Saudi Cultural Missions Theses & Dissertations
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Item Restricted Spatio-temporal Modelling of Irish House Prices(University of Sheffield, 2024) Algadheb, Maha; Johnson, JillIreland, a prosperous nation in Western Europe, has experienced great instability in its property market in recent years. However, the nature, variance, and causative aspects of the recent increases in housing prices in the Republic of Ireland are poorly understood. Thus, this work uses statistical modelling techniques to define the temporal, geographical, and combination space-time patterns of housing prices in Ireland. Irish house price data have been collected for this study. First, several temporal models were used to analyse trends in Irish house prices, incorporating temporal components, and predictions. Among these models, the SARIMAX model was identified as the best fit, demonstrating the highest goodness of fit for Irish housing prices. The analysis revealed a decline in house prices from 2010 to 2013 but, followed by a steady increase, with some seasonal variations peaking in the third quarter. Second, a variety of geographically weighted regression (GWR) models with different distance kernel functions were applied to capture spatial effects and factors that influence house prices in Ireland. Diagnostic indicators showed that the GWR model with a bi-square kernel function and adaptive bandwidth was the most appropriate for fitting and analysing the spatial variability in house prices.The study demonstrated that the heterogeneity of spatial factors across regions or spatial units can be effectively captured by GWR models. Finally, a combined spatio-temporal modelling approach was investigated, simultaneously capturing temporal and spatial effects. Geographically and temporally weighted regression (GTWR) was used to account for both spatial and temporal correlations and dependencies. The study found that the most effective model for representing the fluctuations in the Irish housing market is the GTWR model, featuring an exponential kernel function and adaptive bandwidth. To evaluate the relative strengths of each approach, the study compared the performance of the temporal (SARIMAX), spatial (GWR), and spatio-temporal (GTWR) models. Although each model provided distinct insights, the comparison showed that the GTWR model performed better overall in capturing fluctuations in the Irish housing market, highlighting the significance of considering both spatial and temporal dimensions simultaneously. This research offers valuable insights for understanding the intricate variations in the Irish housing market. Specifically, it highlights the importance of considering both time trends and regional differences when analysing house prices. The findings could be useful for real estate professionals or anyone interested in understanding the Irish property market.9 0Item Restricted Hierarchical Bayesian Modelling of Spatio-Temporal Air Pollution Patterns in Athens, Greece(University of Sheffield, 2024) Aldawsari, Hilah; Johnson, Jill SAir pollution is one of the biggest health risks of our time, mainly due to industrial development worldwide. Persistent exposure to air pollution causes many severe diseases, including lung cancer. It also is responsible for millions of deaths annually worldwide. As a result, various researchers have been developing models to estimate air pollution levels throughout the past decades, aiming to better understand their dynamics. These models can also be used to forecast air pollution levels. This forecasted information is crucial for enabling relevant authorities to adopt precautionary measures and to alert the public in advance, especially when the levels are very harmful to human health. Up to date, modelling air pollution is a challenging task due to the various factors that affect its behaviour. One advanced field that enables the modelling of air pollution levels is spatio-temporal modelling. The main focus of this dissertation is to develop a spatio-temporal model to estimate parameters and forecast air pollution levels across different locations in Athens, Greece. Among various pollutants, this work focuses on exploring the ozone (O3) pollutant, along with three meteorological variables, to examine their influence on O3 levels. Two statistical approaches were developed for modelling and forecasting monthly concentrations of O3 in Athens, Greece. The first approach is based on time series modelling using seasonal autoregressive integrated moving average (SARIMA) and seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) models. The second approach is based on spatio-temporal modelling using hierarchical Bayesian spatiotemporal Gaussian process (GP) models. This model has the ability to capture the complex dynamic of O3 levels, which vary based on several factors, including geographic location and meteorology. This model also has the ability to provide predictions for both space and time. Both SARIMA and SARIMAX models showed a reasonable fit of the data. The SARIMAX outperformed SARIMA models in both training and testing tests in most stations. The hierarchical Bayesian spatio-temporal GP model fits the data well and shows reasonable performance in both forecasting and prediction abilities, with a spatial decay parameter ϕ of 0.13 corresponding to an effective range of 23 km. All statistical data analysis in this dissertation was implemented using the statistical software R.21 0