Data and code for the study of an improved assessment method for urban growth simulations across models, regions and time

Data are the maps of urban patterns and urban expansion drivers for Ningbo, Taizhou, and Wenzhou from 1995 to 2015; codes are UrbanCA run codes.</p

Elastic&ndash;Plastic Numerical Analysis of the Spinning Process of SA-372 Steel Used in High-Pressure Hydrogen Storage Cylinders (&ge;100 MPA)

Elastic&ndash;plastic numerical analysis of the spinning process of SA-372 steel is used in high-pressure hydrogen storage to analyze high-pressure hydrogen storage cylinders with high precision and excellent hydrogen embrittlement resistance. The spinning process of SA-372 steel used to form such a cylinder with a pressure of 100 MPa is investigated through elastic&ndash;plastic finite element analysis. The variations in the stress, strain, pressure, temperature, and wall thickness during the spinning processes are comprehensively examined, and the optimized processing parameters are determined based on the numerical analysis results. Finally, these optimal parameters are used to conduct actual spin-forming experiments. The numerical results are found to be in excellent agreement with the experimental results, which verifies the feasibility and effectiveness of the proposed elastic&ndash;plastic numerical analysis model for the optimization of spinning process parameters. Furthermore, the hydrogen embrittlement test based on ISO 11114-4:2005 method A proves that the cylinder shoulder has a good hydrogen embrittlement resistance

A spatial error-based cellular automata approach to reproducing and projecting dynamic urban expansion

Urban systems are featured by spatial autocorrelation, which may produce clustering of model residuals when simulating urban expansion using cellular automata (CA). Accurate identification of spatial autocorrelation and reduction of residual clustering are essential to accurate CA modeling of urban expansion. We developed a new CA approach (CASEM) using a spatial error model (SEM) that incorporates spatial autocorrelation. Using Zhengzhou City as a case study, we calibrated three types of CA models [e.g., logistic regression (Logit), spatial lag model (SLM) and SEM] from 2000 to 2010. Here, two important issues are the choice of the appropriate method (SLM vs. SEM) for urban expansion modeling and the applicability of CASEM for projecting urban scenarios. We validated the CASEM model from 2010 to 2017 and projected urban scenarios out to the year 2030 using this model. End-state assessment reveals that CASEM yields a higher overall accuracy (91.4%) in the calibration, but lower overall accuracy (83.8%) in the validation. For change assessment, CASEM yields a lower figure-of-merit (FOM; 31.8%) in the calibration but a higher FOM (35.2%) in the validation. We conclude that CASEM can accurately simulate urban expansion at Zhengzhou considering the fit performance of urban land transition rules, and the accuracy assessment of urban patterns and expansion. Scenario prediction using CASEM is therefore valuable for formulating useful urban planning regulations and in supporting sustainable urban development

Spatial patterns of land surface temperature and their influencing factors: a case study in Suzhou, China

Land surface temperature (LST) is a fundamental Earth parameter, on both regional and global scales. We used seven Landsat images to derive LST at Suzhou City, in spring and summer 1996, 2004, and 2016, and examined the spatial factors that influence the LST patterns. Candidate spatial factors include (1) land coverage indices, such as the normalized difference built-up index (NDBI), the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), (2) proximity factors such as the distances to the city center, town centers, and major roads, and (3) the LST location. Our results showed that the intensity of the surface urban heat island (SUHI) has continuously increased, over time, and the spatial distribution of SUHI was different between the two seasons. The SUHIs in Suzhou were mainly distributed in the city center, in 1996, but expanded to near suburban, in 2004 and 2016, with a substantial expansion at the highest level of SUHIs. Our buffer-zone-based gradient analysis showed that the LST decays logarithmically, or decreases linearly, with the distance to the Suzhou city center. As inferred by the generalized additive models (GAMs), strong relationships exist between the LST and the candidate factors, where the dominant factor was NDBI, followed by NDWI and NDVI. While the land coverage indices were the LST dominant factors, the spatial proximity and location also substantially influenced the LST and the SUHIs. This work improved our understanding of the SUHIs and their impacts in Suzhou, and should be helpful for policymakers to formulate counter-measures for mitigating SUHI effects

A moving window-based spatial assessment method for dynamic urban growth simulations

This study proposes a spatial evaluation method for urban growth simulation based on moving windows, where the metrics measured within each window are considered to be those of the central cell. We also applied the generalized additive model to identify the quantitative relationship between the urban growth drivers and the spatial assessment metrics. A case study in Jiaxing city shows that the single-number overall accuracies (OAs) are above 94% and the figure-of-merits (FOMs) are above 27% in both 2010 and 2015. Most regions of the study area yield very high OAs and low FOMs while the regions around the administration centres yield low OAs and high FOMs. The spatial method can well indicate the model’s effects on the urban simulations in different regions. The spatial assessment can report the assessment metrics of each cell to produce assessment maps as well as quantify the relationship between drivers and assessment metrics

The effect of observation scale on urban growth simulation using particle swarm optimization-based CA models

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

Spatially-explicit modeling and intensity analysis of China's land use change 2000–2050

Land use change affected by wide ranges of human activities is a key driver of global climate change. In the last three decades, China has experienced unprecedented land use change accompanied by increasing environmental problems. There is a pressing need to project and analyze long-term land use scenarios that are critical for land use planning and policymaking. Using GlobeLand30 data, we examined China's land use change from 2000 to 2010, and developed a novel LandCA model for scenario projections from 2020 to 2050. The observed and projected land use change (2000–2050) was analyzed in terms of the interval, category, and transition levels. Our findings show that land Exchange intensity is more than 3 times greater than land Quantity intensity from 2000 to 2050, and the overall rate of land use change will decelerate from 2010 to 2050. During 2000–2010, the loss of built-up land to other categories was 12.7% while the gain was 32.5%, with a growth rate 3.4 times larger than that during 2010–2050. The total amount of cultivated land continuously decreases but will not violate the Chinese “Cultivated Land Red-Line Restriction” by 2050. We speculate that the government's goal of 26% forest cover by 2050 may not be achieved, as a result of strict land use policies preventing the transformation from cultivated land to forests. This study contributes to new evaluations of long-term land use change in China for the government to adjust policies and regulations for sustainable development

Urban Growth Modeling and Future Scenario Projection Using Cellular Automata (CA) Models and the R Package Optimx

Cellular automata (CA) is a spatially explicit modeling tool that has been shown to be effective in simulating urban growth dynamics and in projecting future scenarios across scales. At the core of urban CA models are transition rules that define land transformation from non-urban to urban. Our objective is to compare the urban growth simulation and prediction abilities of different metaheuristics included in the R package optimx. We applied five metaheuristics in optimx to near-optimally parameterize CA transition rules and construct CA models for urban simulation. One advantage of metaheuristics is their ability to optimize complexly constrained computational problems, yielding objective parameterization with strong predictive power. From these five models, we selected conjugate gradient-based CA (CG-CA) and spectral projected gradient-based CA (SPG-CA) to simulate the 2005-2015 urban growth and to project future scenarios to 2035 with four strategies for Su-Xi-Chang Agglomeration in China. The two CA models produced about 86% overall accuracy with standard Kappa coefficient above 69%, indicating their good ability to capture urban growth dynamics. Four alternative scenarios out to the year 2035 were constructed considering the overall effect of all candidate influencing factors and the enhanced effects of county centers, road networks and population density. These scenarios can provide insight into future urban patterns resulting from today's urban planning and infrastructure, and can inform future development strategies for sustainable cities. Our proposed metaheuristic CA models are also applicable in modeling land-use and urban growth in other rapidly developing areas