Separate waste collection in mountain municipalities. A case study in Campania

Musella, Gaetano

doi:10.1016/j.landusepol.2019.104408

Cited by 21 publications

(15 citation statements)

References 57 publications

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“…Using weighted regression, morphological and residential dispersion is analysed. A Campania study compared mountain town waste collection with the impact of land characteristics [32], especially how morphology and housing distribution affect separate waste collection (SWC) in mountain communities. In sparsely populated regions, socio-economic variables support the large geographical variation of land characteristics.…”

Section: Review Of Urban Waste Relationships Digital Platforms and Po...mentioning

confidence: 99%

Urban Waste Management and Prediction through Socio-Economic Values and Visualizing the Spatiotemporal Relationship on an Advanced GIS-Based Dashboard

2023

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Enhancing data-driven decision-making is vital for waste authorities. Although few studies have explored the influence of socio-economic indicators on waste tonnage, comprehensive analysis of urban waste data focusing on geographical information is also scarce. There is a dearth of dashboards for visualizing waste tonnage with spatial relationship maps. This study aims to present a prediction model useful for estimating urban waste by using personal income (I), the number of income earners (E), land values (L), the estimated resident population (P) and population density (D), called the IELPD measures. An innovative approach is developed to identify the correlation between urban household waste data and socio-economic factors and develop an advanced dashboard based on a geographic information system (GIS). To accomplish this, relationship maps and regression analysis are deployed to visualize household waste data spanning six years of waste production in New South Wales, Australia, classified into three categories: recyclable, residual and organic (RRO) wastes. Furthermore, this classification enables accessing the association between these three waste categories and the IELPD metrics. And there are four types of visualization generated, that is, thematic mapping, spatial relationship maps, correlation matrices and dashboard development. The regression analysis shows a substantial association between RRO waste tonnage, population changes and a minor correlation with land values. Overall, this study contributes to urban waste data storytelling and its spatiotemporal associations with socio-economic determinants. This paper offers a valuable prediction model of the IELPD metrics to estimate urban waste and visualize them in a dashboard allowing practitioners and decision-makers to track trends in the RRO waste stream in urban waste generally.

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Section: Review Of Urban Waste Relationships Digital Platforms and Po...mentioning

confidence: 99%

Urban Waste Management and Prediction through Socio-Economic Values and Visualizing the Spatiotemporal Relationship on an Advanced GIS-Based Dashboard

2023

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“…Many factors contribute to higher illegal disposal rates. As stated by many researchers, the most common reason is poor waste management infrastructure, such as waste collection facilities and transportation [6][7][8]. Therefore, providing infrastructure may be a solution to reduce these business-as-usual (BAU) practices [9].…”

Section: Introductionmentioning

confidence: 99%

“…These regression models include spatial dependence in the analysis [15]. Agovino and Musella have found several factors that may influence separation activity in mountainous areas [7]. Antczak and Keser et al used the GWR model to predict several independent variables that may affect municipal waste production in Poland and Turkey [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…OLS and SAR can be used to develop a global model appropriate for more extensive spatial data, such as a country or province, where the GWR can explore local variables. Therefore, good model validation will be established by employing GWR analysis in the local context [7].…”

Section: Introductionmentioning

confidence: 99%

“…This study aims to identify the factors that may influence the dumping practices in Semarang City using a geographic information system (GIS) by using subdistrict data collected from the municipal government of Semarang City. No previous researchers have worked on this topic, and most have used spatial analysis tools to find the determinants for waste generation and separation only [7,[17][18][19]. The number of illegal dumping piles, considered illegal dumping generation, is estimated using Ramadan et al's study [1].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Factors Influencing Illegal Waste Dumping Generation Using GIS Spatial Regression Methods

et al. 2023

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Illegal municipal waste dumping practices in developing countries may be impacted by many factors such as socioeconomic, demographic, availability of waste collection facilities, recycling sites, and spatial characteristics. This study uses spatial regression analysis to identify which factors primarily impact illegal waste dumping practices. For this purpose, 8 variables explain the data for the 177 subdistricts used in the spatial regression analysis. This study used ordinary least squares (OLS) and geographically weighted regression (GWR) methods to build a regression model of the factors identified. OLS analysis showed that only elevation and population density were found to become determinants of illegal waste dumping activity based on spatial regression methods. Elevation above sea level is positively correlated while population density is negatively correlated with the number of illegal dumping generations. GWR shows a better statistical value than OLS, where the significance of the adjusted R-square increased from 0.24 to 0.61. This study may help reduce the number of illegal waste dumping practices, especially in a metro city context.

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Convergence or Divergence in Waste Treatment Methods? The Impact of Waste Management Legislation in the Transition to the Circular Economy Model

Musella¹

2023

SpringerBriefs in Environmental Science

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Separate waste collection in mountain municipalities. A case study in Campania

Cited by 21 publications

References 57 publications

Urban Waste Management and Prediction through Socio-Economic Values and Visualizing the Spatiotemporal Relationship on an Advanced GIS-Based Dashboard

Urban Waste Management and Prediction through Socio-Economic Values and Visualizing the Spatiotemporal Relationship on an Advanced GIS-Based Dashboard

Analysis of Factors Influencing Illegal Waste Dumping Generation Using GIS Spatial Regression Methods

Convergence or Divergence in Waste Treatment Methods? The Impact of Waste Management Legislation in the Transition to the Circular Economy Model

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