The expansion of natural catastrophe exposures in East Asia (Pearl River Delta)

• Main Author: Wang, Tinghao
• Other Authors: Pan Tso-Chien
• Format: Final Year Project
• Language: English
• Published: 2017
• Subjects: DRNTU::Engineering::Civil engineering
• Online Access: http://hdl.handle.net/10356/71143
• Institution: Nanyang Technological University

Pearl River Delta, being the world largest megacity, is undergoing rapid urbanisation. This study aims to predict and estimate the growth of the cities in Pearl River Delta Economic Zone, in terms of population in the year 2030. The method of doing so is similar to the method done by Bettencourt et al based on the article “Growth, innovation, scaling, and the pace of life in cities”. As the method was not applied to Pearl River Delta Economic Zone before, therefore it is interesting to see whether the result correlates with the result already achieved. The first step is to identify the urban indicators to be used in this study and find the relevant data from the statistical yearbooks of the cities. The population growth is related to scaling exponent, β. The β value determines the potential of city growth. The next step is to obtain the β value for each urban indicator. The result showed that for urban indicators associated with wealth and innovation, most of β value is high (> 2) instead of the β = 1-1.5 range in the article. For urban indicators associated with individual human needs, only partial of it exhibit similar characteristic with the article. The reason for such error may be the small number of sample size. The limitation of the study is no urban indicators associated with infrastructure is used, thus no comparison can be made with the article to verify its validity. The result also showed that those already established cities with high GDP and population such as Guangzhou will have less potential of growth and smaller developing cities such as Zhaoqing will have higher potential of growth. The next step is the apply the urban growth equation to predict the population size in year 2030. This is done using an iterative process to determine R value and then fix E value to obtain predicted population, N(t). The results showed that different urban indicators produced different population growth trend which lead to an inconclusive result. The time period is too short to see the trend of population growth in the long term to determine the point of time representing year 2030 for different β classification. The E value would affect the population growth trend thus it should not be fixed to same value for all cases. Preliminary population forecast is done using simple growth model to provide a rough population estimation. N(t) from preliminary forecast is used to adjust E value and predict values of urban indicators in year 2030.