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Vitiligo is a skin disorder that is associated with a decreased risk of skin cancer, but it can lead to increased susceptibility to sunburn, psychological distress, and disruptions in daily life, consists of two primary subtypes: segmental and nonsegmental vitiligo, each with distinct underlying mechanisms. However, the reliable identification of diagnostic markers and the ability to differentiate between these subtypes have remained elusive challenges. This study aims to pioneer predictive algorithms for vitiligo diagnosis, harnessing the capabilities of AI (Artificial Intelligence) to amalgamate multisource data and uncover essential features for distinguishing vitiligo subtypes.An ensemble algorithm was thoughtfully developed for vitiligo diagnosis, utilizing a spectrum of machine learning techniques to evaluate the likelihood of vitiligo, whether segmental or nonsegmental. Diverse machine learning methodologies were applied to distinguish between healthy individuals and vitiligo patients, as well as to differentiate segmental from nonsegmental vitiligo. The ensemble algorithm achieved a remarkable AUC (Area Under the Curve) of 0.99 and an accuracy of 0.98 for diagnosing vitiligo. Furthermore, in predicting the development of segmental or nonsegmental vitiligo, the model exhibited an AUC of 0.79 and an accuracy of 0.73. Key parameters for vitiligo identification encompassed factors such as age, FBC (full blood count)-neutrophils, FBC-lymphocytes, LKF(liver and kidney function)-direct bilirubin, LKF-total bilirubin, and LKF-total protein levels. In contrast, vital indicators for monitoring the progression of segmental and nonsegmental vitiligo included FBC-B lymphocyte count, FBC-NK (Natural Killer) cell count, and LKF-alkaline phosphatase levels. This retrospective study underscores the potential of AI-driven analysis in identifying significant risk factors for vitiligo and predicting its subtypes at an early stage. These findings offer great promise for the development of effective diagnostic tools and the implementation of personalized treatment approaches in managing this challenging skin disorder. [ABSTRACT FROM AUTHOR]

Gaining an understanding of the intricate mechanisms between human activity and the built environment can help in promoting sustainable urban development. However, most scholars have focused on residents' life and work behavior and have ignored the third activity (e.g., shopping, eating, and entertainment). In this study, a random forest algorithm and SHapley Additive exPlanation model were utilized to explore the nonlinear influence of the built environment on the attraction of the third activity (other than home and work). A comparative analysis of the inflow of the third activity from home and work was also carried out. The results show that the contributions of all built environment variables to the attraction of the third activity differ between home–other flow (HO) and work–other flow (WO) at the global scale, but their local effects are significantly similar. Furthermore, the nonlinear influence of the built environment on the attractions of the third activity can vary from one factor to another. A significant spatial heterogeneity can be observed on the built environment variables' local effects on the attractions of the third activity. These findings can provide urban planners with insights that will help in the planning and optimization of communities for pursuing the third activity. [ABSTRACT FROM AUTHOR]

Urban thermal environment (UTE) deterioration seriously affects the production, life, and health of urban residents. Therefore, identifying the spatial and temporal evolution of the urban environment and exploring the main factors that affect the UTE are increasingly urgent and important. This study uses multisource spatial data to analyze the changes in surface temperature and its spatial and temporal differentiation patterns of Hangzhou, China in 2000, 2010, and 2021. Then, factors that are intimately connected to the alterations in the UTE are identified. Based on this, the contribution and impact of the UTE are calculated. The findings indicate that from 2000 to 2021, Hangzhou's urban heat island area has been continuously expanding, with an increase of 622.74 km2. In 2021, the maximum surface temperature difference reached 6.2°C, where the heat island area in the Xiaoshan and Yuhang Districts exhibited the largest increase. Hangzhou's urban heat island space showed a scattering distribution in the northwest–southeast direction. High-temperature areas are concentrated in regions that are characterized by dense industrial and commercial distribution, which display local concentration and overall dispersion. Low-temperature areas are concentrated around vegetation-covered regions and bodies of water. The natural and social factors contribute to the changes in surface temperature within the study area. The contribution of social factors on surface temperature surpasses that of natural factors. The leading factors are landscape patterns, the natural environment, and construction activities. The combined effects of various factors increase the surface temperature by 0.965°C for each unit increase in the combined factors within the study area. [ABSTRACT FROM AUTHOR]

Metropolitan areas are important regions with a high concentration of population and public service facilities. The coupling coordination between public service facilities and population plays an important role in the sustainable development of economy and society. However, previous studies have focused on a few core cities, effectively identifying areas with weak coordination of public services and currently lacking corresponding information support and paradigms at the regional scale. Taking the Wuhan Metropolitan Area as an example, this paper strengthens the concern about the spatiality and locality of public service facilities in the region;, applies the improved CRITIC method and coupling coordination model to study the spatiotemporal distribution characteristics of public service facilities at the county and grid levels by using multisource data, and evaluates the intercity differences and dynamic changes of coupling coordination relationships between public service facilities and population. The findings are as follows: (1) Wuhan was the core of public service facilities in the metropolitan area, and a continuous high-level coordinated distribution was gradually developed on the east-west axis from 2016 to 2022; (2) there were differences in the coupling coordination degree of different types of facilities, among which the imbalance between the supply and demand of elderly care facilities was obvious; and (3) the coupling coordination degree of facilities in the Wu-E-Huang-Huang core development area was relatively high. Finally, we summarize the development stages of the coupling coordination between the facilities and population in the Wuhan Metropolitan Area and obtain the typical development characteristics. The research results could provide scientific support for planning decisions. [ABSTRACT FROM AUTHOR]

Abstract Vitiligo is a skin disorder that is associated with a decreased risk of skin cancer, but it can lead to increased susceptibility to sunburn, psychological distress, and disruptions in daily life, consists of two primary subtypes: segmental and nonsegmental vitiligo, each with distinct underlying mechanisms. However, the reliable identification of diagnostic markers and the ability to differentiate between these subtypes have remained elusive challenges. This study aims to pioneer predictive algorithms for vitiligo diagnosis, harnessing the capabilities of AI (Artificial Intelligence) to amalgamate multisource data and uncover essential features for distinguishing vitiligo subtypes.An ensemble algorithm was thoughtfully developed for vitiligo diagnosis, utilizing a spectrum of machine learning techniques to evaluate the likelihood of vitiligo, whether segmental or nonsegmental. Diverse machine learning methodologies were applied to distinguish between healthy individuals and vitiligo patients, as well as to differentiate segmental from nonsegmental vitiligo. The ensemble algorithm achieved a remarkable AUC (Area Under the Curve) of 0.99 and an accuracy of 0.98 for diagnosing vitiligo. Furthermore, in predicting the development of segmental or nonsegmental vitiligo, the model exhibited an AUC of 0.79 and an accuracy of 0.73. Key parameters for vitiligo identification encompassed factors such as age, FBC (full blood count)-neutrophils, FBC-lymphocytes, LKF(liver and kidney function)-direct bilirubin, LKF-total bilirubin, and LKF-total protein levels. In contrast, vital indicators for monitoring the progression of segmental and nonsegmental vitiligo included FBC-B lymphocyte count, FBC-NK (Natural Killer) cell count, and LKF-alkaline phosphatase levels. This retrospective study underscores the potential of AI-driven analysis in identifying significant risk factors for vitiligo and predicting its subtypes at an early stage. These findings offer great promise for the development of effective diagnostic tools and the implementation of personalized treatment approaches in managing this challenging skin disorder.

Chinese urban development has entered the stage of stock planning, and as an important part of urban public space, the construction of street space has a direct impact on urban development. High-quality street space contributes to sustainable urban development.High-quality street spaces are highly important for constructing vibrant and sustainable cities. Taking Jiaozuo city, a medium-sized city, as an example, this article establishes an evaluation index system for commercial street space quality from the aspects of external characterization and objective material space and uses the Pearson correlation coefficient to explore the influencing factors of urban commercial street space quality. This study finds that (1) crowd activities on commercial streets have different temporal and spatial patterns on weekdays and days off; (2) the external characterization of urban street space quality has a significant positive correlation with objective material spatial elements such as the density of public transportation stops, functional density, functional mixing, interface continuity, and spatial green visibility rate; (3) the enhancement of street accessibility and fine-tuning of functions are key to improving the spatial quality of commercial streets.This study provides theoretical basis and ideas for improving the spatial quality of commercial streets in medium-sized cities in China and promoting sustainable urban development.

To meet the growing demand for integrated monitoring of complex power grid equipment, it is necessary to improve the situational awareness model of power transformers. The model is expected to assist monitoring personnel in timely identifying transformers with deteriorating trends among massive and discrete monitoring information, and to make responses in advance. However, the current transformer state awareness technology generally has the problem of single data source and poor timeliness, and still requires monitoring personnel to make artificial analysis and prediction in combination with telemetry information, which cannot fully meet the requirements of power grid equipment monitoring. This paper is based on multi-source data fusion technology, through associating and mining transformer alarm information, equipment maintenance records and power transmission and transformation online monitoring data, to extract the dimension features of transformer operation situation assessment. By constructing a multi-layer perceptron model, a transformer state transition model based on the principle of Markov chain is established, which can predict possible defects 2 h in advance and achieve good results, and determine the transformer state early warning index, providing sufficient time for monitoring personnel to deploy transformer operation and maintenance work in advance. Finally, the effectiveness of the method proposed in this paper is proved by the case of transformer crisis state in a city substation, and the method proposed in this paper has important significance for transformer state early warning. [ABSTRACT FROM AUTHOR]

The field of academic stress detection has gained significant attention recently because mental and physical health is crucial for academic success. The goal of academic stress detection is to identify a student's level of stress during the learning process using observable markers including physiological, behavioral, and psychological data. In recent years, detection methods that utilize wearable and nonwearable sensors have gained increased attention owing to their rich functionalities. In order to discover contemporary developments, coping strategies, limitations, difficulties, and potential research areas for addressing academic stress in educational settings, this study conducted an exhaustive review of the existing literature. First, we discussed how stressful events influence students’ psychological and physical health as well as the statistics frequently utilized to monitor academic stress. Then, using machine learning and deep learning methods, we described academic stress detection models. In addition, we described self-regulated strategy, computer-supported strategy and interactive learning technology-supported strategy. This comprehensive analysis of the latest techniques and recommendations for potential research avenues for tackling academic stress in educational settings will help other researchers in this field carry out and assess user research and build academic stress detection systems. [ABSTRACT FROM AUTHOR]

Urban street vitality has been a perennial focus within the domain of urban planning. This study examined spatial patterns of street vitality in the old city of Nanjing during working days and weekends using real-time user datasets (RTUDs). A spatial autoregressive model (SAM) and a multiscale geographically weighted regression (MGWR) model were employed to quantitatively assess the impact of various factors on street vitality and their spatial heterogeneity. This study revealed the following: (1) the distribution of street vitality in the old city of Nanjing exhibited a structure centered around Xinjiekou, with greater regularity and predictability in street vitality on working days than on weekends; (2) eight variables, such as traffic location, road density, and functional density, are positively associated with street vitality, whereas the green view index is negatively associated with street vitality, and commercial location benefits street vitality at weekends but detracts from street vitality on working days; and (3) the influence of variables such as traffic location and functional density on street vitality is contingent on their spatial position. Based on these results, this study provides new strategies to enhance the street vitality of old cities. [ABSTRACT FROM AUTHOR]

Amid urban expansion, historic districts face challenges such as declining vitality and deteriorating spatial quality. Using the streets of Xi'an's historical and cultural district as examples, this research utilizes multisource data, including points of interest (POIs), street view images, and Baidu heatmaps, alongside analytical techniques such as machine learning. This study explores the determinants of street vitality from the dual perspectives of its external manifestation and spatial carriers. A quantitative framework for measuring street vitality in historic districts is established, thoroughly examining the driving factors behind street vitality. Additionally, the relationship between built environment indicators and street vitality is elucidated through statistical analysis methods. The findings reveal significant, time-varying influences of these spatial carriers on human vitality, with distinct spatial distribution patterns of human activity across different times, and the significance of the influence of external representations of human vitality and various types of spatial carriers varies over time. Based on these insights, this paper proposes strategies for enhancing the vitality of historic streets, aiming to rejuvenate and sustain the diverse and dynamic energy of these districts. It provides a foundation for revitalizing the vigor of cultural heritage zones and offers strategies applicable to similar urban contexts. [ABSTRACT FROM AUTHOR]

Accurate forecasting of duty-free shopping demand plays a pivotal role in strategic and operational decision-making processes. Despite the extensive literature on sustainability, operations management, and consumer behavior in the context of duty-free shopping, there is a noticeable absence of an integrated end-to-end solution for precise demand forecasting. Furthermore, existing forecasting models often encounter limitations in effectively leveraging multi-source data as reliable indicators for duty-free shopping demand. To address these gaps, our study introduces a pioneering deep-learning architecture known as the Attention-Aided Interaction-Driven Long Short-Term Memory-Convolutional Neural Network Model (AI-LCM). Designed to capture intricate cross-correlations within multi-source data, encompassing search queries, COVID-19 impact, economic factors, and historical data; this model represents a significant methodological advancement. Rigorous evaluation against state-of-the-art benchmarks conducted on robust real-world datasets confirms the superior forecasting performance exhibited by our AI-LCM model. We elucidate the manifold implications for various stakeholders while illustrating the extensive applicability of our model and its potential to inform data-driven decision-making strategies. [ABSTRACT FROM AUTHOR]

Automatic matching of multisource data is an important technique for achieving change detection, fusion and updating spatial data. However, most current learning methods for building footprint matching require a large number of samples, and labeling these samples is costly in terms of labor and time. Moreover, multisource building footprint data are complex and diverse leading to recognizing the different matching relationships is a hard task. Thus, this study proposes a learning-based method for recognizing multisource building footprints matching relationships by using a one-class support vector machine (OCSVM). The OCSVM was trained using only positive samples. First, a set of geometric indicators was designed to train a model and realize initial matching recognition. Then, a contextual metric was calculated based on the rough matching results, and geometric and contextual metrics were combined to train the model and realize relaxed matching recognition. Relaxed matching is an optimization process implemented after initial matching to recognize more relaxed matching relationships. In relaxed matching, a convex hull is used to recognize matching relationships besides 1:1, such as 1:n, m:1 and m:n. The experimental results showed that the proposed method outperformed indicator-weighted (weighted average) and learning-based matching methods, such as traditional SVMs and decision trees (DTs). The precision scores of the proposed model were 97.1%, 95% and 97.2% for the Wuhan (China), Beijing (China) and Richmond Hill (Canada) datasets, respectively. Furthermore, the proposed model identified the matching relationships of buildings with complex geometric features and high-density spatial distributions. [ABSTRACT FROM AUTHOR]

Copyright of Acta Scientiarum Naturalium Universitatis Sunyatseni / Zhongshan Daxue Xuebao is the property of Sun-Yat-Sen University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Earlier, hydrological simulation calibration and validation relied on flow observations at hydrological stations, but multisource observations changed the basin hydrological simulation from single-flow validation to multivariate validation, including evaporation, soil water, and runoff. This study used the Soil and Water Assessment Tool (SWAT) distributed hydrological model to simulate and investigate hydrological processes in the Jinghe River Basin in China. After a single-station, single-variable calibration using flow observation data at the Zhangjiashan Hydrological Station, multisource data were used to validate actual evaporation, soil water, and runoff. Using the flow station data from Zhangjiashan station for parameter calibration and validation, the simulated values of R2, NSE, and KGE were all above 0.64, the PBIAS was within 20%, and the values of all the metrics in the calibration period were better than those in the validation period. The results show that the model performed satisfactorily, proving its regional applicability. Qingyang, Yangjiaping, and Zhangjiazhan stations had R2, NSE, and KGE values above 0.57 and PBIAS within 25% during regional calibration, considering spatial variability. Additionally, simulation accuracy downstream increased. R2, NSE, and KGE were above 0.50, and PBIAS was within 25% throughout validation, except for Qingyang, where the validation period was better than the calibration period. The Zhangjiashan station monthly runoff simulation improved after regional calibration. Runoff validation performed highest in the multivariate validation of evaporation–soil water–runoff, followed by actual evaporation and soil water content in China. The evaluation results for each hydrological variable improved after additional manual calibration. Multivariate verification based on multisource data improved the hydrological simulation at the basin scale. [ABSTRACT FROM AUTHOR]

Nighttime economic development is an important driving force in urban economic development, and identification of the levels and boundary ranges of nighttime economic agglomerations is an important part of the management of the nighttime economy. Previous studies have been limited by the use of a single data source to identify nighttime economic agglomerations. To address this limitation, multisource data fusion was used in this study to integrate nighttime lighting data, point of interest data, and check-in data and to assess the nighttime economy more comprehensively from the perspectives of both providers and receivers in the nighttime economy. To identify the hierarchical structure and boundaries of nighttime economic agglomerations accurately, a two-step method was used to identify local hotspots of the nighttime economy, divide the nighttime economic agglomerations into levels, and explore the spatial distribution and functional characteristics of different levels of nighttime economic zones. Comparative experiments showed the method used in this study to be rational and accurate. The methods and results of this study can provide a more comprehensive approach to the precise identification of nighttime economic agglomerations and guidance for the future planning, rational development, and management of nighttime economic agglomerations. [ABSTRACT FROM AUTHOR]

Approaching precipitation forecast refers to the prediction of precipitation within a short time scale, which is usually regarded as a spatiotemporal sequence prediction problem based on radar echo maps. However, due to its reliance on single-image prediction, it lacks good capture of sudden severe convective events and physical constraints, which may lead to prediction ambiguities and issues such as false alarms and missed alarms. Therefore, this study dynamically combines meteorological elements from surface observations with upper-air reanalysis data to establish complex nonlinear relationships among meteorological variables based on multisource data. We design a Residual Spatiotemporal Convolutional Network (ResSTConvNet) specifically for this purpose. In this model, data fusion is achieved through the channel attention mechanism, which assigns weights to different channels. Feature extraction is conducted through simultaneous three-dimensional and two-dimensional convolution operations using a pure convolutional structure, allowing the learning of spatiotemporal feature information. Finally, feature fitting is accomplished through residual connections, enhancing the model's predictive capability. Furthermore, we evaluate the performance of our model in 0–3 h forecasting. The results show that compared with baseline methods, this network exhibits significantly better performance in predicting heavy rainfall. Moreover, as the forecast lead time increases, the spatial features of the forecast results from our network are richer than those of other baseline models, leading to more accurate predictions of precipitation intensity and coverage area. [ABSTRACT FROM AUTHOR]

Accurately estimating aboveground biomass (AGB) in tropical forests is vital for managing the threats posed by deforestation, degradation, and climate change. However, challenges persist in accurately estimating AGB in high AGB regions. This study aims to accurately estimate the AGB of regions with high AGB by using spatial statistical analyses based on AGB estimates made by machine‐learning fusion of multisource data. We hypothesize that incorporating dominant auxiliary factors in the analysis increases the estimation accuracy. This study focuses on tropical forests located in Longyan, Fujian Province, China, covering an area of 19,028 km2. Multisource data are used, including airborne laser scanning, the Shuttle Radar Topography Mission digital elevation model, the Landsat Operational Land Imager, and the National Forest Inventory. Based on GeogDetector's spatial covariance matrix and the spatial similarity principle, we identify key auxiliary factors (dominant tree species, canopy closure, and herbaceous cover) and investigated how auxiliary variables can improve estimation accuracy. Empirical Bayesian kriging regression prediction introduces the main auxiliary factors to refine AGB estimates. These refinements significantly enhance the accuracy of AGB estimates, particularly for high AGB, resulting in a 0.1 increase in R2, a 7.0% reduction in root mean square error, a 13.5% reduction in mean square error, and a 6.6% reduction in mean absolute error when compared with the AGB estimates obtained by using machine learning to fuse multisource data. Thus, incorporating spatial statistical analysis into the integration of multisource data and machine learning for AGB estimation can enhance the accuracy of high‐AGB estimates in intricate forest structures, resulting in precise AGB maps. Plain Language Summary: This study enhances the accuracy with which the aboveground biomass (AGB) of high‐AGB forests is estimated, and the results should be applicable to other subtropical forest regions. Using a machine learning approach to fuse multisource data, we propose to integrate spatial statistical analyses to identify critical cofactors, such as dominant tree species, canopy closure, and herbaceous plant cover. These factors are then incorporated into the analysis to further refine high‐AGB estimates. Applying these methods substantially improves the accuracy of high‐AGB estimates and is crucial in supporting forest management, conservation, and related policy development in subtropical regions. Key Points: Spatial statistical analysis identifies key auxiliary factors and refines estimates of high aboveground biomass (AGB)Incorporating the dominant auxiliary factors increases the accuracy of high‐AGB estimates in tropical forestsSpatial statistics analysis combined with the use of multisource data and machine learning allows for accurate estimates of high‐AGB regions [ABSTRACT FROM AUTHOR]

Dockless bike-sharing (DBS) is a green and flexible travel mode, which has been considered as an effective way to address the first-and-last mile problem. A two-level process is developed to identify the integrated DBS–metro trips. Then, DBS trip data, metro passenger data, socioeconomic data, and built environment data in Shanghai are used to analyze the spatiotemporal characteristics of integrated trips and the correlations between the integrated trips and the explanatory variables. Next, multicollinearity tests and autocorrelation tests are conducted to select the best explanatory variables. Finally, a geographically and temporally weighted regression (GTWR) model is adopted to examine the determinants of integrated trips over space and time. The results show that the integrated trips account for 16.8% of total DBS trips and that departure-transfer trips are greater than arrival-transfer trips. Moreover, the integrated trips are concentrated in the central area of the city. In terms of impact factors, it is found that GDP, government count, and restaurant count are negatively correlated with the number of integrated trips, while house price, entropy of land use, transfer accessibility index, and metro passenger flow show positive relationships. In addition, the results show that the GTWR model outperforms the OLS model and the GWR model. [ABSTRACT FROM AUTHOR]

Using a large Chinese news corpus and Internet search data, we conduct an in‐depth out‐of‐sample forecasting study of Consumer Price Index (CPI) with the monthly macroeconomic database. For this purpose, we combine penalized regression and mixed‐frequency data sampling (MIDAS) methods to deal with the mixed‐frequency and high‐dimensional problems. Then we measure the time‐varying contributions of data from different sources and industries via dynamic model averaging. The results show that different types of data all provide enhancement in CPI forecasting, and the inclusion of media and Internet data further improves CPI forecasts' accuracy and timeliness due to their thorough coverage and fast update. In particular, the importance of alternative data is highlighted when the economy experiencing downturn or high uncertainty. [ABSTRACT FROM AUTHOR]

Accurate soybean futures price prediction is critical to related agricultural production, warehousing, and trading. Interval forecasting can avoid the loss of fluctuation information and evaluate the uncertainty of futures prices. However, most previous studies only consider the single‐type auxiliary variable, which will cause the deficiency of valued information. Moreover, the research concentrating on internet search index ignores the search habits of investors, resulting in subjectivity in keyword selection. Therefore, a novel multisource data‐driven combined forecasting model is proposed that consists of four parts: unstructured data processing, interval multi‐scale decomposition, interval combination forecasting, and model evaluation. First, sentiment analysis technology is used to convert news text into sentiment scores. The internet search keyword screening method based on latent Dirichlet allocation is then constructed to achieve the quantification of investor attention. Second, a decomposition method is applied to decompose the original interval‐valued series into finite more stationary components. Third, interval prediction results are obtained by machine learning‐based multiple predictors. Finally, the model evaluation module comprising error evaluation indicators and comparison experiments is presented to verify the effectiveness. The experimental results show that the proposed model has higher prediction accuracy, which indicates that multisource data and designed keyword screening methods can enhance the forecasting performance of the model. [ABSTRACT FROM AUTHOR]

Precisely predicting natural gas prices (NGPs) is important because it can provide the necessary decision-making basis for energy scheduling, planning and control. However, NGPs are affected by many factors and exhibit the characteristics of nonlinearity and randomness, which makes accurate predictions challenging. Therefore, in this paper, the information gain of multisource data and the global optimization ability of the gray wolf algorithm are used to build a multifactor-driven NGP hybrid forecasting model to improve the prediction performance. First, the emotional tendency and readability of news text are extracted and calculated by using VADER and textstat tools, respectively. Then the network search index is filtered and integrated by using the correlation coefficient method and the CRITIC method to form alternative variables of multisource data (news and search index). Second, the gray wolf optimization algorithm is used to find and determine the best key parameter group in long short-term memory model. Finally, the spot price of natural gas in Henry Hub from March 1, 2012 to February 28, 2022 is selected as the prediction object, and multi-scenario numerical experiments are carried out to verify the effectiveness of the proposed model. The ablation experiment results show that the information gain brought by multisource data can effectively improve the prediction effect of NGPs. Furthermore, the proposed model has the best prediction performance in different scenarios and can be regarded as a promising prediction tool.

The United States (U.S.) is a global leader in the production and exportation of soybeans and corn. Accurate monitoring and estimation of soybean and corn yields in the U.S. is essential for improving global food security. However, there is currently a lack of publicly available spatial distribution datasets with high temporal and spatial resolution for U.S. corn and soybean yields, which hampers related research and policy-making. Therefore, in this study, we proposed a statistical downscaling framework to produce spatially explicit crop yield estimates by utilizing multisource environmental covariates and ensemble machine learning methods. We produced distribution maps of soybean and corn yields in the U.S. from 2006 to 2021 at a 1-km resolution through the optimal Cubist model, resulting in the USASoy&CornYield1km dataset. The results demonstrated stable accuracy, with R2 values for corn ranging from 0.70 to 0.89 (average of 0.80) and for soybeans ranging from 0.74 to 0.90 (average of 0.81) during the period 2006–2021. Comparison with the spatial production allocation model (SPAM) dataset further confirmed the reliability of this dataset, with correlations of 0.84 for soybeans and 0.78 for corn when compared to SPAM2010. Spatial uncertainty analysis showed that the yield estimation uncertainty was 14.04% for soybeans and 20.49% for corn, indicating a generally low level of uncertainty. Overall, the USASoy&CornYield1km dataset offers higher spatial and temporal resolution, captures yield variations within counties, and covers a long time span. This study provides significant insights for analyzing U.S. soybean and corn yields and improving agricultural production.

The joint of multisource remote sensing (RS) data for land cover classification has become a popular research topic. Although studies have shown that the fusion of multisource data can improve the accuracy of classification, the current limitation lies in the inadequate exploitation of information, resulting in spectral confusion categories overlapping, and varying visual differences within the same category. To address these problems, this article proposes a multiscale spectral–spatial attention residual fusion network (MSSARFNet) that aims to enhance the classification performance of multisource RS data through effective spectral–spatial feature extraction and fusion. Specifically, three modules are designed: the multiscale spectral attention residual module (MSpeARM), the multiscale spatial attention residual module (MSpaARM), and multiscale convolution fusion (MCF) module. First, we divide the channels of the features into multiple paths and apply spectral–spatial attention mechanisms on each path. To further enhance connectivity, convolutional operation is utilized to establish connections between different paths, thus forming MSpeARM and MSpaARM. The MSpeARM suppresses redundant features and enhances effective features along the channel dimension to better differentiate spectral-confused categories. The MSpaARM highlights that different visual patterns of objects in the same category can mutually reinforce each other by weighting all positional features, regardless of their spatial differences. Second, to fuse these two sets of features, the MCF module is designed to learn multilevel semantic features and enhance fusion at a granular level. Experimental evaluations on three RS datasets demonstrate that the proposed method achieves excellent classification performance, indicating the effectiveness of MSSARFNet.

Gaining an understanding of the intricate mechanisms between human activity and the built environment can help in promoting sustainable urban development. However, most scholars have focused on residents’ life and work behavior and have ignored the third activity (e.g., shopping, eating, and entertainment). In this study, a random forest algorithm and SHapley Additive exPlanation model were utilized to explore the nonlinear influence of the built environment on the attraction of the third activity (other than home and work). A comparative analysis of the inflow of the third activity from home and work was also carried out. The results show that the contributions of all built environment variables to the attraction of the third activity differ between home–other flow (HO) and work–other flow (WO) at the global scale, but their local effects are significantly similar. Furthermore, the nonlinear influence of the built environment on the attractions of the third activity can vary from one factor to another. A significant spatial heterogeneity can be observed on the built environment variables’ local effects on the attractions of the third activity. These findings can provide urban planners with insights that will help in the planning and optimization of communities for pursuing the third activity.

Urban commercial centers play a critical role in the development of cities, and it is of significant relevance to research the influencing variables of the urban vitality of commercial centers to improve the quality of urban commercial centers. This study employs big data to construct a multiple linear regression model in order to uncover the spatial-distribution characteristics of urban vitality and commercial sectors in commercial centers within the primary urban region of Guangzhou. The findings indicate that the commercial sectors of life, business, finance, and leisure have a substantial influence on the fluctuation of pedestrian flow in commercial centers throughout the day. Conversely, public service commercial sectors do not have a significant impact on pedestrian flow. Furthermore, the effect of various commercial sectors on the vibrancy of urban commercial centers varies, and their performance differs on weekdays and holidays. Additionally, the level of integration among commercial sectors affects the vitality of the city's commercial space. This research presents empirical facts that can be used to optimize the logical allocation of urban commercial resources in urban planning. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Earth Science (1001-8166) is the property of Advances in Earth Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: The cold stress is one of the most important factors for affecting production throughout year, so effectively evaluating frost damage is great significant to the determination of the frost tolerance in lettuce. Methods: We proposed a high-throughput method to estimate lettuce FDI based on remote sensing. Red-Green-Blue (RGB) and multispectral images of open-field lettuce suffered from frost damage were captured by Unmanned Aerial Vehicle platform. Pearson correlation analysiswas employed to select FDIsensitive features from RGB and multispectral images. Then the models were established for different FDI-sensitive features based on sensor types and different groups according to lettuce colors using multiple linear regression, support vector machine and neural network algorithms, respectively. Results and discussion: Digital number of blue and red channels, spectral reflectance at blue, red and near-infrared bands as well as six vegetation indexes (VIs) were found to be significantly related to the FDI of all lettuce groups. The high sensitivity of four modified VIs to frost damage of all lettuce groups was confirmed. The average accuracy of models were improved by 3% to 14% through a combination of multisource features. Color of lettuce had a certain impact on the monitoring of frost damage by FDI prediction models, because the accuracy of models based on green lettuce groupwere generally higher. The MULTISURCE-GREEN-NN model with R² of 0.715 and RMSE of 0.014 had the best performance, providing a high-throughput and efficient technical tool for frost damage investigation which will assist the identification of cold-resistant green lettuce germplasm and related breeding. [ABSTRACT FROM AUTHOR]

Promoting the accessibility of basic public service facilities is key to safeguarding and improving people's lives. Effective public service provision is especially important for the sustainable development of less developed regions. Lincang in Yunnan Province is a typical underdeveloped region in China. In parallel, multisource remote sensing data with higher spatial resolution provide more precise results for small-scale regional accessibility assessment. Thus, we use an assessment method to measure and evaluate the accessibility of three types of infrastructure in Lincang based on multisource geospatial data. We further analyze the matching between public service facility accessibility and the socioeconomic attributes of inhabitant clusters and different poverty groups. The results show that the accessibility of educational facilities is currently better than that of health facilities in Lincang and that of sanitation facilities is relatively poor. Public service facility accessibility varies significantly among different types of inhabitant clusters, with better accessibility in inhabitant clusters with high levels of population density, aging, and income. Accessibility to healthcare, education, and sanitation is negatively correlated to varying degrees of poverty levels of poor groups, and the mean values of accessibility to various types of public facilities vary significantly across poor groups. Our findings can help inform policy formulation and provide theoretical support for planning and optimizing the layout of public facilities. [ABSTRACT FROM AUTHOR]

Strengthening the construction of ecological civilization is an inevitable requirement for promoting high-quality economic and social development. It is of great significance to study the evolutionary trend and relationship between urban spatial structure and ecological efficiency to promote high-quality social development. Taking Shandong Province as an example, this paper obtains data on urban factors such as points of interest, night light, number of employed people at the end of the year and water supply; uses Anselin Local Moran's I index to identify urban centers; analyzes the distribution form characteristics and agglomeration degree of urban space; and studies the spatial distribution characteristics and causes of differences in ecological efficiency based on the Super-SBM DEA model with undesirable output. The results show that all cities in Shandong Province show an inverse S-shaped circle decreasing trend, Laiwu city has the highest compactness (compactness index is 2.96), and Tai 'an city has the lowest compactness index of 0.04. The level of eco-efficiency in Shandong Province is "low in the west and high in the east", and the difference in eco-efficiency between regions is increasing year by year. Urban compactness has a "first increasing and then decreasing" effect on eco-efficiency. Technological innovation and industrial structure narrow the spatial difference in eco-efficiency, and the level of economic development expands it to a certain extent. This study aims to fill the gaps in existing research. By analyzing data on the evolution of urban spatial structure and resource consumption, it will reveal the trends of changes in the urban spatial structure of Shandong Province and study the impact of these changes on ecological benefits. [ABSTRACT FROM AUTHOR]

The rapid pace of urbanization and increasing demands for urban functionalities have led to diversification and complexity in the types of urban surface elements. The conventional approach of relying solely on remote sensing imagery for urban surface element extraction faces emerging challenges. Data-driven techniques, including deep learning and machine learning, necessitate a substantial number of annotated samples as prerequisites. In response, our study proposes a knowledge-driven approach that integrates multisource data with ontology to achieve precise urban surface element extraction. Within this framework, components from the EIONET Action Group on Land Monitoring in Europe matrix serve as ontology primitives, forming a shared vocabulary. The semantics of surface elements are deconstructed using these primitives, enabling the creation of specific descriptions for various types of urban surface elements by combining these primitives. Our approach integrates multitemporal high-resolution remote sensing data, network big data, and other heterogeneous data sources. It segments high-resolution images into individual patches, and for each unit, urban surface element classification is accomplished through semantic rule-based inference. We conducted experiments in two regions with varying levels of urban scene complexity, achieving overall accuracies of 93.03% and 97.35%, respectively. Through this knowledge-driven approach, our proposed method significantly enhances the classification performance of urban surface elements in complex scenes, even in the absence of sample data, thereby presenting a novel approach to urban surface element extraction. [ABSTRACT FROM AUTHOR]

Snow on the Antarctic sea ice is a crucial component of the cryosphere. In response to the dynamic and highly heterogeneous Antarctic snow during the sea ice melting season, this study employed a combined multi-source data and deep learning method to accurately retrieve snow depth on Antarctic sea ice. Initially, we integrate multiple datasets, including satellite remote sensing, geospatial information, and meteorological data. Subsequently, a Convolutional Neural Network (CNN) is utilized to construct a snow depth retrieval model (PSDCNN-5_7 model). Compared to snow depth measurements from Alfred Wegener Institute (AWI) snow buoys, the PSDCNN-5_7 model outperforms existing algorithms, exhibiting a deviation of only -3.38 cm. The uncertainty of the snow depth caused by the model input is only 1.64 cm. In West Antarctica, snow depth is more affected by snowfall (SF), 2-m air temperature (T2m), and sea ice velocity (SIV). Conversely, in East Antarctica, snow depth is primarily influenced by SIV. The proposed approach accurately retrieves snow depth on Antarctic sea ice and facilitates the derivation of long-term variations and trends in snow depth, contributing to a better understanding of the relationship between sea ice, snow, and climate change. [ABSTRACT FROM AUTHOR]

With population aging becoming increasingly prominent, it has been recognized as a critical issue for urban planners whether older adults can access fair, high-quality medical services. Yet, few studies have investigated urban medical accessibility at a departmental level for older adults. Taking Beijing as a case study, this study puts forth a novel framework augmented by multisource data to analyze subdistrict-scale accessibility to urban medical departments, with a particular emphasis on older adults' common diseases. Online registration data were collected to indicate the supply capacity of medical services, with the influence of department reputation taken into account when determining the facilities' service range. A hierarchical two-step floating catchment area method was employed to examine accessibility for older adults to the cardiovascular, orthopedic, endocrinology, respiratory, and neurology departments. Subsequently, Gini indices were calculated to evaluate medical accessibility equity across urban spaces. Additionally, multiple linear regression was used to explore the correlations between medical accessibility and potential influencing factors. Results showed that medical accessibility is typically higher in downtown areas and decreases from the urban center to the peripheral areas in Beijing. Cardiovascular, respiratory, and orthopedic patients face relatively greater difficulty in accessing appropriate medical resources. Furthermore, significant inequity in medical accessibility was observed among the subdistricts at different locations, with accessibility inequity in the ecological conservation area being particularly concerning. Additionally, seven factors were found to significantly affect accessibility, including distance to the urban center, density of arterial and collector roads, proportion of residential land use, proportion of public service land use, proportion of transportation land use, and proportion of vehicle ownership. The proposed framework could identify subdistricts with low accessibility for older adult patients to access different medical departments. Therefore, it could potentially support the policy-making process by offering guidelines to adjust the construction plan of the medical system in metropolises and optimize the direction for resource input. [ABSTRACT FROM AUTHOR]

Accurate forecasting tourism demand is crucial for improving the economic benefits of tourist attractions, but it is a challenging task. In this paper, we propose an effective daily tourism forecast model, principal component analysis‐grey wolf optimizer‐extreme learning machine (PCA‐GWO‐ELM), based on Baidu index data, holiday data, and weather data. Our model uses PCA to reduce the dimensionality of the data and employs the GWO to optimize the number of neural networks in the hidden layer of the ELM model, improving its forecast performance. We conduct an empirical study using the collected tourist data of Mount Siguniang. The results show that the proposed hybrid forecasting model outperforms other models in daily tourism demand forecasting, making it a potential candidate method for practitioners and researchers studying tourism demand forecasting. [ABSTRACT FROM AUTHOR]

Amid urban expansion, historic districts face challenges such as declining vitality and deteriorating spatial quality. Using the streets of Xi’an’s historical and cultural district as examples, this research utilizes multisource data, including points of interest (POIs), street view images, and Baidu heatmaps, alongside analytical techniques such as machine learning. This study explores the determinants of street vitality from the dual perspectives of its external manifestation and spatial carriers. A quantitative framework for measuring street vitality in historic districts is established, thoroughly examining the driving factors behind street vitality. Additionally, the relationship between built environment indicators and street vitality is elucidated through statistical analysis methods. The findings reveal significant, time-varying influences of these spatial carriers on human vitality, with distinct spatial distribution patterns of human activity across different times, and the significance of the influence of external representations of human vitality and various types of spatial carriers varies over time. Based on these insights, this paper proposes strategies for enhancing the vitality of historic streets, aiming to rejuvenate and sustain the diverse and dynamic energy of these districts. It provides a foundation for revitalizing the vigor of cultural heritage zones and offers strategies applicable to similar urban contexts.

Urban street vitality has been a perennial focus within the domain of urban planning. This study examined spatial patterns of street vitality in the old city of Nanjing during working days and weekends using real-time user datasets (RTUDs). A spatial autoregressive model (SAM) and a multiscale geographically weighted regression (MGWR) model were employed to quantitatively assess the impact of various factors on street vitality and their spatial heterogeneity. This study revealed the following: (1) the distribution of street vitality in the old city of Nanjing exhibited a structure centered around Xinjiekou, with greater regularity and predictability in street vitality on working days than on weekends; (2) eight variables, such as traffic location, road density, and functional density, are positively associated with street vitality, whereas the green view index is negatively associated with street vitality, and commercial location benefits street vitality at weekends but detracts from street vitality on working days; and (3) the influence of variables such as traffic location and functional density on street vitality is contingent on their spatial position. Based on these results, this study provides new strategies to enhance the street vitality of old cities.

Earlier, hydrological simulation calibration and validation relied on flow observations at hydrological stations, but multisource observations changed the basin hydrological simulation from single-flow validation to multivariate validation, including evaporation, soil water, and runoff. This study used the Soil and Water Assessment Tool (SWAT) distributed hydrological model to simulate and investigate hydrological processes in the Jinghe River Basin in China. After a single-station, single-variable calibration using flow observation data at the Zhangjiashan Hydrological Station, multisource data were used to validate actual evaporation, soil water, and runoff. Using the flow station data from Zhangjiashan station for parameter calibration and validation, the simulated values of R2, NSE, and KGE were all above 0.64, the PBIAS was within 20%, and the values of all the metrics in the calibration period were better than those in the validation period. The results show that the model performed satisfactorily, proving its regional applicability. Qingyang, Yangjiaping, and Zhangjiazhan stations had R2, NSE, and KGE values above 0.57 and PBIAS within 25% during regional calibration, considering spatial variability. Additionally, simulation accuracy downstream increased. R2, NSE, and KGE were above 0.50, and PBIAS was within 25% throughout validation, except for Qingyang, where the validation period was better than the calibration period. The Zhangjiashan station monthly runoff simulation improved after regional calibration. Runoff validation performed highest in the multivariate validation of evaporation–soil water–runoff, followed by actual evaporation and soil water content in China. The evaluation results for each hydrological variable improved after additional manual calibration. Multivariate verification based on multisource data improved the hydrological simulation at the basin scale.

Nighttime economic development is an important driving force in urban economic development, and identification of the levels and boundary ranges of nighttime economic agglomerations is an important part of the management of the nighttime economy. Previous studies have been limited by the use of a single data source to identify nighttime economic agglomerations. To address this limitation, multisource data fusion was used in this study to integrate nighttime lighting data, point of interest data, and check-in data and to assess the nighttime economy more comprehensively from the perspectives of both providers and receivers in the nighttime economy. To identify the hierarchical structure and boundaries of nighttime economic agglomerations accurately, a two-step method was used to identify local hotspots of the nighttime economy, divide the nighttime economic agglomerations into levels, and explore the spatial distribution and functional characteristics of different levels of nighttime economic zones. Comparative experiments showed the method used in this study to be rational and accurate. The methods and results of this study can provide a more comprehensive approach to the precise identification of nighttime economic agglomerations and guidance for the future planning, rational development, and management of nighttime economic agglomerations.

Introduction: The Leaf area index (LAI) of source region of yellow river basin is an important indicator for environmental sustainability. Most studies focus on the trend of LAI in Yellow River Source Region (YRSR) in accordance with both climate change and human actives. However, quantifying the effect of human activities on LAI is difficult but urgently needed. Specifically, Particle Matter 2.5 (PM2.5) can be an indirect indicator of human activities.Methods: In this study, we explored the potential dependence of LAI on temperature, precipitation, and PM2.5 in different land cover types in YRSR with linear regression and correlation analysis.Results: Over the period of 2001–2020, the climate in the region has been warming and becoming more humid, leading to overall improvements in vegetation. The mean LAI values varied between seasons, with summer having the highest and winter having the lowest LAI. The analysis of the LAI trends revealed that the mean LAI has been steadily increasing, particularly in the eastern region. The correlation analysis showed a significant positive correlation between annual average LAI and both annual precipitation and temperature, indicating that temperature has a greater impact on vegetation growth. The analysis of land cover types showed that most types exhibited a unimodal trend in LAI throughout the year, except for construction land which had two distinct peaks. Human-induced land cover change had a small impact on the overall increase in LAI. Furthermore, the interannual variation of PM2.5 showed a downward trend, with a strong correlation with the trend of LAI. Additionally, multiple linear regression analysis and residual trend analysis showed that climate factors had the strongest impact on LAI.Conclusion: The study highlights the spatiotemporal variations of LAI in the YRSR and its correlation with climatic and human factors. The findings suggest that climate change plays a crucial role in the vegetation growth and LAI in the region.

With a large population and rapid urbanization, there are still many challenges to optimize the ecological-agricultural-urban space. Here, taking Yulin City, situated on the Loess Plateau of China as a case in point, we explored the spatial suitability evaluation of ecological-agricultural-urban space. Building upon the Chinese government’s concept of “resource and environmental carrying capacity and territorial development suitability evaluation” (hereinafter referred to as “double evaluation”), this study applies machine learning to the planning of ecological-agricultural-urban space. It explores an intelligent evaluation method for land space patterns using multi-source data. Based on the random forest (RF) algorithm and geographic information system (GIS), resulting in evaluated spatial patterns for ecological-agricultural-urban in the Yulin area. The results showed the constructed random forest models achieved an accuracy of 93% for ecology, 90% for agriculture, and 92% for urban space in Yulin City on the test dataset. By means of suitability analysis, the results indicated that the extremely important ecological space were predominantly located in the southwestern and eastern regions of the study area, while suitable space for agricultural production were primarily scattered throughout the southeast. In contrast, suitable space for urban construction were concentrated mainly in the central part of the study area. The use of machine learning has proven to be effective in addressing multicollinearity among spatial evaluation factors across three different areas. By eliminating human subjectivity in weight assignment during evaluation, it introduces fresh perspectives for land space planning and status assessment. These findings may offer support for the scientific delineation of ecological-agricultural-urban space (three districts and three lines) in China.

Objectives: The aims of this study were to evaluate the effect of related factors that influence orthodontic treatment time and to identify the predictors of orthodontic treatment duration in a Chinese population. Materials and Methods: Information for 29 sociodemographic, malocclusion and orthodontic treatment characteristics was collected. Patients were divided into two groups: the duration ≤24 months group and duration >24 months group. The treatment dates of the initial visit and end of orthodontic treatment were obtained to calculate duration. The data were subjected to univariate and multivariate logistic regression modelling to quantify the association between characteristics and the treatment duration; odds ratios (ORs) and 95% confidence intervals (95% CIs) are reported. Results: Of 2120 patients, 704 patients (mean age, 15.9 ± 6.0 years) were included. Age, extraction, Bolton ratio (overall), rotated teeth, overjet and crowding (lower arch) were predictors for duration. The nomogram based on predictive factors exhibited strong discrimination ability, with concordance indices of 0.755 (95% CI = 0.712‐0.798) in the training cohort model and 0.717 (95% CI = 0.647‐0.787) in the validation cohort model. The calibration curves for the training and validation cohorts showed that the probabilities of the nomogram agreed well with actual probabilities. Conclusions: Young age, non‐extraction and severe crowding (lower arch) can increase the chance of durations of ≤24 months. Rotated teeth, excessive overjet and Bolton ratio (overall) can increase the chance of durations of >24 months. [ABSTRACT FROM AUTHOR]

Global warming is currently an area of concern. Human activities are the leading cause of urban greenhouse gas intensification. Inversing the spatial distribution of carbon emissions at microscopic scales such as communities or controlling detailed planning plots can capture the critical emission areas of carbon emissions, thus providing scientific guidance for intracity low-carbon development planning. Using the Sino—Singapore Tianjin Eco-city as an example, this paper uses night-light images and statistical yearbooks to perform linear fitting within the Beijing—Tianjin—Hebei city-county region and then uses fine-scale data such as points of interest, road networks, and mobile signaling data to construct spatial characteristic indicators of carbon emissions distribution and assign weights to each indicator through the analytic hierarchy process. As a result, the spatial distribution of carbon emissions based on detailed control planning plots is calculated. The results show that among the selected indicators, the population distribution significantly influences carbon emissions, with a weight of 0.384. The spatial distribution of carbon emissions is relatively distinctive. The primary carbon emissions are from the Sino—Singapore Cooperation Zone due to its rapid urban construction and development. In contrast, carbon emissions from other areas are sparse, as there is mostly unused land under construction. [ABSTRACT FROM AUTHOR]

Urban vitality has been a popular topic in urban planning. Urban vitality was measured using Baidu population vitality data and the constructed environment of the urban area of Hefei City using POI (point of interest) data, urban vector data, and nighttime remote sensing image data. The influence mechanism between the urban vitality and the built environment of Hefei City was analyzed using OLS linear regression and the GWR (geographical weighted regression) model. The research results show as follows. The urban vitality of Hefei City demonstrated spatially differential features of degreasing gradually from the center to the periphery and being partially highly concentrated with an extremely imbalanced spatial distribution. The constructed environment's impact on urban vitality had spatial heterogeneity. Regional vitality can be effectively enhanced on a global scale by improving regional function mixedness, the average number of building storeys, and infrastructural perfection. Due to their spatial position, indices such as population density, block POI density, road intersection density, volume fraction, green water coverage, bicycle sharing density, commercial service facility accessibility, and educational and cultural facility accessibility can either promote or inhibit the urban vitality. Moreover, the nearest distance to a bus stop and a subway station was negatively correlated to urban vitality. The author gave recommendations for urban planning and management combined with research results that can promote fine and differentiated block construction and benign growth of cities. [ABSTRACT FROM AUTHOR]

The degree of coordination between the supply and demand for urban green spaces serves as a vital metric for evaluating urban ecological development and the well-being of residents. An essential principle in assessing this coordination is the precise quantification of both the demand and supply of green spaces, as well as the differential representation of their spatiotemporal structures. This study utilizes the entropy weight method (EWM) and principal component analysis (PCA) to comprehensively measure supply indicators for green space quantity and quality in the central urban area of Shenyang, China. To establish reliable and quantifiable demand indicators, mobile signaling spatial-temporal data are corrected by incorporating static population cross-sectional data. The Gaussian two-step floating catchment area method (Ga2SFCA) is employed to calculate the accessibility of green spaces in each community with ArcGIS 10.2 software, while the Gini coefficient is utilized to assess the equity of green space distribution within the study area. This study employs location entropy to determine the levels of supply and demand for green spaces in each subdistrict. Furthermore, the priority of community-scale green space regulation is accurately determined by balancing vulnerable areas of green space supply and replenishing green space resources for the ageing population. The findings suggest a Gini coefficient of 0.58 for the supply and demand of green spaces in Shenyang's central metropolitan region, indicating a relatively low level of equalization in overall green space allocation. Based on location entropy, the classification of supply and demand at the street level yields the following outcomes: balanced areas comprise 21.98%, imbalanced areas account for 26.37%, and highly imbalanced regions represent 51.65%. After eliminating the balanced regions, the distribution of the elderly population is factored in, highlighting the spatial distribution and proportions of communities with distinct regulatory priorities: Level 1 (S1) constitutes 7.4%, Level 2 (S2) accounts for 60.9%, and Level 3 (S3) represents 31.7%. Notably, the communities in the S1 category exhibit spatial distribution characteristics of aggregation within the inner ring and the northern parts of the third ring. This precise identification of areas requiring urgent regulation and the spatial distribution of typical communities can provide reliable suggestions for prioritizing green space planning in an age-friendly city. [ABSTRACT FROM AUTHOR]

Abstract Background Private clinics are important places for residents to obtain daily medical care. However, previous researches mainly focused on public medical institutions but ignored the issue of systematic allocation of social medical resources such as clinics. It is critical to understand the private clinics distribution to analyze the rational allocation of medical resources and the spatial difference. Methods Based on the field survey, land census, population density, and economic data from Guiyang, this study analyzes the spatial pattern of private clinics in the main urban area of Guiyang and the influencing factors by using spatial analysis methods such as kernel density, standard deviation ellipses, and geo-detector. Results The private clinics in the main urban area of Guiyang are characterized by "inner dense, outer sparse dense," showing an overall spatial clustering feature of "four cores and two belts with many points" and "dense inside and sparse outside." Different types of private clinics have distinct spatial distribution characteristics and agglomeration forms. The growth of private clinics is closely linked to the population growth of mountainous cities. The most important factors influencing the spatial pattern of private clinics are residential land factors, followed by traffic factors and population density. The impact of economic, natural, and spatial factors is minimal. When using a geo-detector, the results of multi-factor interaction differ from those of single factors, and factor interactions have greater explanatory power than single factors in clinic distribution. Conclusion This study investigates the geographic distribution and influencing variables of private clinics in typical mountain cities and identifies the causes of the current disparity in the distribution of healthcare resources. It is necessary to gradually develop the primary healthcare system in mountainous cities with legislation, counterpart support, and social resources. While ensuring equal access to health care for low-income people and mobile populations, various medical needs of community members should be fully considered and implemented as soon as possible.

Approaching precipitation forecast refers to the prediction of precipitation within a short time scale, which is usually regarded as a spatiotemporal sequence prediction problem based on radar echo maps. However, due to its reliance on single-image prediction, it lacks good capture of sudden severe convective events and physical constraints, which may lead to prediction ambiguities and issues such as false alarms and missed alarms. Therefore, this study dynamically combines meteorological elements from surface observations with upper-air reanalysis data to establish complex nonlinear relationships among meteorological variables based on multisource data. We design a Residual Spatiotemporal Convolutional Network (ResSTConvNet) specifically for this purpose. In this model, data fusion is achieved through the channel attention mechanism, which assigns weights to different channels. Feature extraction is conducted through simultaneous three-dimensional and two-dimensional convolution operations using a pure convolutional structure, allowing the learning of spatiotemporal feature information. Finally, feature fitting is accomplished through residual connections, enhancing the model’s predictive capability. Furthermore, we evaluate the performance of our model in 0–3 h forecasting. The results show that compared with baseline methods, this network exhibits significantly better performance in predicting heavy rainfall. Moreover, as the forecast lead time increases, the spatial features of the forecast results from our network are richer than those of other baseline models, leading to more accurate predictions of precipitation intensity and coverage area.

Dockless bike-sharing (DBS) is a green and flexible travel mode, which has been considered as an effective way to address the first-and-last mile problem. A two-level process is developed to identify the integrated DBS–metro trips. Then, DBS trip data, metro passenger data, socioeconomic data, and built environment data in Shanghai are used to analyze the spatiotemporal characteristics of integrated trips and the correlations between the integrated trips and the explanatory variables. Next, multicollinearity tests and autocorrelation tests are conducted to select the best explanatory variables. Finally, a geographically and temporally weighted regression (GTWR) model is adopted to examine the determinants of integrated trips over space and time. The results show that the integrated trips account for 16.8% of total DBS trips and that departure-transfer trips are greater than arrival-transfer trips. Moreover, the integrated trips are concentrated in the central area of the city. In terms of impact factors, it is found that GDP, government count, and restaurant count are negatively correlated with the number of integrated trips, while house price, entropy of land use, transfer accessibility index, and metro passenger flow show positive relationships. In addition, the results show that the GTWR model outperforms the OLS model and the GWR model.

Data scarcity is a key factor impacting the current emphasis on individual indicators and the distribution of large-scale spatial objects in country-level SDG 6 research. An investigation of progress assessments and factors influencing SDG implementation in cities and counties indicates that smaller-scale regions hold greater operational significance for achieving the 2030 Agenda for Sustainable Development from the bottom up; thus, urgent attention should be given to data deficiencies and inadequate analyses related to SDG impact attribution. This study, conducted in the National Innovative Demonstration Zone for Sustainable Development of Lincang City, investigates multisource data sources such as integrated statistics, survey data, and remote sensing data to analyze the progress and status of SDG 6 achievement from 2015–2020, and employs the LMDI decomposition model to identify influential factors. The assessment results demonstrate that the SDG 6 composite index in Lincang increased from 0.47 to 0.61 between 2015 and 2020. The SDG 6 indicators and SDG 6 composite index have significant spatial heterogeneity. The water resources indexes in wealthy countries are high, the water environment and water ecology indexes in developing countries are comparatively high, and the SDG 6 composite index is high in undeveloped counties. Technological and economic advances are the main positive drivers impacting the SDG 6 composite index, and the relative contributions of technology, economy, structure, and population are 61.84%, 54.16%, −4.03%, and −11.96%, respectively. This study shows that integrated multisource data can compensate for the lack of small-scale regional statistical data when quantitative and comprehensive multi-indicator evaluations of the SDGs are conducted. And, policies related to SDG 6.1.1, SDG 6.2.1, and SDG 6.3.1 can be a priority for implementation in undeveloped regions with limited funding. [ABSTRACT FROM AUTHOR]