Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian

Abstract

The spatial evolution of land use in Jinzhou area was simulated using fuzzy cellular automata to determine all factors influencing urban land use change. For this purpose, land use data of multiple sources and remote sensing images from 2003 to 2015 were analyzed. The following results were obtained: (1) real land use data and simulation data for 2015 were tested using four indices. Under the 5 m × 5 m scale, the model shows good performance for simulation of areas with various land use types. (2) Among simulations under four scenarios, the effect of traffic guidance on the development of construction land was more distinct under the economic development mode, which clearly showed the phenomenon of “agglomeration” along major traffic lines. (3) Jinshitan Street is adjacent to the sea, and land use changes are significant under the 4th scenario, and thus related departments should pay more attention. (4) Shortcomings of conventional cellular automata model in processing complex systems could be mitigated through the integration of fuzzy sets.

Landscape pattern and economic factors’ effect on prediction accuracy of cellular automata-Markov chain model on county scale

“…It can be found that population density has a more significant correlation with kappa coefficient than population and income (r = −0.269, p = 0.2812). [22,45,[55][56][57]. Especially, socio-economic data have a confirmatory impact to the simulation of the CA-Markov model [58].…”

“…The greater the population density, the more complicated the land use. The population density is also a significant factor affecting simulation in many studies [22,55,56,59]. Therefore, both population density and kappa coefficient have a correlation that cannot be ignored.…”

“…To understand the process of LULC changes, LULCchange models are inevitable [16]. In recent years, many models have been used to simulate and predict land use change, such as conversion of land use and its effects on small regional extent (CLUE-S) model [17,18], which is one of the most widely used land-use models, Markov model [19], multiagent systems (MAS) model [20,21], cellular automata (CA) model [22,23] and other models [24,25]. In most cases, these models showed relatively good results.…”

“…However, in fact, land use changes are always caused by complex factors. The neighborhood scale, conversion rules, and simulation mechanisms would influence the CA's accuracy of the simulation [22]. Markov models are susceptible to location factors and decision-making processes.…”

Utilisation Efficiency of Construction Land in China’s Coastal Cities Based on Debt Level

“…e mechanisms that influence such efficiency are complex and diverse [20]. A number of studies have shown that fiscal expenditure, urban population density [23], transportation infrastructure [9,24,25], and urban industrial composition, and layout [26] have an impact on urban construction land efficiency [27]. However, researchers are yet to reach a consensus in regard to the quantitative measurements and mechanisms of urban construction land use efficiency.…”