Urban Growth Simulation Model Research Articles

Tele-connecting China's future urban growth to impacts on ecosystem services under the shared socioeconomic pathways

Land change, especially urban land expansion, is increasingly triggered by remote demands for goods and services rather than by purely local drivers, exerting pressure on ecosystem services beyond local boundaries. This effect has been termed as ‘tele-connections’. China has become the world's second largest economy. Understanding the tele-connections between China's future urban growth and its impacts on ecosystem services is essential to reconcile the conflict between socioeconomic developments and ecological protection. To this end, we propose to integrate an urban growth simulation model with the multi-region input-output (MRIO) model, thereby illustrating how urban land consumption in one region can cause ecosystem services' degradation in another. We explore the decline in ecosystem services due to urban land tele-connections under five shared socioeconomic pathway (SSP) scenarios. The results yield the direct loss of ecosystem services by 1.14–5.42% in food production, 0.06–0.44% in carbon sequestration, 0.09–0.59% in soil retention, 0.05–0.29% in sandstorm prevention, 0.12–0.80% in water retention and 0.19–1.04% in habitat provision. Uneven ecological consequences caused by domestic urban land displacement are witnessed not only in China's peripheral regions but also in developed regions. Shanghai, as the largest city in China, is expected to exert great impacts in terms of the quantity of ecosystem services decline and its spatial extent as well. Overall, the presented scenario simulations can support the establishment of effective compensation strategies toward balancing the responsibility and rights of stakeholders associated with ecological services protection.

Modeling Patterns of Land Use in Chinese Cities Using an Integrated Cellular Automata Model

This paper introduces an urban growth simulation model applied to the full scope of China. The model uses a multicriteria decision analysis to calculate the land conversion probability and then integrates it with a cellular automata model. A nonlinear relationship is incorporated in to the model to interpret the impacts of different Land Use and Cover Change driving forces. The Analytical Hierarchical Process is also implemented to compute the variance between weights of different factors. Multiple sizes of neighborhood and different urban ratios in the model rules are tested, and a 5 × 5 neighborhood and an urban threshold of 0.33 are chosen. The study demonstrates the importance of spatial analysis on socioeconomic factors, population, and Gross Domestic Product in land use change simulation modeling. The model fills the gap between the purely economic theory simulation model and the geographic simulation model. The nationwide urban simulation is an example that addresses the lack of urban simulation studies in China and among large-scale simulation models.

Extending the SLEUTH model to integrate habitat quality into urban growth simulation

This study aims to support sustainable urban and environmental planning by using urban growth simulation models, in which environmental quality is employed as one of the inputs. We proposed an extended SLEUTH urban growth model (UGM) for the regions threatened by environmental quality degradation caused by uncontrolled urban expansion. In this model, habitat quality is assessed by the InVEST model and is used to represent environmental quality, which is utilized in urban growth simulation. The habitat quality map is used to replace the slope layer as input for the SLEUTH model's urban growth simulation for cities where relatively flat topography makes this layer of minimal explanatory value. The extended SLEUTH UGM was calibrated using data for Changzhou city, China in 1990, 2000, 2010, and 2014. The best value of the Optimal SLEUTH Metric (OSM) was calculated for both the standard SLEUTH UGM and the extended SLEUTH UGM independently. The OSM value for the latter model was much higher than that of the former model, which indicated that the extended model provided a better explanation of urban growth in the study area. The calibrated extended SLEUTH UGM was applied to predict growth in Changzhou city from 2014 to 2030. The result showed that the urban area is expected to expand about 626 km2 by 2030. Comparison with the prediction result by using standard SLEUTH UGM showed that the area with high habitat quality could be reserved and the urban expansion could be limited by using our model. The findings demonstrate that the extended SLEUTH UGM could be a valuable tool for sustainable urban and environmental planning and management in developing regions where environmental protection should be considered as one of the major land-use objectives in their rapid urbanization process.

INVESTIGATION OF URBAN GROWTH EFFECTS ON FOREST AREA BY CREATING DIFFERENT SIMULATION SCENARIOS

Abstract. It is necessary to keep urban growth under control according to the understanding of sustainable urban management in rapidly growing cities. In other words, sustainable urban management requires estimating how the land cover will change and in which direction the urbanization will be in the coming years, as well as knowing the current structures of the cities. Therefore, simulation models are frequently used for monitoring urban growth. The results of simulation models help to obtain background information that will be the basis for the formation of a “sustainable urban life” by allowing the determination of the natural areas that can face the threat of urbanization. The cadastral structure is one of the basic variables affecting the growth of a city. Therefore, the purpose of the study is to investigate urban growth by producing cadastral parcel-based simulation models. The land cover data required to create a simulation model were generated from cadastral maps and land registry data, in four different time periods. Within the scope of the study, cellular automata-based urban growth simulation models for the years 2030, 2050 and 2070 were produced, and the land cover changes that occurred in Sancaktepe were investigated.

A meta-modeling approach for spatio-temporal uncertainty and sensitivity analysis: an application for a cellular automata-based Urban growth and land-use change model

ABSTRACTThe paper presents a computationally efficient meta-modeling approach to spatially explicit uncertainty and sensitivity analysis in a cellular automata (CA) urban growth and land-use simulation model. The uncertainty and sensitivity of the model parameters are approximated using a meta-modeling method called polynomial chaos expansion (PCE). The parameter uncertainty and sensitivity measures obtained with PCE are compared with traditional Monte Carlo simulation results. The meta-modeling approach was found to reduce the number of model simulations necessary to arrive at stable sensitivity estimates. The quality of the results is comparable to the full-order modeling approach, which is computationally costly. The study shows that the meta-modeling approach can significantly reduce the computational effort of carrying out spatially explicit uncertainty and sensitivity analysis in the application of spatio-temporal models.

SIMULATING URBAN GROWTH IN THE GEORGE TOWN CONURBATION

Simulation of land use changes has been discussed at length, with increasing papers proposing new methods to predict urban growth. Simulating urban growth can provide a clearer view of urban expansion which gave huge impact in developing a city. This phenomenon, however, also has driven the encroachment into agricultural and forest land in order to cater urban growth expansion. Therefore, this paper aims to develop an urban growth simulation model using GIS-based CA-Markov approach, incorporated with driving forces of urban growth in the Malaysian context. Land use data based on year 2006, 2010 and 2014 of the study area were prepared in a GIS database and used to develop and validate a CA-Markov Model using Idrisi Kilimanjaro software. The findings indicated new or expanded built-up areas of 2018 are found to be located in close proximity to existing growth centers and settlements.

Transition index maps for urban growth simulation: application of artificial neural networks, weight of evidence and fuzzy multi-criteria evaluation.

Transition index maps (TIMs) are key products in urban growth simulation models. However, their operationalization is still conflicting. Our aim was to compare the prediction accuracy of three TIM-based spatially explicit land cover change (LCC) models in the mega city of Mumbai, India. These LCC models include two data-driven approaches, namely artificial neural networks (ANNs) and weight of evidence (WOE), and one knowledge-based approach which integrates an analytical hierarchical process with fuzzy membership functions (FAHP). Using therelative operating characteristics (ROC), the performance of thesethree LCC models were evaluated. The results showed 85%, 75%, and 73% accuracy for the ANN, FAHP, and WOE. The ANN was clearly superior compared to the other LCC models when simulating urban growth for the year 2010; hence, ANN was used to predict urban growth for 2020 and 2030. Projected urban growth maps were assessed using statistical measures, including figure of merit, average spatial distance deviation, producer accuracy, and overall accuracy. Based on our findings, we recomend ANNs as an and accurate method for simulating future patterns of urban growth.

Configuring the neighbourhood effect in irregular cellular automata based models

ABSTRACTCellular automata (CA) models have been widely employed to simulate urban growth and land use change. In order to represent urban space more realistically, new approaches to CA models have explored the use of vector data instead of traditional regular grids. However, the use of irregular CA-based models brings new challenges as well as opportunities. The most strongly affected factor when using an irregular space is neighbourhood. Although neighbourhood definition in an irregular environment has been reported in the literature, the question of how to model the neighbourhood effect remains largely unexplored. In order to shed light on this question, this paper proposed the use of spatial metrics to characterise and measure the neighbourhood effect in irregular CA-based models. These metrics, originally developed for raster environments, namely the enrichment factor and the neighbourhood index, were adapted and applied in the irregular space employed by the model. Using the results of these metrics, distance-decay functions were calculated to reproduce the push-and-pull effect between the simulated land uses. The outcomes of a total of 55 simulations (5 sets of different distance functions and 11 different neighbourhood definition distances) were compared with observed changes in the study area during the calibration period. Our results demonstrate that the proposed methodology improves the outcomes of the urban growth simulation model tested and could be applied to other irregular CA-based models.

Housing land transaction data and structural econometric estimation of preference parameters for urban economic simulation models.

This paper describes a dataset of 6284 land transactions prices and plot surfaces in 3 medium-sized cities in France (Besançon, Dijon and Brest). The dataset includes road accessibility as obtained from a minimization algorithm, and the amount of green space available to households in the neighborhood of the transactions, as evaluated from a land cover dataset. Further to the data presentation, the paper describes how these variables can be used to estimate the non-observable parameters of a residential choice function explicitly derived from a microeconomic model. The estimates are used by Caruso et al. (2015) to run a calibrated microeconomic urban growth simulation model where households are assumed to trade-off accessibility and local green space amenities.

Simulating urban growth driven by transportation networks: A case study of the Istanbul third bridge

Increasing populations and the related transport volumes are problematic for metropolitan regions in Istanbul especially due to the separation of the city by the Bosphorus strait, which creates significant congestion, delays, and carbon dioxide emissions. A third bridge north of the two existing bridges that will bypass the urban areas in Istanbul is under construction to address these challenges. The new Yavuz Sultan Selim Bridge project will transect the most important natural areas. The main purpose of this paper is to investigate urban growth driven by transport networks as observed after the construction of the two existing bridges and the probable impact the third bridge will have on land use and land cover (LULC) via a cellular automata (CA) based simulation technique. The time periods selected for model calibration were 1972, 1987, 2002, and 2009 because the first bridge (Bosphorus Bridge) was built in 1973 and the second bridge (Fatih Sultan Mehmet Bridge) in 1988. An urban growth simulation model was generated for the year 2030, and a change detection analysis was performed between 1972–2009 and 2009–2030 to determine the probable natural areas threatened by urbanization. According to the results, Istanbul will continue to grow northward with 41% of forested areas and 28% of fragile ecosystem areas transforming into urban areas between 2009 and 2030.

Multi-agent based modeling of spatiotemporal dynamical urban growth in developing countries: simulating future scenarios of Lianyungang city, China

Urbanization is the most typical form of land use/cover change, and exploration of the driving mechanism of urban growth and the prediction of its future changes are very important for achieving urban sustainable development. In view of the ability of a multi-agent system to simulate a complex spatial system and from the perspective of combining macroscopic and microscopic decision-making behaviors of agents, a spatiotemporal dynamical urban growth simulation model based on the multi-agent systems has been developed. In this model, macroscopic land use planning behaviors implemented by macroagents and microscopic land use selection behaviors autonomously generated by microagents interact within two-dimensional spatial cells. Furthermore, the urbanization process is promoted through joint decision-making by macroagents and microagents. Considering the central region of the coastal industrial city Lianyungang as the study area, we developed three target scenarios on the basis of current trends, economic development priorities, and environmental protection priorities. Moreover, the corresponding urban growth scenarios were simulated and analyzed. The simulation results show that by combining the macroscopic and microscopic decision-making behaviors of agents to simulate spatiotemporal dynamical urban growth based on the multi-agent systems, the proposed model can provide a useful spatial exploratory tool for explaining the driving mechanism of urbanization and providing decision-making support for urban management.

Planning Storm-Drinage Systems for Urban Growth

A package is developed that integrated and interfaces an urban growth simulation model and an urban hydrology model, so as to explicitly evaluate the effectiveness of nonstructural measures, e.g., zoning as far as the drainage system is concerned. Alternate growth scenarios can thus be directly related to the corresponding storm-drainage systems of specified standards of performance. Among the remaining “Best Management Practice” control options, the on-site storage, treatment and controlled overflow are retained as accurately representing the facets of urban storm-water management relevant at the planning stage. This is achieved by including these controls in a simulation of the hydrologic behavior of each local drainage basin of an urban agglomeration separately. An application is given for a medium size community in Indiana.