Feature selection (FS) is vital in hyperspectral image (HSI) classification, it is an NP-hard problem, and Swarm Intelligence and Evolutionary Algorithms (SIEAs) have been proved effective in solving it. However, the high dimensionality of HSIs still leads to the inefficient operation of SIEAs. In addition, many SIEAs exist, but few studies have conducted a comparative analysis of them for HSI FS. Thus, our study has two goals: (1) to propose a new filter–wrapper (F–W) framework that can improve the SIEAs’ performance; and (2) to apply ten SIEAs under the F–W framework (F–W–SIEAs) to optimize the support vector machine (SVM) and compare their performance concerning five aspects, namely the accuracy, the number of selected bands, the convergence rate, and the relative runtime. Based on three HSIs (i.e., Indian Pines, Salinas, and Kennedy Space Center (KSC)), we demonstrate how the proposed framework helps improve these SIEAs’ performances. The five aspects of the ten algorithms are different, but some have similar optimization capacities. On average, the F–W–Genetic Algorithm (F–W–GA) and F–W–Grey Wolf Optimizer (F–W–GWO) have the strongest optimization abilities, while the F–W–GWO requires the least runtime among the ten. The F–W–Marine Predators Algorithm (F–W–MPA) is second only to the two and slightly better than F–W–Differential Evolution (F–W–DE). The F–W–Ant Lion Optimizer (F–W–ALO), F–W–I-Ching Divination Evolutionary Algorithm (F–W–IDEA), and F–W–Whale Optimization Algorithm (F–W–WOA) have the middle optimization abilities, and F–W–IDEA takes the most runtime. Moreover, the F–W–SIEAs outperform other commonly used FS techniques in accuracy overall, especially in complex scenes.

The summer ozone pollution of Shandong province has become a severe problem in the period 2014–2018. Affected by the monsoon climate, the monthly average ozone concentrations in most areas were unimodal, with peaks in June, whereas in coastal areas the concentrations were bimodal, with the highest peak in May and the second highest peak in September. Using the empirical orthogonal function method, three main spatial distribution patterns were found. The most important pattern proved the influences of solar radiation, temperature, and industrial structure on ozone. Spatial clustering analysis of the ozone concentration showed Shandong divided into five units, including Peninsula Coastal area (PC), Lunan inland area (LN), Western Bohai area (WB), Luxi plain area (LX), and Luzhong mountain area (LZ). Influenced by air temperature and local circulation, coastal cities had lower daytime and higher nighttime ozone concentrations than inland. Correlation analysis suggested that ozone concentrations were significantly positively correlated with solar radiation. The VOCs from industries or other sources (e.g., traffic emission, petroleum processing, and chemical industries) had high positive correlations with ozone concentrations, whereas NOx emissions had significantly negatively correlation. This study provides a comprehensive understanding of ozone pollution and theoretical reference for regional management of ozone pollution in Shandong province.

Intensive land use (ILU) is a multi-objective optimization process that aims to simultaneously improve the economic, social, and ecological benefits, as well as the carrying capacity of the land, without increasing additional land, and evaluation of the ILU over long time series has a guiding significance for rational land use. To tackle inefficient extraction of information, subjective selection of dominant factor, and lack of prediction in previous evaluation studies, this paper proposes a novel framework for evaluation and analysis of ILU by, first, using Google Earth Engine (GEE) to extract cities’ built-up land information, second, by constructing an index system that links economic, social and ecological aspects to evaluate the ILU degree, third, by applying Geodetector to identify the dominant factor on the ILU, finally, by using the S-curve to predict the degree. Based on the case study data from northern China’s five fast-growing cities (i.e., Beijing, Tianjin, Shijiazhuang, Jinan, Zhengzhou), the findings show that the ILU degree for all cities has increased over the past 30 years, with the highest growth rate between 2000 and 2010. Beijing had the highest degree in 2018, followed by Tianjin, Zhengzhou, Jinan, and Shijiazhuang. In terms of the time dimension, the dominant factor for all cities shifted from the output-value proportion of secondary and tertiary industries in the early stage to the economic density in the late stage. In terms of the space dimension, the dominant factor varied from cities. It is worth noting that economic density was the dominant factor in the two high-level ILU cities, Beijing and Tianjin, indicating that economic strength is the main driver of the ILU. Moreover, cities with high-level ILU at the current stage will grow slowly in the ILU degree from 2020 to 2035, while Zhengzhou and Jinan, whose ILU has been in the midstream recently, will grow the most among the cities.

The urban development of China is changing from incremental expansion to stock renewal mode. The study of urban functional areas has become one of the important fundamental works in current urban renewal and high-quality urban development. In recent years, big spatiotemporal data has been well applied in the urban function field. However, the study of spatial–temporal evolution characteristics and forecasting optimization for mixed-use urban functional areas has not been examined well. Thus, in this study, we proposed a new approach that applies a revised information entropy method to analyze the degrees of mixing for urban functional areas. We applied our approach in Jinan City, Shandong Province as the study area. We used Point-of-Interest, OpenStreetMap and other datasets to identify the mixed-use urban functional areas in Jinan. Then, the CA–Markov model simulated the urban layout in 2025. The results showed that: (1) the combination of road network and kernel density method has the highest accuracy of identifying urban functional areas. (2)The mixing degree model is constructed by using the improved information entropy, which makes up for the shortcoming of identifying the mixed functional areas simply by the frequency ratio of POI data. (3) The “residence and business” functional area has the highest proportion in the central area of Jinan from 2015 to 2020, and the total area of mixed-use unban functional areas continuously increased during this period. (4) The total area of the central area in Jinan has significantly increased in 2025. The optimization of urban functions should expand mixed-use functional areas and increase the proportion of infrastructure. Also, Jinan should improve the efficiency of space development.

Evaluation of intensive land use (ILU) over long time series is essential for the rational use of land and urban development. We propose a novel framework for analyzing ILU in the Pearl River Delta (PRD) region of China. First, we used Google Earth Engine (GEE) to obtain cities’ built-up land information. Second, we calculated the ILU degree and constructed an evaluation index system based on the Pressure–State–Response (PSR) theoretical framework. Third, we employed Geodetector to determine the dominant influencing factors on ILU. The findings are as follows: (1) It is accurate and effective to extract land use data using GEE. From 2000 to 2020, all cities’ built-up areas increased, but the increases differed by city. (2) While the ILU level in all cities has increased over the past 20 years, the ILU level in each city varies. Specifically, Shenzhen had the highest ILU degree in 2020, followed by core cities such as Guangzhou, Dongguan, and Zhuhai, while cities on the PRD region’s periphery, such as Zhaoqing and Jiangmen, had relatively low ILU levels. (3) In terms of time, the dominant factors influencing ILU in the PRD region have shifted over the past two decades. During this period, however, two factors (economic density and disposable income per capita) have always played a dominant role. This suggests that improving economic output efficiency and the city’s economic strength is a feasible way to raise the ILU level at this time.