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

The Effect of Observation Scale on Urban Growth Simulation Using Particle Swarm Optimization-Based CA Models

Cellular automata (CA) is a bottom-up self-organizing modeling tool for simulating contagion-like phenomena such as complex land-use change and urban growth. It is not known how CA modeling responds to changes in spatial observation scale when a larger-scale study area is partitioned into subregions, each with its own CA model. We examined the impact of changing observation scale on a model of urban growth at UA-Shanghai (a region within a one-hour high-speed rail distance from Shanghai) using particle swarm optimization-based CA (PSO-CA) modeling. Our models were calibrated with data from 1995 to 2005 and validated with data from 2005 to 2015 on spatial scales: (1) Regional-scale: UA-Shanghai was considered as a single study area; (2) meso-scale: UA-Shanghai was partitioned into three terrain-based subregions; and (3) city-scale: UA-Shanghai was partitioned into six cities based on administrative boundaries. All three scales yielded simulations averaging about 87% accuracy with an average Figure-of-Merit (FOM) of about 32%. Overall accuracy was reduced from calibration and validation. The regional-scale model yielded less accurate simulations as compared with the meso- and city-scales for both calibration and validation. Simulation success in different subregions is independent at the city-scale, when compared with regional- and meso-scale. Our observations indicate that observation scale is important in CA modeling and that smaller scales probably lead to more accurate simulations. We suggest smaller partitions, smaller observation scales and the construction of one CA model for each subregion to better reflect spatial variability and to produce more reliable simulations. This approach should be especially useful for large-scale areas such as huge urban agglomerations and entire nations

Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules

The dynamic relationships between land use change and its driving forces vary spatially and can be identified by geographically weighted regression (GWR). We present a novel cellular automata (GWR-CA) model that incorporates GWR-derived spatially varying relationships to simulate land use change. Our GWR-CA model is characterized by spatially nonstationary transition rules that fully address local interactions in land use change. More importantly, each driving factor in our GWR model contains effects that both promote and resist land use change. We applied GWR-CA to simulate rapid land use change in Suzhou City on the Yangtze River Delta from 2000 to 2015. The GWR coefficients were visualized to highlight their spatial patterns and local variation, which are closely associated with their effects on land use change. The transition rules indicate low land conversion potential in the city’s center and outer suburbs, but higher land conversion potential in the inner near suburbs along the belt expressway. Residual statistics show that GWR fits the input data better than logistic regression (LR). Compared with an LR-based CA model, GWR-CA improves overall accuracy by 4.1% and captures 5.5% more urban growth, suggesting that GWR-CA may be superior in modeling land use change. Our results demonstrate that the GWR-CA model is effective in capturing spatially varying land transition rules to produce more realistic results, and is suitable for simulating land use change and urban expansion in rapidly urbanizing regions

Calibration of cellular automata models using differential evolution to simulate present and future land use

A key issue in cellular automata (CA) modeling is the minimization of the differences between the actual and simulated patterns, which can be mathematically formulated as an objective function. We develop a new hybrid model (termed DE-CA) by integrating differential evolution (DE) into CA to solve the objective function and retrieve the optimal CA parameters. Constrained relations among factors were applied in DE to generate different sets of CA parameters for prediction of future scenarios. The DE-CA model was calibrated using historical spatial data to simulate 2016 land use in Kunming and predict multiple scenarios to the year 2026. Assessment of quantitative accuracy shows that DE-CA yields 92.4% overall accuracy, where 6.8% is the correctly captured urban growth; further, the model reported only 5.0% false alarms and 2.6% misses. Regarding the simulation ability, our new CA model performs as well as the widely applied genetic algorithm-based CA model, and outperforms both the logistic regression-based CA model and a no-change NULL model. We projected three possible scenarios for the year 2026 using DE-CA to adequately address the baseline urban growth, environmental protection and urban planning to show the strong prediction ability of the new model

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

How current and future urban patterns respond to urban planning? An integrated cellular automata modeling approach

While many publications predict future urban scenarios, few have deliberated the impact of issued urban planning on scenario prediction. We propose a planning-constrained model (named PCGA-CA) that integrates cellular automata (CA) and genetic algorithm (GA) to simulate current and future urban patterns under the spatial constraints of urban planning. The planning regulations include three types: fully allowed area (FAA), partially allowed area (PAA), and strictly prohibited area (SPA), where we propose a planning implementation parameter (PIP) to represent the stringency in PAA. Under different PIPs, we apply the PCGA-CA model to simulate the 2015 urban patterns and predict the 2030 and 2045 scenarios for Ningbo city, China. The results show that the regulations substantially affect the simulation accuracy and urban pattern. As the planning regulations become less stringent, the accuracy decreases from 90.3% to 89.4% and the urban pattern becomes less compact. In particular, the urban pattern is the most compact when the regulations are not imposed. The PCGA-CA predicts the quantity and location of illegal urban development, and identifies spatially varying urban growth across planning regulations. For the same year, the urban patterns with different PIPs illustrate substantial differences in landscape metrics. The simulations of the current urban pattern should help urban planners and local authorities assess past implementations of urban planning, while the scenario predictions can offer a view of the future by evaluating the consequences of different planning regulations

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