Open AccessThe Prediction of Urban Growth Trends and Patterns using Spatio-temporal CA-MC Model in Seremban Basin
Author(s) -
Rabia Ajeeb,
Maher Milad Aburas,
Faisal Baba,
Abdelsalam Ali,
Motasem Y. D. Alazaiza
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/540/1/012028
Subject(s) - markov chain , markov model , urban area , environmental science , computer science , econometrics , calibration , statistics , mathematics , economics , economy
Urban growth, a dynamic and demographic phenomenon, refers to the increased spatial value of urban areas, such as cities and towns, due to social and economic forces. Nowadays, urban lands are rapidly increasing, replacing non-urban lands such as agricultural, forest, water, rural, and open lands. In this study, a CA-Markov model was utilized to predict the growth of urban lands and their spatial trends in Seremban, Malaysia. The performance of the CA-Markov model was also assessed. The Markov chain model was applied to produce the quantitative values of transition probabilities for urban and non-urban lands. Subsequently, the CA model was used to predict the dynamic spatial trends of land changes. The change in urban and non-urban land use from 1984 to 2010 was modeled using the CA-Markov model for calibration purposes and to compute optimal CA transition rules as well as to predict future urban growth. In the accuracy assessment process, the CA-Markov model was validated using a Kappa coefficient. The overall accuracy of the Kappa index statistics was 83%, which indicates the excellent performance of the model proposed in this study. Finally, based on the CA transition rules and the transition area matrix produced from the calibration process using the Markov Chain model, future urban growth in Seremban for 2020 and 2030 was simulated.