A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS

The fast-paced urbanization of recent decades entails that many regions are facing seemingly uncontrolled land-use changes (LUCs) that go hand in hand with a range of environmental and socio-economic challenges. In this paper, we use an integrated cellular automata–Markov chain (CA–MC) model to analyze and predict the urban expansion of and its impact on LUC in the city of Bahir Dar, Ethiopia. To this end, the research marshals high-resolution Landsat images of 1991, 2002, 2011, and 2018. An analytical hierarchy process (AHP) method is then used to identify the biophysical and socioeconomic factors underlying the expansion in the research area. It is shown that, during the period of study, built-up areas are rapidly expanding in the face of an overall decline of the farmland and vegetation cover. Drawing on a model calibration for 2018, the research predicts the possible geographies of LUC in the Bahir Dar area for 2025, 2034, and 2045. It is predicted that the conversions of other land-use types into built-up areas will persist in the southern, southwestern, and northeastern areas of the sprawling city, which can mainly be traced back to the uneven geographies of road accessibility, proximity to the city center, and slope variables. We reflect on how our findings can be used to facilitate sustainable urban development and land-use policies in the Bahir Dar area.

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Spatiotemporal Monitoring and Prediction of Land Use Integrating the Markov Chain and Cellular Automata in the Coastal Chaouia

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c5760.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 1704-1711

Keyword(s):

Land Use ◽

Markov Chain ◽

Cellular Automata ◽

Rapid Development ◽

Urban Expansion ◽

World Population ◽

Industrial Sectors ◽

The Face ◽

Agricultural Use ◽

The Impact

In the last decades, the world population rate has been gradually increasing, this population growth has faced intense urban expansion and the rapid development of the agricultural and industrial sectors. This change had an impact on the mode of land use. In the face of this problem, several strategies have been created for monitoring and predicting possible future scenarios on rhythm of land use change. The CA-Markov model used in this research allows to predict future land use trends on the basis of the classified maps of 1987, 1999, 2011 and 2019. Simulating and tracking these maps is a major challenge. The latter provides important information in terms of data, methods and models to be used to create a realistic and sustainable process of territory planning for environmentalists, planners and local authorities. The combination of the Markov chain and cellular automata has been used to qualitatively and quantitatively simulate and evaluate future land use trends in coastal Chaouia, Morocco. To achieve this purpose, two maps were developed for the two years of 2027 and 2035. By using kappa, the global success of the modelling was 89.22% and 82.12% respectively in 2011 and 2019 for the projected land use map. The results confirm that forests have been affected by intensive agricultural uses. This increase in agricultural use is due to the impact of the constant increase in the development of the agroeconomic and demographic sectors. This situation indicates the need to create new approach to management to protect the sustainability of land use in coastal Chaouia.

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A multi-objective Markov Chain Monte Carlo cellular automata model: Simulating multi-density urban expansion in NYC

Computers Environment and Urban Systems ◽

10.1016/j.compenvurbsys.2021.101602 ◽

2021 ◽

Vol 87 ◽

pp. 101602

Author(s):

Ahmed Mustafa ◽

Amr Ebaid ◽

Hichem Omrani ◽

Timon McPhearson

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Cellular Automata ◽

Urban Expansion ◽

Cellular Automata Model ◽

Multi Objective

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Dynamic simulation of urban expansion based on Cellular Automata and Markov Chain Model: a case study in Siliguri Metropolitan Area, West Bengal

Modeling Earth Systems and Environment ◽

10.1007/s40808-019-00626-7 ◽

2019 ◽

Vol 5 (4) ◽

pp. 1723-1732 ◽

Cited By ~ 1

Author(s):

Apurba Sarkar ◽

Pradip Chouhan

Keyword(s):

Markov Chain ◽

Cellular Automata ◽

Dynamic Simulation ◽

Metropolitan Area ◽

West Bengal ◽

Markov Chain Model ◽

Urban Expansion ◽

Chain Model

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ESTIMATING BUILT-UP AREA CHANGE IN ULAANBAATAR CITY, MONGOLIA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b4-2020-143-2020 ◽

2020 ◽

Vol XLIII-B4-2020 ◽

pp. 143-147

Author(s):

P. Myagmartseren ◽

I. Myagmarjav ◽

N. Enkhtuya ◽

G. Byambakhuu ◽

T. Bazarkhand

Keyword(s):

Land Use ◽

Markov Chain ◽

Cellular Automata ◽

Urban Expansion ◽

Land Development ◽

Urban Land ◽

Urban Land Use ◽

Chain Model ◽

Rapid Urbanization ◽

Study Results

Abstract. Long-term urban built-up area changes of the Ulaanbaatar city has accelerated since the 1950s and due to rapid urbanization most of the Mongolian population, or about 68%, live in urban areas. The systematic understanding of urban land expansion is a crucial clue for urban land use planning and sustainable land development. Therefore, in this paper, we used a Markov chain model and cellular automata (CA) to simulate and predict current and future built-up areas expansion is Ulaanbaatar. Landsat imageries (Landsat TM 5, Landsat ETM 7 and Landsat OLI 8) of 1988, 1998, 2008, and 2017 were used to derive main land use classes. Clark Lab’s (Clark University) Geospatial Monitoring and Model software had been used for the urban expansion prediction. The results are innovated to comparable to validate with other study results by using a different kind of methods. Built-up area expansion modeled and predicted 2028’s trends based on a historical expansion of the Ulaanbaatar city between 1988 and 2017, which are prepared according to input model requirements. The built-up area was 7282 hectares (ha) in 1988 and has expanded to 31144 ha in 2017. The built-up area growth of the Ulaanbaatar city has reached 4.3 times over the past 30 years, and from 2017 to 2028 the expansion of the built-up area will be 1.5 times. A comparison of urban expansion from 1988 to 2017 has revealed a rapid built-up invasion to the previous areas of agriculture, grassland, and forest. Simulation performance of Markov chain with the cellular automata model can be used for an improvement in the understanding of the urban expansion processes while allowing helpful for better planning of Ulaanbaatar city, as well as for other rapidly developing towns of Mongolia.

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