Simulating urban expansion and scenario prediction using a cellular automata urban growth model, SLEUTH, through a case study of Karaj City, Iran

2014 ◽  
Vol 30 (4) ◽  
pp. 591-611 ◽  
Author(s):  
Yousef Sakieh ◽  
Bahman Jabbarian Amiri ◽  
Afshin Danekar ◽  
Jahangir Feghhi ◽  
Sadeq Dezhkam
Keyword(s):  
Cellular Automata ◽  
Growth Model ◽  
Urban Growth ◽  
Urban Expansion ◽  
Urban Growth Model ◽  
Scenario Prediction

scholarly journals Modelling and simulating ‘informal urbanization’: An integrated agent-based and cellular automata model of urban residential growth in Ghana

2022 ◽  
pp. 239980832110688
Author(s):  
Felix S. K. Agyemang ◽  
Elisabete Silva ◽  
Sean Fox
Keyword(s):  
Cellular Automata ◽  
Growth Model ◽  
Urban Growth ◽  
Urban Expansion ◽  
City Region ◽  
African City ◽  
Local Data ◽  
Support Tool ◽  
Agent Based ◽  
Urban Growth Model

The global urban population is expected to grow by 2.5 billion over the next three decades, and 90% of this growth will occur in African and Asian countries. Urban expansion in these regions is often characterised by ‘informal urbanization’ whereby households self-build without planning permission in contexts of ambiguous, insecure or disputed property rights. Despite the scale of informal urbanization, it has received little attention from scholars working in the domains of urban analytics and city science. Towards addressing this gap, we introduce TI-City, an urban growth model designed to predict the locations, legal status and socio-economic status of future residential developments in an African city. In a bottom-up approach, we use agent-based and cellular automata modelling techniques to predict the geospatial behaviour of key urban development actors, including households, real estate developers and government. We apply the model to the city-region of Accra, Ghana, drawing on local data collection, including a household survey, to parameterise the model. Using a multi-spatial-scale validation technique, we compare TI-City’s ability to simulate historically observed built-up patterns with SLEUTH, a highly popular urban growth model. Results show that TI-City outperforms SLEUTH at each scale, suggesting the model could offer a valuable decision support tool in similar city contexts.

scholarly journals A Cellular Automata Urban Growth Model for Water Resources Strategic Planning

10.29007/w43g ◽  
2018 ◽  
Author(s):  
Dionysios Nikolopoulos ◽  
Konstantina Risva ◽  
Christos Makropoulos
Keyword(s):  
Water Resources ◽  
Strategic Planning ◽  
Cellular Automata ◽  
Growth Model ◽  
Urban Growth ◽  
Management Practices ◽  
High Reliability ◽  
Population Increase ◽  
Water Quality Control ◽  
Urban Growth Model

The alarming rate of urbanization poses immediate problems to water resources management, mainly, but not limited to water supply, flood risk management, wastewater treatment and water quality control. Ideally, strategic planning of water systems should be fully aware of the prospects of future urban growth in order to maintain high reliability of services provided and satisfy customers in the long term. Typically, urban growth is handled in a static manner via the development of future scenarios based on previous urban planning studies. Generally, these scenarios focus solely on population increase and ignore the spatial allocation dynamics. Modern urban water strategic thinking needs to incorporate robust tools and methodologies in management practices, able to predict and quantify the outcome possibility of future urban growth. To cope with the aforementioned challenge, this study proposes a novel cellular automata urban growth model as well as, a supplementary remote sensing methodology to preprocess input data.

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