A cellular automata-based approach for spatio-temporal modeling of the city center as a complex system: The case of Kastamonu, Türkiye

IŞINKARALAR, Öznur; Varol, Çiğdem

doi:10.1016/j.cities.2022.104073

Cited by 34 publications

(9 citation statements)

References 76 publications

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“…City centers can be defined as the most complex area of the city by nature and it is very difficult to explain their dynamic relationships [8,[35][36][37][38]. The public spaces in the city centers can be accepted as the primary communication object of society.…”

Section: Resultsmentioning

confidence: 99%

Urban Public Spaces, Public Health, and Heavy Metal Pollution Threatening in Ankara City Center: Strategies for Urban Planning

IŞINKARALAR

BAYRAKTAR

2022

KUJES

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In the last century, worldwide population growth and concentration in urban areas cause many problems, especially in the central areas of metropolitan cities. One of these problems is air pollution it reduces the quality of life of the citizens and threatens public health. Among the components of air pollution, heavy metals are the most dangerous because they accumulate in metabolism, some of them are toxic and poisonous even at low concentrations, and they are deadly for human health at high concentrations. Studies on the determination of air pollution are limited to revealing the current situation and do not offer solutions for urban planning. In this context, the accumulation of copper elements, which is extremely dangerous for public health, in plants located in the city center was investigated by using landscape plants as a tool in the research. Within the scope of the study, the change of copper concentration in five plant species grown in areas with no traffic, less dense, and dense areas were investigated. As a result of the research, the increase in the concentration of copper in the air due to traffic has been revealed statistically. The research emphasizes the relationship between heavy metal pollution, which poses a serious threat to public health, and urban planning, and reveals the basic strategies in terms of urban planning.

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Section: Resultsmentioning

confidence: 99%

Urban Public Spaces, Public Health, and Heavy Metal Pollution Threatening in Ankara City Center: Strategies for Urban Planning

IŞINKARALAR

BAYRAKTAR

2022

KUJES

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“…The nutrient element content of the soil is one of the most important factors influencing the root development of plants [32][33][34]. As with all other organisms, the phenotypical characteristics and development of plants are shaped under the effects of plants' genetic structures [35][36][37] and environmental factors [38][39][40][41].…”

Section: Discussionmentioning

confidence: 99%

Change of Calcium Concentrations in Forest Soils by Plant Species and Soil Depth

Erdem

2022

KUJES

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One of the most important factors playing role in plant development is the nutrient element content of the soil. Being one of the fundamentally necessary macronutrients for plant nourishment, calcium (Ca) plays an important role in the growth and development of a plant. Thus, even though many studies have been carried out on the change of Ca concentration in agricultural soils, the number of studies examining this subject for forest soils is very limited. In the present study, it was aimed to compare the concentrations of Ca in soil and plant organs for the soils, where different forest trees are grown. Within this scope, leaf, bark, wood, cone, and root samples were collected from Turkish fir, black pine, Scotch pine, and Oriental beech species and soil samples were taken from the surface, mid-deep, and deep soil levels. Then, the Ca concentrations were compared. As a result, it was determined that Ca concentrations statistically significantly vary between leaves, roots, and woods of plants and between mid-deep and deep soils by species (p

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“…Based on mathematical theories and observed data, a Markov chain can be used to predict the probability of changes in LULC, starting from the initial state while avoiding the data-independence assumption required by statistical methods [26,27]. Therefore, numerous studies have coupled Markov models with CAs to predict land conversion demand and simulate changes in LULC [28]. Were the analysis period restricted to several years or decades, the dynamics of land use would have their own inertia and correlation between different land-use types [29], which means that a Markov transition probability matrix based on a historical empirical analysis should accurately predict the dynamics of land-use conversion over a relatively short period [30].…”

Section: Introductionmentioning

confidence: 99%

Coupling Random Forest, Allometric Scaling, and Cellular Automata to Predict the Evolution of LULC under Various Shared Socioeconomic Pathways

2023

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Accurately estimating land-use demand is essential for urban models to predict the evolution of urban spatial morphology. Due to the uncertainties inherent in socioeconomic development, the accurate forecasting of urban land-use demand remains a daunting challenge. The present study proposes a modeling framework to determine the scaling relationship between the population and urban area and simulates the spatiotemporal dynamics of land use and land cover (LULC). An allometric scaling (AS) law and a Markov (MK) chain are used to predict variations in LULC. Random forest (RF) and cellular automata (CA) serve to calibrate the transition rules of change in LULC and realize its micro-spatial allocation (MKCARF-AS). Furthermore, this research uses several shared socioeconomic pathways (SSPs) as scenario storylines. The MKCARF-AS model is used to predict changes in LULC under various SSP scenarios in Jinjiang City, China, from 2020 to 2065. The results show that the figure of merit (FoM) and the urban FoM of the MKCARF-AS model improve by 3.72% and 4.06%, respectively, compared with the MKCAANN model during the 2005–2010 simulation period. For a 6.28% discrepancy between the predicted urban land-use demand and the actual urban land-use demand over the period 2005–2010, the urban FoM degrades by 21.42%. The growth of the permanent urban population and urban area in Jinjiang City follows an allometric scaling law with an exponent of 0.933 for the period 2005–2020, and the relative residual and R2 are 0.0076 and 0.9994, respectively. From 2020 to 2065, the urban land demand estimated by the Markov model is 19.4% greater than the urban area predicted under scenario SSP5. At the township scale, the different SSP scenarios produce significantly different spatial distributions of urban expansion rates. By coupling random forest and allometric scaling, the MKCARF-AS model substantially improves the simulation of urban land use.

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A cellular automata-based approach for spatio-temporal modeling of the city center as a complex system: The case of Kastamonu, Türkiye

Cited by 34 publications

References 76 publications

Urban Public Spaces, Public Health, and Heavy Metal Pollution Threatening in Ankara City Center: Strategies for Urban Planning

Urban Public Spaces, Public Health, and Heavy Metal Pollution Threatening in Ankara City Center: Strategies for Urban Planning

Change of Calcium Concentrations in Forest Soils by Plant Species and Soil Depth

Coupling Random Forest, Allometric Scaling, and Cellular Automata to Predict the Evolution of LULC under Various Shared Socioeconomic Pathways

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