Predict and Simulate Sustainable Urban Growth by Using GIS and MCE Based CA. Case of Famagusta in Northern Cyprus (original) (raw)
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2013
This thesis utilizes "Geographical Information Systems" (GIS), "Multi Criteria Evaluation" (MCE) with "Cellular Automata" (CA) for simulating Sustainable urban growth scenarios for Famagusta and represents "Do Nothing" and "Sustainable" scenario-based spatial simulations of the City. Under Do Nothing scenario, Markov Chain probability analysis with CA models is used with temporal land use datasets based on the images from 2002 and 2011. It shows that, Famagusta is diverging from sustainable development. Future expansions of both medium dense and low dense urban zones are generally sited close to the existing built-up urban areas that are connected with road network. A similar model is employed for the application of Sustainable Urban Development policies by Policy Driven Scenario. As a main goal, Sustainable Urban Development includes three main criteria, Compactness,
Cellular Automata Based Model of Urban Spatial Growth
Journal of The Indian Society of Remote Sensing, 2010
In the study reported in this paper an attempt has been made to develop a Cellular Automata (CA) model for simulating future urban growth of an Indian city. In the model remote sensing data and GIS were used to provide the empirical data about urban growth while Markov chain process was used to predict the amount of land required for future urban use based on the empirical data. Multi-criteria evaluation (MCE) technique was used to reveal the relationships between future urban growth potential and site attributes of a site. Finally using the CA model, land for future urban development was spatially allocated based on the urban suitability image provided by MCE, neighbourhood information of a site and the amount of land predicted by Markov chain process. The model results were evaluated using Kappa Coefficient and future urban growth was simulated using the calibrated model
Remote Sensing, 2015
In this study, urban growth of the Atakum District in Samsun, Turkey, was simulated by Cellular Automata-Markov Chain (CA-MC) and Multi-layer Perceptron-Markov Chain (MLP-MC) hybrid models in a geographical information system (GIS) environment. Historical land use/land cover (LU/LC) data were extracted from 1989, 2000 and 2013 Landsat TM/ETM+/OLI images. Using the LU/LC data for the years 1989 and 2000, the urban growth for 2013 was simulated using the CA-MC and MLP-MC models. The simulation results were compared with the 2013 LU/LC data to assess the validity of the simulation. The MLP-MC method provided the best results according to the validation based on the kappa index of agreement. Based on this result, the urban growth for the year 2025 was simulated using MLP-MC. The simulation estimated an urban growth rate of 35.2% between 2013 and 2025, an increase in the area of artificial surfaces from 1681.9 ha to 2274.3 ha and the destruction of 511.7 ha of agricultural land and 4.4 ha of forest. The results of this study demonstrate that the urban growth models provide a better understanding of the current patterns and temporal dynamics and can predict future changes according to past and current dynamics. The results also show that simulations are most accurate when using a model that best conforms to the changes in the given study area.
Alexandria Engineering Journal, 2018
This paper aims at predicting the future urban growth and its impact on the Yemeni city of Ibb. It adopts a combined urban simulation model using both Cellular Automata (CA) and Fuzzy Set, integrated in a Geographic Information Systems (GIS) platform. The research methodology includes three main stages: (1) preparation of historical data on land uses (2003, 2013), (2) simulation of data using urban simulation models (CA & Fuzzy set) in LanduseSim software environment, and (3) visualizing data. The model was validated using the pixel matching method for both the simulation map and actual map for the year of 2013. The match ratio was 93.76% for all layers and 89.40% for the urban layer. Accordingly, the final simulation was completed to the year 2033. The results show a horizontal distribution of urban growth with a high percentage of increase in urban areas; from 28.41% in 2013 to 43.11% in 2033. This increase occurs at the expense of agricultural and natural land. The study recommends a reconsideration of the the city expansion strategies by decision makers in government in order to ensure ecological balance.
American Journal of Environmental Science and Engineering, 2019
Increase in population and the desire to seek new opportunities has contributed to urban growth, putting pressure on facilities in urban centres. Bungoma town, being the headquarter of Bungoma County has undergone radical changes in its physical form, not only in territorial expansion, but also through internal physical transformation. In the process of urbanization, physical characteristic of the town is gradually changing as cropland (agricultural land), vegetation and wetland has been converted to built-up areas. This new urban fabric needs to be analysed to understand the impact of these changes. The aim of this research was to evaluate the suitability of Bungoma town setting, model its growth and predict the future growth of the town based on land cover changes (1985-2015). Landsat satellite images were classified with five land cover classes followed by change detection. To simulate land cover map for Bungoma town in 2030, Markov Chain model and Cellular Automata Markov (CA-Markov) model were used. It was found that built-up area increased over the study period. The major contributors to this change are cropland, vegetation and wetland land cover types. The CA-Markov model results showed that 52% of the total study area will be converted into built-up area, 19% to cropland, 20% to vegetation, 5% to open spaces and 3% to wetland by 2030. This would have negative implication on food security in the region which is a major source of income for the inhabitants. There is need therefore for proper land use planning in the area. In addition, vertical urban development should be encouraged to control rapid expansion of the town.
Prediction of Urban Growth through Cellular Automata-Markov Chain
Bulletin de la Société Royale des Sciences de Liège, 2016
Urban growth modelling can provide a useful tool to help decision-makers and urban planners evaluate different planning scenarios. Predicting the land uses and covers for sustainable utilization of lands cover is crucial due to rapid changes in the operations mainly arising from population growth and urbanization or perhaps because of changes in the stature of a city. The application of urban growth models such as CA-Markov could raise awareness about the future growth of the city aimed at consciously controlling and changing the land use planning in the future. The results of reviewing the urban growth process and land use changes around the city in the past mainly indicated the lands converted from agricultural use to urban areas across Bojnoord. Moreover, the hybrid model of CA-Markov was employed to predict the land use changes for the next 50 years with 10-year intervals between 2020 and 2070. The results showed that if the process of urban growth and land use changes in areas ...
6 Tourism department, Rawanduz Private Technical Institute, Rawanduz Abstract Many cities in the Kurdistan Region have witnessed a rapid change in land use during the last two decades. Geographic information systems (GIS) and remote sensing have been broadly utilized to monitor and detect urban growth prediction. In this paper, three Landsat TM 5 and one Landsat 8 of Sulaimaniya city were used to identify and develop an urban growth map for 1991, 1998, 2006 and 2014. A supervised classification approach was applied; in order to predict urban growth, the Markov chain and CA-Markov models were used. The result demonstrates that validation of CA-Markov to forecast 2006 land cover map is ineffective in reasonably predicting land coverage for this time period; however this model had significant validation for the year 2014 and also has a good forecast power for 2024.
Urban Climate, 2020
Efficient Land Use and Land Cover (LULC) monitoring and management require awareness of previous dynamics, current trends, and predictions of future developments. Understanding such an urban dynamics is, thus, necessary to deliberate a proper urban growth management approach. The study is aimed to simulate the LULC dynamics and develop a scenario-based LULC prediction for sustainable urban growth planning and management in the case of Addis Ababa and the surrounding area. The research employed a hybrid Cellular Automata, Markov chain (CA-Markov) and Multi-criteria Analytical Hierarchy Process (AHP) modeling approach. Accordingly, the research depicted continuous historical increment of Built-up spaces by consuming other ecologically valuable LULC classes. The quantitative measures of landscape metrics confirmed the benefit of Ecologically Sensitive Scenario (ESS) modeling as compared to Business As Usual Scenario (BAUS) as it keeps the dynamism of the city region more sustainable. ESS modeling enables an urban system to grow into a better way by making built-up augmentation relatively mild and controlling water bodies, forests and cultivated land losses. Therefore, this scenariobased simulation of the LULC dynamics providing decision-making options for those who strive for sustainable urban growth planning and management not only in the study region but also other similar cities.
Cellular Automata and Markov Chain Based Urban Growth Prediction
International Journal of Environment and Geoinformatics
Remote sensing and Geographic Information System (GIS); plays a vital role for studying Land Use Land Cover (LULC) and identifying the main factors for useful outcomes. Assessment of the urban growth pattern is extremely essential as sprawl is seen as one of the potential threats for urban planning. The project has been carried out for the Land Use Land Cover classification of Gandhinagar district of Gujarat state. Gandhinagar city has experienced wide change in LULC in last few decades. It is located at 23.2156° N & 72.6369° E in Gujarat. LULC mapping of Gandhinagar was carried out using LANDSAT Multispectral, TM, ETM+, and OLI/TIRS images for the years 1972, 1977, 1987, 1994, 2000, 2008, 2015 and 2019. Landsat data covers Gandhinagar’s vegetation, Water Bodies, Open Area, Agriculture, and Settlement. The area of interest of Gandhinagar was generated from Landsat data using the digitized boundary of Gandhinagar district. The main objective of this project is to generate LULC using ...
Towards Modeling Urban Growth with Using Cellular Automata (CA) and GIS
2005
Abstract The study of changing urban structure is one of the important global issues particularly with the new paradigms on sustainable development. Thus, modeling and simulating urban growth can be high useful for exploring the interaction between built environment and natural environment to help urban planning regarding decisionmaking complexity. This goal would be achieved through the integration remote sensing (RS) data, GIS and simulator toolkits.