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

Author

Listed:
  • Shixiong Xu

    (School of Built Environment, University of New South Wales Sydney, Sydney, NSW 2052, Australia)

  • Sara Shirowzhan

    (School of Built Environment, University of New South Wales Sydney, Sydney, NSW 2052, Australia)

  • Samad M. E. Sepasgozar

    (School of Built Environment, University of New South Wales Sydney, Sydney, NSW 2052, Australia)

Abstract

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.

Suggested Citation

  • Shixiong Xu & Sara Shirowzhan & Samad M. E. Sepasgozar, 2023. "Urban Waste Management and Prediction through Socio-Economic Values and Visualizing the Spatiotemporal Relationship on an Advanced GIS-Based Dashboard," Sustainability, MDPI, vol. 15(16), pages 1-38, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12208-:d:1213966
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    References listed on IDEAS

    as
    1. Agovino, Massimiliano & Musella, Gaetano, 2020. "Separate waste collection in mountain municipalities. A case study in Campania," Land Use Policy, Elsevier, vol. 91(C).
    2. Khanali, Majid & Ghasemi-Mobtaker, Hassan & Varmazyar, Hossein & Mohammadkashi, Naghmeh & Chau, Kwok-wing & Nabavi-Pelesaraei, Ashkan, 2022. "Applying novel eco-exergoenvironmental toxicity index to select the best irrigation system of sunflower production," Energy, Elsevier, vol. 250(C).
    3. Keihan Hassanzadehkermanshahi & Sara Shirowzhan, 2022. "Measuring Urban Sustainability over Time at National and Regional Scale for Addressing United Nations Sustainable Development Goal (SDG) 11: Iran and Tehran as Case Studies," Sustainability, MDPI, vol. 14(12), pages 1-25, June.
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