Spatiotemporal Analysis Research Articles

A Metrology Informatics Investigation of Conversion Therapy in Hepatocellular Carcinoma: 2014–2023

Background: With advancements in drug therapy, local treatments, and evolving concepts, conversion therapy has shown benefits for patients initially diagnosed with unresectable hepatocellular carcinoma (HCC). Over the past 10 years, the conversion therapy field has accumulated a vast amount of underutilized data, necessitating in-depth bibliometric evaluation. Methods: This cross-sectional retrospective study collected a substantial amount of research on conversion therapy published between 2014 and 2023, adhering to strict search criteria. The primary outcomes were publication volume, citation count, and interstudy relationships. Comprehensive analysis was conducted using unsupervised hierarchical clustering, spatiotemporal analysis, regression model, and the Walktrap algorithm. Results: Over the past decade, conversion therapy has demonstrated significant progress, with an annual growth rate of 23.0%. Post-2020, these metrics saw a marked increase, reaching 116 publications and 1943 citations by 2023. Cluster analysis grouped 244 authors into 17 clusters, highlighting early and sustained contributions from Western authors compared with later-emerging Eastern authors. Research characteristics in HCC conversion therapy were classified into 5 clusters, with Cluster 2 (Target Therapy and Immunotherapy) emerging as a new focus. Thematic analysis categorized research characteristics into 4 quadrants, identifying “immune checkpoint inhibitor” and “combination therapy” as highly relevant and rapidly developing themes, while “hepatic arterial infusion chemotherapy” and “radioembolization” show high potential for future research. Conclusions: This study highlights key contributors and emerging trends and provides important predictions for future research directions. To achieve effective conversion therapy for HCC, researchers may prioritize immunotherapy and locoregional treatments such as hepatic arterial infusion chemotherapy or radioembolization.

Spatiotemporal Analyses of Lightning and Tornado Exposure to Large Outdoor Gatherings in the Conterminous United States

Abstract Lightning and tornadoes kill more than 100 people per year on average throughout the United States. This study examines potential human exposure to lightning and tornadoes for United States large outdoor public gathering (LOG) events such as amusement parks, concerts, festivals, and sporting events. Specifically, LOG event attributes such as location, event date, number of events throughout the year, and maximum venue capacity are combined with climatological estimates of lightning and tornado activity to estimate potential worst-case LOG attendee exposure for 477 LOG venues across 16,232 individual LOG events throughout the year. Analyses permitted the creation of monthly LOG lightning (LOGLT) and tornado (LOGEF0+ and LOGEF2+) risk indices that denote potential lightning and tornado exposure for the LOG events assessed in this study. Findings reveal that amusement parks, Major League Baseball (MLB) games, and state fairs during warm season months (i.e., March through August) have the greatest potential exposure to lightning and tornadoes compared to all other LOG events. The New Orleans Jazz and Heritage Festival during May ranks highest in terms of potential lightning exposure, while Coors Field in Denver, CO (June) and Six Flags Over Texas in Arlington (April) encompass the greatest potential exposure to all (EF0+) and significant (EF2+) tornadoes. Based on these findings, LOG venue operators and governing bodies should continue to develop risk reduction strategies aimed at improving lightning and tornado safety, especially as lightning and tornado hazard threats evolve over time and across geographic space.

From Conservation to Development: A Study of Land Use and Ecological Changes to Vegetation Around the Hainan Tropical Rainforest National Park

Global ecosystems, particularly in biodiversity-rich tropical rainforests, are increasingly under pressure from human activities. As socio-economic development continues and populations steadily grow, the effective planning of areas surrounding national parks has become a global challenge. This study, based on remote sensing data and utilizing landscape ecology tools, such as ArcGIS 10.8, GeoDa 1.20, and Fragstats 4.2, combines spatial statistical methods, trend analysis, and the Hurst index to conduct a long-term analysis and forecast future trends in vegetation ecological quality indicators (VEQI) and landscape pattern changes within and around the Hainan Tropical Rainforest National Park. VEQI changes across various buffer zones were also assessed. Our results show that both arable and built-up land increased, especially from 2002 to 2022. Arable land decreased from 5566.8 km2 to 4796.8 km2, then increased to 5904.6 km2; built-up land expanded from 163.97 km2 to 314.59 km2, reflecting urbanization. Spatiotemporal analysis revealed that 42.54% of the study area experienced significant VEQI changes, with a 24.05% increase (mainly in the northwest) and an 18.49% decrease (mainly in the southeast). The VEQI improvements were consistent across all buffer zones, with the most significant growth in the 7.5 km zone. Landscape indices indicated high fragmentation in coastal areas, while inland areas remained stable, reflecting the tension between conservation and urbanization. These findings provide a theoretical basis for future ecological development and buffer zone policies in the park.

Grid independence studies applied to a field‐scale computational fluid dynamic (CFD) model using the detached eddy simulation (DES) technique along a reach of the Colorado River in Marble Canyon

AbstractGrid independence studies have emerged as essential methodological frameworks for comprehending the impact of domain resolution on simulating anisotropic turbulence at the river‐reach scale using large eddy simulation models. This study proposes a methodology to assess the loss of information in turbulent flow patterns when coarsening the computational domain, examined in a 1‐km transect of the Colorado River along Marble Canyon. Seven computational domain resolutions are explored to analyse the sensitivity of turbulent flow to spatial resolution changes, utilizing the turbulent kinetic energy (TKE) spectrum technique and spatiotemporal analysis of eddy structures via statistical metrics such as root mean square error (RMSE), Kullback‐Leibler (KL) divergence, Nash‐Sutcliffe model efficiency coefficient (NSE), wavelet power spectrum and grid convergence index (GCI). Based on physical principles and statistics, these metrics quantify information loss and assess domain resolutions. A computational fluid dynamic (CFD) model is developed by employing the detached eddy simulation (DES) technique, with boundary condition (BC) integrating the rough wall extension of the Spallart‐Allmaras model in cells near the bed. Evaluation of domain resolutions aims to identify grid cell sizes capturing flow behaviour and hydraulic characteristics, including primary and secondary flows, return currents, shear layers and primary and secondary eddies. The study observes an increase in data representation of the TKE spectrum with finer spatial domain resolution. Additionally, surface analysis, conducted via RMSE, KL and NSE metrics, identifies specific areas within the flow field showing high sensitivity to refining the grid cell sizes.

Mapping malaria in Thailand: A Bayesian spatio-temporal analysis of national surveillance data.

Malaria, caused by protozoan parasites of the genus Plasmodium, remains prevalent in tropical and subtropical regions. This study employed Bayesian spatio-temporal analysis to assess malaria incidence patterns and identify environmental and climatic correlates across Thailand at the district level. We analysed national malaria surveillance data using Bayesian hierarchical models to examine spatio-temporal patterns in malaria incidence. The model incorporated random effects to account for unobserved heterogeneity across locations and over time, enabling robust inferences on the relationships between environmental and climatic factors and malaria incidence. This analysis revealed seasonal malaria incidence patterns related to environmental and climatic factors, particularly Plasmodium vivax and Plasmodium falciparum. A 1°C increase in maximum temperature at a 6-month lag was associated with an 8% increase in P. vivax incidence (relative risk [RR] = 1.08; 95% credible interval [CrI]: 1.06-1.10). Additionally, a 0.1-unit increase in normalised difference vegetation index corresponded to an 11.96-fold increase in P. vivax cases (95% CrI: 9.36-15.38), while each 100 mm increase in precipitation led to an 8% rise (RR: 1.08; 95% CrI: 1.06-1.09). For P. falciparum, a 0.1-unit increase in normalised difference vegetation index correlated with an 11.59-fold increase in incidence (95% CrI: 8.29-16.16). The risk of P. falciparum increased by 15% per 100 mm increase in precipitation (RR = 1.15; 95% CrI: 1.13-1.17) and by 4% for each 1°C rise in maximum temperature (RR = 1.04; 95% CrI: 1.02-1.06). Elevated incidence was predominantly observed along the Thai-Cambodian and Thai-Myanmar borders, with central Thailand classified as low risk. These findings highlight the significance of integrating environmental and climatic factors into malaria control strategies. The insights gained can guide the Thai government's resource allocation for effective surveillance, treatment, and preventive measures, ultimately supporting malaria control and elimination efforts in the region.

Spatiotemporal analysis and GIS-based dashboard development for urban household waste

PurposeThis paper aims to develop a methodology for the spatiotemporal analysis of urban household waste data and a geographic information system (GIS)-based dashboard for interactive outcomes that identifies emerging trends and spatial distribution.Design/methodology/approachThe study visualized the emerging hotspot analysis of household waste data covering the waste in selected areas from 2014 to 2019 in New South Wales, Australia. Through analyses in ArcGIS Pro, multiple maps and diagrams can be created to display these results in ArcGIS Insights. To enable the spatial waste analysis outcomes accessible, a GIS-based dashboard including maps and charts, spatiotemporal visualization of household waste tonnage, and emerging hotspots was created.FindingsBased on the development of the dashboard in the ArcGIS Suites, there is an accessible data pipeline from ArcGIS Pro to Insights. The cloud-mapping system in ArcGIS online serves as a foundation for temporary data storage. The results also show the emerging hotspots of recyclable, residual and organic (RRO) waste in the Greater Sydney Region, Wollongong, Newcastle and Tweed. This study found an emerging cold spot in Wagga Wagga.Practical implicationsA dashboard for monitoring waste streams can be developed to enable GIS specialists to use historical spatiotemporal datasets in ArcGIS suites easily. Policymakers, strategy developers, urban waste managers and organizations dealing with urban waste can utilize this analytical dashboard to identify the issues, patterns and trends concerning urban waste for better decision-making in allocating required resources to overcome the identified issues to make informed decisions and develop strategies to alleviate the trends and patterns of ongoing problems. Indeed, the GIS-based dashboard developed in this research provides deep analysis and insights from the spatial waste data, allowing them to understand the included insights at a glance quickly.Originality/valueDeriving location information for urban household waste data is crucial for waste management since it offers a better understanding of urban household waste data patterns, issues and historical trends. Small-scale studies have examined spatial waste patterns, but the investigation of urban household waste focusing on RRO waste is limited. Moreover, there is a lack of GIS-based dashboard development to enable spatiotemporal waste analysis outcomes to be publicly accessible.

Sustainable Trends and Determinants of Wheat Cultivation in Poland (2004–2023): A Spatiotemporal Analysis of Productivity, Resilience, and Climate Adaptation

Wheat farming is of utter importance in every country around the world, since it is a critical crop that contributes to food security and rural development. Given this importance, this research work investigates trends, determinants, and spatial variability in Polish wheat production between the years 2004 and 2023, with a key interest in productivity, resilience, and sustainable farming. This work will apply spatiotemporal analysis, statistical modeling, and ARIMA forecasting in the identification of the environmental, economic, and policy factors that interact with the wheat yield. Determinants include climatic variables, temperature, and precipitation, production costs, and market prices. These results point to great regional disparities in yield; the apparently better regions, such as Opole and Pomerania, enjoy exceptionally good environmental conditions with good access to modern technology, while regions like Podlasie and Subcarpathia are characterized by poor soil quality and a shortage of resources. This paper has pointed out the need for differential intervention policies that could contribute to reducing such yield gaps, increasing resilience to climate change, and hence contributing to sustainable wheat production growth. The stabilized yields projected underline the resilience of the sector to various challenges, from climate variability to market fluctuations. The results also indicate how innovative practices, supported by enabling policy frameworks, are essential in the promotion of wheat production in an environmentally friendly way, such as precision agriculture. The present research work will provide useful tools for policymakers, researchers, and other stakeholders by providing active insights into how to achieve equitable and sustainable agricultural development in Poland.

The Mutations in RcMYB114 Affect Anthocyanin Glycoside Accumulation in Rose

In plants, the R2R3-MYB transcription factors are one of the largest MYB gene families. These MYB transcription factors are very important for regulating plant growth and development. RcMYB114, RcbHLH, and RcWD40 promote anthocyanin accumulation by forming the MBW (MYB-bHLH-WD40) complex and determine the rose flower’s color. RcMYB114 genomic sequences differ between the red petal and white varieties. Two non-synonymous substitutions were found in the open reading frame. It leads to a change in amino acids. Here, the anthocyanin content showed that there was no anthocyanin in white petals, while the anthocyanin content in red petals increased firstly at stage 2, decreased slightly at stage 4, and then increased again at stage 5. The spatiotemporal expression pattern analysis showed that RcMYB114 was not expressed in all petals and tissues of white petals at different flower development stages. In red petal varieties, RcMYB114 was highly expressed in petals, followed by styles, and not expressed in stems, young leaves, and stage 1 of flower development. However, RcMYB114 has the highest expression level at the blooming stage. The RcMYB114 sequence contains 9 SNPs in the coding region, 7 of which were synonymous substitutions that had no effect on the translation product and 2 of which were non-synonymous substitutions that resulted in amino acid alteration at positions 116 and 195, respectively. The RcMYB114 gene in red rose was named RcMYB114a, and in white rose was RcMYB114b. RcMYB114c was mutated into leucine via artificial mutation; it was valine at position 116 of RcMYB114a, and Glycine mutated into Arginine at position 195 of RcMYB114a was RcMYB114d. RcMYB114b was the double mutation at positions 116 and 195 of RcMYB114a. The results of yeast two-hybrid experiments showed that RcMYB114a and its missense mutations RcMYB114b, RcMYB114c, and RcMYB114d could both interact with RcbHLH and RcWD40 to form the MYB-bHLH-WD40 complex. A transient transformation experiment in tobacco confirmed that RcMYB114a and its missense mutations RcMYB114b, RcMYB114c, and RcMYB114d could significantly promote the high expression of related structural genes in tobacco, together with the RcbHLH gene, which led to the accumulation of anthocyanins and produced the red color of the leaves. The RcMYB114a gene and its missense mutations RcMYB114b, RcMYB114c, and RcMYB114d interacted with the RcbHLH gene and significantly regulated the accumulation of anthocyanins. The two non-synonymous mutations of RcMYB114 do not affect the function of the gene itself, but the content of the anthocyanins accumulated was different. This study should provide clues and references for further research on the molecular mechanism underlying the determination of rose petal color.

Examining the Effective Role of Artificial Intelligence in the Interconnected Crisis of Climate Change and Human Migration

Examining the Effective Role of Artificial Intelligence in the Interconnected Crisis of Climate Change and Human Migration

Risk-oriented geoinformation airspace modeling for calculating civil aviation unmanned aerial vehicles optimal routes

Currently, there is an urgent need to create a high-quality automated risk assessment tool for the use of (unmanned aircraft) UAVs. There is no universal approach to risk management in unmanned civil aviation, and the risk assessment of the operator is largely individual. At the moment, no tool has been developed for plotting optimal routes for UAV flights in airspace, which would avoid piloting in areas with unacceptable risk. The article suggests the use of fully functional geographic information systems (GIS) to assess the risks of performing a flight mission. For a qualitative assessment of the risks of a particular flight assignment, it is proposed to take into account the situational component in the relevant segment of airspace and the ground (surface) situation. The article systematizes the main groups of factors that are important for assessing the risks of using BV. UAV flights are exposed to environmental factors, while posing a danger to surrounding objects. A formula for analyzing the spatial and temporal distribution of risk values in the airspace is derived. The minimum size of the simulation cell is proposed. A universal approach to assessing the risks of a UAV flight by various operators is substantiated, and a methodology for spatiotemporal analysis of the distribution of risk values based on the use of GIS is given. The results of the analysis of spatial and temporal information in the GIS environment make it possible to zone the airspace according to the degree of flight acceptability and build the optimal route outside areas with an increased risk of an aviation incident or accident. The developed spatio-temporal risk-oriented model can be used to support management decision-making in terms of building optimal routes for the movement of UVs.

Machine Learning and Spatio-Temporal Patterns in Climate Change

Climate change is one of our planet's most significant concerns, affecting ecosystems, weather patterns, and human populations all around the globe. Understanding the complex spatiotemporal pattern associated with climate change is critical for making sound judgments and developing effective mitigation solutions. In recent years, the analysis of large and complex climate data sets has been revolutionized with the help of machine learning methods. This study uses datasets from the Kaggle website and for data preparation the Min-Max method is used to ensure the data which is suitably scaled. Following that, the decision tree machine learning technique is used for feature extraction, allowing the identification of relevant climate-related elements. Finally, the study employs spatiotemporal analysis to create prediction models for predicting climate change trends. This interdisciplinary approach emphasizes the important connection between machine learning and climate science, giving vital insights for climate researchers and policymakers dealing with the issues presented by climate change. The suggested approaches greatly outperform current existing models, obtaining an astounding accuracy of 97.99%. This collaboration allows for the creation of precise prediction models, which help in climate forecasting, early warning systems, and climate impact assessments.

Spatial interpolation and spatiotemporal scanning analysis of human brucellosis in mainland China from 2012 to 2018

Despite the considerable efforts made to address the issue of brucellosis worldwide, its prevalence in dairy products continues to be difficult to estimate and represents a significant public health concern globally. The aim of the present study was to gain a better understanding of the epidemiology of this disease in mainland China. We set out to investigate the annual spatial distribution and potential hotspots of the disease. Data on the incidence rate of brucellosis from 2012 to 2018 was obtained from the China Disease Control and Prevention Information System (CDCIS). ArcGIS 10.6 software was employed to perform kriging interpolation analysis and to create a predictive distribution map for brucellosis. Additionally, SaTScan software was utilized to conduct spatial-temporal scanning analysis to identify potential spatial-temporal changes in the incidence rate of brucellosis in China. There is a seasonal trend in the incidence of brucellosis in China, with higher rates observed during the warm season, particularly peaking in May. The results of the exploratory analysis of kriging data indicate that the average incidence map, generated using the second-order Gaussian semi-variance model with log-kriging interpolation, demonstrates the highest accuracy. Spatial and temporal clustering analyses reveal a primary clustering area centered in Heilongjiang, along with three secondary clustering areas located in Tibet, Shanxi, and Hubei. Additionally, the predictive distribution map for brucellosis in China, along with the analysis of the scanning statistic, indicates that the high-incidence area is situated in the northwest region of mainland China, although there is a noticeable trend of shifting towards the south. There are distinct spatial patterns of brucellosis in China. In high-incidence areas, it is essential to allocate additional resources for prevention and control to effectively contain the spread of brucellosis epidemics. In low-incidence areas, it is vital to promptly identify favorable factors that can help mitigate the occurrence of brucellosis.

Novel Numerical Modeling of a Groundwater Level-Lowering Approach Implemented in the Construction of High-Rise/Complex Buildings

Controlling groundwater levels is essential for the safe construction of complex or high-rise buildings. In México, dewatering regulations lack detailed references, and piezometric data are limited, making precise groundwater control a challenge. This study aimed to develop a numerical groundwater model by translating a conceptual hydrogeological model into a calibrated MODFLOW simulation using the graphical user interface ModelMuse, developed by the United States Geological Survey (USGS). For the project “Torre Tres Ríos”, field measurements recorded a water-table level of 33 m above sea level (masl) in July, rising to 35.74 masl in October due to rainy season recharge and the influence of the Tamazula River, then decreasing to 35.20 masl in November. The model, calibrated with a mean absolute error of 0.15 m and a standard deviation of 0.174 m, effectively represented steady and transient states. A spatiotemporal analysis based on the calibrated numerical model enabled the evaluation of different dewatering scenarios. Initially, deep wells with a pumping rate of 120 L per second (lps) were required for dewatering; however, a wellpoint system was proposed, showing improved performance with a reduced impact on groundwater flow and the surrounding environment during the critical August–November period. This study highlights the importance of numerical modeling in refining dewatering system designs, ensuring adaptability to fluctuating groundwater conditions. By providing a methodology for optimizing dewatering strategies, it contributes to more efficient and sustainable construction practices in regions with complex hydrogeological conditions.

The impact of meteorological and socio-economic factors on malaria in mainland China (2006-2015): A study using spatiotemporal analysis and Bayesian model.

The impact of meteorological and socio-economic factors on malaria in mainland China (2006-2015): A study using spatiotemporal analysis and Bayesian model.

Spatio-temporal analysis and prediction for raw water quality of drinking water source by improved RNN algorithm

Spatio-temporal analysis and prediction for raw water quality of drinking water source by improved RNN algorithm

Leveraging spatiotemporal Bayesian analysis to unravel polysubstance use and overdose risk: Opportunities and challenges.

Leveraging spatiotemporal Bayesian analysis to unravel polysubstance use and overdose risk: Opportunities and challenges.

Multiscale exploration of spatiotemporal dynamics in China's largest urban agglomeration: An interactive coupling perspective on human activity intensity and ecosystem health.

Multiscale exploration of spatiotemporal dynamics in China's largest urban agglomeration: An interactive coupling perspective on human activity intensity and ecosystem health.

Spatiotemporal survival analysis for movement trajectory tracking in virtual reality

We present a novel method for analyzing response trajectory tracking data. Limiting behavioral experiments to discrete, key-press response measures, such as reaction times and accuracy, is unsatisfactory for observing the ongoing dynamics of cognition. We assessed the utility of continuous response tracking in Virtual Reality (VR) by comparing it to key-press responses in a classical N-back matching task. For elucidatory purposes, in both classical and VR versions of the task we first worked through analyses of discrete measures, before drawing information from the continuous trajectory tracking data in VR. Classical ANOVAs reproduced effects of visual working memory load in an N-back task. Violations of ANOVA assumptions suggested effects were buried in the noise; some of these were revealed in subsequent survival analyses, namely frequency neglect (a strong preference for match responses despite the infrequency of this response category) in the fast responses and category frequency-tuned response in the slow responses. Spatiotemporal survival analysis (StSA), our newly proposed method of analyzing response trajectories, revealed that all these effects also occur in the VR conditions. In addition, initial divergences towards the wrong responses were corrected later in the course of trajectories in the non-Match trials. While the StSA method is theory-free and can be used for exploratory purposes, we present examples of hypothesis testing in StSA.

Spatio-temporal temperature distribution and heat transfer analysis during subcooled nucleate pool boiling on plates

Spatio-temporal temperature distribution and heat transfer analysis during subcooled nucleate pool boiling on plates

Assessing the Role of Energy Mix in Long-Term Air Pollution Trends: Initial Evidence from Poland

Air pollution remains a critical environmental and public health issue, requiring diverse research perspectives, including those related to energy production and consumption. This study examines the relationship between Poland’s energy mix and air pollution trends by integrating national statistical data on primary energy consumption and renewable energy sources over the past 15 years with air pollution measurements from the last eight years. The air pollution data, obtained from reference-grade monitoring stations, focus on particulate matter (PM). To address discrepancies in temporal resolution between daily PM measurements and annual energy sector reports, a bootstrapping method was applied within a regression framework to assess the overall impact of individual energy components on national air pollution levels. Seasonal decomposition techniques were employed to analyze the temporal dynamics of specific energy sources and their contributions to pollution variability. A key aspect of this research is the role of renewable energy sources in air quality trends. This study also investigates regional variations in pollution levels by analyzing correlations between geographic location, industrialization intensity, and the proportion of green areas across Poland’s administrative regions (Voivodeships). This spatially explicit approach provides deeper insights into the linkages between energy production and pollution distribution at a national scale. Poland presents a unique case due to its distinct energy mix, which differs significantly from the EU average, its persistently high air pollution levels, and recent regulatory changes. These factors create an ideal setting to assess the impact of energy sector transitions on environmental quality. By employing high-resolution spatiotemporal big data analysis, this study leverages measurements from over 100 monitoring stations and applies advanced statistical methodologies to integrate multi-scale energy and pollution datasets. From a PM perspective, the regression analysis showed that High-Methane Gas had a neutral impact on PM concentrations, making it a suitable transition energy source, while renewables exhibited negative regression coefficients and coal-based sources showed positive coefficients. The findings offer new perspectives on the long-term environmental effects of shifts in national energy policies.