Simulation Of Urban Expansion Research Articles

Simulation of urban expansion using geographical similarity transition rules and neighbourhood sizes

Simulation of urban expansion using geographical similarity transition rules and neighbourhood sizes

A high‐performance cellular automata model for urban expansion simulation based on convolution and graphic processing unit

AbstractCellular automata (CA) models are effective tools for simulating future urban expansion. With the widespread use of high‐resolution geospatial data for CA simulation, the computational intensity of CA models has increased. Additionally, due to the continuous development of CA modeling research, many scholars have made improvements to the models to enhance their simulation accuracy, resulting in an increasing computational complexity of the model. Consequently, the simulation task based on CA requires vast computing time and memory space. In recent years, deep learning (DL) has experienced rapid development. Many open‐source DL frameworks support graphic processing unit (GPU) parallel computing and provide efficient application programming interfaces (APIs) that can be easily called to handle tasks of interest. In this study, a high‐performance CA model was constructed based on the similarity between the neighborhood effect calculation process of the CA model and the convolutional process in a convolutional neural network (CNN). The convolution function in the DL library is used to calculate the neighborhood effect of the CA model to reduce the time and memory consumption of CA‐based simulation. The experimental results show that compared with the conventional CA model, the execution time of the GPU‐convolution‐CA model proposed in this study has been reduced by more than 98%.

Urban expansion simulation with an explainable ensemble deep learning framework

Urban expansion simulation is of significant importance to land management and policymaking. Advances in deep learning facilitate capturing and anticipating urban land dynamics with state-of-the-art accuracy properties. In this context, a novel deep learning-based ensemble framework was proposed for urban expansion simulation at an intra-urban granular level. The ensemble framework comprises i) multiple deep learning models as encoders, using transformers for encoding multi-temporal spatial features and convolutional layers for processing single-temporal spatial features, ii) a tailored channel-wise attention module to address the challenge of limited interpretability in deep learning methods. The channel attention module enables the examination of the rationality of feature importance, thereby establishing confidence in the simulated results. The proposed method accurately anticipated urban expansion in Shenzhen, China, and it outperformed all the baseline methods in terms of both spatial accuracy and temporal consistency.

Spatiotemporal patterns, driving mechanism, and multi-scenario simulation of urban expansion in Min Delta Region, China

Urban agglomerations are important spatial carriers for regional economic development, but sustained urban expansion has triggered a series of issues such as conflicts between “people and land” and “built-up areas and green spaces”. However, many studies have rarely incorporated spatiotemporal patterns and driving mechanism into scenario simulation of urban growth, and has also overlooked subsequent discussions on the specific integration of simulation results with spatial planning. Considering the case of Min Delta Region, this study analyzes the spatiotemporal evolution of urban expansion based on multi-source data and explore the influential driving mechanism using Random forest algorithm. A combination of Markov and FLUS models was used to dynamically simulate future urban growth patterns. The results showed that 1) From 1995 to 2015, the urban expansion increased by 2.26 times, showing a trend of first accelerating and then slowing down. It was also discovered that there is an anisotropy toward expansion in the trajectory of urban growth. 2) With marginal increase and enclave increase, general urban expansion was observed to be changing from diffusion to agglomeration, and the urban expansion hotspots mainly located in the southeast coastal area. 3) Humanistic links, geographic position, social and economic considerations all played a role in the development of the Min Delta region, and can be summarized as the “four forces” driving mechanism model. 4) By 2035, the trend of continuous development with the Min Delta built-up area as the main body will be basically formed, and incorporating ecological restrictions can effectively restrain urban growth's encroachment into ecological spaces. Based on the simulation results, Min Delta region gradually showing a spatial structure evolution trend from the single core to the dual core mode, and then to the multi-center and networked mode. This study would serve as a multi-angle decision-making reference for regional spatial and ecological protection planning and urban growth management, and as a scientific foundation for high-quality development planning in the region.

Simulating the impact of urban expansion on ecosystem services in Chinese urban agglomerations: A multi-scenario perspective

Urban agglomerations (UAs) are the most active areas of urban land expansion. The encroachment of urban land on large-scale ecological land has caused severe ecological problems and threatens the ecological sustainability of UAs. Understanding the characteristics of multi-scenario urban land expansion and its impacts on ecosystem services (ESs) in UAs can provide guidance for formulating ecologically sustainable management and control policies in UAs. However, few studies have simulated the characteristics of multi-scenario urban expansion and determined its impacts on ESs in Chinese UAs. In this study, we used a patch-generating land use simulation (PLUS) model and the benefit transfer method to conduct a multi-scenario simulation of urban land expansion in Chinese UAs in 2030 and measure its impacts on ESs. The simulation indicates that urban land continued to expand and expanded most severely under an urban development scenario. In the future, under an ecological protection scenario, ESs would be maintained at their highest levels. In general, the global Moran's I indices of the ESs and urban expansion were negative at the 0.001 significance level, indicating that ESs and urban expansion in Chinese UAs were strongly spatially concentrated. The impacts of urban expansion on the ESs varied among the UAs. In national and regional UAs, ESs were affected negatively by patch density (PD) and percentage of like adjacencies (PLADJ), whereas the loss of ESs in local UAs was not only affected by PD and PLADJ, but also by the proportion of urban land. These findings provide effective guidance for improving ESs, urban development planning, and high-quality development in Chinese UAs.

Modelling urban expansion with cellular automata supported by urban growth intensity over time

ABSTRACT The simulation of urban expansion has become an important means to assist urban development planning and ecological sustainable development. However, the spatial and temporal heterogeneities of urban expansion has been a major challenge for modelling urban expansion. This study designed three features from the perspective of spatiotemporal heterogeneity to improve the accuracy of CA model. The new features cover the trends effects of long time-series data on urban expansion, urban spatial growth intensity based on urban growth kernel estimation and allocation probability of the newly generated urban cells from global neighbourhood effects. Finally, urban expansion in Huizhou, China, was simulated and predicted. The experimental results show that the new features can effectively reduce the prediction error for the total amount of urban growth with a deviation of about 2%, and the overall accuracy of urban expansion is as high as 0.93. The features designed in this paper are shown to be effective and can be applied to urban simulations and scenario prediction with various models.

Urban Expansion Simulation Coupled with Residential Location Selection and Land Acquisition Bargaining: A Case Study of Wuhan Urban Development Zone, Central China’s Hubei Province

The urban expansion process involves multiple stakeholders whose interactions and decision-making behaviors have a complex impact on urban land conversion. In this study, we established an urban expansion simulation model that couples two sub-models: the residential location selection model and the land acquisition bargaining model. Those sub-models include four types of agents: resident agent (RA), real estate developer agent (DA), government agent (GA), and farmer agent (FA). The residential location selection model is composed of three agents, RA, DA, and GA, and is first used to select residential locations, while an artificial neural network (ANN) is used to define the behavior rules of RA and RA selects pixels as candidate locations according to the joint decision probability. Then the land acquisition bargaining model is used, which is composed of GA and FA. If the land acquisition is successful, a pixel is converted into urban land, which is occupied by the corresponding RA; otherwise, the RA selects the next pixel and enters the bargaining process again, and so on, until the RA successfully selects a residential location. Each iteration represents the selection process of an agent. We used this model to simulate urban expansion within the Wuhan Urban Development Zone (WHUDZ) of central China from 2009 to 2019. The overall accuracy and Kappa coefficient of the simulation results were 92.78% and 55.24%, respectively, which were higher than the results using logistic regression cellular automata. Moreover, we obtained the relative contributions of various influencing factors in the ANN on the residential location selection, revealing the influence of the land acquisition process on land expansion. In addition, the coupled model predicted that the WHUDZ’s urban land area will reach 1415.82 km2 in 2029, mainly through extensional expansion, and the southeast and northwest will be expansion hot spots.

A whale optimization algorithm–based cellular automata model for urban expansion simulation

• A whale optimization algorithm-based cellular automaton (WOA-CA) model for simulating urban expansion is innovatively proposed in this research. • A new objective function is designed in this research to help mining the transition rules. • The comparison results show that our proposed model outperforms five commonly used representative models in terms of the accuracy and computational efficiency. Cellular automata (CA) has proved to be effective and efficient in conducting urban expansion simulation. The generation of cell transition rules is a crucial step for a CA model. In this research, a whale optimization algorithm–based CA (WOA-CA) model was innovatively proposed. In the proposed model, a WOA was adapted to help mining the transition rules of the CA model, which was also evaluated and utilized in the case study of Guangzhou, simulating urban expansion from the year of 2000 to 2010. The experiment results demonstrated that the proposed model is effective and the simulation result is able to reach an overall accuracy of 92.16% with a Kappa coefficient of 0.744, and the value of Moran’s I is also quite close to that of the actual urban expansion. In addition, the proposed model has also been compared with a few representative CA models, including multi-criteria evaluation-based CA (MCE-CA), artificial neural network-based CA (ANN-CA), bat algorithm-based CA (BA-CA), convolution neural network for united mining-based CA (UMCNN-CA), and gray wolf optimizer-based CA (GWO-CA). The comparison results showd that our proposed model outperforms all these models in terms of overall accuracy and computational efficiency.

Urban Growth Boundaries Delineation under Multi-Objective Constraints from the Perspective of Humanism and Low-Carbon Concept

Urban growth boundaries (UGBs) play an important role in controlling urban sprawl and protecting natural ecosystems. Traditional methods mainly focus on the heterogeneity of regional resources and environment rather than residents’ behavioral activities. However, residents’ behavioral activities are one of the most important factors influencing urban spatial form. Fortunately, the emergence of big data, especially phone signaling data, provides alternative data sources to understand the dynamic resident behavior activity space, which is significant for people-oriented urban development. Therefore, we propose a novel framework for UGB delineation based on multi-source big data and multi-objective constraints, which emphasizes humanism and the low-carbon concept in urban expansion simulation. The multi-objective constraints are constructed from the evaluation of resident activity space expansion potential, the evaluation of urban construction suitability, the evaluation of ecological conservation importance, and the human survival materials limitation. We apply the framework to Ningbo, and the results show that the framework under multi-objective constraints from a people-oriented and low-carbon perspective is more reliable and comprehensive than that without constraints. The findings also show that the UGB delineation based on multi-source big data has higher accuracy and better performance. The conceptual and methodological advances of this study are also applicable to other cities to help UGBs delineation.

A Multi-Scenario Land Expansion Simulation Method from Ecosystem Services Perspective of Coastal Urban Agglomeration: A Case Study of GHM-GBA, China

Balancing urban development and ecosystem conservation in the context of natural resource scarcity can provide scientific guidance for land use planning. We integrated research methods, such as ecosystem services (ES) assessment, coastal vulnerability assessment, multi-objective linear planning, and land use change simulation, to develop a new model framework for multi-scenario urban land expansion simulation based on ecosystem services. In relation to the land use scale and constraints, we simulated three types of scenarios in 2035, including a status quo continuity scenario (SCS), economic development scenario (EDS), and ecological protection scenario (EPS), to explore the ideal land use optimization strategies to enhance ES and land use efficiency. The results indicated that the scale of construction land under the three scenarios grew, and arable land and grassland had the largest losses. The continued urban expansion in the Guangdong–Hong Kong–Macao Greater Bay Area has already had a significant negative impact on ecosystem services and could result in a total ESV loss of USD 28.1 billion by 2035 if an economic-first development model is adopted. Based on the hotspots of urban construction land expansion in the ecological–economic priority game, we proposed a classification and optimization strategy for land use, including proactive restoration of damaged ecological spaces with high ESVs (Zhaoqing City and Huizhou City), optimization of green space quality and formation of ecological corridors (Guangzhou City, Shenzhen City, Hong Kong, and Macao), and implementation of natural resource conservation planning and spatial regulation in the urban–rural integration area (Foshan City and Dongguan City). This research framework scientifically allocates the “quality” of ecosystem values and “quantity” of natural resources and provides a reference for regional “bottom-up” territorial spatial planning.

Modelling microscale impacts assessment of urban expansion on seasonal surface urban heat island intensity using neural network algorithms

Investigation of surface urban heat island (SUHI) results from rapid urbanization and upsurge of land surface temperature (LST) has substantial socioeconomic and environmental impacts. This study investigates and simulates the impacts of rapid urbanization on LST and SUHI patterns in Sylhet City, Bangladesh, from 1995 to 2030. Landsat images and machine learning algorithms have been used to identify the urban growth, LST and UHI distribution patterns in several city directions. In addition, correlation analysis has been conducted between LST, SUHI and spectral indices (NDBI, NDBSI, NDVI, NDWI). Results suggested that urban expansion increased LST by 7 °C in summer and 6 °C in winter from 1995 to 2020. Increment has also occurred in summer high SUHI from 0.25 km2 to 2.65 km2. Pearson correlation demonstrated that built-up areas have a strong positive relationship with LST (0.96) and SUHI (0.911). Future simulation of urban expansion for 2025 and 2030 shows a 9 % increase, leading to a significant increase in moderate to high SUHI intensity. The study's findings can act as an effective guideline for sustainable infrastructural development and ensure environmental stability by increasing the thermal comfort level of the city.

Simulation of urban land expansion in China at 30 m resolution through 2050 under shared socioeconomic pathways

ABSTRACT Rapid urbanization has profoundly impacted social and environmental systems. China is the world’s largest developing country. Projecting future urban expansion in China is critical to alleviate any adverse impacts and achieve sustainable development goals. However, the existing national-scale urban expansion simulations at low or medium resolutions produce significant distortions in urban spatial patterns, limiting the utility of future projections. High-resolution simulation of urban expansion is challenging on a large scale owing to high computational demands. In this study, we used a high-performance cellular automata model (Tensor-FLUS) to simulate the urban expansion of China from 2015 to 2050, at 30 m spatial resolution, under shared socioeconomic pathways. The high-resolution (30 m) urban expansion simulations preserve greater spatial details and avoid 34.07–37.60% underestimation of the urban area, compared with simulation at 1 km resolution. The environmental impact assessments revealed that future urban expansion mainly encroaches on cropland (88.00–88.29%), with a greater likelihood of occupying productive cropland, placing substantial pressure on food production. Although the proportion of occupied natural land is relatively small (11.32–11.60%), newly expanded urban areas will tend to consume woodland and grassland of high ecological value, leading to profound impacts on the ecosystem. In general, the produced high-resolution future simulations can reduce the uncertainty of environmental impact assessment at the national scale. Furthermore, it can provide consistent projection data for research at the provincial or metropolitan scales to support urban planning and local climate change mitigation.

Analysis of time–space dynamic simulation on urban expansion in mountainous area based on the weight-CA model

As one of the central cities in the Wuling Mountains, Huaihua is regarded as the main body and core economic development area in the process of Wuling’s future new urbanization, exerting a pivotal influence on the five provincial border areas (Hunan, Hubei, Chongqing, Guizhou and Guangxi). But studies on urban expansion in this city are few, so this paper makes efforts to simulate its urban land expansion by constructing the Cellular Automata model (the Weight-CA model) taking into account differences in land types conversion. Firstly, the paper obtains the time-series Landsat spectral bands (Red, NIR) and NDVI images using the time–space fusion model; then the optimal combination of classification features is screened based on JBh distance and the object-oriented random forest algorithm is applied for land cover classification; the paper finally constructs the Weight-CA model for the simulation and validation of urban land expansion in Huaihua. Study results in the paper have shown that the Landsat spectral bands obtained with the ESTARFM have a relatively high correlation with real Landsat images. The land cover classification based on the optimal classification feature combination and the object-oriented random forest algorithm is high in accuracy, the total of which exceeding 84 % over 4 years. Meanwhile, based on the Weight-CA model, the simulation accuracy of urban land expansion in Huaihua in 2010 and 2015 reached 89.98 % and 89.60 % compared with the real values. It is operationally ready and practical and of strong practical significance to extract land cover types by Landsat and MODIS data fusion, and simulate urban land expansion using the Weight-CA method.

SDG-oriented multi-scenario sustainable land-use simulation under the background of urban expansion.

With the continuous improvement of urbanization level and the continuous expansion of city scale, there are some unreasonable land development and utilization problems, which will make the contradiction between people and land more prominent and the risk of ecological environment deterioration more serious. It hinders the sustainable land use (SLU) and then affects the realization of regional Sustainable Development Goals (SDGs). SDGs have become a programmatic document for all countries in the world to implement sustainable development. It provides a guideline and direction for the sustainable urban expansion. The sustainable urban expansion promotes the realization of SDGs worldwide. By combining the SDGs with the urban SLU, this paper obtains the optimized future land-use demand of the multi-scenario city and the urban expansion simulation scenario to the SDGs under the multi-scenario through the uncertain mathematical model (MIFCCP) and the spatial simulation model (PLUS). We find that firstly, the net profit of land use (LNB) reaches the highest value under the economic development scenario (ED), when the probability of environmental constraint violation p = 0.01 and p = 0.15, LNB = [2625.48, 3244.98] × 109 CNY, and [2646.95, 3271.51] × 109 CNY. Ecosystem service value (ESV) reached the highest value under the sustainable development scenario (SD), when p = 0.01 and p = 0.15, ESV = [75.34, 93.12] × 109 CNY, and [72.62, 95.56] × 109 CNY. The net carbon emissions from land use (LNC) reached the minimum value in SD scenario, and when p = 0.01 and p = 0.15, the LNC reached [57.46, 71.02] × 106 ton and [56.12, 76.04] × 106 ton. Secondly, the contribution degree of 15 driving factors to the change of local types is excavated, among which, the driving factors of traffic stations have the highest contribution degree to the change of construction land and cultivated land, and the third-class roads have the highest contribution degree to the change of wetland area. Furthermore, by analyzing the indicators related to SLU in SDGs, we can build an optimization model of land use quantity structure under uncertain conditions, and the optimized results can meet the targets of economic benefits, ecological benefits, and net carbon emissions of land use under different development plans. By linking SDGs and SLU, the coupled model framework can provide scientific basis for urban land expansion strategy based on ecological environment constraints and scientific support for sustainable management of land use.

Offshore Island Connection Line: A new perspective of coastal urban development boundary simulation and multi-scenario prediction

ABSTRACT The urban development boundary (UDB) is a core issue in territorial spatial planning and remains a research focus in urban development. However, in the past, land use simulations of coastal cities have had some limitations. For example, new urban land outside the research boundary could not be effectively simulated, resulting in a large gap between the simulation results and reality. In this study, an artificial neural network (ANN) cellular automata (CA) model based on a new perspective, the Offshore Island Connection Line (collectively, OICL-ANN-CA), was developed to address this problem. We first proposed the delineation principles and methods of OICL and applied it to the Jinpu New Area of Dalian City, Liaoning Province, China. Based on the conversion probability and land use simulation results obtained by ANN and CA, this study validated the UDB simulations for 2000–2020 in the Jinpu New Area and predicted the UDB for 2020–2035 under three scenarios: historical inertia development, ecological security protection, and ecological and economic balance. The results indicate that, compared with the traditional perspective model (the ANN-CA model based on the sea–land boundary), the OICL-ANN-CA model exhibited better simulation accuracy (the figure of merit was approximately 35% higher) and effectively simulated new urban land outside the traditional boundary. This simulation of urban land expansion is more consistent with recent development in the study area. In addition, the predicted results of UDB in 2035 also demonstrate the benefits of this model for detecting reclaimed land. This study illustrates that the OICL-ANN-CA model is a more capable method for capturing changes in new coastal urban land and can produce realistic simulations. It provides a reference for delimiting the UDB and defining the research boundary for the Jinpu New Area and other fast-growing coastal cities in China.

Optimization of differentiated regional land development patterns based on urban expansion simulation—A case in China

AbstractThe advancing global urbanization puts great pressure on the society and ecosystem, especially in developing countries. Reasonable land‐use policies adapted to local conditions are the key issues to prevent disordered urban expansion. Multi‐scenario simulation provides a new perspective for differentiated policy formulation based on regional heterogeneity. Regarding the Shaanxi Province, a rapid urbanized area with high spatial heterogeneity in western China, as the study case, we tracked its past land‐use changes and predicted the characteristics of urban expansion using the Future Urban‐Regional Environment Simulation model. We found that (1) during the past 35 years, built‐up land evidently increased accompanied by the loss of cropland, grassland, and unused land. (2) The urban expansion mainly will occur in the areas with flat terrain under three scenarios. (3) The urban expansion will transit into the “requiring land from mountains” pattern in the future, which means that it will gradually spread to areas with higher slopes. (4) Differentiated and optimal development patterns are proposed for different subregions by accounting the loss of ecosystem service in the procedure of urban expansion. This research can help local governments formulate differentiated future macro control of territorial spatial planning to optimize subregional land development.

Three-Dimensional Simulation Model for Synergistically Simulating Urban Horizontal Expansion and Vertical Growth

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.

Impacts and Projections of Land Use and Demographic Changes on Ecosystem Services: A Case Study in the Guanzhong Region, China

Land use change and demographic factors directly or indirectly affect ecosystem services value, and the analysis of ecosystem services contributes to optimization of land planning, which is essential for regional sustainable development. In this study, ArcGIS 10.2, IDRISI 17.0 Selva and MATLAB software, value coefficient method, CA-Markov prediction model and population growth model were applied to analyze the spatial and temporal changes of land use trends and ecosystem service values in Guanzhong region, and further predict the impacts of land type changes and population changes on ecosystem services in the context of urbanization. Results showed that the expansion of construction land was the most intense, and the transfer process mainly crowded out arable land; the total ecosystem services value grew spatially in a “low center-high periphery” ring with large differences at the bottom, and forest land was the most important value provider. The total ecosystem services value was estimated to decline in the future, with low-value areas spreading northward and differences in the per capita ecosystem services value increasing. This study provides a reference for optimal simulation of urban expansion and ecological conservation.

Model construction of urban agglomeration expansion simulation considering urban flow and hierarchical characteristics

Model construction of urban agglomeration expansion simulation considering urban flow and hierarchical characteristics

Analysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China

Quantifying the characteristics of urban expansion as well as influencing factors is essential for the simulation and prediction of urban expansion. In this study, we extracted the built-up regions of 14 central cities in the Hunan province using the DMSP-OLS night light remote sensing datasets from 1992 to 2018, and evaluated the spatial and temporal characteristics of the built-up regions in terms of the area, expansion speed, and main expansion direction. The backpropagation (BP) neural network and autoregressive integrated moving average (ARIMA) model were used to predict the area of the built-up regions from 2019 to 2026. The model predictions were based on the GDP, ratio of the secondary industry output to the GDP, ratio of the tertiary industry output to the GDP, year-end urban population, and urban road area. The results demonstrated that the built-up area and expansion speed of the central cities in the eastern part of the Hunan province were significantly higher than those in the western part. The main expansion directions of the 14 central cities were east and south. The urban road area, year-end urban population, and GDP were the main driving factors of the expansion. The urban expansion model based on the BP neural network provided a high prediction accuracy (R = 0.966). It was estimated that the total area of urban built-up regions in the Hunan province will reach 2463.80 km2 by 2026. These findings provide a new perspective for predicting urban areas rapidly and simply, and it also provides a useful reference for studying the spatial expansion characteristics of central cities and formulating a sustainable urban development strategy during the 14th Five-Year Plan of China.