Analysis Of Spatio-temporal Data Research Articles

Driving mechanism of urban expansion in the Bohai Rim urban agglomeration from the perspective of spatiotemporal dynamic analysis

The urban agglomeration represents the predominant form of new urbanisation, yet the evolution of its internal spatial structure exhibits pronounced spatial and temporal heterogeneity. This study concentrates on the Bohai Rim urban agglomeration, one of three major urban agglomerations in China, which has received comparatively limited research attention but has also undergone substantial urbanisation. Therefore, we reassessed and explored the spatial-temporal evolution of the spatial structure of urban expansion using Exploratory Spatiotemporal Data Analysis (ESTDA), and summarized the driving mechanisms using Geographically and Temporally Weighted Regression (GTWR). Our study found that the urban land expansion intensity of Bohai Rim urban agglomeration presented the differences between megacities and large cities, coastal and inland cities and the regional disparities exhibited a gradual narrowing and balancing over time and space, with the high-expansion intensity zones undergoing alternation. The dynamics of the local spatial structure performed a clear positive correlation with the development level and a negative correlation with the exploitation time. The urban expansion intensity was influenced by various factors, including population, economic and other variables. Over time, the main drivers shifted from emphasis on economic quantity to quality, and from financial support to government policy. The objective of this study is to substantiate the impact of socio-economic factors on the spatio-temporal heterogeneity of urban expansion and to propose strategies for alleviating urban land pressure in the Bay Area urban agglomerations. This study enriches the research on the urban expansion mechanisms of different types of urban agglomerations and enhances a comprehensive understanding of the spatiotemporal heterogeneity of urban expansion within it. The findings can provide valuable theoretical support for urban planners in future urban development and construction and can also serve as exemplars for urbanisation in other urban agglomerations.

Spatial association measures for time series with fixed spatial locations

Spatial time series (STS), which refers to time-series data collected at fixed spatial locations, is crucial for understanding the spatiotemporal dynamics of geographical phenomena. Measuring the spatial association based on STS similarity provides valuable insights into the exploratory analysis of spatiotemporal data. However, existing methods are not effective in accurately quantifying such spatial association. To address this gap, this study proposes a conceptual model and a statistical method for identifying spatial clusters that exhibit significantly similar time-varying characteristics within a set of STS data. Conceptually, three representative patterns are defined: positive, negative, and no associations. A positive pattern occurs when spatially adjacent STSs show similar time-varying characteristics, while a negative pattern occurs when they show dissimilar ones. Technically, this study introduces a distance metric to measure similarities among STSs. The spatial association of STS at global and local scales is quantified according to the spatial concentration of these similarities. The validity and applicability of the proposed statistics are verified through synthetic and real-world examples, demonstrating their potential as effective tools for understanding spatiotemporal dynamics from a new perspective.

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The Lapindo mudflow disaster at the PT Lapindo Brantas drilling site in Ronokenongo Village, Porong District, Sidoarjo Regency, East Java caused the loss of agricultural and residential areas. The research aimed to detect the areas that are affected by Lapindo mudflow 2006-2022 using Landsat 7 ETM and Landsat 8 OLI-TIRS imageries, as well as visualize their impact using the cloud computing-based Google Earth Engine/GEE platform. Spatiotemporal data analysis was performed on the GEE platform using random forest machine learning as algorithm for supervised land use classification, while visualization was carried out through Earth Engine Apps. The results showed an increase in the mudflow-affected area from 2006 (204.57 ha) to 2012 (542.32 ha) with northeast direction, whereas the increase was insignificant at the following years. Within the detection period, agricultural land was the most affected area, followed by residential areas and bare land. The area ordering was similar during all detected years. The increasing size of the affected area can potentially have both direct and indirect impacts on the surrounding area. Therefore, special action is needed for the surrounding area, such as relocating settlements to safer areas against the Lapindo mudflow disaster.

Modeling land-degradation vulnerability in coal-mined environs through geospatial and AHP techniques: potential strategies for eco-restoration.

Coal is the most important and abundant source of energy in India, where 66% of the thermal power is generated from the combustion of coal. Therefore, understandinghow coal-mining activities increase land-degradation risks and vulnerabilities in different land uses is essential to devise site-specific restoration plans for complementing the national targets of land degradation neutrality (LDN). This study was carried out in the Chirimiri coal-mining region of Chhattisgarh, India, employing geospatial methods. The Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) data corresponding to 2000, 2010, and 2020 were digitally classified using maximum-likelihood algorithm in ERDAS IMAGINE and ArcGIS platforms. Spatio-temporal satellite data analysis revealed that an area encompassing ~ 112 km2 (48.9%) of forests and 6.63 km2 (37.6%) of waterbodies was lost due to the expanse of mining between 2000 and 2020. Consequently, the degraded land and built-up areas rapidly expanded during the same period accounting for a net increase of 101.1 km2 (47%) and 9.13 km2 (322%). The mining disturbances degenerated the soil health, drastically reducing soil pH, soil organic carbon (SOC), and available nitrogen (N) in the mined area exacerbating the region's vulnerability to degraded lands. Land Degradation Vulnerability Index (LDVI) map shows that mined area was highly vulnerable, followed by barren wasteland, agriculture, and forest area, while destructive mining and other associated activities in the past 20years made 52% of the landscape vulnerable. The study discusses the implications of effective eco-restoration for minimizing the land degradation processes and building resilient ecosystems for mitigating land degradation in coal-mined landscapes.

Spatial Correlation Network Characteristics and Influencing Factors of Industrial Carbon Emission Efficiency of Cities in Pearl River Basin

China has experienced rapid development of urbanization and industrialization, and urban industrial carbon reduction has become the key to green and sustainable development. Exploring urban industrial carbon emissions and its spatial correlation characteristics is of great importance for promoting collaborative cooperation between cities and achieving the "dual carbon" goal. Considering 49 cities in the Pearl River Basin as research objects, the super slacks-based measure model was used to measure the urban industrial carbon emission efficiency from 2010 to 2020, and exploratory spatiotemporal data analysis was used to explore its spatial pattern. On this basis, we used social network analysis and quadratic assignment procedure （QAP） methods to explore the spatial correlation network characteristics and influencing factors of urban industrial carbon emission efficiency. The results indicated that the industrial carbon emission efficiency of the Pearl River Basin showed a downward trend. Additionally, a strong spatial imbalance and heterogeneity was observed. The Pearl River Delta Region always dominated the spatial correlation network of carbon emission efficiency, forming a multi-level composite network system with the core network as the primary component and the conventional network as the auxiliary component. Economically developed cities, such as Guangzhou, Shenzhen, and Dongguan have long been in the center of the network and have extensive connections with other cities, while playing an intermediary role. The level of urbanization, science and technology, and industrialization have an increasingly significant positive impact on the strength of the connection between urban industrial carbon emission networks. The Pearl River Basin industrial carbon emission efficiency network has significant individual differences and hierarchical characteristics. According to the individual centrality, network role, and clustering characteristics, combined with the corresponding influencing factors, the Pearl River Basin urban collaborative carbon emission reduction strategy was formulated. This study can provide reference for the formulation and implementation of regional low-carbon collaborative development strategies.

Experimental investigation of the radial structure of energetic particle driven GAM in TCV

Abstract High amplitude energetic particle geodesic acoustic mode (EGAM) oscillations driven by Neutral Beam (NBI) injected in the direction counter to the toroidal plasma current are observed in the TCV tokamak. The modes appear at frequencies close to the geodesic acoustic mode (GAM) frequency, corresponding to the mode radii calculated without plasma elongation corrections. The spatial structure of the EGAM density oscillation is analyzed using multichannel soft x-ray (SXR) and broadband light emission diagnostics. The analysis of spatiotemporal emission data shows a non-rotating structure of the density oscillations. The non-rotating mode discrimination is the additional new capability of the multichannel spatiotemporal SXR data analysis technique in TCV. We present the discrimination method of assessing the standing character of the EGAM wave. The structure of EGAM density oscillations in TCV is consistent with the theoretical GAM poloidal structure, namely a m= 1 standing wave with density oscillations amplitude proportional to sin(θ), where the poloidal angle θ is measured from the equatorial plane. The poloidal structure of magnetic the field oscillations is analyzed using a poloidal array of magnetic coils. The structure of the EGAM-induced magnetic field oscillations in TCV is a m = 2 standing wave. Time evolution of EGAMs suggests that a nonlinear EGAM chirping is observed. The chirping depends on the EGAM radial location, which varies as the NBI deposition is varied through a vertical shift of the plasma magnetic axis. The chirping disappears at the plasma periphery. A fast periodic radial shift of the EGAMs radial location is also observed to occur during the single chirp.

Urban Ecological Economic Resilience in China: Spatio-Temporal Evolution, Influencing Factors, and Trend Prediction

This article adopted exploratory spatio-temporal data analysis (ESTDA), geographic detector, and spatial Markov chain model to analyze the spatio-temporal evolution characteristics, main influencing factors, and future trend predictions of urban ecological economic resilience (EER). The results show that EER has been significantly enhanced, and high-level cities have a “rhombus” spatial distribution pattern. EER has a noticeable spatial agglomeration effect and the range of high–high agglomeration areas has gradually expanded. The LISA time path reflects that the spatial structure of EER is relatively stable, and urban units and neighboring cities show a more apparent synergistic growth trend. Social development, economic support, ecological restoration, and innovation and transformation strongly influence the development of EER, and the interaction between factors is more significant. In the future, EER will still tend to maintain the existing stable and unchanged state, and cross-grade leapfrogging development will not be achieved.

Spatiotemporal evolution and driving factors of carbon emission intensity from urban land use in China

The analysis of spatial differentiation and driving factors of urban land use carbon intensity in China can provide scientific theoretical basis and empirical support for urban control of land use carbon intensity. Based on the calculation of land use carbon intensity of 284 cities at or above the prefecture level in China from 2006 to 2021, this paper uses exploratory spatio-temporal data analysis to investigate the spatio-temporal evolution of urban carbon intensity in China, and introduces a multi-scale geographical weighted regression model to explore the spatial heterogeneity of its driving factors. The results showed that: (1) the carbon emission intensity of cities in China and the three regions showed a decreasing trend over time, and the spatial and temporal distribution of cities had a strong spatial positive correlation. (2) In terms of local spatial structure and dependence of carbon emission intensity, the volatility of cities in central and western regions is higher, while cities in eastern regions are relatively more stable; In the spatial pattern, there are some characteristics of transfer inertia and locking. (3) The main driving factors of carbon reduction effect at different spatial and temporal scales are different, and there is a certain spatial heterogeneity. This study comprehensively discussed the temporal and spatial transition, convergence trend and driving factors of urban land carbon emission intensity in China, attempting to provide a reference for differentiated emission reduction strategies for cities, which is of great significance for the transformation of cities from "dual control of energy consumption" to "dual control of carbon emission" and the green transformation of economic and social structure.

Spatio-Temporal Data Analysis of Single Droplet Breakup by High Velocity Gas Flow

Spatio-Temporal Data Analysis of Single Droplet Breakup by High Velocity Gas Flow

A bibliometric analysis of domestic and international research on hemorrhagic fever with renal syndrome over the past 2 decades.

Bibliometrics and statistics were used to determine and analyze the research status of hemorrhagic fever with renal syndrome (HFRS) from 2004 to 2023, and objectively reflect the development and future trend of HFRS at home and abroad through comparison. To search the research literature on HFRS in China National Knowledge Network and Web of Science databases from January 2004 to December 2023, CiteSpace and VOSviewer were used to visually analyze the annual publication trends, authors, research institutions, countries, co-cited literature, keywords and other contents of the included literatures. A total of 4460 Chinese literatures and 2372 foreign literatures were included. The number of HFRS published in the Web of Science database showed a trend of positive growth, while the number of HFRS published in China National Knowledge Network showed a trend of decline. Bai Xuefan and Wang Zhiqiang were the most published authors in China, and foreign scholars Vaheri, Antti, Ahlm, Clas. The main research institutions in the domestic literature were Zhejiang Provincial Center for Disease Control and Prevention, Liaoning Provincial Center for Disease Control and Prevention, while foreign research institutions concentrated on the University of Helsinki and Ministry of Health. The top 3 countries in the literature research of Web of Science are the USA, China, and Germany. The analysis results of hot spots and trends suggested that we need to develop more reliable tools and methods in the monitoring and spatio-temporal analysis of HFRS epidemic data in the future, so as to provide references for the surveillance and early warning of zoonotic diseases in the field of public health research.

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.

An isotopically-labelled temporal mass spectrometry imaging data analysis workflow to reveal glucose spatial metabolism patterns in bovine lens tissue

Application of stable isotopically labelled (SIL) molecules in Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) over a series of time points allows the temporal and spatial dynamics of biochemical reactions to be tracked in a biological system. However, these large kinetic MSI datasets and the inherent variability of biological replicates presents significant challenges to the rapid analysis of the data. In addition, manual annotation of downstream SIL metabolites involves human input to carefully analyse the data based on prior knowledge and personal expertise. To overcome these challenges to the analysis of spatiotemporal MALDI-MSI data and improve the efficiency of SIL metabolite identification, a bioinformatics pipeline has been developed and demonstrated by analysing normal bovine lens glucose metabolism as a model system. The pipeline consists of spatial alignment to mitigate the impact of sample variability and ensure spatial comparability of the temporal data, dimensionality reduction to rapidly map regional metabolic distinctions within the tissue, and metabolite annotation coupled with pathway enrichment modules to summarise and display the metabolic pathways induced by the treatment. This pipeline will be valuable for the spatial metabolomics community to analyse kinetic MALDI-MSI datasets, enabling rapid characterisation of spatio-temporal metabolic patterns from tissues of interest.

A tutorial: Analyzing eye and head movements in virtual reality.

This tutorial provides instruction on how to use the eye tracking technology built into virtual reality (VR) headsets, emphasizing the analysis of head and eye movement data when an observer is situated in the center of an omnidirectional environment. We begin with a brief description of how VR eye movement research differs from previous forms of eye movement research, as well as identifying some outstanding gaps in the current literature. We then introduce the basic methodology used to collect VR eye movement data both in general and with regard to the specific data that we collected to illustrate different analytical approaches. We continue with an introduction of the foundational ideas regarding data analysis in VR, including frames of reference, how to map eye and head position, and event detection. In the next part, we introduce core head and eye data analyses focusing on determining where the head and eyes are directed. We then expand on what has been presented, introducing several novel spatial, spatio-temporal, and temporal head-eye data analysis techniques. We conclude with a reflection on what has been presented, and how the techniques introduced in this tutorial provide the scaffolding for extensions to more complex and dynamic VR environments.

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.