017)
Glass (750 ml)	555	Amienyo et al. (2013)
Aluminium can (330 ml)	312	Amienyo et al. (2013)
Plastic bottle (500 ml)	293	Amienyo et al. (2013)
Rubber/latex (per piece)	210	Usubharatana and Phungrassami (2018)
Cloth (per t-shirt – fibre production)	200	Sandin et al. (2019)

Based on Table 2, estimate calculation will be made based on a number of litters collected from Phase 1. Collected litter in Phase 1 will be categorized based on the type of litter; paper, glass, plastic bottle, rubber/latex and cloth. The total carbon footprint can be estimated by simply multiplying a number of captured litter by the carbon footprint in Table 2. Following equation (8) shows how to calculate the estimated number of carbon footprints.

N_{l} \times carbon footprint (g) = ~ Total carbon footprint (g),

(8)

where N_l is the number of litters.

Results and discussion

Litter pattern identification

Average nearest neighbour is performed on the litter distribution. Geotag data is used as a data input to perform the analysis. Two study areas are presented and compared. The study areas are BBKP and TSPP. A summary of all eight study areas is presented in Table 2.

Bandar Baru Kangkar Pulai

The value of the nearest neighbour ratio is 0.248, which is <1. The ratio indicated that the litter pattern in BBKP is clustered. Based on the report summary, the return value of the z-score is −42.737, which inclines towards the negative sides of standard normal distribution. The (negative) z-score is either very high or very low in the normal distribution, which correlates with a very small p-value. The p-value (probability) obtained for BBKP is 0, which is very small, which means that the observed spatial pattern is the result of random processes and may reject the null hypothesis (Ho). Based on this pattern distribution, it can be concluded that litter at the BBKP study area is concentrated at the exact point location and produces a cluster pattern. Based on this pattern, it is expected that the suggested location of recycle bins will be placed nearby to one another.

Taman Sri Pulai

Another analysis is performed for the litter dataset of the TSP study area. A number of 1814 litters are used as data input. The analysis shows that the pattern of point distribution is clustered. The report summary for the analysis can be obtained from the Table 3. The value of the nearest neighbour ratio is 0.057, which <1. The z-score return value is −58.635, which correlates with the small p-value, which is 0. Thus, the same conclusion can be summarized for the litter data of TSP. The litter is thrown at the exact point location and cause the clustered pattern.

Table 3.

Report summary of nearest neighbour analysis.

Study area	Observed means distance (me)	Expected mean distance (m)	Nearest neighbour ratio	z-Score	Litter pattern	Litter occurrence rank
Bandar Baru Kangkar Pulai	3.2874	13.2217	0.2486	−42.7371	Clustered	3
Kampung Melayu Kangkar Pulai	11.9329	25.7915	0.4626	−16.4472	Clustered	8
Kangkar Pulai	7.3192	21.1161	0.3466	−19.6049	Clustered	7
Sri Pulai Perdana 2	3.7177	13.3570	0.278335	−38.7059	Clustered	5
Taman Pulai Emas	3.6061	12.7671	0.2824	−34.9168	Clustered	4
Taman Sri Pulai	0.8232	14.2663	0.0577	−58.6357	Clustered	1
Taman Sri Pulai Perdana	3.7729	15.3527	0.2457	−46.3990	Clustered	6
Taman Teratai	3.0950	15.8501	0.1952	−46.4410	Clustered	2

The summary report for eight study areas is presented in the following Table 3. Based on the summary, the nearest neighbour ratios for all study areas are <1, indicating that all litters distribution is clustered. The z-value for all study areas is also very low, between −16.447 and −58.635. Since the z-value is relatively small, this explains the consistent value of the p-value for all study areas, which is 0. Thus, the null hypothesis Ho can be rejected, and it can be concluded that litter distribution for all study areas is clustered. The findings are then analysed, and it is determined that litter is concentrated in similar areas, such as in front of businesses and along roadways. Thus, the recycling container should be positioned in this high-traffic area to attract attention and encourage recycling habits.

From Table 3, we can also indicate the litter occurrence based on the observed mean distance of litter distribution. For example, litter can be found at the average of 3-m distance in BBKP and 11-m distance for KMKP. The number of litter occurrences is ranked based on the observed mean distance values. Among these eight study areas, TSP is at the first (1st) rank, where litter can be found at an average distance of 0.8 m. KMKP is at the eighth (8th) rank, where the average distance to find a litter is 11.9 m. This rank clearly shows that litter is concentrated in the study area. Thus, there is a need to strategize to reduce public littering and motivate residents’ recycling habits.

Recycle point identification

The optimal recycle point is identified based on the combination of the k-means clustering algorithm and the Elbow Method of WCSS. Figure 5(a) shows the relationship between the number of clusters and WCSS. The number of clusters k can be identified through the plot. Based on Figure 5(a), the WCSS plot dramatically drops with the increasing value from k = 1 to 2 and from k = 2 to 3, then the optimal cluster elbow is k color4. Then, the distortion goes down gradually and becomes stable. After obtaining the value of k, we can determine the cluster centre point of data using the k-means clustering algorithm. The location of the recycle bin can be proposed based on this cluster centre. The output of the proposed recycle bin locations for BBKP is represented in Figure 5(b). There are four proposed locations for the recycle bin in BBKP study areas (yellow dots). However, the proposed location is considered as raw location. Further verification is needed to confirm the location optimality.

Figure 5.

(a) Within-cluster-sum-square plot and (b) number of proposed recycle bin locations.

The proposed recycle bin locations for another seven study areas are described in Figure 6. Based on the results, it is shown that the Taman Pulai Emas (Figure 6(b)) study areas require more bins placement than other study areas. It is due to the concentration of litter at several spots is high. Furthermore, the boundary area of Taman Pulai Emas is larger than others, which justifies why the area requires more bins than other areas. Based on the concentration, eight group clusters are produced by k-means clustering and WCSS.

Figure 6.

Proposed recycle bin locations (yellow dots) for each study area: (a) produced cluster centre k = 6 for KP, (b) produced cluster centre k = 7 for Taman Pulai Emas, (c) produced cluster centre k = 5 for TSPP 2, (d) produced cluster centre k = 4 for TSP, (e) produced cluster centre k = 6 for TSPP, (f) produced cluster centre k = 5 for TT, (g) produced cluster centre k = 5 for BBKP and (h) produced cluster centre k = 6 for KMKP.

Location verification

Based on the proposed location, several conditions need to be addressed to maximize the suitability of the location. Some of the proposed locations are not suitable because they are located in front of the residential house, on top house unit roof, on the main roads or streets and in an inaccessible area. So, there is a need to suggest a new location of recycling points for better accessibility and the resident’s convenience. Figure 7(a) shows the proposed locations for TSPP recycling bins in the OpenStreetMap. The proposed locations need to be adjusted, as shown in Figure 7(b). The adjusted location of the recycling bins must be within the cluster boundary to maintain the average distance ratio based on point distribution. There are six locations proposed for the TSPP study area, and only three locations which are Point 2, Point 4 and Point 6, need to be adjusted.

Figure 7.

(a) Proposed location of recycling bins and (b) adjusted locations for TSPP.

Total carbon footprint

In this study, the calculation used for the litter does not precisely describe the carbon footprint because the litter is not weighted using designated tools. However, it can be used to estimate the carbon footprint that can be reduced by utilizing the WCPI approach. The result for the total carbon footprint is described in the Table 4. From Table 4, it is shows that the litter from a plastic bottle produced 2731.34 kg of carbon footprint, the highest among others. Meanwhile, rubber-based litter produced the lowest carbon footprint, 17.64 kg. Suppose the WCPI approach is realized and managed to motivate consumers to recycle public litter. In that case, there are possibilities that the total of 3396.89 kg of carbon emissions can be reduced and, at the same time, could save the energy digestive and reduce the use of other natural resources. It is indicated that placing recycle bins at an optimal location is important to support a low carbon footprint. Table 4 shows the total carbon footprint produced for each type of litter in Phase 1.

Table 4.

Carbon footprint produced for each litter.

Type of litter	Number of litter	Carbon footprint (g) per piece	Total carbon footprint (kg)
Paper	2322	647	150.75
Glass	669	555	371.30
Aluminium	293	312	91.46
Plastic bottle	9322	293	2731.34
Rubber	84	210	17.64
Cloth	172	200	34.40
Total	–	–	3396.89

Conclusion

This study introduced a new approach to identifying optimized recycle point distribution; WCPI. WCPI used comprehensive spatial analysis distribution to describe the litter pattern for eight study areas in Johor Bahru, Malaysia. Currently, the local authorities of study areas do not have any specific model to identify the optimal location of the recycle bin. Meanwhile, other practices and studies only consider distance and weight in their model to locate the bin. None of the current models consider the litter pattern distribution for the bin location-allocation model.

Our findings proved that public littering is still happening in the study areas, and most of the litter is recyclable. A total of 11,945 litter are found in the study areas, and most of the litter is PET bottles. The following theoretical implications can be drawn from the litter insights.

Public littering is still common, which contradicts the Theory of Waste Management. Based on the theory, it is expected to manage waste management efficiently to prevent the waste from causing harm to human health and the environment and, at the same time, promote resource use optimization (Pongrácz et al., 2004).

Our findings also confirm that WCPI incorporates clustering approaches to propose the location of the bin. The results show that WCPI successfully processed the data input and the litter distribution and proposed a suitable location based on litter concentration. The number of proposed recycle points may vary depending on the number of litter and litter distribution. However, the location needs to be verified in terms of its suitability and accessibility. Findings from other domains suggest that bin placement may play a significant role in embarking on consumer motivations to dispose of litter in the recycle bin (Geertman and van Gent, 2018). Nevertheless, whether the strategic location will contribute to litter disposable remains to be investigated.

This study also provides an insight into the number and type of litter disposed of in the study area. Based on that information, 3396.89 kg of carbon footprint can be saved if the disposed of litter is sent for recycling. This approach will be in line with the aim of Iskandar Malaysia to achieve a low carbon society as planned in the Low Carbon Society Blueprint for Iskandar Malaysia 2025. Therefore, we recommend that a standardized approach be employed to manage public littering and, at the same time, strategize carbon footprint reduction via waste management.

Despite several findings, we also note that our knowledge measure (particularly on carbon footprint) had rather low reliability since it is based on the estimation and could be improved in future work. Finally, our research focused on litter distribution as a factor in identifying optimal recycling bin’s locations. Future research should consider cross-examination after the execution of the proposed approach.

In conclusion, we believe that the findings of this study could help the local authorities to strategize a plan for a better recycling environment for achieving the SDG 2030. The optimal location is essential to embark on consumers’ motivation to dispose of recycling waste, reduce litter and transform Malaysia into a clean and sustainable nation, aiming to achieve Agenda 2030.

Footnotes

Acknowledgements

The authors would like to express special thanks to the student of Bachelor of Science (Geoinformatics) course SGHG3513 Spatial Analysis – Session 2019/2020 and Bachelor of Engineering (Geomatic) course SGHU4843 Environmental Studies – Session 2018/2019 & 2019/2020 for data collection.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Ministry of Education (MOE) through Fundamental Research Grant Scheme (FRGS/1/2022/WAB07/UTM/02/3) and Universiti Teknologi Malaysia Industry/International Incentive Grant (IIIG) ‘Spatial Classification and Clustering of Waste Recycle Point Distribution Towards Green City Initiatives’, Vot Q.J130000.3652.02M78.

ORCID iD

Suhaibah Azri

References

Amienyo

Gujba

Stichnothe

, et al. (2013) Life cycle environmental impacts of carbonated soft drinks. The International Journal of Life Cycle Assessment 18: 77–92.

Anagnostopoulos

Kolomvatsos

Anagnostopoulos

, et al. (2015) Assessing dynamic models for high priority waste collection in smart cities. Journal of Systems and Software 110: 178–192.

Arthur

Vassilvitskii

(2006) How slow is the k-means method? In: Proceedings of the twenty-second annual symposium on computational geometry, SCG’06, 5–7 June 2006, Sedona, Arizona, USA, pp. 144–153. New York, NY: ACM.

Azri

Ujang

Abdul Rahman

(2019) 3D Geo-clustering for wireless sensor network in smart city. In: 5th International Conference on Geoinformation Science – GeoAdvances 2018, 10–11 October 2018, Casablanca, Morocco, pp. 11–16.

Azri

Ujang

Castro

, et al. (2016a) Classified and clustered data constellation: An efficient approach of 3D urban data management. ISPRS Journal of Photogrammetry and Remote Sensing 113: 30–42.

Azri

Ujang

Rahman

, et al. (2016b) 3D geomarketing segmentation: A higher spatial dimension planning perspective. In: International Conference on Geomatic and Geospatial Technology (GGT) 2016, 3–5 October 2016, Kuala Lumpur, Malaysia, pp. 1–7.

Bahçelioğlu

Buğdaycı

Doğan

, et al. (2020) Integrated solid waste management strategy of a large campus: A comprehensive study on METU campus, Turkey. Journal of Cleaner Production 265: 121715.

Bergman

(2017) Improving the Location of Existing Recycling Stations Using GIS. Stockholm: KTH.

Boskovic

Jovicic

(2015) Fast methodology to design the optimal collection point locations and number of waste bins: A case study. Waste Management & Research 33: 1094–1102.

10.

Boskovic

Jovicic

Jovanovic

, et al. (2016) Calculating the costs of waste collection: A methodological proposal. Waste Management & Research 34: 775–783.

11.

Cao

Chen

Shi

, et al. (2016) WEEE recycling in Zhejiang Province, China: generation, treatment, and public awareness. Journal of Cleaner Production 127: 311–324.

12.

Chung

(2019) Study shows low rate of recycling plastic bottles in M’sia. The Star, 14 November.

13.

Cohen-Addad

Klein

Mathieu

(2019) Local search yields approximation schemes for k-means and k-median in Euclidean and minor-free metrics. SIAM Journal on Computing 48: 644–667.

14.

Conke

(2018) Barriers to waste recycling development: Evidence from Brazil. Resources, Conservation and Recycling 134: 129–135.

15.

Department of Statistics Malaysia (2020) Press Release Compendium of Environment Statistics. Putrajaya: Department of Statistics Malaysia.

16.

Digiacomo

DWL

Lenkic

, et al. (2018) Convenience improves composting and recycling rates in high-density residential buildings. Journal of Environmental Planning and Management 61: 309–331.

17.

Ebrahimi

North

Yan

(2017) GIS applications in developing zero-waste strategies at a mid-size American university. In: 2017 25th international conference on geoinformatics (ed IEEE), 2–4 August, pp. 1–6. Buffalo, NY: CPGIS.

18.

Economic Planning Unit, Malaysia (2017) Malaysia Sustainable Development Goals Voluntary National Review 2017. Putrajaya: Economic Planning Unit, Malaysia.

19.

El-Hallaq

Mosabeh

(2019) Optimization of municipal solid waste management of bins using GIS. A case study: Nuseirat City. Journal of Geographic Information System 11: 32.

20.

Erfani

SMH

Danesh

Karrabi

, et al. (2017) A novel approach to find and optimize bin locations and collection routes using a geographic information system. Waste Management & Research 35: 776–785.

21.

Friggstad

Rezapour

Salavatipour

(2019) Local search yields a PTAS for k-means in doubling metrics. SIAM Journal on Computing 48: 452–480.

22.

García

Crawford

Soto

, et al. (2018) A k-means binarization framework applied to multidimensional knapsack problem. Applied Intelligence 48: 357–380.

23.

Geertman

Van Gent

(2018) Circulair Wood for the Neighbourhood: Understanding Willingness to Recylce Wood in the Dapperbuurt, Amsterdam. Amsterdam: Hogeschool van Amsterdam.

24.

Genc

Zeydan

Sarac

(2019) Cost analysis of plastic solid waste recycling in an urban district in Turkey. Waste Management & Research 37: 906–913.

25.

(2018) Exploring the social dimensions of multi-residential recycling. Resources, Conservation and Recycling 132, 77–78.

26.

Hughes

MÜ

Mcconnell

Groner

(2019) A community-based social marketing anti-littering campaign: Be the street you want to see. In: Social Marketing in Action, pp. 339–358. Cham: Springer.

27.

Jereme

Chamhuri

Alam

(2014) Waste recycling in Malaysia: Transition from developing to developed country. Indian Journal of Education and Information Management 4: 1–14.

28.

Jesri

Saghafipour

Koohpaei

, et al. (2021) Mapping and spatial pattern analysis of COVID-19 in central Iran using the Local Indicators of Spatial Association (LISA). BMC Public Health 21: 2227.

29.

Kattoua

Al-Khatib

Kontogianni

(2019) Barriers on the propagation of household solid waste recycling practices in developing countries: State of Palestine example. Journal of Material Cycles and Waste Management 21: 774–785.

30.

Kim

Kiniry

(2018) Two-phase simulation-based location-allocation optimization of biomass storage distribution. Simulation Modelling Practice and Theory 86: 155–168.

31.

Leeabai

Areeprasert

Khaobang

, et al. (2021) The effects of color preference and noticeability of trash bins on waste collection performance and waste-sorting behaviors. Waste Management 121: 153–163.

32.

Letelier

Blazquez

Paredes-Belmar

(2021) Solving the bin location–allocation problem for household and recycle waste generated in the commune of Renca in Santiago, Chile. Waste Management & Research: The Journal for a Sustainable Circular Economy 40: 154–164.

33.

Pang

Zhang

, et al. (2020) Mapping urban spatial structure based on POI (point of interest) data: A case study of the central city of Lanzhou, China. ISPRS International Journal of Geo-Information 9: 92.

34.

Matsumoto

(2018) Time allocation and recycling activities. Journal of Material Cycles and Waste Management 20: 2062–2067.

35.

Miller

Meindl

Caradine

(2016) The effects of bin proximity and visual prompts on recycling in a university building. Behavior and Social Issues 25: 4–10.

36.

Munguía-López

ADC

Zavala

Santibañez-Aguilar

, et al. (2020) Optimization of municipal solid waste management using a coordinated framework. Waste Management 115, 15–24.

37.

Nagalingeswari

Satamraju

(2017) Efficient garbage management system for smart cities. International Journal of Engineering Trends and Technology (IJETT) 50: 260–265.

38.

Nainggolan

Perangin-Angin

Simarmata

, et al. (2019) Improved the performance of the K-means cluster using the sum of squared error (SSE) optimized by using the Elbow Method. Journal of Physics: Conference Series 1361, 012015.

39.

Nidheesh

Abdul Nazeer

Ameer

(2017) An enhanced deterministic K-means clustering algorithm for cancer subtype prediction from gene expression data. Computers in Biology and Medicine 91: 213–221.

40.

Perumal

Boopathi

Chellaiyan

, et al. (2021) Sources, spatial distribution, and abundance of marine debris on Thondi coast, Palk Bay, Southeast coast of India. Environmental Sciences Europe, 33: 136.

41.

Pongrácz

Phillips

Keiski

(2004) Evolving the theory of waste management: defining key concepts. In: Popov

Itoh

Brebbia

Kungolos

(eds) Waste Management and the Environment II, pp. 471–480.

42.

Rathore

Sarmah

Singh

(2019) Location–allocation of bins in urban solid waste management: A case study of Bilaspur city, India. Environment, Development and Sustainability 22: 3309–3331.

43.

Regla

Pour Yousefian Barfeh

Hernandez

(2019) Clustering of riding in tandem incidents using K-means: A case study in the Philippines. In: Conference: 2019 international conference on computational intelligence and knowledge economy (ICCIKE), Dubai, 11–12 December 2019. Amity University Dubai.

44.

Sandin

Roos

Spak

, et al. (2019) Environmental assessment of Swedish clothing consumption – six garments, sustainable futures. Mistra Future Fashion report number 2019:05. Mistra Future Fashion.

45.

Scott

(2015) Spatial Pattern, Analysis of. In: Wright

(ed.) International Encyclopedia of the Social & Behavioral Sciences (2nd ed.).

46.

Sheau-Ting

Sin-Yee

Weng-Wai

(2016) Preferred attributes of waste separation behaviour: An empirical study. Procedia Engineering 145: 738–745.

47.

Stoeva

Alriksson

(2017) Influence of recycling programmes on waste separation behaviour. Waste Management 68: 732–741.

48.

Struk

(2017) Distance and incentives matter: The separation of recyclable municipal waste. Resources, Conservation and Recycling 122: 155–162.

49.

Tibshirani

Walther

Hastie

(2001) Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63: 411–423.

50.

Tiew

Basri

NEA

Deng

, et al. (2019) Comparative study on recycling behaviours between regular recyclers and non-regular recyclers in Malaysia. Journal of Environmental Management 237: 255–263.

51.

Tîrnăucă

Gómez-Pérez

Balcázar

, et al. (2018) Global optimality in k-means clustering. Information Sciences 439: 79–94.

52.

United Nations (2020) SDG indicators [Online]. Available at: https://unstats.un.org/sdgs/metadata?Text=&Goal=12&Target=12.5 (accessed 28 May 2021).

53.

Usubharatana

Phungrassami

(2018) Carbon footprints of rubber products supply chains (Fresh latex to rubber glove). Applied Ecology and Environmental Research 16: 1639–1657.

54.

Kaddoum

(2017) A waste city management system for smart cities applications. In: 2017 Advances in Wireless and Optical Communications (RTUWO), Riga, Latvia, 2–3 November 2017, pp. 225–229.

55.

Wang

Gittens

Mahoney

(2019) Scalable kernel K-means clustering with Nyström approximation: relative-error bounds. The Journal of Machine Learning Research 20: 431–479.

56.

Zhang

, et al. (2018) The spatial distribution pattern of enterprises in Beijing and its influencing factors analysis based on POI data. Chinese Sociological Dialogue 3: 148–159.

57.

Xiao

Dong

Geng

, et al. (2018) An overview of China’s recyclable waste recycling and recommendations for integrated solutions. Resources, Conservation and Recycling 134: 112–120.

58.

Yue

Cai

, et al. (2017) Carbon footprint of copying paper: considering temporary carbon storage based on life cycle analysis. Energy Procedia 105: 3752–3757.

59.

Zhang

(2020) The spatial pattern and influencing factors of tourism development in the Yellow River Basin of China. PLOS One 15: e0242029.

60.

Zhao

Deng

Ngo

(2018) k-means: A revisit. Neurocomputing 291: 195–206.

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

Abstract

Keywords

Introduction

Materials and methods

Phase 1: Geotagging litter distribution and data profiling

Phase 2: Litter pattern identification

Phase 3: Data clustering

Carbon footprint estimation

Results and discussion

Litter pattern identification

Bandar Baru Kangkar Pulai

Taman Sri Pulai

Recycle point identification

Location verification

Total carbon footprint

Conclusion

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

ORCID iD

References