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Simultaneously simulate vertical and horizontal expansions of a future urban landscape: a case study in Wuhan, Central China

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Abstract

While there are extensive studies of urban 2D forms, research on the varying geometric features and spatial distribution patterns of urban 3D spaces is comparatively rare. In this paper, we propose a coupled model, known as BPANN-CBRSortCA, which is based on a back propagation artificial neural network (BPANN) and case-based reasoning technology with sort cellular automaton (CBRSortCA) to simulate future urban building heights and their spatial distribution. BPANN–CBRSortCA uses BPANN to predict the vertical extrusion of building heights and uses CBRSortCA to simulate horizontal urban expansion. The BPANN–CBRSortCA model is innovative because of its capabilities to simultaneously project urban growth in the vertical and horizontal dimensions. The proposed model also overcomes the limitations of the traditional cellular automata models that cannot simulate ‘diffused’ urban expansion. This research used Wuhan City as a case study to simulate vertical and horizontal urban expansion from 2015 to 2025. The results showed the following: (1) in the next 10 years, new build-up will mainly appear along the edge of Hongshan and Hanyang Districts or will occupy bare land in the form of ‘filling’ and (2) the tallest buildings will be mainly located to the south of East Lake in Hongshan District and on undeveloped land within the city. These simulation results can provide a reference for future urban planning.

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... Wuhan's urban space shows a trend of radiating and expanding from the central urban area to the surrounding areas ( Figure 10), especially in the south and northeast of Wuhan, which is closely related to Wuhan's policies to accelerate the construction of the Yangtze River Economic Belt and is in line with the urban space development trend of "one master and four deputies". Moreover, He et al. (2017) and Zhai et al. (2021) found that when simulating the urban spatial expansion of Wuhan, it mainly concentrated in the edges of the Hanyang and Hongshan Districts [49,50], which is consistent with the findings of this study. In addition, the UDB of Wuhan is lower than the expansion scale of the built-up area predicted by Liang et al. (2021) and Wang et al. (2021) [51,52]. ...
... Wuhan's urban space shows a trend of radiating and expanding from the central urban area to the surrounding areas ( Figure 10), especially in the south and northeast of Wuhan, which is closely related to Wuhan's policies to accelerate the construction of the Yangtze River Economic Belt and is in line with the urban space development trend of "one master and four deputies". Moreover, He et al. (2017) and Zhai et al. (2021) found that when simulating the urban spatial expansion of Wuhan, it mainly concentrated in the edges of the Hanyang and Hongshan Districts [49,50], which is consistent with the findings of this study. In addition, the UDB of Wuhan is lower than the expansion scale of the built-up area predicted by Liang et al. (2021) and Wang et al. (2021) [51,52]. ...
... How to achieve the rational, orderly, and sustainable development of cities has always been a hot topic in China's urbanization research. Moreover, the rational delineation of the urban development boundary as an effective means to control the disorderly expansion and restrict the healthy development of cities has attracted attention from scholars [49,50]. In this context, a series of mathematical models for the urban development boundary have emerged [12,51,52]. ...
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In order to control the development of urban space, it is important to explore scientific methods to provide a reference for regional territorial space planning. On the basis of the minimum cumulative resistance (MCR) model and the cellular automaton (CA)-Markov model, we constructed a new technical method for delineating urban development boundaries, exploring the temporal and spatial distribution characteristic of land use in Wuhan from 2010 to 2020 through nighttime and remote sensing images, and simulating the urban development boundaries of Wuhan from 2025 to 2035. The results show that: (1) the scales of Wuhan City’s built-up areas in 2010, 2015, and 2020 were 500 km2, 566.13 km2, and 885.11 km2, respectively, and the trends of expansion run to the east and southeast, and (2) on the basis of the MCR model, the urban development boundary scale of Wuhan City in 2025, 2030, and 2035 from the perspective of actual supply will be 903.52 km2, 937.48 km2, and 1021.44 km2, respectively, and based on the CA-Markov model, the urban development boundary scales of Wuhan City in 2025, 2030, and 2035 from the perspective of ideal land demand will be 912.75 km2, 946.40 km2, and 1041.91 km2, respectively. By combining the results of the two methods, we determined areas of 901.62 km2, 944.39 km2, and 1015.36 km2 as the urban development boundaries of Wuhan City in 2025, 2030, and 2035, respectively. According to the principle of supply–demand balance, the urban development boundary delineated by the integration of the MCR model and CA-Markov model, which is in line with the spatial expansion trend of growing cities, could optimize the urban development pattern; solve the contradiction between urban development, farmland protection, and ecological protection; and provide a methodological reference and decision-making basis for planning practice.
... complex evolution forms containing internally connected dots, the expansion of planes and the integrated interactions between dots and planes [7,8]. The growth of "dots" occurs in different periods of urban development [6]. In the early stage of urban expansion, a large number of small construction patterns emerge quickly around the original main city pattern [9,10], which manifest as "diffusion" and can be abstracted as "dots" in growth [5,11]. ...
... This phenomenon of disjoint and spatial separation can also be abstractly referred to as "dot" growth. In the traditional CA model, none of these "dot" growth processes can be effectively simulated [5,6]. ...
... As a result, SLEUTH may not be able to simulate a real urban proliferation scenario. Liu et al. proposed the SMDUGT model to simulate diffusion and aggregation in urban morphology evolution [6]. The basic idea of SMDUGT is to divide the expansion candidate areas of different expansion types based on regression results, and then consider neighborhood factors to construct the adjacent (edge and infill) and outlying (outlying) types. ...
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Restricted by urban development stages, natural conditions, urban form and structure, diffusional growth occupies a large proportion of area in many cities. Traditional cellular automata (CA) has been widely applied in urban growth studies because it can simulate complex system evolution with simple rules. However, due to the limitation of neighborhood conditions, it is insufficient for simulating urban diffusional growth process. A maximum entropy mode was used to estimate three layers of probability spaces: the probability layer of cell transformation from non-urban status to urban status (PLCT), the probability layer for aggregated growth (PLAP), and the probability layer for diffusional growth (PLOP). At the same time, a maxent category selected CA model (MaxEnt-CSCA) was designed to simulate aggregated and diffusional urban expansion processes simultaneously. Luoyang City, with a large proportion of diffusional urban expansion (65.29% in 2009–2018), was used to test the effectiveness of MaxEnt-CSCA. The results showed that: (1) MaxEnt-CSCA accurately simulated aggregated growth of 47.40% and diffusional growth of 37.13% in Luoyang from 2009 to 2018, and the overall Kappa coefficient was 0.78; (2) The prediction results for 2035 showed that future urban expansion will mainly take place in Luolong District and the counties around the main urban area, and the distribution pattern of Luolong District will change from the relative diffusion state to the aggregation stage. This paper also discusses the applicable areas of MaxEnt-CSCA and illustrates the importance of selecting an appropriate urban expansion model in a region with a large amount of diffusional growth.
... Lin, Huang, Chen, and Huang (2014) developed an urban CA model to simulate vertical urban growth with a predefined set of 'IF-THEN' rules. He, Liu, Zeng, Chaohui, and Tan (2017) used a back propagation ANN to predict vertical urban growth based on the urban extents that were generated by an urban CA model. These two studies consistently represented vertical urban growth as the change in the types of building heights. ...
... In short, existing models for simulating vertical urban growth suffer from several important issues: (1) Several recent studies have simplified the simulations of vertical urban growth into the simulations of change in the discrete types of, for example, building heights (e.g., 'low-rise', 'high-rise', etc.) (Chen, Xie, et al., 2021;He et al., 2017;Huang et al., 2021). Besides the modeling uncertainties, additional uncertainties could exist in both the definition and classification of the types, and the simluations may not be comparable from one case to another. ...
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Most contemporary urban cellular automata (CA) models primarily focus on the simulation of urban land expansion, and cannot effectively simulate vertical urban growth. This study addresses this drawback by extending a patch-based urban CA model with a component that can predict the building volumes of an urban land expansion scenario. The proposed model is evaluated through a case study in the Guangzhou-Foshan metropolitan area, China. The horizontal urban growth simulations achieve a mean ‘Figure-of-merit’ value of 0.1406 at the cell level and an agreement of 97% at the pattern level. The building volume prediction made by the methods of random forest and k-nearest-neighbor has a testing R² of 0.90 and a mean percentage absolute error of 22%. The proposed model is applied to the urban growth projections under the shared socioeconomic pathways (SSPs). The results successfully reflect the influences that different SSPs have on vertical urban developments. These results also complement related research of urbanization projections under the SSPs, because most existing studies consider the impacts of horizontal urban growth only. As building volumes and heights are fundamental parameters to urban climate modeling, the ability of the proposed model to project future change in vertical urban developments can support the mitigation of climate change effects on human settlements.
... In the early stage, the urban land growth in remote areas will still account for the main part of the newly added urban area. Later, the growth area of remote areas gradually will decrease, and the urban land growth form will enter a very mature stage [55]. In addition, the spatial development pattern of Wuhan in the future will appear a form of multi-centered urban development [56]. ...
... In order to adapt to the future social-economic development, the morphological characteristics of urban development tend to be increasingly based on multi-center development [10,57,58]. The EULG scenario, and the urban development form appears to be more compact [55]. The formulation of specific urban planning should be flexible accordingly [59]. ...
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As one of the 17 Sustainable Development Goals, it is sensible to analysis historical urban land use characteristics and project the potentials of urban sustainable development for a smart city. The cellular automaton (CA) model is the widely applied in simulating urban growth, but the optimum parameters of variables driving urban growth in the model remains to be continued to improve. We propose a novel model integrating an artificial fish swarm algorithm (AFSA) and CA for optimizing parameters of variables in the urban growth model and make a comparison between AFSA-CA and other five models, which is used to study a 40-year urban land growth of Wuhan. We found that the urban growth types from 1995 to 2015 appeared relatively consistent, mainly including infilling, edge-expansion and distant-leap types in Wuhan, which a certain range of urban land growth on the periphery of the central area. Additionally, although the genetic algorithms (GA)-CA model and the AFSA-CA model among the six models due to the distance variables, the parameter value of the GA-CA model is −15.5409 according to the fact that the population (POP) variable should be positively. As a result, the AFSA-CA model regardless of the initial parameter setting is superior to the GA-CA model and the GA-CA model is superior to all the other models. Finally, it is projected that the potentials of urban growth in Wuhan for 2025 and 2035 under three scenarios (natural urban land growth without any restrictions (NULG), sustainable urban land growth with cropland protection and ecological security (SULG), and economic urban land growth with sustainable development and economic development in the core area (EULG)) focus mainly on existing urban land and some new town centers based on AFSA-CA urban growth simulation model. An increasingly precise simulation can determine the potential increase area and quantity of urban land, providing a basis to judge the layout of urban land use for urban planners.
... We selected seventeen driving factors from natural, ecological, socioeconomic and transportation sources ( Figure 4). The growth and development of building heights is a manifestation of vertical urban growth and is related to topography, ecology, economy, society, location, and policy [3,26,30,31]. Seventeen spatial factors of building heights were selected based on the actual situation of the study area and available data sources, as shown in Figure 5. However, the spatial scale of urban land use data in the horizontal direction was 30 m. ...
... We selected seventeen driving factors from natural, ecological, socioeconomic and transportation sources ( Figure 4). The growth and development of building heights is a manifestation of vertical urban growth and is related to topography, ecology, economy, society, location, and policy [3,26,30,31]. Seventeen spatial factors of building heights were selected based on the actual situation of the study area and available data sources, as shown in Figure 5. (c) distance to city center; (d) distance to district centers; (e) distance to railways; (f) distance to highways; (g) distance to national roads; (h) distance to provincial roads; (i) distance to railway stations; (j) distance to subway stations; (k) distance to ocean; (l) distance to lakes; (m) distance to rivers; (n) density of urban road network; (o) population density; (p) PM2.5; (q) GDP. (c) distance to city center; (d) distance to district centers; (e) distance to railways; (f) distance to highways; (g) distance to national roads; (h) distance to provincial roads; (i) distance to railway stations; (j) distance to subway stations; (k) distance to ocean; (l) distance to lakes; (m) distance to rivers; (n) density of urban road network; (o) population density; (p) PM2.5; (q) GDP. ...
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Urban expansion studies have focused on two-dimensional planar dimensions, ignoring the impact of building height growth changes in the vertical direction on the urban three-dimensional (3D) spatial expansion. Past 3D simulation studies have tended to focus on simulating virtual cities, and a few studies have attempted to build 3D simulation models to achieve the synergistic simulation of real cities. This study proposes an urban 3D spatial expansion simulation model to achieve a synergistic simulation of urban horizontal expansion and vertical growth. The future land use simulation model was used to simulate urban land use changes in the horizontal direction. The random forest (RF) regression algorithm was used to predict building height growth in the vertical direction. Furthermore, the RF algorithm was used to mine the patterns of spatial factors affecting building heights. The 3D model was applied to simulate 3D spatial changes in Shenzhen City from 2014 to 2034. The model effectively simulates the horizontal expansion and vertical growth of a real city in 3D space. The crucial factors affecting building heights and the simulation results of future urban 3D expansion hotspot areas can provide scientific support for decisions in urban spatial planning.
... It must be noted that our attention to urban expansion issue has thus FAR been limited to the horizontal level. However, as a three-dimensional space, the urban expansion also has three-dimensional attributes [8][9][10][11]. In other words, urban vertical expansion is an important part of urban expansion that cannot be ignored [11,12]. Urban vertical expansion can effectively reflect the concentration of population and economy in the city with the improvement of building height as its most obvious feature [13]. ...
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Urban expansion is not only reflected in the increase in horizontal urban area, but also in the increase in vertical urban height, that is, the city’s vertical expansion. Exploring the spatiotemporal evolution of urban vertical expansion and its influencing factors is critical for furthering urban expansion research. This paper characterized the degree of urban vertical expansion using the floor area ratio (FAR) of newly added residential land in Jiangsu Province, China, from 2009 to 2018, and discussed the spatiotemporal characteristics, regional differences and influencing factors of urban vertical expansion. The results indicate the following: (1) The degree of urban vertical expansion in Jiangsu Province exhibited an inverted “U” shape that increased and then decreased. Spatially, it presented a pattern of high in the west and low in the east areas, while high in the north and low in the south areas. (2) There were significant α-convergence, β-convergence and club convergence phenomena in Jiangsu Province’s urban vertical expansion. The convergence speed was fast in the north and south areas but slow in the middle. (3) Real estate development investment and slope had a significant positive impact on urban vertical expansion, while urban per capita disposable income and economic structure had a non-linear impact on urban vertical expansion intensity. Finally, this paper highlighted the importance of further investigation into urban expansion from multiple dimensions. The government should strengthen its control over the various land plot ratios to ensure the city’s orderly expansion and healthy development.
... K is the total number of independent variables. Based on previous researches (He, Liu, Chen, Yin, & Tan, 2017a;Müller, Steinmeier, & Küchler, 2010;Thapa & Murayama, 2010) and expertise knowledge, a set of spatial factors, including slope, elevation, population density, and distances to the Yangtze River, highways, national roads, provincial roads, urban main roads, residential areas, the urban center, public transport stations, building blocks, existing urban areas and commercial centers, were selected as candidate independent variables. Before regression, the multi-collinearity and significance of candidate variables were examined, and the variables with VIF (variance inflation factor) greater than 10 or not significant at the 90 % confidence level were excluded. ...
Article
Rapid urbanization causes great changes of carbon metabolism. Current research mainly focuses on carbon consequences of urban expansion projections, but rarely explores how carbon management strategies affect future urban growth trajectories. Here, we propose a hierarchy of low-carbon management strategies and incorporate it into an integrated cellular automata model to obtain sustainable urban development plans. In the hierarchy, a top-down strategy regarding carbon emission reduction is used to adjust future urban land demand, while a bottom-up strategy regarding carbon sequestration conservation of land patches is used to constrain land use conversions. We design four expansion scenarios based on different combinations of two low-carbon strategies for Wuhan in 2025, including business as usual (BAU), the scenario with top-town strategy (T-UES), the scenario with bottom-up strategy (B-UES), and the scenario with both two strategies (TB-UES). Our results demonstrate that the proposed method can generate promising urban expansion plans with less ecological loss, and promote compact and infilling urban development.
... The land use data for 2005-2015 were obtained from the National Land Use/Cover Database of China at the 1:100,000 scale. In accordance with the land resource and utilization attributes, six classes of land uses-cropland, woodland, grassland, water body, built-up land, and unused land-were identified [6]. A 30 ? 30 m gridded database of land use classification, which is considered to be an accurate and reliable dataset for the monitoring, forecasting, and driving analysis of land use change at a regional scale, has been accomplished [37]. ...
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Air pollution in China is a serious problem and an inevitable threat to human health. This study evaluated the relationship between air quality and urban growth pattern in China by conducting empirical research involving 338 prefecture-level and above cities. Spatial regression techniques considering spatial autocorrelation were applied to correct the calculation bias. To obtain local and accurate results, a conception of eight economic zones was adopted to delineate cities into different groups and to estimate regression separately. An additional six urban form and socioeconomic indicators served as controlling variables. Significant and positive relationships between the aggregated urban growth pattern index and air pollution were observed in Northeast China, northern coastal China, and Northwest China, indicating that a high degree of urban aggregation is associated with poor air quality. However, a negative parameter was obtained in southern coastal China, showing an opposite association on urban aggregation and air quality. Nonsignificant connections among the other four zones were found. The findings also highlighted that land use mix, population density, and city size exerted varied and significant influence on air quality across eight economic zones. Overall, this study indicated that understanding the quantitative relationships between urban forms and air quality can provide policymakers with alternative ways to improve air quality in rapidly developing China.
... The CA model is a temporal and spatial model of global changes evolving from local behaviors among individuals. It is favored by many researchers when dealing with complex and dynamic urban spatial systems (Clarke and Gaydos 1998;Batty 2007;Chen et al. 2014;He et al. 2017). Since the presentation of CA theory by Ulan and Von Neumann in the late 1940s (Neumann 1966), it has been used extensively for research into urban spatial growth prediction and land use simulation (Batty, Xie, and Sun 1999;Santé et al. 2010;Zheng et al. 2015). ...
Article
In recent years, the rapid expansion of urban spaces has accelerated the mutual evolution of landscape types. Analyzing and simulating spatio-temporal dynamic features of urban landscape can help to reveal its driving mechanisms and facilitate reasonable planning of urban land resources. The purpose of this study was to design a hybrid cellular automata model to simulate dynamic change in urban landscapes. The model consists of four parts: a geospatial partition, a Markov chain (MC), a multi-layer perceptron artificial neural network (MLP-ANN), and cellular automata (CA). This study employed multivariate land use data for the period 2000–2015 to conduct spatial clustering for the Ganjingzi District and to simulate landscape status evolution via a divisional composite cellular automaton model. During the period of 2000–2015, construction land and forest land areas in Ganjingzi District increased by 19.43% and 15.19%, respectively, whereas farmland, garden lands, and other land areas decreased by 43.42%, 52.14%, and 75.97%, respectively. Land use conversion potentials in different sub-regions show different characteristics in space. The overall land-change prediction accuracy for the subarea-composite model is 3% higher than that of the non-partitioned model, and misses are reduced by 3.1%. Therefore, by integrating geospatial zoning and the MLP-ANN hybrid method, the land type conversion rules of different zonings can be obtained, allowing for more effective simulations of future urban land use change. The hybrid cellular automata model developed here will provide a reference for urban planning and policy formulation.
... Edge-expansion and outlying expansion lead to a more dispersed urban form whereas infilling leads to a more compact urban form. (Xu et al., 2007;Shi et al., 2012;Jiao et al., 2015;Sun et al., 2013;He et al., 2017b). As such, this paper utilizes LEI to identify the expansion type for newly developed areas. ...
Article
The emergence of geographical ‘big data’ provides new opportunities for studying urban issues. This study uses geographical ‘big data’ on point of interest density (POID), degree of urban function mixing (MIX), location check-in density (CIQD), housing prices (HP), and population change (POPC) to measure the urban vitality of patches of new development that occurred in Chinese cities from 2005 to 2015. The study uses association rule analysis to explore the relationship between different urban growth patterns on urban vitality, and the results indicate that different forms of urban growth have different effects on urban vitality. Infilling is characterized by high values for point of interest density and location check-in density with low values for urban function mixing and mixed values for population change. Edge-expansion is associated with high values for population change and urban function mixtures. Outlying expansion is associated with several negative values for urban vitality, particularly variables related to interactions between people and the environment around them (CIQD). The results indicate that cities may utilize these different forms of urban growth patterns to achieve different goals; for example, infilling may be more effective for office-style development in areas with existing higher population density and urban function mixtures, and edge-expansion may be effective for rapidly absorbing large populations and hosting urban functions that require larger footprints. As such, Chinese cities currently undergoing early stages of development should pursue high-intensity edge-expansion development. To the best of the authors’ knowledge, this is the first attempt to study the relationship between urban expansion types and urban vitality through the use of ‘big data,’ and the results of this study can provide guidance on urban spatial development for government leaders and researchers in the future.
... On the basis of the New Urbanization Development Plan of the State (2014-2020) issued by the State Council of China in 2014, the urbanization level has increased from 17.9% in 1978 to 57.35% in 2016 and is expected to reach 60% by 2020. The resulting urban growth has caused a series of social and ecological problems, which have restrained the sustainable development of the economy and environment (He et al., 2017a(He et al., , 2017bXia et al., , 2019bZhang et al., 2019). Thus, how to effectively control the disorderly expansion of cities and achieve a reasonable development is an important issue for urban China. ...
... Other studies simulated the process of urban expansion in Surrey, Canada, and Wuhan, China. Koziatek et al. [2] observed that in Surrey high-rise buildings will be more common near transportation networks, urban centers, and higher population densities, while He et al. [29] noted that in Wuhan the highest buildings will occur near the lake and undeveloped land. Existing 3D urban expansion studies intend to focus on megacities with high economic. ...
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The development of cities in the vertical dimension is important in valley-type cities where physical growth is limited by terrain. However, little research has focused on three-dimensional urban expansion of valley-type cities. Lanzhou is a typical valley-type city in China and Chengguan District is the core area of Lanzhou City. This research is aimed at understanding the development of valley-type cities through the analysis of the three-dimensional urban expansion of Lanzhou Chengguan District and providing a reference for urban planning. We extracted five periods of architectural contours and height information between 1975 to 2018 with the support of multi-source remote sensing and network data. We used overlay analysis and mathematical statistical methods to analyze urban horizontal expansion and used the building density, floor area ratio, vertical expansion speed, fluctuation degree, and skyline to analyze urban vertical expansion. We found that the mode of horizontal expansion of Chengguan District shifted from adjacency to enclave through mountain area reclamation. The area with the fastest vertical expansion speed first appeared in the horizontal expansion completed area, and then in both the rapid horizontal expansion area and in the horizontal expansion completed area. Before 2007, the speed of horizontal expansion increased and reached its peak while the vertical expansion speed was relatively stable. After that, the former decreased, and the vertical expansion increased rapidly and dominated the urban development. The vertical expansion of the valley-type city gradually dominates urban development. Urban planning should consider the three-dimensional expansion, especially in the vertical dimension.
... Therefore, urban growth in WMA was not efficient in the last two decades. To control the over expansion of urban land, future planning can pay attention to the longitudinal growth of urban with more floors in the buildings to satisfy the housing and commercial demands for land, which has been considered in the planning document of Wuhan (He, Liu, Zeng, Yin, & Tan, 2017). Factories should be encouraged to speed up renovation and upgrading to improve efficiency to mitigate the increasing demands for industrial land. ...
Article
Accurate forecasting of future urban land expansion can provide useful information for policy makers and urban planners to better plan for the impacts of future land use and land cover change (LULCC) on the ecosystem. However, most current studies do not emphasize spatial variations in the influence intensities of the various driving forces, resulting in unreliable predictions of future urban development. This study aimed to enhance the capability of the SLEUTH model, a cellular automaton model that is commonly used to measure and forecast urban growth and LULCC, by embedding an urban suitability surface from geographically weighted logistic regression (GWLR). Moreover, to examine the performance of the loosely-coupled GWLR-SLEUTH model, a layer with only water bodies excluded and a layer combining the former with an urban suitability surface from logistic regression (LR) were also used in SLEUTH in separate model calibrations. This study was applied to the largest metropolitan area in central China, the Wuhan metropolitan area (WMA). Results show that the integrated GWLR-SLEUTH model performed better than either the traditional SLEUTH model or the LR-SLEUTH model. Findings demonstrate that spatial nonstationarity existed in the drivers' impacts on the urban expansion in the study area and that terrain, transportation and socioeconomic factors were the major drivers of urban expansion in the study area. Finally, with the optimal calibrated parameter sets from the GWLR-SLEUTH model, an urban land forecast from 2017 to 2035 was conducted under three scenarios: 1) business as usual; 2) under future planning policy; and 3) ecologically sustainable growth. Findings show that future planning policy may promise a more sustainable urban development if the plan is strictly obeyed. This study recommended that spatial heterogeneity should be taken into account in the process of land change modeling and the integrated model can be applied to other areas for further validation and forecasts.
... The rapid development of cities has led to fast growth of urban space in China [1,2]. In many large cities, high-rise buildings have been constructed to meet the demands of the rapid growth of urban population. ...
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The quantitative relationship between the spatial variation of building's height and the associated land surface temperature (LST) change in six Chinese megacities is investigated in this paper. The six cities involved are Beijing, Shanghai, Tianjin, Chongqing, Guangzhou, and Shenzhen. Based on both remote sensing and building footprint data, we retrieved the LST using a single-channel (SC) algorithm and evaluate the heating/cooling effect caused by building-height difference via correlation analysis. The results show that the spatial distribution of high-rise buildings is mainly concentrated in the center business districts, riverside zones, and newly built-up areas of the six megacities. In the urban area, the number and the floor-area ratio of high to super high-rise buildings (>24m) account for over 5% and 4.74%, respectively. Being highly urbanized cities, most of urban areas in the six megacities are associated with high LST. Ninety-nine percent of the city areas of Shanghai, Beijing, Chongqing, Guangzhou, Shenzhen, and Tianjin are covered by the LST in the range of 30.2~67.8˚C, 34.8~50.4˚C, 25.3~48.3˚C, 29.9~47.2˚C, 27.4~43.4˚C, and 33.0~48.0˚C, respectively. Building's height and LST have a negative logarithmic correlation with the correlation coefficients ranging from-0.701 to-0.853. In the building's height within range of 0~66m, the LST will decrease significantly with the increase of building's height. This indicates that the increase of building's height will bring a significant cooling effect in this height range. When the building's height exceeds 66m, its effect on LST will be greatly weakened. This is due to the influence of building shadows, local wind disturbances, and the layout of buildings.
... The literature on VUG is novel, but important for urban planning. For example, [2] employs statistical techniques to examine VUG in Indonesia; [4] develops a tool to represent VUG over time using multi-criteria evaluation; [7] builds a model with genetic algorithms to predict VUG in the city of Tokyo (Japan); [13] designs a model based on satellite image classification using Suport Vector Machines to estimate VUG; [3] predicts UVG with an ANN programmed in Matlab. On the other hand, there is research that analyses perception, attitudes, concerns and acceptance of VUG [6], [11] and [10]. ...
... The average annual temperature is 15.8°C-17.5°C. In past few years, accompanied by accelerated development of the economy and increased population in the region, significant changes in land use and landscape pattern were observed in previous studies [9,10]. ...
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Based on the land use data of 2000, 2010 and 2020, using GIS technology along with landscape ecology methods, this paper monitored the changes in land use and landscape pattern in Wuhan. The findings are as follows: (1) the main features of land use change in Wuhan were the expansion of urban area and the decline of cropland, forest, wetland and water in recent 20 years; (2) forest, wetland and water kept a transfer-out trend while urban kept a transfer-in trend; (3) the fragmentation degree of forest, grassland and urban landscapes decreased from 2000 to 2020; (4) the patch shapes of almost all landscapes tended to be more regular under the human interventions. It is thereby worth reducing the interference intensity of human activities on landscape pattern in the process of urban growth.
... Expect eco-environmental studies, the FA is potentially useful for many other socio-economic studies, such as CO 2 emissions , CO 2 stock and absorption (Xi et al., 2016), material stock flows in built environment and urban infrastructure (Han et al., 2018;Schiller et al., 2020), and other socio-economic activities. Urbanization is not only the expansion of built-up area, but also the increase of building heights caused by old city reconstruction and urban renewal (He et al., 2017). The FA map could provide great support for urban governor to carry out comprehensive planning and management of urban horizontal and vertical development. ...
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Urbanization studies are of global interest and mainly focus on mapping urban areas and areas of expansion using remote sensing data. However, information about the 3-dimensional characteristics or expansion of urban buildings is absent due to difficulties in data acquisition. Quantifying the urban floor area is crucial for assessing urban 3-D morphology. We used a random forest regression model to predict the first urban floor area of mainland China at a 130-m spatial resolution based on high spatial resolution nighttime light LUOJIA 1-01 images (130-m), a population map (100-m), and a single building dataset encompassing 71 cities. The predicted floor area (PFA) map for mainland China was estimated from the single building dataset of 50 cities, and data from the other 21 cities were used to estimate the accuracy. The results showed that the total accuracy of the PFA map is strong (R² = 0.68, RMSE = 7277.46 m²/ha). The PFA map overestimated the values in low value areas and underestimated the values in high value areas. The accuracy was also acceptable at the single city scale based on the results from six cities (R² > 0.6). The calculated floor area map for 71 cities was merged with the PFA map by replacing the values in the corresponding locations to generate the final predicted floor area (FPFA) map, which enabled higher accuracy. The total floor area is 76038.39 km², which is 0.79% of the total area of China. The general distribution of the floor area amount and intensity showed that the coastline had a higher intensity than the inner region of the country and the southern region had a higher intensity than the northern urban area along the coastline. The floor area distribution was extremely uneven among the provinces. The top six provinces represent 50.01% of the total floor area; however, the last six provinces represent only 3.31%. The high spatial resolution FPFA map of mainland China calculated by us has great potential application in urban ecology research, such as the impact of FPFA on heat island and haze.
... cluster.AffinityPropagation 函数实现。 2.2 近邻传播聚类元胞自动机流程 APCA 与 Logistic-CA (后文称之为 TCA) 的不同在于 APCA 迭代过程中会自动决定 新增元胞的类型是飞地型还是邻接型 (边缘型和填充型的统称) 。它将 AP 确定的聚类中 心作为扩散型增长的"种子点" ,在"种子点"周围增长不受满足邻域内一定数量的城市 元胞要求限制;另一方面原有城市斑块周边或者空隙内的增长过程在本文中被称为邻接 增长,需要满足邻域限制。APCA 总体思路是使用 AP 在非城市元胞集合中寻找一定数量 的聚类中心作为扩散型增长的"种子点" ,并依照"种子点"与原有城市元胞的相似程度 设计"异步迭代"的策略,使得相似度更高的"种子点"周围能更快的形成新的城市元 胞;对于邻接型增长则基于一种排序元胞自动机的策略,使得转变潜力高的非城市元胞 优先发生状态转变。APCA 的总体流程图如图 1 所示。具体方案流程包括: (1) 确定模拟的开始基期,提取出所有的非城市用地 (编码 0) 和城市用地 (编码 1) 。 使用逻辑回归计算每个非城市元胞转向城市元胞的概率 [23] 。参考以往研究以及结合研究 区实际 [8,13] ...
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Accurate modeling of urban growth is an extremely important component of urban geographic studies and is also vital for future urban planning. The trajectories of urban growth can be monitored and modeled by the use of geographic information system techniques, remote sensing data, and statistical analysis. In this study, we couple game theory with an integrated agent-cellular method to develop a model of the major determinants controlling urban development, which not only accounts for socioeconomic driving forces but also captures human actions. Wuhan, the largest city in central China, is undergoing rapid urbanization and is facing uncontrolled urban expansion. The city proper region of Wuhan is selected as the case study area to simulate urban growth during the period between 2003 and 2023. The results indicate that the social conflicts between the different stakeholders in urban development can be identified by utilizing a game tree. The game-theory based agent-cellular model is shown to be more effective than a pure cellular automata model in urban growth simulation. The results also show that, from 2013 to 2023, the urban area of the Wuhan city proper region is predicted to grow to 442.77 km2, which is almost two times the area in 2003. This research is the first study to use empirical data and game theory to analyze the decision-making process in urban development in the Wuhan area.
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Urban development is a complex spatio-temporal process that involves both horizontal and vertical growth. Despite growing recognition of the significance of horizontal development, models of urban vertical growth remain limited. This study aims to develop a GIS-based cellular automata model for exploring the vertical complexities of urban growth. Taking into account a series of variables, including accessibility, population density and building density and height, an “IF-THEN” rule base is designed and employed to simulate different height states of building growth. The model is validated through application to a case study of Guangzhou city for the period of 2001–2010. The results of the proposed model are compared with Guangzhou Urban Planning Bureau reference data for newly authorized construction buildings and then tested using an error matrix for 2001–2005 (overall accuracy 81.2% and Kappa coefficient 74.2%) and a fractal dimension for 2006–2010. Several conclusions are made based on the fractal analysis: (1) low-rise buildings tend to “spread outward,” while high-rise buildings exhibit a trend of “compact development”; (2) a “hot zone” of vertical growth in Guangzhou demonstrates that the city is now undergoing a “phase transition” from a mono-center to a bi-center; and (3) low-, moderate-, and high-state buildings are being co-developed and are thus beginning to constitute an important feature of the urban and smart growth landscape.
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China’s economic reforms and unprecedented growth have generated many fascinating issues for scholarly research. An understanding of urbanization and land use change in China is required for appropriate strategies and policies to facilitate future sustainable development. This paper reviews the literature on urbanization, land use and sustainable development in China with a focus on land use change. We argue that land use and environmental research are embedded in the complex economic-geographical processes and multiple trajectories of development and urbanization in China. This paper highlights the important role of space–time modeling in a multi-disciplinary setting in the study of urbanization, land use and sustainable development. It also points out potential areas for future research.
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Over half of the earth’s terrestrial surface has been modified by humans. This modification is called land use change and its pattern is known to occur in a non-linear way. The land use change modeling community can advance its models using data mining tools. Here, we present three data mining land use change models, one based on Artificial Neural Network (ANN), another on Classification And Regression Trees (CART) and another Multivariate Adaptive Regression Splines (MARS). We reconfigured the three data mining models to concurrently simulate multiple land use classes (e.g. agriculture, forest and urban) in South-Eastern Wisconsin (SEWI), USA (time interval 1990–2000) and in Muskegon River Watershed (MRW), Michigan, USA (time interval 1978–1998). We compared the results of the three data mining tools using relative operating characteristic (ROC) and percent correct match (PCM). We found that ANN provided the best accuracy in both areas for three land use classes (e.g. urban, agriculture and forest). In addition, in both regions, CART and MARS both showed that forest gain occurred in areas close to current forests, agriculture patches and away from roads. Urban increased in areas of high urban density, close to roads and in areas with few forests and wetlands. We also found that agriculture gain is more likely for the areas closer to the agriculture and forest patches. Elevation strongly influenced urbanization and forest gain in MRW while it has no effect in SEWI.
Article
Modeling urban growth and generating scenarios are essential for studying the impact and sustainability of an urban hydrologic system. Urban systems are regarded as complex self-organizing systems, where the dynamic transitions from one form of landuse to another occur over a period of time. Therefore, a modeling framework that captures and simulates this complex behavior is essential for generating urban growth scenarios. Cellular Automata (CA)-based models have the potential to model such discrete dynamic systems. In this study, a constraint-based binary CA model was used to predict the future urban growth scenario of the city of Roorkee (India). A hydrologic model was applied on the simulated urban catchment to study its hydrologic response. The Natural Resources Conservation Service Curve Number (NRCS-CN) method, which is suitable for ungauged urban watersheds, was adopted to determine the impact of urban growth on the quantity of storm water runoff over a period of time. The results indicate that urban growth has a linear relationship with peak discharge and time to peak for the catchment under investigation.
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The effective modeling of the urban landscape dynamics in a megalopolitan cluster area (MCA) is essential to understanding its spatial evolution process. However, existing urban landscape dynamic models based on cellular automata (CA) are limited in that they do not consider urban flows (e.g., flows of people, material, and information) between the different cities/towns in an MCA. This paper proposes a new megalopolitan landscape dynamic model (MLDM) that is better suited for simulating the urban landscapes in an MCA by combining a gravitational field model (GFM) with a CA model. The GFM was used to model the influence of inter-city urban flows and to refine the transition rules of the CA model. The MLDM was applied to simulate the urban landscape in the MCA of Beijing–Tianjin–Tangshan, and produced more accurate simulation results than the CA model that did not account for urban flows. The MLDM-based prediction of future landscapes suggested that urbanization will continue in the region through 2020, especially in a few ‘hotspot’ areas. Close attention should be paid to these areas for strategic regional planning and environmental protection in this heartland of China.
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Peri-urban areas are sites currently experiencing the most active urbanization. In order to quantify the complexity in urban growth patterns and processes, based on the common boundary and Landscape Expansion Index (LEI) analysis, we developed six spatial rules to identify three urban growth types of infilling, edge-expansion and outlying in 2000–2008. In addition, growth density (GD) with buffer zone analysis was applied to ascertain urban movement from the city center. Results indicated the prominent urban growth type was edge-expansion, with continued urbanization most prominently in the peri-urban areas. The infilling growth increment proportion in the second study period (2004–2008) suggested the study area was experiencing an increased coalescence pattern and declined diffusion pattern. However, the highest infilling GD zones that occurred primarily near the city center did not vary significantly in 2000–2008. The intensive urban growth zones in the first study period (2000–2004) followed by later efficient infill growth also suggested urban growth was more compact in the second study period. Furthermore, two land use change maps showed that substantial arable land was lost by urban growth in 2000–2008, and the establishment of industrial parks encroached on large salt marsh in the second study period. Further research is required to delineate a suitable development management plan to sustain a baseline for urban growth. Furthermore, the integration of a zoning approach associated with the green belt is suggested to play a key role in a transition to continued urbanization.
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In this study, Hangzhou, the capital city of Zhejiang Province in eastern China was selected as a case study. Based on time series Landsat MSS/TM/ETM + imagery and historical census data, analysis of the relationship between land use dynamics, built-up land expansion patterns, and underlying driving forces from 1978 to 2008 was performed, using an integrated approach of remote sensing (RS) and geographic information system (GIS) techniques and statistical methods. The results showed that rapid expansion of built-up land in the Hangzhou Metropolitan Area (HMA) led to accelerated land use conversion. The built-up land increased from 319.3 km2 in 1978 to 862.5 km2 in 2008. Expansion patterns of built-up land in the HMA were essentially characterized by axial expansion centered on the former city proper before 1991. In 1996 and 2001, two significant administrative division adjustments for the former city proper and two neighboring municipalities occurred. This led to the success in implementing strategies of “frog-leaping development along the Qiantang River” and “crossing the Qiantang River and developing southward”. Spatially, a closer linkage between the former city proper and two neighboring municipalities was established. Consequently, rapid development of infrastructures, facilities, intensive industrial parks, and urban and rural settlements along the Qiantang River resulted in the eastward and southward expansion of built-up land. Thus, from 1991 to 2008 the model of urban expansion resulted in a multi-nuclei pattern. Furthermore, as shown with detailed analysis, the growth pattern of built-up land of the HMA is highly correlated with socio-economic factors, including the gross domestic product (GDP), per capita disposable income, population growth, and processes of industrialization and urbanization, which represent the dominant driving factors for spatiotemporal patterns of built-up land in the HMA.
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Alternative patterns of residential development, going by names such as cluster housing and conservation subdivisions, opt to preserve large areas of shared outdoor space by increasing housing density on portions of the parcel. These alternative approaches arguably help preserve environmental quality, but how do they affect the people who live there? This study explored the impacts of residential density and nature areas on residents' satisfaction with their neighborhood. Survey results from 361 participants in nine different residential subdivisions showed that density and proximity to shared nature areas did not have a large impact on neighborhood satisfaction. More important were opportunities to visit nearby shared space and having views of nature from the home.
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Urban growth is a key concern for planners as it has considerable ecological impact. In both Switzerland and the European Union this growth has mostly proceeded at the expense of agricultural land. It is not yet, however, well understood what drives this extensive land-use change. This study assesses the influence of proximity to motorway exits on urban growth and analyses urban growth along some of the main motorways in Switzerland. The analysis is based on two data collection campaigns from the Land Use Statistics with a time difference of 12 years. Proximity is measured as the distance from a motorway exit, which we related to changes in the entire urban areas and their subclasses ‘Building areas’, ‘Industrial areas’ and ‘Transportation areas’. Linear regression revealed a significant distance trend whereby the closer an area lies to a motorway exit, the higher the rate of urban growth. Industrial areas show the strongest distance trend. Further, variance partitioning revealed the exclusive explanatory power of distance from a motorway exit by partialling out two further potential predictors, the previous urban area and the local relief. We found significant effects of distance, e.g. on industrial areas in the Central Plateau and on building areas in the Central Alps. There, we can assume a causal relationship between proximity to motorway exits and urban growth. Regarding ecoregions or urban subclasses, no uniform picture emerged. We thus recommend discussing urban sprawl separately for different areas and subcategories of urban land.
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Following a discussion of recent policies for the city centre in Britain, the paper explores the contribution of residential development to aspects of sustainability, drawing on a range of survey evidence in Bristol and Swansea. The residents are frequent shoppers, helping to sustain the local daytime economy. Sustainability goals are also supported because large proportions of residents walk to city centre attractions, and many also to their places of work, showing reduced reliance on the private car. Support for the expanding nighttime economy reflects the age, gender and social class composition of the resident population, with different attractions receiving different levels of support from different social groups, but with younger adults as the mainstay. Sustainability in the city centre context appears best served by a majority of young adult residents, ameliorated by a sizeable proportion of older adults, and an absence of households with children. Grandiose government sustainability aims of creating the truly balanced community which includes many children, should be modified in this local context.
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Economic valuation of the amenities of urban open spaces will lend support to urban planning and development. In the absence of an explicit market for these amenities, the hedonic approach is often employed. Hitherto there have been a relatively limited number of studies on hedonic valuation of urban open spaces in China. This paper employs Geographic Field Model (GFM) to specify the externalities of urban open spaces with regard to their specific scale of influence, and builds a GFM-based spatial hedonic model to value the environmental amenities. The GFM quantification of open space variables overcomes the potential bias caused by traditional distance measures without influence scale limitations and the discontinuousness of the dichotomous index indicating the proximity to open space. A GFM-based spatial hedonic analysis was conducted in Wuhan, a metropolis in central China with various open spaces. Proximity to the Changjiang River recreation space and the East Lake were found to exert remarkable and positive impacts on apartment price. But proximity to other lakes and rivers were not significant in the result. The study showed that city level parks have significant amenity values, but district level parks do not. The amenity values of these urban open spaces were demonstrated by measuring the value they added to apartment prices. Some unexpected findings, such as the positive effect of noise and the powerful impact of floor height on housing price, may be common rules in densely populated cities in China.
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Both modelers and social scientists attempt to find better explanations of complex urban systems. They include development paths, underlying driving forces and their expected impacts. So far, land-use research has predominantly focused on urban growth. However, new challenges have arisen since urban shrinkage entered the research agenda of the social and land-use sciences. Therefore, the focus of this paper is a twofold one: Using the example of urban shrinkage, we first discuss the capacity of existing land-use modeling approaches to integrate new social science knowledge in terms of land-use, demography and governance because social science models are indispensable for accurately explaining the processes behind shrinkage. Second, we discuss the combination of system dynamics (SD), cellular automata (CA) and agent-based model (ABM) approaches to cover the main characteristics, processes and patterns of urban shrinkage. Using Leipzig, Germany, as a case study, we provide the initial results of a joint SD-CA model and an ABM that both operationalize social science knowledge regarding urban shrinkage.
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Urban expansion has been a hot topic not only in the management of sustainable development but also in the fields of remote sensing and geographic information science (GIS). After land reform initiated in 1987, Chinese cities are facing a new development wave, which is the mixture of urban expansion and redevelopment. Local urban planners are also facing a huge challenge to require the understanding of complex urban growth process, which involves various actors with different patterns of behavior. Modeling an urban development pattern is the prerequisite to understanding the process. This paper presents a spatial data analysis method to seek and model major determinants of urban growth in the period 1993–2000 by a case study of Wuhan City in PR China. The method comprises exploratory data analysis and spatial logistic regression technique. The former is able to visually explore the spatial impacts of each explanatory variable. The latter can provide a systematic confirmatory approach to comparing the variables. The study shows that the major determinants are urban road infrastructure and developed area, and master planning is losing its role in the specific period.
Article
Urbanization has profoundly transformed many landscapes throughout the world, and the ecological consequences of this transformation are yet to be fully understood. To understand the ecology of urban systems, it is necessary to quantify the spatial and temporal patterns of urbanization, which often requires dynamic modeling and spatial analysis. In this paper, we describe an urban growth model, the Phoenix Urban Growth Model (PHX-UGM), illustrate a series of model calibration and evaluation methods, and present scenario-based simulation analyses of the future development patterns of the Phoenix metropolitan region. PHX-UGM is a spatially explicit urban landscape model and is a modified version of the Human-Induced Land Transformations (HILT) model originally developed for the San Francisco Bay Area. Using land use and other data collected for the Phoenix area, existing growth rules were selectively modified and new rules were added to help examine key ecological and social factors. We used multiple methods and a multi-scale approach for model calibration and evaluation. The results of the different evaluation methods showed that the model performed reasonably well at a certain range of spatial resolutions (120–480 m). When fine-scale data are available and when landscape structural details are desirable, the 120-m grain size should be used. However, at finer levels the noise and uncertainty in input data and the exponentially increased computational requirements would considerably reduce the usefulness and accuracy of the model. At the other extreme, model projections with too coarse a spatial resolution would be of little use at the local and regional scales. A series of scenario analyses suggest that the Metropolitan Phoenix area will soon be densely populated demographically and highly fragmented ecologically unless dramatic actions are to be taken soon to significantly slow down the population growth. Also, there will be an urban morphological threshold over which drastic changes in certain aspects of landscape pattern occur. Specifically, the scenarios indicate that, as large patches of open lands (including protected lands, parks and available desert lands) begin to break up, patch diversity declines due partly to the loss of agricultural lands, and the overall landscape shape complexity also decreases because of the predominance of urban lands. It seems that reaching such a threshold can be delayed, but not avoided, if the population in the Phoenix metropolitan region continues to grow. PHX-UGM can be used as a tool for exploring the outcome of different urban planning strategies, and the methods illustrated in this paper can be used for evaluating other urban models.
Article
Constrained cellular automata (CA) are frequently used for modeling land use change and urban growth. In these models land use dynamics are generated by a set of cell state transition rules that incorporate a neighborhood effect. Generally, neighborhoods are relatively small and therefore only a limited amount of spatial information is included. In this study a variable grid CA is implemented to allow incorporation of more spatial information in a computationally efficient way. This approach aggregates land uses at greater distances, in accordance with a hierarchical concept of space. More remote areas are aggregated into consecutively larger areas. Therefore the variable grid CA is capable of simulating regional as well as local dynamics at the same time. The variable grid CA is used here to model urban growth in the Greater Vancouver Regional District (GVRD) between 1996 and 2001. Calibration results are tested for goodness of fit at the cellular level by means of the kappa statistic and for land use patterns by means of cluster size analysis and radial analysis. Kappa results show that the model performs considerably better than a neutral allocation model. Cluster and radial analysis indicate that the model is capable of producing realistic urban growth patterns.
Article
Revealing spatially varying relationships between urban growth patterns and underlying determinants is important for better understanding local dimensions of urban development. Through a case study of Nanjing, China, we employ both global and local logistic regressions to model the probability of urban land expansion against a set of spatial variables. We found that compared with other fast growing coastal cities, Nanjing remains a relatively compact city. The orthodox logistic regression found the significance of proximity, neighborhood conditions, and urban agglomeration in urban land change. The logistic GWR significantly improves the global logistic regression model in terms of better model goodness-of-fit and lower level of spatial autocorrelation of residuals. More importantly, the local estimates of parameters of spatial variables enable us to investigate spatial variations of the influences of spatial variables on urban growth. We have found distinctive local patterns and effects of urban growth in Nanjing, shaped by local urban spatial and institutional structures. A probability surface of urban growth, which is generated from raster calculations among the parameter and variable surfaces, provides a clear scenario of urban growth patterns and can be useful for decision making. This study also shows the importance of policy studies and fieldwork in the interpretation of results generated from statistical and GIS modeling.
Article
An emerging branch of geocomputing involves the modelling of spatial processes. A variety of techniques are being used, the most important being traditional regionalized system dynamics approaches, multi-agent systems, and cellular automata (CA). The techniques are frequently combined to model processes operating at different spatial scales. Urban and regional models based on CA give good representations of the spatial dynamics of land use. In a current application, a cellular model of The Netherlands at 500 m resolution is driven by a macro-scale dynamical spatial interaction model defined on 40 economic regions; this model is in turn driven by national planning projections and policy goals. Given the national totals, the macro-scale model generates regional demands for population and a number of economic activities. These demands are translated into demands for cell space, which the CA then attempts to locate. In turn, information on conditions at the cellular level, such as the quantity and quality of land available to various activities and actual densities at the cellular scale, are returned to the regional model to modify parameter values there. Linking the two models operating at the two scales improves the performance of both. The results of high-resolution modelling of spatial dynamics raise several methodological issues. One of the most pressing concerns evaluation of the results. Another issue concerns predictability. To the extent that these models capture the evolving nature of real cities and regions, they cannot be strictly predictive.
Article
In the context of rapid urbanization, accurate assessment of urban growth has become increasingly necessary for understanding environmental impacts and supporting urban planning toward a sustainable development. In this paper, we present an integrated system dynamics and cellular automata model not only in socio-economic driving forces analysis but also in urban spatial pattern evaluation. Shanghai city in China is selected as a case to fulfill the tasks. The major findings are summarized as follows: (1) the integrated model is proved to be competent in monitoring and projecting the dynamics of urban growth. (2) From 2000 to 2020, the urban area of Shanghai is predicted to increase at an annual rate of 3%, and amount 1474 km2 in 2020. Spatially, the newly increased urban land is most likely to expand around the vicinity of city center or sub-centers, and mainly along a west–east axis and a north–south axis. Road network planning plays an important role in directing the development of newly urbanized land.
Article
In recent years, cellular automata (CA) models for urban growth simulation have proliferated because of their simplicity, flexibility and intuitiveness, and particularly because of their ability to incorporate the spatial and temporal dimensions of the processes. Though apparently simple, CA models are capable of modeling complex dynamic systems such as urban systems. Currently, one of the main problems in actually applying CA models to urban planning practice is the choice or design of the most suitable CA model. For this reason, a review of urban CA models applied to real-world cases is provided, along with an analysis of their capabilities and limitations. The review and classification of CA models based on the main characteristics of the models has allowed for the analysis of their strengths and weaknesses. Finally, a discussion of the needs for further research is presented.