000			02153nam a22002537a 4500
003			ZW-GwMSU
005			20240910131520.0
008			240910b |||||||| |||| 00| 0 eng d
022			_a07339488
040			_aMSU _bEnglish _cMSU _erda
050	0	0	_aHT169 JOU
100	1		_aGao, Yujie _eauthor
245	1	0	_aProcess modeling for urban growth simulation with cohort component method, cellular automata model and GIS/RS : _bcase study on surrounding area of Seoul, Korea/ _ccreated by Yujie Gao and Dae-Sik Kim
264	1		_aReston : _bASCE, _c2016.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_aJournal of urban planning and development _vVolume 142, number 2
520	3		_aThis study developed a process model that includes three steps for urban growth simulation. A preprocessing step is required to classify three satellite images acquired in 1990, 2000, and 2009 into six land-use types using remote sensing (RS) and geographic information systems (GIS). The first step of the process model is to project the population using a cohort component method for 2014. The second step is to quantify the demand for urban land use based on a regression model between population and urban land use. The third step is to optimize the weighting values for six criteria using the weighted scenario method (WSM), cellular automata (CA) model, and GIS in order to make a grid-based optimal potential suitability map for urban growth. Two accuracy assessment methods, pixel-by-pixel comparison and calculation of zonal statistics, were adopted to evaluate the accuracy of simulation results. This study also showed that the process model can still be used according to population growth scenarios even if the population increases or decreases suddenly due to socioeconomic or political factors that cannot be projected using the cohort component method.
650			_aProcess modelling _vUrban growth simulation _xPopulation forecasting _zSeoul, South Korea
700			_aKim, Dae-Sik _eauthor
856			_uhttps://doi.org/10.1061/(ASCE)UP.1943-5444.0000260
942			_2lcc _cJA
999			_c167073 _d167073