UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Modelling land use using demographic forecasting and local optimisation: A case study of general education provision in Riyadh, Saudi Arabia

Altuwariki, Salman; (2023) Modelling land use using demographic forecasting and local optimisation: A case study of general education provision in Riyadh, Saudi Arabia. Doctoral thesis (Ph.D), UCL (University College London). Green open access

[thumbnail of Alutwariki_10167551_ Thesis.pdf]
Preview
Text
Alutwariki_10167551_ Thesis.pdf

Download (5MB) | Preview

Abstract

Globally accepted guidelines for land use allocation in Riyadh, Saudi Arabia have been based on an outmoded practice that was created over a century ago. This approach is based on a mix of predetermined population densities, walking distances, and per person area ratios. The latter criterion is essentially based on a worldwide average for facility areas and user numbers. The fundamental criticism levelled at such practices is their insensitivity to population trends and limited land resources. In this context, this research is aimed at updating common practice in the light of population growth and residential mobility projections at the city and district levels. The models introduced aim to provide comprehensive and adaptable simulation tools for optimising any type of land use provision standard over a specified time period. The simulation environment makes use of an agent-based framework that adapts and integrates a number of well-known methodologies, including Cohort Component Modelling (CCM) for population projection, Spatial Interaction (SI) modelling for residential mobility, and AutoRegressive Integrated Moving Average (ARIMA) for various ratio extrapolation. Additionally, new hybrid concepts and approaches have been evaluated, including a household based CCM and the use of Neural Network algorithms (NN) to forecast residential mobility. The case study focuses on Saudi populations in Riyadh, Saudi Arabia where the three general education stages at elementary, middle, and secondary levels were optimised for both genders. Moreover, the optimisation time horizon spans 50 years, from 2020 to 2070 while the focus of research at the city level optimises the conventional ratio of area per student based on the present stock of education allocated land and a land consumption ratio defined for every five years. The district level optimisation, on the other hand, balances the demand and supply of education over 50 years by utilising the Ministry of Education's (MOE) predesigned school prototypes. The research findings demonstrate the feasibility of developing a tool for optimising land use guidelines that is capable of producing acceptable outcomes while being sensitive to demographic change and land resource availability.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Modelling land use using demographic forecasting and local optimisation: A case study of general education provision in Riyadh, Saudi Arabia
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2023. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/10167551
Downloads since deposit
85Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item