Document Type : CASE STUDY
Authors
Department of Geomatics, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran
Abstract
BACKGROUND AND OBJECTIVES: Urbanization and rapid population growth are significant global challenges, especially in cities with outdated planning systems. Effective urban growth models, which utilize historical data, land use patterns, and population dynamics, are essential for predicting future expansion. In Kirkuk, Iraq, urban growth has occurred unregulated due to an outdated master plan, and a new plan has been introduced. However, no local studies have assessed the effectiveness of urban growth models against this plan. This study aims to fill this gap by evaluating urban development in Kirkuk from 1993 to 2023 and forecasting growth to 2037 using remote sensing and Geo-spatial Information System-based models.
METHODS: This research applied two urban growth models: Cellular Automata Markov chain modeling (CA-MARCOV) and SLEUTH, to analyze urban expansion in Kirkuk. Satellite imagery from 1993, 2003, 2013, and 2023 was used for land use classification. The Cellular Automata Markov chain model used transition probability matrices derived from land use data, while the SLEUTH model incorporated urban density, road networks, and slope data for simulations. Both models were tested for accuracy by comparing predicted urban growth for 2023 with actual land use data. The study also used these models to forecast urban expansion for 2037, comparing their predictions with the city’s new master plan.
FINDINGS: The land use classification indicated significant urban growth in Kirkuk, from 41.57 km² in 1993 to 155.5 km² in 2023. The accuracy of the predictions for 202F3 showed that the SLEUTH model achieved 87% accuracy, outperforming the Cellular Automata Markov chain model, which had 76% accuracy. For 2037, the Cellular Automata Markov chain model projected urban growth to 201.43 km², while the SLEUTH model predicted a larger expansion of 219.78 km². The SLEUTH model also excluded water bodies and restricted zones (such as oil fields and airports), which were manually identified, while the Cellular Automata Markov chain model did not account for these exclusions.
CONCLUSION: The SLEUTH model provided more accurate urban growth predictions and was selected for comparison with the new master plan. The results indicated discrepancies between the SLEUTH model’s predictions and the master plan, particularly in areas experiencing rapid growth, suggesting the need for adjustments. The southeastern and southwestern regions showed alignment with the plan. These findings highlight the importance of using accurate urban growth models for sustainable planning in Kirkuk, offering valuable insights for urban development through 2037
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