An optimised cellular automata model based on adaptive genetic algorithm for urban growth simulation

This paper presents an improved cellular automata (CA) model optimized using an adaptive genetic algorithm (AGA) to simulate the spatiooral process of urban growth. The AGA technique can be used to optimize the transition rules of the CA model defined through conventional methods such as logistic regression approach, resulting in higher simulation efficiency and improved results. Application of the AGA-CA model in Shanghai's Jiading District, Eastern China demonstrates that the model was able to generate reasonable representation of urban growth even with limited input data in defining its transition rules. The research shows that AGA technique can be integrated within a conventional CA based urban simulation model to improve human understanding on urban dynamics

Simulating the impact of economic and environmental strategies on future urban growth scenarios in Ningbo, China

Coastal cities in China are challenged by multiple growth paths and strategies related to demands in the housing market, economic growth and eco-system protection. This paper examines the effects of conflicting strategies between economic growth and environmental protection on future urban scenarios in Ningbo, China, through logistic-regression-based cellular automata (termed LogCA) modeling. The LogCA model is calibrated based on the observed urban patterns in 1990 and 2015, and applied to simulate four future scenarios in 2040, including (a) the Norm-scenario, a baseline scenario that maintains the 1990-2015 growth rate; (b) the GDP-scenario, a GDP-oriented growth scenario emphasizing the development in city centers and along economic corridors; (c) the Slow-scenario, a slow-growth scenario considering the potential downward trend of the housing market in China; and (d) the Eco-scenario, a slow-growth scenario emphasizing natural conservation and ecosystem protections. The CA parameters of the Norm-and Slow-scenarios are the same as the calibrated parameters, while the parameters of proximities to economic corridors and natural scenery sites were increased by a factor of 3 for the GDP-and Eco-scenarios, respectively. The Norm-and GDP-scenarios predicted 1950 km(2) of new growth for the next 25 years, the Slow-scenario predicted 650 km2, and the Eco-scenario predicted less growth than the Slow-scenario. The locations where the newly built-up area will emerge are significantly different under the four scenarios and the Slow-and Eco-scenarios are preferable to achieve long-term sustainability. The scenarios are not only helpful for exploring sustainable urban development options in China, but also serve as a reference for adjusting the urban planning and land policies

Projection of land surface temperature considering the effects of future land change in the Taihu Lake Basin of China

Land surface temperature (LST) is an important environmental parameter that is significantly affected by land use and landscape composition. Despite the recent progress in LST retrieval algorithms and better knowledge of the relationship between LST and land coverage indices, predictive studies of future LST patterns are limited. Here, we project LST patterns in the Taihu Lake Basin to the year 2026 based on projected land use pattern and simulated land coverage indices that include normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI) and normalized difference water index (NDWI). We derived the spatiotemporal LST patterns in the Taihu Lake Basin from 1996 to 2026 using thermal infrared data from Landsat imagery. A CA-Markov model was applied to project the 2026 land use pattern in the basin based on spatial driving factors, using the 2004 land use as the initial state. We simulated the NDBI, NDVI and NDWI indices for 2026 using the projected land use patterns, and then generated the 2026 LST in the study area. Our results showed that LST has been increasing and the warming areas have been expanding since 1996, especially in the Su-Xi-Chang urban agglomeration. The mean LST in Su-Xi-Chang has increased from 31 degrees C in 2004 and has risen to about 33 degrees C in 2016, and the projection suggests that LST will reach about 35 degrees C in 2026. Our results also suggest that mean LST increased by 2 degrees C per decade in this highly urbanized area between 1996 and 2026. We present a preliminary method to produce future LST patterns and provide reasonable LST scenarios in the Taihu Lake Basin, which should help develop and implement management strategies for mitigating the effects of urban heat island

Mining of CA based land transition rules through self-adaptive genetic algorithm for urban growth modeling

This paper presents a method for mining the land use transition rules and parameters of a cellular automata urban growth model using a self-adaptive genetic algorithm (SAGA) method. It builds on the evolutionary computation technique to search for and optimize a set of spatial parameters representing various spatial factors impacting on urban land use change. The application of the SAGA-CA model to simulate the spatio-temporal processes of urban land change in Southeast Queensland’s Gold Coast City, Australia from 1991 to 2006 demonstrates that the self-adaptive genetic algorithm can be integrated within a conventional urban CA model to improve the performance of the model, therefore enhance our understanding of urban landscape dynamics

Fractal dimension as an indicator for quantifying the effects of changing spatial scales on landscape metrics

While geographers and ecologists are well aware of the scale effects of landscape patterns, there is still a need for quantifying these effects. This paper applies the fractal method to measure the scale (grain or cell size) sensitivity of landscape metrics at both landscape and class levels using the Gold Coast City in Southeast Queensland, Australia as a case study. By transforming the original land use polygon data into raster data at eleven aggregate scales, the fractal dimensions of 57 landscape metrics as defined in FRAGSTATS were assessed. A series of linear log-log regression models were constructed based on the power law to obtain the coefficient of determination (COD or R) of the models and the fractal dimension (FD) of the landscape metrics. The results show that most landscape metrics in the area and edge, shape and the aggregation groups exhibit a fractal law that is consistent over a range of scales. The six variations of several landscape metrics that belong to both the area/edge and shape groups show different scale behaviours and effects. However, the metrics that belong to the diversity group are scale-independent and do not accord to fractal laws. In addition, the scale effects at the class level are more complex than those at the landscape level. The quantitative assessment of the scale effect using the fractal method provides a basis for investigating landscape patterns when upscaling or downscaling as well as creating any scale-free metric to understand landscape patterns

A cellular automata model based on nonlinear kernel principal component analysis for urban growth simulation

In this paper we present a cellular automata (CA) model based on nonlinear kernel principal component analysis (KPCA) to simulate the spatiotemporal process of urban growth. As a generalisation of the linear principal component analysis (PCA) method, the KPCA method was developed to extract the nonspatially correlated principal components amongst the various spatial variables which affect urban growth in high-dimensional feature space. Compared with the linear PCA method, the KPCA approach is superior as it generates fewer independent components while still maintaining its capacity to reduce the noise level of the original input datasets. The reduced number of independent components can be used to better reconstruct the nonlinear transition rules of a CA model. In addition, the principal components extracted through the KPCA approach are not linearly related to the input spatial variables, which accords well with the nonlinear nature of complex urban systems. The KPCA-based CA model (KPCA-CA) developed was fitted to a fast-growing region in China's Shanghai Metropolis for the sixteen-year period 1992-2008. The simulated patterns of urban growth matched well with the observed urban growth, as determined from historical remotely sensed images for the same period. The KPCA-CA model resulted in significant improvements in locational accuracy when compared with conventional CA models and acted to reduce simulation uncertainty

Scenario prediction of emerging coastal city using CA modeling under different environmental conditions: a case study of Lingang New City, China

The world's coastal regions are experiencing rapid urbanization coupled with increased risk of ecological damage and storm surge related to global climate and sea level rising. This urban development issue is particularly important in China, where many emerging coastal cities are being developed. Lingang New City, southeast of Shanghai, is an excellent example of a coastal city that is increasingly vulnerable to environmental change. Sustainable urban development requires planning that classifies and allocates coastal lands using objective procedures that incorporate changing environmental conditions. In this paper, we applied cellular automata (CA) modeling based on self-adaptive genetic algorithm (SAGA) to predict future scenarios and explore sustainable urban development options for Lingang. The CA model was calibrated using the 2005 initial status, 2015 final status, and a set of spatial variables. We implemented specific ecological and environmental conditions as spatial constraints for the model and predicted four 2030 scenarios: (a) an urban planning-oriented Plan Scenario; (b) an ecosystem protection-oriented Eco Scenario; (c) a storm surge-affected Storm Scenario; and (d) a scenario incorporating both ecosystem protection and the effects of storm surge, called the Ecostorm Scenario. The Plan Scenario has been taken as the baseline, with the Lingang urban area increasing from 45.8\ua0km(2) in 2015 to 66.8\ua0km(2) in 2030, accounting for 23.9\ua0% of the entire study area. The simulated urban land size of the Plan Scenario in 2030 was taken as the target to accommodate the projected population increase in this city, which was then applied in the remaining three development scenarios. We used CA modeling to reallocate the urban cells to other unconstrained areas in response to changing spatial constraints. Our predictions should be helpful not only in assessing and adjusting the urban planning schemes for Lingang but also for evaluating urban planning in coastal cities elsewhere