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|Title:||Modelling and assessing linkages between urban growth and road network for Southeast Asian cities||Authors:||Kamarajugedda, Shankar Acharya||Keywords:||Engineering::Civil engineering::Spatial information/surveying||Issue Date:||2020||Publisher:||Nanyang Technological University||Source:||Kamarajugedda, S. A. (2020). Modelling and assessing linkages between urban growth and road network for Southeast Asian cities. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||The key objectives in this PhD research are to analyze and quantify the spatio-temporal urban growth dynamics, specifically growth in urban area for major Southeast Asian (SEA) cities and also to develop quantitative relationships amongst urban growth, city infrastructure and socio-economic indicators. To meet the objectives, a multi-stage approach was used to model and analyze urban growth in SEA cities. Firstly, a macro-scale analysis was used to assess spatio-temporal urban growth using remotely sensed night-time light (NTL) data. This analysis using yearly NTL data from 1992 to 2012 resulted in identification and assessment of urban growth patterns for 15 SEA cities and also the extraction of three urban categories (specifically, countryside (CS), peri-urban and (PU) core-urban (CU)) using a brightness gradient (BG) approach for each city. The PU and CU urban categories were generally found to increase over time whereas CS urban category decreased implying significant spatial and temporal trends in urbanization. Significant CS to PU transitions were evident in all the studied cities. Most cities have negligible CS-CU transition. The urbanization trends showed strong correlation with the population growth for the SEA cities with inland cities showing an unrestrained spatial growth with population. The studied SEA cities also expanded spatially into the surrounding countryside generally following a linear trend with population increase. A further multi-scale analysis was performed to assess correlations between urban expansion, road infrastructure and socioeconomic indicators such as GDP and population at inter-city and intra-city scales. This analysis was performed at a higher spatial resolution using remotely sensed Landsat data over a 30-year time change period using two years of 1987 and 2017 in extracting the urban expansion patterns for 9 major SEA cities. Strong correlations were identified amongst urban growth, road infrastructure, population and GDP between cities at inter-city scale. Furthermore, the intra-city analysis results showed that the logarithmic of the intra-urban expansion rate follows an inverted concave pattern with road density, with the rising/falling limbs indicating gradual increase/decrease of the expansion rate. A “turning point” where the curve starts dipping is shown to differentiate amongst different grades of urban expansion. These turning point thresholds were further used to develop zoning maps within each city highlighting different intra-city urban expansion processes. Similar trends were also identified between the logarithmic population growth rates with road density. Lastly, considering Bangkok as a case study, an urban growth prediction model (SLEUTH – Cellular Automata) was applied using the same 30 year temporal datasets to identify the future urban scenarios and to correlate the projected urban expansion patterns (future year of 2027) w.r.t. the linkages identified. The results show that Bangkok has a radially outward growing trend with around 287.6 km2 expanded area over a historical 30-year (1987-2017) period, and with a SLEUTH predicted expanded area of 129.9 km2 over 2017 to 2027. This case study led to better understanding of the future infrastructure demand and expansion zones. Overall, the linkages amongst urban expansion, road infrastructure and socio-economic indicators such as these developed in the PhD research shed light on the developmental processes for SEA cities which, in turn can inform on the planning for sustainable and resilient developmental activities. This is especially relevant for developing economies, such as in improving their land use efficiency and infrastructure planning.||URI:||https://hdl.handle.net/10356/140554||DOI:||10.32657/10356/140554||Rights:||This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
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Updated on Oct 28, 2021
Updated on Oct 28, 2021
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