Mapping of city growth with socio-economic growth data

• Main Author: Mun, Cassandra Ji Kay
• Other Authors: Lo Yat-Man, Edmond
• Format: Final Year Project
• Language: English
• Published: 2018
• Subjects: DRNTU::Engineering::Civil engineering
• Online Access: http://hdl.handle.net/10356/74555
• Institution: Nanyang Technological University

In Southeast Asia, the urban population increased from 32% to 47% between 1990 and 2014, signifying a rapid trend of urbanization (United Nations, 2014) and the growth of cities. Their growth entails increasing urban populations and assets that are exposed and vulnerable to the impacts of natural disasters. Hence, extensive and quality data on the trends of city growth are crucial for the development of effective urban plans and introduction of appropriate risk mitigation measures. In this study, the reported Capital Stock data of physical structures as the selected indicator of exposed assets is downscaled from a national level to a 1km by 1km grid resolution using three different indicators (i) Population, (ii) Land Cover and (iii) Road Network Density. The objectives are to (1) track urban growth patterns and (2) develop a suitable estimator of Capital Stock exposed with the indicators available. Year 2000, 2010 and 2020 are selected to track the trends over two ten-year periods and a twenty-year period for the specific Southeast Asian cities of Jakarta and Manila. It was found that Capital Stock distribution by Land Cover and Road Network Density are more homogenous in comparison to distribution by Population. Grids with higher Capital Stock proportions relative to the total Capital Stock value are concentrated around the central metropolitan regions. Percentage of Capital Stock of a district in the city (proportion of Capital Stock relative to city’s total Capital Stock value) is generally proportional to its percentage area (proportion of district’s area relative to city’s total area). Capital Stock Growth is observed to be independent of Population size.The variability of Capital Stock values with different indicators was useful in finding out if a suitable indicator or a combination of more than one could be chosen to be a Capital Stock estimator. However, as correlation between cities differ, the results were city-specific and inconclusive to select a suitable indicator and more cities need to be studied.