Analysis Of Spatio-temporal Data Research Articles

Impact of high-speed railway network on county-level accessibility and economic linkage in Jiangxi Province, China: A spatio-temporal data analysis

Abstract High-speed railway (HSR) networks have profoundly influenced interregional accessibility and economic linkages. This study examined 100 counties (cities) and districts in Jiangxi Province, using spatial and temporal data to measure weighted average travel time, daily accessibility, the accessibility coefficient, and the total and intensity of economic linkages. This study analyzed the impacts of operating a single HSR line versus an entire network. The findings revealed that (1) the HSR network has created a balanced spatial–temporal convergence effect, narrowing the gap between most locations and showing a clear “corridor effect”; (2) the network has strengthened economic connections among counties and has significantly enhanced overall economic output by creating two high-level economic linkage belts, formed around the Shanghai–Kunming and Beijing–Hong Kong HSRs; and (3) the network amplifies the “Siphon effect” and “Matthew effect,” further disadvantaging non-connected regions. Therefore, the province should promote a well-designed, HSR network, enhancing economic exchanges among counties and fostering a high-level economic belt characterized by diversity, complementary advantages, and coordinated development.

LEST: Large language models and spatio-temporal data analysis for enhanced Sino-US exchange rate forecasting

Exchange rate forecasting has a significant impact on a country’s balance of international payments, financial security and stability. Under the context of Sino-US trade war, a series of news events have happened thus increasing the abnormal fluctuations in the RMB exchange rate, and these fluctuations have made it more difficult to forecast the exchange rate. The analysis of news events’ impact on exchange rate fluctuations focused on assigning emotional labels to different economic news and political events by building an emotional lexicon. However, the previous method of using an offline emotional lexicon to analyze events could not be updated promptly making it difficult to better exploit implicit information of news events. Also, the offline emotional lexicon requires manual annotation of events which is unavoidable and subjective and therefore influences the sentiment analysis of the event. Therefore, we build a fusion forecasting framework of spatio-temporal and emotional information for accurate, real-time exchange rate forecasting. First, in the preprocessing phase, we leveraged the situational learning capabilities of Large Language Models (LLMs), specifically ChatGPT, combined with prompt engineering to enable ChatGPT to provide real-time, online, and precise automated sentiment analyses of Sino-US trade events. Second, we proposed the SG-TL fusion model, which integrates Spatio-Temporal Graph Convolutional Networks (STGCN) and Gated Recurrent Unit (GRU) models in the feature processing approach to accurately extract exchange rate features in spatial and temporal dimensions. We also employed Transformer and Long Short-Term Memory (LSTM) models in the forecasting phase to forecast the Sino-US exchange rates. Finally, Experiment results demonstrate that the exchange rate forecasting framework LEST can accurately capture the exchange rate fluctuations during the period of Sino-US trade war. Furthermore, its forecasting performance is more precise, efficient, and stable than existing works. The source code of our developed method is publicly available at https://gitee.com/andyham_andy.ham/lest-forecasting-framework.

The whole process route planning algorithm based on AIS spatio-temporal big data analysis

ABSTRACT This paper proposes a route planning algorithm that can comprehensively integrate ship safety, economic benefits and algorithm universality. Firstly, the Polynomial Approximation with the Exponential Kernel method was used to smooth the repaired trajectory and the similarity of the smoothed trajectory was evaluated using Hausdorff distance. The results showed that the similarity of ship trajectories was effectively improved by 36.53%. Using the Douglas Pucker algorithm to compress the smoothed trajectory, the compression rate exceeded 92%. Then, the HDBSCAN self-adjusting clustering analysis method was used to cluster the ship trajectory and the silhouette coefficient index was used to quantitatively evaluate the clustering effect, the calculation result was 0.9032. Finally, using the average centre method to extract the centres of each cluster, 299 key turning points were obtained and connected to construct an undirected route network. Based on this, the Dijkstra algorithm was used to achieve optimal route planning.

Authors’ Response to Peer Reviews of “Human Brucellosis in Iraq: Spatiotemporal Data Analysis From 2007-2018”

Authors’ Response to Peer Reviews of “Human Brucellosis in Iraq: Spatiotemporal Data Analysis From 2007-2018”

Optimizing the detection of emerging infections using mobility-based spatial sampling

Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, integrating the spatial structure of the target, holds promise as an approach for testing allocation in detecting infections, and leveraging information on individuals’ movement and contact behavior can enhance targeting precision. This study introduces a spatial sampling framework informed by spatiotemporal analysis of human mobility data, aiming to optimize the allocation of testing resources for detecting emerging infections. Mobility patterns, derived from clustering point-of-interest and travel data, are integrated into four spatial sampling approaches at the community level. We evaluate the proposed mobility-based spatial sampling by analyzing both actual and simulated outbreaks, considering scenarios of transmissibility, intervention timing, and population density in cities. Results indicate that leveraging inter-community movement data and initial case locations, the proposed Case Flow Intensity (CFI) and Case Transmission Intensity (CTI)-informed spatial sampling enhances community-level testing efficiency by reducing the number of individuals screened while maintaining a high accuracy rate in infection identification. Furthermore, the prompt application of CFI and CTI within cities is crucial for effective detection, especially in highly contagious infections within densely populated areas. With the widespread use of human mobility data for infectious disease responses, the proposed theoretical framework extends spatiotemporal data analysis of mobility patterns into spatial sampling, providing a cost-effective solution to optimize testing resource deployment for containing emerging infectious diseases.

Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru.

Amid the COVID-19 pandemic, understanding the spatial and temporal dynamics of the disease is crucial for effective public health interventions. This study aims to analyze COVID-19 data in Peru using a Bayesian spatio-temporal generalized linear model to elucidate mortality patterns and assess the impact of vaccination efforts. Leveraging data from 194 provinces over 651 days, our analysis reveals heterogeneous spatial and temporal patterns in COVID-19 mortality rates. Higher vaccination coverage is associated with reduced mortality rates, emphasizing the importance of vaccination in mitigating the pandemic's impact. The findings underscore the value of spatio-temporal data analysis in understanding disease dynamics and guiding targeted public health interventions.

Spatiotemporal Analysis of Traffic Data: Correspondence Analysis with Fuzzified Variables vs. Principal Component Analysis Using Weather and Gas Price as Extra Data

Spatiotemporal Analysis of Traffic Data: Correspondence Analysis with Fuzzified Variables vs. Principal Component Analysis Using Weather and Gas Price as Extra Data

GHCU, a Molecular Chaperone, Regulates Leaf Curling by Modulating the Distribution of KNGH1 in Cotton.

Leaf shape is considered to be one of the most significant agronomic traits in crop breeding. However, the molecular basis underlying leaf morphogenesis in cotton is still largely unknown. In this study, through genetic mapping and molecular investigation using a natural cotton mutant cu with leaves curling upward, the causal gene GHCU is successfully identified as the key regulator of leaf flattening. Knockout of GHCU or its homolog in cotton and tobacco using CRISPR results in abnormal leaf shape. It is further discovered that GHCU facilitates the transport of the HD protein KNOTTED1-like (KNGH1) from the adaxial to the abaxial domain. Loss of GHCU function restricts KNGH1 to the adaxial epidermal region, leading to lower auxin response levels in the adaxial boundary compared to the abaxial. This spatial asymmetry in auxin distribution produces the upward-curled leaf phenotype of the cu mutant. By analysis of single-cell RNA sequencing and spatiotemporal transcriptomic data, auxin biosynthesis genes are confirmed to be expressed asymmetrically in the adaxial-abaxial epidermal cells. Overall, these findings suggest that GHCU plays a crucial role in the regulation of leaf flattening through facilitating cell-to-cell trafficking of KNGH1 and hence influencing the auxin response level.

Hydrogeological trends in an alluvial valley in the Brazilian semiarid: Impacts of observed climate variables change and exploitation on groundwater availability and salinity

Study regionMimoso Alluvial Valley, Semiarid Brazil. Study focusThis study aims to assess the influence of climate variability and exploitation on groundwater accessibility, quantity and quality, based on a spatiotemporal data analysis from long-term monitoring field campaigns, conducted monthly from 2000 to 2019. This study successfully identified representative stable monitoring points, piezometers and wells, for piezometric and salinity levels employing the technique of relative differences, taking into account aquifer hydraulic properties. Trend analysis was then carried out adopting the Mann-Kendall Method, Sen's Slope test, Pettitt test, and the Seasonal Trend decomposition through the Loess (STL) method. Principal Component Multivariate Analysis (PCA) was also employed to validate long-term analysis of annual groundwater levels, rainfall, evapotranspiration, and pumping rates. New hydrological insights for the regionThe spatial-temporal variability of salinity is closely linked to the salt balance in regions with low hydraulic permeability and groundwater use for irrigation. Groundwater salinity presented a strong link between rainfall and groundwater levels. A temporal rise in evapotranspiration alongside declines in rainfall and groundwater levels was observed, potentially exacerbating groundwater scarcity alongside intensified aquifer exploitation. Furthermore, groundwater salinity presented a decreasing trend for the pumping wells and no trends for piezometers. This could potentially disrupt future irrigation plans and cause long-term water shortages in a region already under severe water scarcity. Hence, groundwater pumping constitutes a positive management alternative for controlling groundwater salinity in the domain.

Spatiotemporal clustering of dam settlement monitoring using instrumentation data (case study: Eyvashan Earth Dam)

The integrity of monitoring data is important for studying the amount and location of deformation in earth dams. In this research, the meeting of the Eyvashan Earth Dam was analyzed using the results of precision instruments over a period of eight years during three periods of construction, first impoundment, and exploitation. To verify the accuracy of the results, the numerical model of the largest section of the dam was constructed with Plaxis software and analyzed according to the Mohr–Coulomb behavior model. The results showed that since the beginning of construction, the occurrence of settlements in the core of the dam has been increasing, and with the passage of time, settlements reach a constant value in the operation phase. The maximum settlement of the dam core at the end of the construction stage was 809 mm, which was equal to 1.2% of the dam height in the middle level. Additionally, an approach based on spatiotemporal clustering has been presented to improve the interpretability of the settlement behavior of earth dams. Clustering analysis of spatiotemporal data is used as a measure to determine the similarity of settlements and to select relevant data. Therefore, k-means clustering algorithms and two-step clustering were used to determine the displacement of earth dams. By comparing the correlation results, the proposed method of detecting settlement areas using a spatiotemporal clustering model can be considered a reliable method for monitoring the settlement of earth dams.

Analyzing urban crash incidents: An advanced endogenous approach using spatiotemporal weights matrix

AbstractContemporary spatial statistics studies often underestimate the complexity of road networks, thereby inhibiting the strategic development of effective interventions for car accidents. In response to this limitation, the primary objective of this study is to enhance the spatiotemporal analysis of urban crash data. We introduce an innovative spatial‐temporal weight matrix (STWM) for this purpose. The STWM integrates external covariates, including road network topological measurements and economic variables, offering a more comprehensive view of the spatiotemporal dependence of road accidents. To evaluate the functionality of the presented STWM, random effect eigenvector spatial filtering analysis is employed on Boston's traffic accident data from January to March 2016. The STWM improves analysis, surpassing distance‐based SWM with a lower residual standard error of 0.209 and a higher adjusted R2 of 0.417. Furthermore, the study emphasizes the influence of road length on crash incidents, spatially and temporally, with random standard errors of 0.002 for spatial effects and 0.026 for non‐spatial effects. This is particularly evident in the north and center of the study area during specific periods. This information can help decision‐makers develop more effective urban development models and reduce future crash risks.

Understanding spatiotemporal patterns of COVID-19 incidence in Portugal: A functional data analysis from August 2020 to March 2022.

During the SARS-CoV-2 pandemic, governments and public health authorities collected massive amounts of data on daily confirmed positive cases and incidence rates. These data sets provide relevant information to develop a scientific understanding of the pandemic's spatiotemporal dynamics. At the same time, there is a lack of comprehensive approaches to describe and classify patterns underlying the dynamics of COVID-19 incidence across regions over time. This seriously constrains the potential benefits for public health authorities to understand spatiotemporal patterns of disease incidence that would allow for better risk communication strategies and improved assessment of mitigation policies efficacy. Within this context, we propose an exploratory statistical tool that combines functional data analysis with unsupervised learning algorithms to extract meaningful information about the main spatiotemporal patterns underlying COVID-19 incidence on mainland Portugal. We focus on the timeframe spanning from August 2020 to March 2022, considering data at the municipality level. First, we describe the temporal evolution of confirmed daily COVID-19 cases by municipality as a function of time, and outline the main temporal patterns of variability using a functional principal component analysis. Then, municipalities are classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. Our findings reveal disparities in disease dynamics between northern and coastal municipalities versus those in the southern and hinterland. We also distinguish effects occurring during the 2020-2021 period from those in the 2021-2022 autumn-winter seasons. The results provide proof-of-concept that the proposed approach can be used to detect the main spatiotemporal patterns of disease incidence. The novel approach expands and enhances existing exploratory tools for spatiotemporal analysis of public health data.

Multivariate Hawkes processes with spatial covariates for spatiotemporal event data analysis

Multivariate Hawkes processes with spatial covariates for spatiotemporal event data analysis

Construction and Application of User Check-in Spatiotemporal Knowledge Graph Based on Neo4j

With the rapid acceleration of urbanization and the widespread adoption of social networks, A vast amount of spatiotemporal check-in data from social media users has been generated. Traditional relational databases have limitations in handling massive spatiotemporal data, whereas graph databases, with their efficiency in data querying and processing, offer a novel solution for the analysis of spatiotemporal data. The paper introduces a method for constructing a user check-in spatiotemporal knowledge graph based on the Neo4j graph database, utilizing a designed graph model to create a visual knowledge graph. By incorporating temporal and spatial characteristics into entities, we realize efficient management and complex query of spatiotemporal data of social media user check-ins. By adding sentiment attributes to relationships through sentiment analysis of check-in text, the application of check-in text is realized. Ultimately, employing Cypher to achieve knowledge querying, we analyze the hot spots of urban activities and categories.

Mortality from leading cancers in districts of England from 2002 to 2019: a population-based, spatiotemporal study

Cancers are the leading cause of death in England. We aimed to estimate trends in mortality from leading cancers from 2002 to 2019 for the 314 districts in England. We did a high-resolution spatiotemporal analysis of vital registration data from the UK Office for National Statistics using data on all deaths from the ten leading cancers in England from 2002 to 2019. We used a Bayesian hierarchical model to obtain robust estimates of age-specific and cause-specific death rates. We used life table methods to calculate the primary outcome, the unconditional probability of dying between birth and age 80 years by sex, cancer cause of death, local district, and year. We reported Spearman rank correlations between the probability of dying from a cancer and district-level poverty in 2019. In 2019, the probability of dying from a cancer before age 80 years ranged from 0·10 (95% credible interval [CrI] 0·10-0·11) to 0·17 (0·16-0·18) for women and from 0·12 (0·12-0·13) to 0·22 (0·21-0·23) for men. Variation in the probability of dying was largest for lung cancer among women, being 3·7 times (95% CrI 3·2-4·4) higher in the district with the highest probability than in the district with the lowest probability; and for stomach cancer for men, being 3·2 times (2·6-4·1) higher in the district with the highest probability than in the one with the lowest probability. The variation in the probability of dying was smallest across districts for lymphoma and multiple myeloma (95% CrI 1·2 times [1·1-1·4] higher in the district with the highest probability than the lowest probability for women and 1·2 times [1·0-1·4] for men), and leukaemia (1·1 times [1·0-1·4] for women and 1·2 times [1·0-1·5] for men). The Spearman rank correlation between probability of dying from a cancer and district poverty was 0·74 (95% CrI 0·72-0·76) for women and 0·79 (0·78-0·81) for men. From 2002 to 2019, the overall probability of dying from a cancer declined in all districts: the reductions ranged from 6·6% (95% CrI 0·3-13·1) to 30·1% (25·6-34·5) for women and from 12·8% (7·1-18·8) to 36·7% (32·2-41·2) for men. However, there were increases in mortality for liver cancer among men, lung cancer and corpus uteri cancer among women, and pancreatic cancer in both sexes in some or all districts with posterior probability greater than 0·80. Cancers with modifiable risk factors and potential for screening for precancerous lesions had heterogeneous trends and the greatest geographical inequality. To reduce these inequalities, factors affecting both incidence and survival need to be addressed at the local level. Wellcome Trust, Imperial College London, UK Medical Research Council, and the National Institute of Health Research.

Spatiotemporal evolution of healthcare service capacity at township health centers in China.

This study analyzes the efficiency, spatiotemporal evolution, and influencing factors of provincial township health centers' healthcare service capacity in China. It utilizes an unexpected output super-efficiency slacks-based measure (SBM) model, exploratory spatiotemporal data analysis methods, and a quantile regression model. The results show that the healthcare service capacity of township health centers is better in provinces with a larger proportion of hierarchical diagnoses and treatments pilot projects in cities, and the regional efficiency trend is ordered central > eastern > western > northeastern. The healthcare service capacity of provincial township health centers mainly shows significant spatial correlation and a spatiotemporal distribution pattern of "high agglomeration, low differentiation." Rural population density and per capita GDP significantly improve the healthcare service capacity of township health centers, while local governments' healthcare and health expenditure increases the healthcare service capacity of township health centers in certain quantiles. The urbanization rate and per capita disposable income inhibit the improvement of the healthcare service capacity of township health centers in certain quantiles. The provinces should accelerate the promotion of hierarchical diagnoses and treatment pilot projects in cities and establish national cooperative development models to promote public health.

RESEARCH ON EFFICIENT INDEXING OF LARGE-SCALE GEOSPATIAL DATA BASED ON MULTI-LEVEL GEOGRAPHIC GRID

Abstract. With the implementation of unified natural resource management in China, national geographic conditions monitoring data have been identified as fundamental data for natural resource survey and monitoring. The efficiency of information extraction from massive spatio-temporal data to support natural resource management has emerged as a critical indicator for maximizing the value of geographic conditions monitoring data and enhancing data-driven decision management. Traditional spatial indices are computationally intensive, and when confronted with immense data volume or uneven data scale, issues such as extensive index computations and poor scale adaptability arise, impeding the efficient retrieval of complex geospatial data. In response to the need for efficient indexing of massive geospatial monitoring data at a scale of 100 million, a multi-level geographic spatial index framework based on geographic grids is proposed. Within the geographic conditions spatio-temporal database, a three-level spatial index of "zone-grid-space" is constructed, utilizing massive land cover data for analysis and testing. The results demonstrate that the multi-level spatial index method exhibits excellent scale adaptability, and grid coding dimensionality reduction and numerical operations effectively reduce the computational load of spatial retrievals of complex vector patches. This method significantly improves the retrieval efficiency of large-scale national geographic conditions data, providing an efficient technique for lightweight information extraction of large-scale monitoring geospatial data within spatial computing systems. The method holds reference value for on-demand retrieval, analysis, and decision-making of natural resource spatio-temporal big data.

Spatio-temporal pattern evolution of China’s provincial tourism efficiency and development level based on DEA-MI model

The spatial differences of efficiency and development level of regional tourism are evident, and the dynamic grasp of their spatiotemporal evolution characteristics and coupling coordination relationship is important to promote high-quality sustainable tourism development. Herein, we measured the tourism development level of 31 provincial units in China during 2000–2020, introduced the data envelopment analysis-based Malmquist productivity index to determine tourism efficiency, used exploratory spatiotemporal data analysis methods to explore the spatiotemporal dynamic characteristics of regional tourism differences and spatial structure, constructed a coupled coordination model of tourism efficiency and development level and analyzed their coupling excellence and synergistic consistency. The results were as follows: (1) The spatial differences of comprehensive tourism efficiency from 31 provinces were evident, and the average situation showed high and low distribution characteristics in the east and west, respectively. The interannual changes showed a fluctuating downward trend, with the scale efficiency playing a supporting role for comprehensive efficiency and technical efficiency playing an influencing and restraining role. (2) Less fluctuation existed in the local spatial structure of tourism efficiency and development level; the direction of dependence was more stable, tourism development level was slightly more volatile, and the spatial dependency direction changes were similar. The tourism efficiency of local structure competition posture was stronger than that of collaboration, with the tourism development level collaboration integration being stronger. (3) The local spatial structure of tourism development level was more stable, the relative position of provincial units was more difficult to change, tourism efficiency of local spatial structure was unstable, and provincial units exhibited a greater possibility of change. (4) Tourism efficiency and the scale of the overall coupling degree and coupling coordination gradually improved, with similar spatiotemporal values. The overall coupling degree and coupling coordination degree of tourism efficiency and development level gradually improved, with similar spatiotemporal heterogeneity and volatility of local evolution. The area with a higher value coupling coordination degree spread slower and more widely.

A Survey of Deep Learning-Based Lightning Prediction

The escalation of climate change and the increasing frequency of extreme weather events have amplified the importance of precise and timely lightning prediction. This predictive capability is pivotal for the preservation of life, protection of property, and maintenance of crucial infrastructure safety. Recently, the rapid advancement and successful application of data-driven deep learning across diverse sectors, particularly in computer vision and spatio-temporal data analysis, have opened up innovative avenues for enhancing both the accuracy and efficiency of lightning prediction. This article presents a comprehensive review of the broad spectrum of existing lightning prediction methodologies. Starting from traditional numerical forecasting techniques, the path to the most recent breakthroughs in deep learning research are traversed. For these diverse methods, we shed light on their progression and summarize their capabilities, while also predicting their future development trajectories. This exploration is designed to enhance understanding of these methodologies to better utilize their strengths, navigate their limitations, and potentially integrate these techniques to create novel and powerful lightning prediction tools. Through such endeavors, the aim is to bolster preparedness against the growing unpredictability of climate and ensure a proactive stance towards lightning prediction.

Multi-spatial-scale land/use land cover influences on seasonally dominant water quality along Middle Ganga Basin.

Studying spatiotemporal water quality characteristics and their correlation with land use/land cover (LULC) patterns is essential for discerning the origins of various pollution sources and for informing strategic land use planning, which, in turn, requires a comprehensive analysis of spatiotemporal water quality data to comprehend how surface water quality evolves across different time and space dimensions. In this study, we compared catchment, riparian, and reach scale models to assess the effect of LULC on WQ. Using various multivariate techniques, a 14-year dataset of 20 WQ variables from 20 monitoring stations (67,200 observations) is studied along the Middle Ganga Basin (MGB). Based on the similarity and dissimilarity of WQPs, the K-means clustering algorithm classified the 20 monitoring stations into four clusters. Seasonally, the three PCs chosen explained 75.69% and 75% of the variance in the data. With PCs > 0.70, the variables EC, pH, Temp, TDS, NO2 + NO3, P-Tot, BOD, COD, and DO have been identified as dominant pollution sources. The applied RDA analysis revealed that LULC has a moderate to strong contribution to WQPs during the wet season but not during the dry season. Furthermore, dense vegetation is critical for keeping water clean, whereas agriculture, barren land, and built-up area degrade WQ. Besides that, the findings suggest that the relationship between WQPs and LULC differs at different scales. The stacked ensemble regression (SER) model is applied to understand the model's predictive power across different clusters and scales. Overall, the results indicate that the riparian scale is more predictive than the watershed and reach scales.