CA-Markov Analysis of Constrained Coastal Urban  Growth Modeling: Hua Hin Seaside City, Thailand

Kityuttachai, Kritsana; Tripathi, Nimisha; Tipdecho, Taravudh; Shrestha, Rajendra P.

doi:10.3390/su5041480

Cited by 76 publications

(62 citation statements)

References 14 publications

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“…Although the demographic and economic data were introduced as inputs of the model already, much literature [50][51][52][53] also suggests that it will improve the good performance of the model to incorporate the population and economic growth rate for future predictions. This study considers that reason to integrate both important data to transition rate calculation through the statistical extrapolation and SD.…”

Section: Model Applicationmentioning

confidence: 99%

Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

et al. 2016

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Urban expansion is considered as one of the most important problems in several developing countries. Bangkok Metropolitan Region (BMR) is the urbanized and agglomerated area of Bangkok Metropolis (BM) and its vicinity, which confronts the expansion problem from the center of the city. Landsat images of 1988Landsat images of , 1993Landsat images of , 1998Landsat images of , 2003Landsat images of , 2008, and 2011 were used to detect the land use and land cover (LULC) changes. The demographic and economic data together with corresponding maps were used to determine the driving factors for land conversions. This study applied Cellular Automata-Markov Chain (CA-MC) and Multi-Layer Perceptron-Markov Chain (MLP-MC) to model LULC and urban expansions. The performance of the CA-MC and MLP-MC yielded more than 90% overall accuracy to predict the LULC, especially the MLP-MC method. Further, the annual population and economic growth rates were considered to produce the land demand for the LULC in 2014 and 2035 using the statistical extrapolation and system dynamics (SD). It was evident that the simulated map in 2014 resulting from the SD yielded the highest accuracy. Therefore, this study applied the SD method to generate the land demand for simulating LULC in 2035. The outcome showed that urban occupied the land around a half of the BMR.

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Section: Model Applicationmentioning

confidence: 99%

Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

et al. 2016

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“…This larger increase of vehicles and travel time induced by infrastructure development can directly be indicative of generating accident [41], health risks such as air pollution [42], and local climate and ecological system changes [43] in the study area. Therefore, there must be According to the above analyses, it can be concluded that travel distance of local population has been associated with substantial changes in the increase of the urban area in the stage of urban formation in our study area.…”

Section: Application Of Mobility Patterns To Land Cover Changementioning

confidence: 99%

Human Mobility Analysis for Extracting Local Interactions under Rapid Socio-Economic Transformation in Dawei, Myanmar

Kimijima

Nagai

2017

Sustainability

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Understanding human mobility patterns provides knowledge about impacts of a socio-economic transformation in a rapidly urbanizing environment. This study assesses a long-term mobility data which uses a face-to-face questionnaire and GPS logger-based method of data collection for extracting socio-economic impacts from the rapid transformation. Conversion of mobility related information such as travel distance, direction, and time from the questionnaire survey into spatiotemporal information was carried out by developing an algorithm. To illustrate the proposed approach, a case study in Dawei Special Economic Zone, Myanmar was conducted. The results show that the questionnaire-based mobility data can be associated with GPS-based mobility data and diverse mobility patterns are found for different social groups in the stage of urban formation. The results enabled an understanding of the human dynamics in interactions, which can be used for monitoring rural sustainability and its challenges in the future with the background of the accelerated project development in the area.

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“…Finally, a Markovian process is used to allocate land for each claimants of a host class based on the derived quantity of change. The LCM has been used extensively for Reducing Emissions from Deforestation and Forest Degradation projects (REDD; e.g., [50][51][52][53]), as well as to estimate urbanization [54][55][56].…”

Section: Land Change Modelermentioning

confidence: 99%

Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models

Pickard

Gray

Meentemeyer

2017

Land

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Abstract:The growing numbers of land change models makes it difficult to select a model at the beginning of an analysis, and is often arbitrary and at the researcher's discretion. How to select a model at the beginning of an analysis, when multiple are suitable, represents a critical research gap currently understudied, where trade-offs of choosing one model over another are often unknown. Repeatable methods are needed to conduct cross-model comparisons to understand the trade-offs among models when the same calibration and validation data are used. Several methods to assess accuracy have been proposed that emphasize quantity and allocation, while overlooking the accuracy with which a model simulates the spatial configuration (e.g., size and shape) of map categories across landscapes. We compared the quantity, allocation, and configuration accuracy of four inductive pattern-based spatial allocation land change models (SLEUTH, GEOMOD, Land Change Modeler (LCM), and FUTURES). We simulated urban development with each model using identical input data from ten counties surrounding the growing region of Charlotte, North Carolina. Maintaining the same input data, such as land cover, drivers of change, and projected quantity of change, reduces differences in model inputs and allows for focus on trade-offs in different types of model accuracy. Results suggest that these four land change models produce representations of urban development with substantial variance, where some models may better simulate quantity and allocation at the trade-off of configuration accuracy, and vice versa. Trade-offs in accuracy exist with respect to the amount, spatial allocation, and landscape configuration of each model. This comparison exercise illustrates the range of accuracies for these models, and demonstrates the need to consider all three types of accuracy when assessing land change model's projections.

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CA-Markov Analysis of Constrained Coastal Urban Growth Modeling: Hua Hin Seaside City, Thailand

Cited by 76 publications

References 14 publications

Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models

Human Mobility Analysis for Extracting Local Interactions under Rapid Socio-Economic Transformation in Dawei, Myanmar

Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models

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