Spatial dynamic modeling of urban areas :a case study of Thimphu, Bhutan | |
| Author | Dorji, Yeshi |
| Call Number | AIT Thesis no.RS-06-10 |
| Subject(s) | Cities and towns--Bhutan--Thimphu--Growth City planning--Bhutan--Thimphu |
| Publisher | Asian Institute of Technology |
| Series Statement | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science |
| Abstract | It is estimated that about 20% of Bhutan's population today lives in urban areas, majority of it in Thimphu. Urban growth of Thimphu is largely prompted by rapid population growth, driven by rural-urban migration and infrastructure developments. Between 1990 and 2002 urban area has increased by 25% with corresponding population growth by 32%. By 2020 urban area of Thimphu will increase by 72%. Urban growth planning must be founded on the clear understanding of trends and future scenarios. The aim of modeling spatial dynamics of urban areas is to predict future scenarios based on spatial-temporal trends. Due to complex non linear nature of urban systems urban growth models have never been free from errors and uncertainties. Remote Sensing and GIS have greatly enhanced data collection, processing and eventually improved predictive capabilities of urban growth models. Statistical based Binary Logistic Regression model and the principle of CA were integrated to handle urban spatial complexity in a better way. Binary Logistic Regression analyzed relationships of predictors to establish coefficients and CA based neighborhood counting handled the inherent influences of complex relationships to compute transition probabilities. Corresponding values of slope, road and urban neighbors against urban status of cells in 2002 were recorded in LUT and analyzed by using Binary Logistic Regression. The cells were urbanized in descending order of transition probabilities; probabilities of becoming urban. It was found that as the difference between base year and predict year widens, cells with transition probabilities less than 0.5 were also urbanized, which contradicts the default transition cut-off value of binary logistic regression. Therefore, base year and predict year were sequentially increased with one year difference |
| Year | 2006 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing (RS) |
| Chairperson(s) | Chen, Xiaoyong |
| Examination Committee(s) | Samarakoon, Lal;Susaki, Junichi;Taravudh Tipdecho |
| Scholarship Donor(s) | Government of Austria |
| Degree | Thesis (M.Sc.) - Asian Institute of Technology, 2006 |