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(2024)ThesisIn the era of ‘Big data’, the implementation of the Smart and Sustainable City concept is progressively being recognized as a pivotal trend of urban management and city governance. Through growing cities horizontally or vertically, the quantity of waste management has become a complex challenge with the acceleration of population growth in cities. From the classification of waste, household waste plays a crucial role in citizens’ lives. Available datasets in the State of NSW, Australia shows the variation of data for household waste between 2014 and 2019, which was categorized into residual, recyclable, organics (RRO) waste, in the local government areas (LGAs). While the visualization of accurate and historical spatial data is the key to gleaning insights from waste stream datasets, a significant gap exists in comprehensive spatio-temporal waste data considering various types of waste data such as residual, recyclable and organic waste. In addition, there is lack of GIS dashboard to make this spatio-temporal analysis available and accessible by inclusion of web maps and interactive analytics results. This type of dashboard could enable decision makers to analyse historical waste tonnage trends and connect socio-economic metrics to the same spatial context. By presenting the results of spatial analysis, these dashboards can elucidate the spatio-temporal patterns, thereby enhancing the interpretability of waste stream data for partitioners and policymakers and linking to socio-economic metrics in local communities. The objectives of this research were multifaceted: first, to visualize the RRO waste data in space and time in specific year, second, to conduct a series of spatio-temporal analysis, identify the spatial patterns in RRO waste over a year; third, to explore spatial relationships with socio-economic metrics including personal income, population, no. of income earners (ABS, 2020), and land values. The study identified the statistical significance between multiple metrics and waste generation. In addition, the fourth aim is to develop a dashboard for the inclusion of such information and insight from waste data. To achieve these aims, this thesis designed a methodology comprising geocoding, data pre-processing in python, use of thematic map, space-time cube, emerging hot spot analysis, relationship map, Pearson Correlation, and dashboard development. The findings of the spatio-temporal analysis for waste data in NSW shows the variation of data in household waste between 2014 and 2019, which was divided into the three categories of RRO waste in each LGA. The emerging hot spots are in Greater Sydney, Wollongong, Newcastle, and Tweed and there is a cold spot in Wagga Wagga. Also, a high correlation is in the relationship map between RRO waste and population growth over the six years. The low level of correlation is between RRO waste and land values annually. The developed analytical dashboard displays the outcome emerging hot spots analysis and spatial temporal analysis through ArcGIS Insight. Meanwhile, the thematic maps and relationship maps in Spatial Waste Visualization Dashboard (SWVD) from ArcGIS Experience Builder, can be accessed in an interactive way. Once the RRO waste datasets are connected to socio-economic metrics, the relationship and correlation to waste behaviour pattern will display valuable insight for local communities. Through the short-term analytics, the spatial relationships between waste tonnage and the social-economic metrics could predict the trends of household waste tonnage. In addition, the dashboard development would show the spatial pattern in historical waste tonnage monitoring and future prediction in the short term. There are potential research opportunities in waste stream data with higher resolution datasets.