Spatio-temporal Process Research Articles

Bayesian tip-dated phylogeny and biogeography of Cissampelideae (Menispermaceae): Mitigating the effects of homoplastic morphological characters.

The integration of morphological and molecular data is essential to understand the affinities of fossil taxa and spatio-temporal evolutionary processes of organisms. However, homoplastic morphological characters can mislead the placement of fossil taxa and impact downstream analyses. Here, we provide an example of how to mitigate effectively the effect of morphological homoplasy on the placement of fossil taxa and biogeographic inferences of Cissampelideae. We assembled three data types, morphological data only, morphological data with a molecular scaffold and combined morphological and molecular data. By removing high-level homoplastic morphological data or reweighting the morphological characters, we conducted 15 parsimony, 12 undated Bayesian and four dated Bayesian analyses. Our results show that the 14 selected Cissampelideae fossil taxa are placed poorly when based only on morphological data, but the addition of molecular scaffold and combination of morphological and molecular data greatly improve the resolution of fossil nodes. We raise the monotypic Stephania subg. Botryodiscia to generic status and discover that three fossils previously assigned to Stephania should be members of Diploclisia. The Bayesian tip-dated tree recovered by removing homoplastic morphological characters with a Rescaled Consistency Index <0.25 has the highest stratigraphic fit and consequently generates more reasonable biogeographic reconstruction for Cissampelideae. Cissampelideae began to diversify in Asia in the latest Cretaceous and subsequently dispersed to South America around the Cretaceous-Palaeogene boundary. Two dispersal events from Asia to Africa occurred in the Early Eocene and the Late Eocene-Late Oligocene, respectively. These findings provide guidelines and practical methods for mitigating the effects of homoplastic morphological characters on fossil placements and Bayesian tip-dating, as well as insights into the past tropical floristic exchanges among different continents.

Knowledge Graph Representation of Multi-Source Urban Storm Surge Hazard Information Based on Spatio-Temporal Coding and the Hazard Events Ontology Model

Coastal cities are increasingly vulnerable to urban storm surge hazards and the secondary hazards they cause (e.g., coastal flooding). Accurate representation of the spatio-temporal process of hazard event development is essential for effective emergency response. However, current knowledge graph representations face the challenge of integrating multi-source information with various spatial and temporal scales. To address this challenge, we propose a new information model for storm surge hazard events, involving a two-step process. First, a hazard event ontology is designed to model the components and hierarchical relationships of hazard event information. Second, we utilize multi-scale time segment integer coding and geographical coordinate subdividing grid coding to create a spatio-temporal framework, for modeling spatio-temporal features and spatio-temporal relationships. Using the 2018 typhoon Mangkhut storm surge event in Shenzhen as a case study and the hazard event information model as a schema layer, a storm surge event knowledge graph is constructed, demonstrating the integration and formal representation of heterogeneous hazard event information and enabling the fast retrieval of disasters in a given spatial or temporal range.

The Spatiotemporal Clustering of Short‐Duration Rainstorms in Shanghai City Using a Sub‐Hourly Gauge Network

AbstractImpacted by both the large‐scale climate change and the local hydrometeorological atmosphere, the spatiotemporal structure and properties of short‐duration extreme rainfall events in urban areas remain a complex issue. This study explores the spatiotemporal pattern variability of short‐duration rainstorm events using an exceptional sub‐hourly gauge network in Shanghai, China. A catalog of 207 “independent” rainstorms is extracted. The hierarchical clustering method is used to examine the spatial and temporal features including rainfall duration, intensity, and coverage for each district with different degrees of urbanization in the 2014–2018 period. The results point to a pronounced spatial heterogeneity with heavier rainfall magnitude in the north. In the urban core area, intensive short‐duration rainstorms hit more frequently than in other districts. Rainstorms can be divided into three types from the spatiotemporal process: Type delayed‐slow, Type advanced‐fast, and Type advanced‐slow. The delayed‐peak with a higher slope occurs more in the west and the advanced‐peak more along the coastal line. The other type of advanced‐peak storm with a reverse trend of intensity and coverage concentrates in June–August of the year and the 16:00–20:00 of the day. While 65.6% of the delayed‐peak storms only occur in one district, more than half of the advanced‐peak appear without other types at the same time. These findings show the importance of observation‐based analysis in understanding the spatiotemporal pattern of short‐duration storms and provide a reference for storm design of sub‐regions in urban areas.

ADP-Based Event-Triggered Constrained Optimal Control on Spatiotemporal Process: Application to Temperature Field in Roller Kiln.

The precise control of the spatiotemporal process in a roller kiln is crucial in the production of Ni-Co-Mn layered cathode material of lithium-ion batteries. Since the product is extremely sensitive to temperature distribution, temperature field control is of great significance. In this article, an event-triggered optimal control (ETOC) method with input constraints for the temperature field is proposed, which takes up an important position in reducing the communication and computation costs. A nonquadratic cost function is adopted to describe the system performance with input constraints. First, we present the problem description of the temperature field event-triggered control, where this field is described by a partial differential equation (PDE). Then, the event-triggered condition is designed according to the information of system states and control inputs. On this basis, a framework of the event-triggered adaptive dynamic programming (ETADP) method that is based on the model reduction technology is proposed for the PDE system. A critic network is used to approach the optimal performance index by a neural network (NN) together with that an actor network is used to optimize the control strategy. Furthermore, an upper bound of the performance index and a lower bound of interexecution times, as well as the stabilities of the impulsive dynamic system and the closed-loop PDE system, are also proved. Simulation verification demonstrates the effectiveness of the proposed method.

Crystallization processes of quartz in a granitic magma: Implications for the magma chamber processes of Okueyama granite, Kyushu, Japan

Cathodoluminescence (CL) characterization combined with titanium concentration of quartz crystals in granite is a prevalent tool for elucidating the crystal growth of quartz in granitic magmas. The CL pattern yields to the internal structure, and the titanium concentration allows crystallization temperature determination based on TitaniQ geothermometry. Using two approaches, 1) observations of quartz crystals using thin sections and 2) observations based on multiple sections of separated quartz, we identified the overall characteristics of quartz crystals within a magma chamber and the 3D internal structure and growth characteristics of quartz grains. Thin section observations allow for the characterization of quartz containing zircon inclusions, indicating that zircon crystallization was followed by quartz crystallization; therefore, the onset of quartz crystallization occurred later than the onset of zircon crystallization in the cooling magma chamber. Such petrological restrictions permit allow for suitable geothermometer use and appropriate TiO2 activity estimation, and thus, accurate quartz crystallization temperature determination. Observations using multiple sections of separated quartz crystals provide 3D internal structures and true onset parts within the grains. Progressive variation in local activities of TiO2 in the concentration boundary layer at the growing quartz crystal interface in the magma chamber can be evaluated using a combination of the crystallization temperature and variations in titanium concentration within the grain, yielding a sequential crystallization model of oscillatory zoning quartz with local activities of TiO2 in the cooling magma chamber. The difference in CL characteristics and crystallization temperature among the lithofacies in granite contributes to clarifying the spatiotemporal magma chamber processes.

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equations, thus demanding extensive computational resources. In contrast, data-driven approaches leverage deep learning algorithms to describe system evolution in low-dimensional spaces. We introduce an architecture, termed Latent Dynamics Network, capable of uncovering low-dimensional intrinsic dynamics in potentially non-Markovian systems. Latent Dynamics Networks automatically discover a low-dimensional manifold while learning the system dynamics, eliminating the need for training an auto-encoder and avoiding operations in the high-dimensional space. They predict the evolution, even in time-extrapolation scenarios, of space-dependent fields without relying on predetermined grids, thus enabling weight-sharing across query-points. Lightweight and easy-to-train, Latent Dynamics Networks demonstrate superior accuracy (normalized error 5 times smaller) in highly-nonlinear problems with significantly fewer trainable parameters (more than 10 times fewer) compared to state-of-the-art methods.

Severe convective storms’ reproduction: empirical analysis from the marked self-exciting point processes point of view

The paper focuses on the evaluation of hailstorms’ and thunderstorms winds’ events in the United States of America, in the period from 1996 to 2022, under the marked spatio-temporal self-exciting point processes point of view. The aim of the present article is the assessment and description of the spatio-temporal spontaneous and reproducing activity of severe hailstorms’ and thunderstorms winds’ processes. The present application shows how the spatio-temporal pattern is well-fitted and clearly explainable, according to the flexible semi-parametric ETAS model fitting.

Developmental loss of NMDA receptors results in supernumerary forebrain neurons through delayed maturation of transit-amplifying neuroblasts

Developmental neurogenesis is a tightly regulated spatiotemporal process with its dysregulation implicated in neurodevelopmental disorders. NMDA receptors are glutamate-gated ion channels that are widely expressed in the early nervous system, yet their contribution to neurogenesis is poorly understood. Notably, a variety of mutations in genes encoding NMDA receptor subunits are associated with neurodevelopmental disorders. To rigorously define the role of NMDA receptors in developmental neurogenesis, we used a mutant zebrafish line (grin1−/−) that lacks all NMDA receptors yet survives to 10 days post-fertilization, offering the opportunity to study post-embryonic neurodevelopment in the absence of NMDA receptors. Focusing on the forebrain, we find that these fish have a progressive supernumerary neuron phenotype confined to the telencephalon at the end of embryonic neurogenesis, but which extends to all forebrain regions during postembryonic neurogenesis. This enhanced neuron population does not arise directly from increased numbers or mitotic activity of radial glia cells, the principal neural stem cells. Rather, it stems from a lack of timely maturation of transit-amplifying neuroblasts into post-mitotic neurons, as indicated by a decrease in expression of the ontogenetically-expressed chloride transporter, KCC2. Pharmacological blockade with MK-801 recapitulates the grin1−/− supernumerary neuron phenotype, indicating a requirement for ionotropic signaling. Thus, NMDA receptors are required for suppression of indirect, transit amplifying cell-driven neurogenesis by promoting maturational termination of mitosis. Loss of suppression results in neuronal overpopulation that can fundamentally change brain circuitry and may be a key factor in pathogenesis of neurodevelopmental disorders caused by NMDA receptor dysfunction.

Technical note: Accurate, reliable, and high-resolution air quality predictions by improving the Copernicus Atmosphere Monitoring Service using a novel statistical post-processing method

Abstract. Starting from the regional air quality forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS), we propose a novel post-processing approach to improve and downscale results on a finer scale. Our approach is based on the combination of ensemble model output statistics (EMOS) with a spatio-temporal interpolation process performed through the stochastic partial differential equation–integrated nested laplace approximation (SPDE-INLA). Our interpolation approach includes several spatial and spatio-temporal predictors, including meteorological variables. A use case is provided that scales down the CAMS forecasts on the Italian peninsula. The calibration is focused on the concentrations of several air quality pollutants (PM10, PM2.5, NO2, and O3) at a daily resolution from a set of 750 monitoring sites, distributed throughout the Italian country. Our results show the key role that conditioning variables play in improving the forecast capabilities of ensemble predictions, thus allowing for a net improvement in the calibration with respect to ordinary EMOS strategies. From a deterministic point of view, the performance of the predictive model shows a significant improvement in the performance of the raw ensemble forecast, with an almost-zero bias, significantly reduced root mean square errors, and correlations that are almost always higher than 0.9 for each pollutant; moreover, the post-processing approach is able to significantly improve the prediction of exceedances, even for very low thresholds, such as those recently recommended by the World Health Organisation. This is particularly significant if a forecasting approach is used to predict air quality conditions and plan adequate human health protection measures, even for low alert thresholds. From a probabilistic point of view, the quality of the forecast was verified in terms of reliability and credible intervals. After post-processing, the predictive probability density functions were sharp and much better calibrated than the raw ensemble forecast. Finally, we present some additional results based on a set of gridded (4 km × 4 km) maps covering the entire Italian country for the detection of areas where pollution peaks occur (exceedances of the current and/or proposed regulatory thresholds).

Contemporary and historical human migration patterns shape hepatitis B virus diversity.

Infection by hepatitis B virus (HBV) is responsible for approximately 296 million chronic cases of hepatitis B, and roughly 880,000 deaths annually. The global burden of HBV is distributed unevenly, largely owing to the heterogeneous geographic distribution of its subtypes, each of which demonstrates different severity and responsiveness to antiviral therapy. It is therefore crucial to the global public health response to HBV that the spatiotemporal spread of each genotype is well characterized. In this study, we describe a collection of 133 newly sequenced HBV strains from recent African immigrants upon their arrival in Belgium. We incorporate these sequences-all of which we determine to come from genotypes A, D, and E-into a large-scale phylogeographic study with genomes sampled across the globe. We focus on investigating the spatio-temporal processes shaping the evolutionary history of the three genotypes we observe. We incorporate several recently published ancient HBV genomes for genotypes A and D to aid our analysis. We show that different spatio-temporal processes underlie the A, D, and E genotypes with the former two having originated in southeastern Asia, after which they spread across the world. The HBV E genotype is estimated to have originated in Africa, after which it spread to Europe and the Americas. Our results highlight the use of phylogeographic reconstruction as a tool to understand the recent spatiotemporal dynamics of HBV, and highlight the importance of supporting vulnerable populations in accordance with the needs presented by specific HBV genotypes.

Stelfi: An R package for fitting Hawkes and log-Gaussian Cox point process models.

Modelling spatial and temporal patterns in ecology is imperative to understand the complex processes inherent in ecological phenomena. Log-Gaussian Cox processes are a popular choice among ecologists to describe the spatiotemporal distribution of point-referenced data. In addition, point pattern models where events instigate others nearby (i.e., self-exciting behaviour) are becoming increasingly popular to infer the contagious nature of events (e.g., animal sightings). While there are existing R packages that facilitate fitting spatiotemporal point processes and, separately, self-exciting models, none incorporate both. We present an R package, stelfi, that fits spatiotemporal self-exciting and log-Gaussian Cox process models using Template Model Builder through a range of custom-written C++ templates. We illustrate the use of stelfi's functions fitting models to Sasquatch (bigfoot) sightings data within the USA. The structure of these data is typical of many seen in ecology studies. We show, from a temporal Hawkes process to a spatiotemporal self-exciting model, how the models offered by the package enable additional insights into the temporal and spatial progression of point pattern data. We present extensions to these well-known models that include spatiotemporal self-excitation and joint likelihood models, which are better suited to capture the complex mechanisms inherent in many ecological data. The package stelfi offers user-friendly functionality, is open source, and is available from CRAN. It offers the implementation of complex spatiotemporal point process models in R for applications even beyond the field of ecology.

Non-Separable Covariance Kernels for Spatiotemporal Gaussian Processes Based on a Hybrid Spectral Method and the Harmonic Oscillator

Non-Separable Covariance Kernels for Spatiotemporal Gaussian Processes Based on a Hybrid Spectral Method and the Harmonic Oscillator

Geodetic imaging and dynamic modeling of saline mudflat using time-series InSAR in Howz-e-Soltan Salt Lake, Qom, Iran

Due to the physicochemical properties of salt swelling and collapsibility, the potential geological hazards threaten the large-scale infrastructure and residential areas in the saline-soil region. Geodetic imaging and dynamic monitoring of the spatiotemporal evolution process of saline soil are vital to detect the potential geological hazards in a vast area. Therefore, this paper presents a coupled deformation model synthesizing environmental factors and trigonometric function model for estimating the time-series InSAR deformation and interpreting the dynamic evolutionary mechanism of the saline mudflat. To demonstrate the advantages of the proposed model, the sulphate saline mudflat is selected as the study area, which is traversed by the Tehran-Isfahan high-speed railway in Howz-e-Soltan Salt Lake, Qom, Iran. Moreover, 259 interconnecting interferograms were selected for the time-series InSAR estimation, composed of 87 Sentinel-1A images obtained from October 2014 through December 2018. Then, the time-series deformations, including the annual deformation velocity and the accumulated deformation, are extracted by the original estimation, polynomial, trigonometric and environmental models. The comparison results show that the environmental model's RMSEs (root-mean-square errors) are smaller than others. It should be noted that the linear deformation, periodic oscillation, and deformation derived from the environmental factors (i.e., precipitation and temperature) are extracted by the environmental model. Furthermore, the dynamic evolutionary mechanism of the saline mudflat is interpreted according to the physicochemical properties of sulphate/carbonate saline soil. The results derived from the environmental model suggested that the saline-soil deformation is directly related with the environmental changes: rapid collapsibility occurs when the precipitation increases from September to the next May, while uplift due to the salt swelling in the dry and rainless season from May to September every year. Our findings provide an opportunity to detect, prevent and control the potential geological hazards in saline mudflats.

Field study on endogenous perfluoroalkyl acid release and their spatiotemporal distribution processes induced by inland navigation

Dense populations and industries in regions with developed inland waterways have caused the significant discharge of perfluoroalkyl acids (PFAAs) into surrounding waterways. Despite being the dominant energy input in the waterways, the impact of ship navigation on endogenous PFAA release is unclear. In this study, a field experiment was carried out in the Wangyu River (Taihu Basin, China) to investigate the spatiotemporal distribution processes of PFAAs in the water column after passage of ships with different tonnages, speeds, and draughts. The results showed that the PFAA contents did not decrease continuously with time but increased with a lag after the passing ship triggered a transient massive dissolution of PFAAs into the overlying water. In addition, PFAA contents in suspended particulate matter (SPM) exhibited a fluctuating downward trends after their peak at the moment of ship passage. Vertically, the PFAA concentrations among the layers of overlying water were relatively homogeneous, whereas SPM exhibited substantial heterogeneity in its distribution and adsorption of PFAAs. Moreover, the differences in jet scouring velocity (u), disturbance duration (t), and draught (h) of ships resulted in large variability in PFAA contents in the water column. Variance partitioning analysis further quantified the effects of u, t, and h on total PFAAs in the water column, with individual contributions of 53 %, 12 %, and 6 %, respectively. Furthermore, the release of endogenous PFAAs induced by ship passage involved rapid and slow processes, the former determining the overall PFAA release and the latter affecting PFAA concentration recovery in the water column. The findings provide in-situ observational data on spatiotemporal variations of PFAAs in multiphase media following ship passage, enhancing our understanding of endogenous pollution in inland waterways.

Reducing the Environmental Impact of Wireless Communication via Probabilistic Machine Learning

Machine learning methods are increasingly adopted in communications problems, particularly those arising in next generation wireless settings. Though seen as a key climate mitigation and societal adaptation enabler, communications related energy consumption is high and is expected to grow in future networks in spite of anticipated efficiency gains in 6G due to exponential communications traffic growth. To make meaningful climate mitigation impact in the communications sector, a mindset shift away from maximizing throughput at all cost and towards prioritizing energy efficiency is needed. Moreover, this must be adopted in both existing (without incurring further embodied carbon costs through equipment replacement) and future network infrastructure, given the long development time of mobile generations. To that end, we present summaries of two such problems, from both current and next generation network specifications, where probabilistic inference methods were used to great effect: using Bayesian parameter tuning we are able to safely reduce the energy consumption of existing hardware on a live communications network by 11% whilst maintaining operator specified performance envelopes; through spatiotemporal Gaussian process surrogate modeling we reduce the overhead in a next generation hybrid beamforming system by over 60%, greatly improving the networks' ability to target highly mobile users such as autonomous vehicles. The Bayesian paradigm is itself helpful in terms of energy usage, since training a Bayesian optimization model can require much less computation than, say, training a deep neural network.

GAN-argcPredNet v2.0: a radar echo extrapolation model based on spatiotemporal process enhancement

Abstract. Precipitation nowcasting has important implications for urban operation and flood prevention. Radar echo extrapolation is a common method in precipitation nowcasting. Using deep learning models to extrapolate radar echo data has great potential. The increase of lead time leads to a weaker correlation between the real rainfall evolution and the generated images. The evolution information is easily lost during extrapolation, which is reflected as echo attenuation. Existing models, including generative adversarial network (GAN)-based models, have difficulty curbing attenuation, resulting in insufficient accuracy in rainfall prediction. To solve this issue, a spatiotemporal process enhancement network (GAN-argcPredNet v2.0) based on GAN-argcPredNet v1.0 has been designed. GAN-argcPredNet v2.0 curbs attenuation by avoiding blurring or maintaining the intensity. A spatiotemporal image correlation (STIC) prediction network is designed as the generator. By suppressing the blurring effect of rain distribution and reducing the negative bias by STIC attention, the generator generates more accurate images. Furthermore, the discriminator is a channel–spatial (CS) convolution network. The discriminator enhances the discrimination of echo information and provides better guidance to the generator in image generation by CS attention. The experiments are based on the radar dataset of southern China. The results show that GAN-argcPredNet v2.0 performs better than other models. In heavy rainfall prediction, compared with the baseline, the probability of detection (POD), the critical success index (CSI), the Heidke skill score (HSS) and bias score increase by 18.8 %, 17.0 %, 17.2 % and 26.3 %, respectively. The false alarm ratio (FAR) decreases by 3.0 %.

Spreading- and evaporation-mediated 2D colloidal assemblies on fluid interfaces

Fluid interfaces exhibit remarkable capabilities and significant application prospects in fabricating two-dimensional (2D) materials and colloidal assemblies. However, the efficient realization of the fluid-mediated self-assembly of colloidal particles has become challenging owing to the complex evolution of spatiotemporal processes and dynamic behaviors at fluid interfaces. In this study, we hypothesized that the superspreading of a volatile droplet directed the ultrafast self-assembly of colloidal particles on an insoluble fluid interface. To validate this hypothesis, employing high-speed and large-field microscopic observation experiments in tandem with theoretical analysis, the time evolution processes of droplet spreading, evaporation, dewetting, and particle assembly were captured, and the multi-scale dynamic behavior of volatile colloidal droplets on an immiscible liquid substrate was studied. Our findings revealed the power law of droplet spreading dynamics at the macroscale, evaporation-induced dewetting of the liquid film and aggregation of colloidal particles at the mesoscopic scale, and colloidal self-assembly under the action of capillary and DLVO forces at the microscopic scale. Moreover, we realized the ultrafast fabrication of 2D colloidal assemblies on liquid interfaces. This work presents a simple, robust, and efficient method for self-assembling and manufacturing 2D-ordered structures and materials based on a platform of fluid interfaces.

Graph spatiotemporal process for multivariate time series anomaly detection with missing values

The detection of anomalies in multivariate time series data is crucial for various practical applications, including smart power grids, traffic flow forecasting, and industrial process control. However, real-world time series data is usually not well-structured, posting significant challenges to existing approaches: (1) The existence of missing values in multivariate time series data along variable and time dimensions hinders the effective modeling of interwoven spatial and temporal dependencies, resulting in important patterns being overlooked during model training; (2) Anomaly scoring with irregularly-sampled observations is less explored, making it difficult to use existing detectors for multivariate series without fully-observed values. In this work, we introduce a novel framework called GST-Pro, which utilizes a graph spatiotemporal process and anomaly scorer to tackle the aforementioned challenges in detecting anomalies on irregularly-sampled multivariate time series. Our approach comprises two main components. First, we propose a graph spatiotemporal process based on neural controlled differential equations. This process enables effective modeling of multivariate time series from both spatial and temporal perspectives, even when the data contains missing values. Second, we present a novel distribution-based anomaly scoring mechanism that alleviates the reliance on complete uniform observations. By analyzing the predictions of the graph spatiotemporal process, our approach allows anomalies to be easily detected. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods, regardless of whether there are missing values present in the data. Our code is available: https://github.com/huankoh/GST-Pro.

A Composite Likelihood-based Approach for Change-point Detection in Spatio-temporal Processes

This paper develops a unified and computationally efficient method for change-point estimation along the time dimension in a non-stationary spatio-temporal process. By modeling a non-stationary spatio-temporal process as a piecewise stationary spatio-temporal process, we consider simultaneous estimation of the number and locations of change-points, and model parameters in each segment. A composite likelihood-based criterion is developed for change-point and parameter estimation. Under the framework of increasing domain asymptotics, theoretical results including consistency and distribution of the estimators are derived under mild conditions. In contrast to classical results in fixed dimensional time series that the localization error of change-point estimator is , exact recovery of true change-points is possible in the spatio-temporal setting. More surprisingly, the consistency of change-point estimation can be achieved without any penalty term in the criterion function. In addition, we further establish consistency of the change-point estimator under the infill asymptotics framework where the time domain is increasing while the spatial sampling domain is fixed. A computationally efficient pruned dynamic programming algorithm is developed for the challenging criterion optimization problem. Extensive simulation studies and an application to the U.S. precipitation data are provided to demonstrate the effectiveness and practicality of the proposed method.

Spatial co-location patterns between early COVID-19 risk and urban facilities: a case study of Wuhan, China.

COVID-19, being a new type of infectious disease, holds significant implications for scientific prevention and control to understand its spatiotemporal transmission process. This study examines the diverse spatial patterns of COVID-19 within Wuhan by analyzing early case data alongside urban infrastructure information. Through co-location analysis, we assess both local and global spatial risks linked to the epidemic. In addition, we use the Geodetector, identifying facilities displaying unique spatial risk characteristics, revealing factors contributing to heightened risk. Our findings unveil a noticeable spatial distribution of COVID-19 in the city, notably influenced by road networks and functional zones. Higher risk levels are observed in the central city compared to its outskirts. Specific facilities such as parking, residence, ATM, bank, entertainment, and hospital consistently exhibit connections with COVID-19 case sites. Conversely, facilities like subway station, dessert restaurant, and movie theater display a stronger association with case sites as distance increases, hinting at their potential as outbreak focal points. Despite our success in containing the recent COVID-19 outbreak, uncertainties persist regarding its origin and initial spread. Some experts caution that with increased human activity, similar outbreaks might become more frequent. This research provides a comprehensive analytical framework centered on urban facilities, contributing quantitatively to understanding their impact on the spatial risks linked with COVID-19 outbreaks. It enriches our understanding of the interconnectedness between urban facility distribution and transportation flow, affirming and refining the distance decay law governing infectious disease risks. Furthermore, the study offers practical guidance for post-epidemic urban planning, promoting the development of safer urban environments resilient to epidemics. It equips government bodies with a reliable quantitative analysis method for more accurately predicting and assessing infectious disease risks. In conclusion, this study furnishes both theoretical and empirical support for tailoring distinct strategies to prevent and control COVID-19 epidemics.