Multi-source Data Analysis Research Articles

Mapping heat vulnerability in Australian capital cities: A machine learning and multi-source data analysis

Mapping heat vulnerability in Australian capital cities: A machine learning and multi-source data analysis

A Review of Multi-Source Data Fusion and Analysis Algorithms in Smart City Construction: Facilitating Real Estate Management and Urban Optimization

In the context of the booming construction of smart cities, multi-source data fusion and analysis algorithms play a key role in optimizing real estate management and improving urban efficiency. In this review, we comprehensively and systematically review the relevant algorithms, covering the types, characteristics, fusion techniques, analysis algorithms, and their synergies of multi-source data. We found that multi-source data, including sensors, social media, citizen feedback, and GIS data, face challenges such as data quality and privacy security when being fused. Data fusion algorithms are diverse and have their own advantages and disadvantages. Data analysis algorithms help urban management in areas such as spatial analysis and deep learning. Algorithm collaboration can improve decision-making accuracy and efficiency and promote the rational allocation of urban resources. In the future, algorithm development will focus on data quality, real-time, deep mining, interdisciplinary research, privacy protection, and collaborative application expansion, providing strong support for the sustainable development of smart cities.

Analyzing failure mechanisms and predicting step-like displacement: Rainfall and RWL dynamics in lock-unlock landslides

Lock-unlock landslides have thick sliding zones that store a lot of energy. This makes them start quickly, happen suddenly, and have serious consequences. Therefore, it becomes urgent to study the deformation and failure mechanisms of such landslides and develop rational predictive models. Taking the Jiuxianping landslide as an example, this study investigates the regularity of landslide displacement changes using multi-source data, focusing on the abrupt displacement patterns in the unlock phase. Furthermore, employing Transient Release and Inhalation Method tests combined with Geo-Studio’s SEEP/W and SIGMA/W modules for fluid–solid coupled simulation calculations, the evolution process of landslide failure mechanisms and deformation characteristics is analyzed and discussed. Lastly, utilizing data mining analysis of multi-source data, a hybrid optimized machine learning predictive model is established for model prediction comparison. The study reveals that: (1) The rise in infiltration line elevates pore water pressure, affecting the stability of the sliding zone, leading to “unlock effects” and step-like displacement deformation; (2) Simulation shows that YY208 is closer to the actual situation, located at the far bank position, while YY210 is greatly influenced by the “buoyancy effect”, resulting in a slowdown in deformation velocity; (3) After data preprocessing, overall actual displacement prediction performs better than simulation displacement prediction in terms of Mean Absolute Error, Mean Squared Error and Correlation Coefficient, but noise reduction processing can improve the periodic prediction effect of simulation displacement.

“Raman plus X” dual‐modal spectroscopy technology for food analysis: A review

AbstractRaman spectroscopy, a nondestructive optical technique that provides detailed chemical information, has attracted growing interest in the food industry. Complementary spectroscopic methods, such as near‐infrared (NIR) spectroscopy, nuclear magnetic resonance (NMR), terahertz (THz) spectroscopy, laser‐induced breakdown spectroscopy (LIBS), and fluorescence spectroscopy (Flu), enhance Raman spectroscopy's capabilities in various applications. The integration of Raman with these techniques, termed “Raman plus X,” has shown significant potential in agri‐food analysis. This review highlights the latest advances and applications of dual‐modal spectroscopy methods combining Raman spectroscopy with NIR, NMR, THz, LIBS, and Flu in food analysis. Key applications include detecting harmful contaminants, evaluating food quality, identifying adulteration, and characterizing structure. The synergistic use of Raman‐based dual‐modal spectroscopy provides more comprehensive information and improves modeling accuracy compared to single techniques. The review also explores the role of data fusion in multisource spectral analysis and discusses challenges and prospects of “Raman plus X,” including the development of integrated hardware and advanced data fusion algorithms. These advancements aim to streamline multisource data analysis, offering valuable insights to select appropriate analytical methods for practical applications in the food industry.

Investigation on deformation behavior of unstable rock belt based on multi-source data analysis

Investigation on deformation behavior of unstable rock belt based on multi-source data analysis

The paradox of dual orientation in human resource practices and its cross-level effect on innovation

This paper leverages paradoxical theorizing to develop the scale for Ambidextrous Human Resource Practices (AHRP) and contributes to the literature on emerging economies by providing a guide for managers on best practices for implementing ambidexterity within HR. A mixed −methods approach was used. Study 1 generated the item pool for the AHRP scale with the help of extant literature and in-depth interviews conducted with 37 managers from five large Chinese firms, including two Fortune 500 companies. The items generated in Study 1 were tested for validation through a second study with a sample of 216 employees. Study 3 employed multilevel structural equation modeling on data from 257 employees across 64 teams from 31 firms. The AHRP construct was developed with two dimensions: commitment-oriented HR practices (HRP-CM) and cooperation-oriented HR practices (HRP-CO). Analysis of multisource data indicates that AHRPs at the firm level had a strong positive relationship with innovation performance at the team level.

Students' study activities before and after exam deadlines as predictors of performance in STEM courses: A multi-source data analysis

Many college students struggle with regulating the time and effort they invest in classes. We used digital trace data from a learning management system to examine students' behavioral engagement and associations with course performance in four chemistry courses (N = 1596). Results from Study 1a show that behavioral engagement declined across the course, except for high spikes in exam weeks. Students with higher regularity and continued engagement after midterm exams obtained higher course grades, whereas steep increases in study activities shortly before exams did not predict performance. Using a selective subsample of students (n = 51, with 510 observations over time) who identified chemistry as a challenging course, Study 1b explores whether intentions to regulate learning behaviors with goal-directed control strategies lead to changes in behavioral engagement. Intentions to use control strategies lead to short-term changes in behavioral engagement, but students did not implement planned adjustments to their study behaviors in the long run. Educational relevance statementThis study shows that consistent behavioral engagement in a learning management system over the course of a semester and early increases in learning activities before critical course exams predicted students' academic success in chemistry college courses. Students showed increased behavioral engagement immediately before course exams, but such short-term increases did not lead to better course performance. Instead, regular course engagement, as indicated by click activity in a learning management system, was significantly related to students' end-of-term course performance. Findings from a small and selective subsample of students who perceived the course as particularly challenging (study 1b) further suggest that students' intentions to change their behavioral engagement for the following exam(s) predicted only short-term changes in observed engagement in the learning management system. Thus, these students might benefit from further support to effectively regulate their learning behaviors. Studies 1a and 1b suggest that digital trace data from the course's learning management system can be informative in identifying struggling students, particularly using trace data from weeks around exams.

Investigating relationships between ridesourcing and public transit using big data analysis and nonlinear machine learning: A case study of Shanghai, China

Ridesourcing has transformed the landscape of passenger transportation systems in many cities worldwide, but whether it competes with or complements public transport (PT) is still debated, and the literature is limited. Therefore, this study aims to address this knowledge gap by measuring the relationships between the two systems and examining their determinants using a multisource big data analysis and nonlinear machine learning approach, with Shanghai, China, as the study case. First, we used the observed ridesourcing data in Shanghai to compute the fastest PT alternative for each ridesourcing trip based on the Amap open platform and subsequently compared the travel patterns (i.e., distance, duration, and generalized cost) of the two systems. Second, we propose a technical framework that considers the spatiotemporal availability and generalized cost acceptability of PT services, as well as the inclusivity of ridesourcing services, to accurately classify and identify the relationship between ridesourcing and PT systems. Finally, we explored the importance of four types of determinants, namely, ridesourcing characteristics, PT service, built environment, and weather, and their nonlinear effects on different relationships based on extreme gradient boosting and Shapley additive explanations. Our results show that the fastest PT alternative involves an average travel distance, generalized travel time, and generalized cost that are 1.16, 2.13, and 1.15 times greater, respectively, than those of ridesourcing. Competitive trips account for 36% of urban areas but only 16% in the suburbs. Furthermore, more than 70% and 10% of the ridesourcing trips in suburban areas are used to complement and integrate PT, respectively. The nonlinear machine learning framework identified the top three determinants of integration as travel cost, distance to the CBD, and travel time. Notably, determinants such as the distance to the CBD and temperature have nonlinear effects on these relationships. These findings offer valuable insights for designing multimodal transportation options that integrate the benefits of ridesourcing and PT.

Triple Collocation-Based Uncertainty Analysis and Data Fusion of Multi-Source Evapotranspiration Data Across China

Accurate estimation of evapotranspiration (ET) is critical for understanding land-atmospheric interactions. Despite the advancement in ET measurement, a single ET estimate still suffers from inherent uncertainties. Data fusion provides a viable option for improving ET estimation by leveraging the strengths of individual ET products, especially the triple collocation (TC) method, which has a prominent advantage in not relying on the availability of “ground truth” data. In this work, we proposed a framework for uncertainty analysis and data fusion based on the extended TC (ETC) and multiple TC (MTC) variants. Three different sources of ET products, i.e., the Global Land Evaporation and Amsterdam Model (GLEAM), the fifth generation of European Reanalysis-Land (ERA5-Land), and the complementary relationship model (CR), were selected as the TC triplet. The analyses were conducted based on different climate zones and land cover types across China. Results show that ETC presents outstanding performance as most areas conform to the zero-error correlations assumption, while nearly half of the areas violate this assumption when using MTC. In addition, the ETC method derives a lower root mean square error (RMSE) and higher correlation coefficient (Corr) than the MTC one over most climate zones and land cover types. Among the ET products, GLEAM performs the best, while CR performs the worst. The merged ET estimates from both ETC and MTC methods are generally superior to the original triplets at the site scale. The findings indicate that the TC-based method could be a reliable tool for uncertainty analysis and data fusion.

North Atlantic and the Barents Sea variability contribute to the 2023 extreme fire season in Canada

In the late spring to summer season of 2023, Canada witnessed unprecedented wildfires, with an extensive burning area and smoke spreading as far as the East Coast of the United States and Europe. Here, using multisource data analysis and climate model simulations, we show that an abnormally warm North Atlantic, as well as an abnormally low Barents Sea ice concentration (SIC), are likely key climate drivers of this Canadian fire season, contributing to ~80% of the fire weather anomaly over Canada from June to August 2023. Specifically, the warm North Atlantic forms an anomalous regional zonal cell with ascending air over the Atlantic and descending air encircling Canada, creating hot and dry local conditions. Meanwhile, reduced Barents SIC leads to a high-pressure center and reinforces the dry northern winds in Canada through Rossby wave dynamics. These exacerbated dry and hot conditions create a favorable environment for the ignition and spread of fires, thus contributing to the prolonged and extreme fire season in Canada. These teleconnections can extend to decadal scales and have important implications for understanding and predicting decadal fire activity in Canada and the surrounding regions.

Interval state estimation method for distribution networks considering multi-attribute decision making and multi-source measurement data uncertainty

The operation of distribution networks faces significant challenges due to uncertainties and measurement errors. This paper presents an innovative interval state estimation method that leverages multisource measurement data and multi-attribute decision-making to enhance accuracy and reliability. We first extract deterministic uncertainty measures from heterogeneous measurement data. By analyzing the interplay between determinacy and uncertainty, we construct an interval representation space that enables precise extraction, integration, and analysis of multisource data. Subsequently, we develop an interval multi-attribute decision-making model that reflects the dynamic preferences of the distribution network, resulting in a robust interval state estimation model that incorporates uncertainty. An improved Krawczyk-Moore (KM) algorithm facilitates iterative solutions, while Monte Carlo simulations validate the model's effectiveness in handling multisource data fusion and uncertainty analysis. Simulation results demonstrate a 0.02 improvement in measurement error statistics, with estimation error statistics remaining stable and the estimated interval coverage probability decreasing by approximately 0.4. Overall, this method outperforms traditional approaches, particularly in scenarios characterized by data uncertainty and multisource data integration, thereby enhancing estimation accuracy.

Right-Siting Care and Decongesting Specialist Outpatient Clinics - A Singapore Case Study

Abstract Issue Globally, Specialist Outpatient Clinics (SOCs) face a growing burden due to rising chronic diseases. This strain on resources leads to long appointment wait times (LAWTs), delaying diagnoses and treatments. This is particularly of concern in countries with rapidly ageing populations such as Singapore. Description The Right-Site Care Program is a healthcare transformation initiative implemented by individual healthcare clusters within Singapore to improve access to timely SOC care by directing patients to the most appropriate care level (right-siting). This transitions stable chronic disease patients to primary care providers (PCPs) for ongoing management. A standardised approach utilises evidence-based guidelines for patient selection, followed by identification and transfer of suitable patients to PCPs. This multi-center program offers a real-world example of healthcare system optimisation for chronic disease management. Results A multi-source data analysis, including program reports and peer-reviewed studies from participating healthcare clusters, was conducted to evaluate program impact. The analysis focused on LAWTs, patient outcomes and implementation challenges. Right-siting yielded mixed results across various conditions. Right-sited patients had better survival rates, likely due to closer monitoring and management of chronic conditions with higher total outpatient attendance frequencies. (1) Improvements were particularly significant for diabetes mellitus patients who successfully transitioned to PCPs. (2) However, challenges such as patient scepticism about PCP competence and a preference for specialist care have been identified. (3) Lessons learned Right-siting care can improve patient outcomes for certain chronic diseases. The program’s impact varied across different chronic diseases, highlighting the need for tailored strategies. Educational interventions addressing patient concerns can facilitate smoother patient transitions and program success. Key messages • Right-siting care shows promise, with improved patient outcomes for conditions like diabetes mellitus. • Patient education is crucial to overcome patient scepticism and preference for specialists.

Evaluation and maintenance method for general speed railway turnouts based on multi-source data

In recent years, with the increasing passengers' requirements for safety and comfort, as well as the measures of increasing revenue and reducing the expenditure of railway company, more efficient, more scientific and more comprehensive requirements have been put forward for the maintenance of railway turnouts. This paper develops a maintenance strategy based on the Turnout Service State Indicator (TSSI), derived from an analysis of multi-source data concerning turnout conditions. The weights of critical indicators affecting turnout conditions are determined using Analytical Target Cascading (ATC) and Analytic Hierarchy Process (AHP). Subsequently, turnouts are classified into four categories—great, good, general, and poor—based on their TSSI values, guiding specific maintenance strategies. The proposed assessment method was validated using data from four sets of turnouts collected on actual tracks, showing consistency between the assessment results and field technicians' evaluations. A comparative analysis of the ATC and AHP suggests that the latter provides a more scientific and objective assessment of turnout conditions. The established methods for evaluating the service state and deciding on turnout maintenance actions have been proven feasible and practical through field validation. These methods can be broadly applied to maintain general speed railway turnouts to enhance repair efficiency and reduce maintenance costs.

Machine learning and multisource data analysis approach towards traffic accident risk prediction

Traffic accidents are continually a concern across the world bringing with them huge economic costs and high rates of fatality and injuries each single year. Predictive modeling can lessen such risks if the accident hotspots and timing can easily be ascertained. This study collects various data including historical accident reports, water and meteorological conditions, road characteristics, and telematics data, to form traffic accident risk predictive models. To construct and analyze these predictive models, we use various machine learning techniques such as logistic regression, decision trees, and ensemble methods (Random Forest and Gradient Boosting). The results bear out as efficient and effective, ensemble models are streets ahead than regression-based techniques with Gradient Boosting achieving AUC-ROC scores of up to 0.89. The results of this research can also assist in applying effective safety measures in a more cost-effective manner as well as facilitate the development of efficient transport management systems and policies.

Extraction of deterioration and analysis of vegetation impact effects on the south palace wall of Weiyang Palace

Weiyang Palace, the royal palace of the Western Han Dynasty, is a part of the Silk Roads: the Routes Network of Chang’an-Tianshan Corridor on the World Heritage list. The south palace wall of Weiyang Palace is a well-preserved earthen heritage site within the palace, but it is undergoing continuous deterioration due to the influence of vegetation and external environmental factors. This study pioneers the integration of high-resolution three-dimensional LiDAR scanning with multi-source data analysis, including unprecedented on-site botanical surveys, to explore the subtle effects of different vegetation types on the structural integrity of the south palace wall. Through contour line analysis and facade grid analysis, we extracted the deterioration locations of typical sections of the earthen heritage sites. And we classified the overlying vegetation types on the wall using an object-oriented classification algorithm. Our findings reveal a complex interaction between vegetation and earthen structures: paper mulberry exhibits protective qualities against erosion, while Ziziphus jujuba significantly exacerbates structural vulnerabilities. The methodologies applied in this study for extracting deterioration at earthen heritage sites and integrating multi-source spatial data can serve as a technical application model for monitoring and analyzing the driving forces of earthen heritage sites along the entire Silk Road network, thereby better guiding the conservation of earthen heritage sites.

Research on simulation and validation methods of aerosol radiative forcing on the Tibetan Plateau based on satellite and ground-based remote sensing observations over the past 20 years

Atmospheric radiative changes induced by aerosol radiative forcing are the most uncertain factors in climate change, affecting a comprehensive understanding of aerosol's role in the climate system and ecosystem, with current research mainly focused on densely populated and heavily polluted regions. This study utilizes satellite and ground-based remote sensing data to establish a multi-source data processing and analysis workflow suitable for the Qinghai-Tibet Plateau region, and based on atmospheric radiative transfer models, constructs methods for simulating and validating regional aerosol radiative forcing, optimizing the long-term observational, simulation, and variation studies of aerosol radiative forcing at regional scales. The results indicate: (1) Key input parameters for simulating aerosol radiative forcing regions were determined through sensitivity tests of radiative transfer model parameters to be AOD, surface albedo, atmospheric column water vapor content, and total atmospheric ozone. A method for simulating aerosol direct radiative forcing regions was constructed. Comparison and validation against aerosol radiative forcing site simulations based on ground-based remote sensing observations at the Yangbajing station in Tibet showed R2 values above 0.8 and NRMSE values between 0.25 and 0.39, indicating high accuracy of the method, suitable for the Qinghai-Tibet Plateau. (2) Utilizing satellite remote sensing data, aerosol direct radiative forcing simulations for the Qinghai-Tibet Plateau region over the past 20 years were conducted based on the constructed method. Results showed: ① The annual mean aerosol radiative forcing at the top of the atmosphere was −3.03 W/m2, gradually increasing from west to east; monthly means were negative, decreasing by an average of 0.0025 W/m2 per year, with decreases mainly in February to May. ② The annual mean surface aerosol radiative forcing was −13.56 W/m2, gradually increasing from west to east; monthly means were negative, decreasing by an average of 0.015 W/m2 per year, with decreases mainly in February, June to July, and October to December. ③ The annual mean atmospheric aerosol radiative forcing was 10.6 W/m2, gradually increasing from southwest to northeast; monthly means were positive, increasing by 0.007 W/m2 per year, with increases mainly in October to December. Overall, the annual and monthly mean aerosol direct radiative forcing values at the top of the atmosphere and surface were negative, indicating a cooling effect, while those in the atmosphere were positive, indicating a heating effect; the strongest aerosol radiative forcing occurred in summer at the top of the atmosphere, and in spring for both surface and atmosphere; April showed the fastest variation.

Adjoint-based sensitivity analysis and assimilation of multi-source data for the inference of spatio-temporal parameters in a 2D urban flood hydraulic model

This contribution presents a novel approach for the calibration of distributed parameters in a 2D urban flood hydraulic model. It focuses on the challenging issue of inferring distributed friction parameters from multi-source heterogeneous spatio-temporal observations of their hydraulic signatures in the context of an urban flash flood in a complex street network. A variational data assimilation algorithm is used to infer high-dimensional multi-variate parameters (spatialized friction and inflow discharge time series) using multi-source observations. This method relies on a differentiable 2D shallow water hydraulic model which enables to generate high-resolution sensitivity maps of local gradients and Derivative-based Global Sensitivity Measures (DGSM), enabling to guide adequate definition of parameter spatialization for the data assimilation process. Assimilated data include real local limnigraphic measurements and high-water marks collected after a major flood event, as well as modeled flow velocity used in twin experiments setups. This study is the first to leverage high-water marks with a variational method for the calibration of distributed parameters in an urban flood model. The multi-source data is used to infer inflow hydrographs and distributed friction parameters in setups of varying complexity. In the main setup, the complex structure of the street network, along with the sensitivity maps and hydraulics expertise led to define a model configuration with 45 friction patches. A high-dimensional parameter vector composed of these friction values and an upstream inflow is inferred simultaneously by assimilating real limnigraphic data and high-water marks. This leads to an increase in model fit to observations and satisfying parameter estimates.

Integrative in silico approaches to analyse microRNA-mediated responses in human diseases.

Advancements in sequencing technologies have facilitated omics level information generation for various diseases in human. High-throughput technologies have become a powerful tool to understand differential expression studies and transcriptional network analysis. An understanding of complex transcriptional networks in human diseases requires integration of datasets representing different RNA species including microRNA (miRNA) and messenger RNA (mRNA). This review emphasises on conceptual explanation of generalized workflow and methodologies to the miRNA mediated responses in human diseases by using different in silico analysis. Although,there have been many prior explorations in miRNA-mediated responses in human diseases, the advantages, limitations and overcoming the limitation through different statistical techniques have not yet been discussed. This review focuses on miRNAs as important gene regulators in human diseases, methodologies for miRNA-target gene prediction and data driven methods for enrichment and network analysis for miRnome-targetome interactions. Additionally, it proposes an integrative workflow to analyse structural components of networks obtained from high-throughput data. This review explains how to apply the existing methods to analyse miRNA-mediated responses in human diseases. It addresses unique characteristics of different analysis, its limitations and its statistical solutions influencing the choice of methods for the analysis through a workflow. Moreover, it provides an overview of promising common integrative approaches to comprehend miRNA-mediated gene regulatory events in biological processes in humans. The proposed methodologies and workflow shall help in the analysis of multi-source data to identify molecular signatures of various human diseases.

A Review of Research Progress on the Impact of Urban Street Environments on Physical Activity: A Comparison between China and Developed Countries

Creating healthy street environments to encourage physical activity is an effective strategy against non-communicable diseases exacerbated by rapid urbanization globally. Developing countries face more significant health challenges than developed ones. However, existing research predominantly focuses on the perspective of developed countries. To address the health challenges in developing nations, studies should not only draw on the findings from developed countries but also clearly define unique research processes and pathways. Consequently, this study conducts a comparative analysis between China, representing developing countries, and developed nations, using databases like China National Knowledge Infrastructure (CNKI) and Web of Science (WOS) and tools such as Citespace, Bicomb, and Statistical Package for the Social Sciences (SPSS) to explore research hotspots, developmental trajectories, thematic categories, and trends. The findings reveal a shift in developed countries from macro-material to micro-environmental elements under multidisciplinary scrutiny, while future topics may include street space evaluations and psychological healing. In China, research has been dominated by different disciplines at various stages, starting with medical attention to chronic disease prevention, which then shifted to traffic engineering’s focus on constructing green travel environments, and finally expanded to disciplines like landscape architecture examining the impact of street environment elements on pedestrian behavioural perceptions. Future themes will focus on promoting elderly health and urban health transport systems. Generally, research in developed countries exhibits a “bottom-up” approach, with practical issues at a “post-evaluation” stage, primarily based on the “socio-ecological model” and emphasizing multidisciplinary collaboration. Chinese research shows a “top-down” characteristic, driven by national policies and at a “pre-planning” stage, integrating theories such as Maslow’s hierarchy of needs and attention restoration theory, with relatively loose disciplinary cooperation. Overall, research is shifting from macro to human-centric scales and is progressively utilizing multi-source and multi-scale big data analysis methods. Based on this, future research and development recommendations are proposed for developing countries, with China as a representative example.

Application Research of Unmanned Aerial Vehicle Remote Sensing Technology in Agricultural Pest and Disease Monitoring

This study aims to explore the application of unmanned aerial vehicle (UAV) remote sensing technology in agricultural pest and disease monitoring to improve monitoring efficiency and accuracy. Through literature review and empirical research, the current application status, advantages, and challenges of UAV remote sensing technology in agricultural pest and disease monitoring were systematically studied. The study first analyzed and summarized the principles and characteristics of UAV remote sensing technology, and then conducted case studies and field investigations on its specific application in agricultural pest and disease monitoring. The research results show that UAV remote sensing technology has the advantages of high resolution, high spatiotemporal coverage, and high efficiency in agricultural pest and disease monitoring. It can provide multi-source data acquisition and analysis during crop growth to achieve rapid identification and dynamic monitoring of agricultural pest and disease. The findings of this study have important implications for guiding the application and promotion of UAV remote sensing technology in agricultural pest and disease monitoring.