Spatiotemporal Behavior Research Articles

Convolutional neural network-based spatiotemporal prediction for deformation behavior of arch dams

Deformation directly reflects the behavior of arch dams, and deformation behavior models have been widely used for structural health monitoring (SHM) in accordance with monitoring data. The deformation field brings advantages to the SHM of arch dams in terms of the spatiotemporal scale. In this paper, a spatiotemporal deformation field behavior model based on a convolutional neural network (CNN) was proposed. The temperature field of the dam fusing the climatological data, including air temperature, weather conditions, and solar radiation, was introduced. Furthermore, the water pressure and constraint fields were also input into the CNN-based model. The physical law and structural continuity were considered, and the parameters of the CNN-based model were general and uniform for each predicted location. The accuracy and extrapolation ability of the proposed model were evaluated. The results illustrate that the CNN-based model predicts accurate deformation at the spatiotemporal scale of the arch dam. The accuracy of the prediction of single-point deformation time series from the CNN-based model is close to that from the traditional hydraulic-seasonal-time (HST) model and finite element model (FEM). For the prediction of the spatial deformation field, the mean MAE of the CNN-based model is 2.68 mm, while it is 5.66 mm for the FEM model, showing much higher prediction accuracy of the CNN-based model. The proposed globally uniform CNN-based model can derive a high-precision and high-density deformation field of arch dams with sparse monitoring data. It can also be continuously updated in real-time according to the latest monitoring data without inverse analysis and parameter adjustment like the FEM model.

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Cell polarity is a macroscopic phenomenon established by a collection of spatially concentrated molecules and structures that culminate in the emergence of specialized domains at the subcellular level. It is associated with developing asymmetric morphological structures that underlie key biological functions such as cell division, growth, and migration. In addition, the disruption of cell polarity has been linked to tissue-related disorders such as cancer and gastric dysplasia. Current methods to evaluate the spatiotemporal dynamics of fluorescent reporters in individual polarized cells often involve manual steps to trace a midline along the cells' major axis, which is time consuming and prone to strong biases. Furthermore, although ratiometric analysis can correct the uneven distribution of reporter molecules using two fluorescence channels, background subtraction techniques are frequently arbitrary and lack statistical support. This manuscript introduces a novel computational pipeline to automate and quantify the spatiotemporal behavior of single cells using a model of cell polarity: pollen tube/root hair growth and cytosolic ion dynamics. A three-step algorithm was developed to process ratiometric images and extract a quantitative representation of intracellular dynamics and growth. The first step segments the cell from the background, producing a binary mask through a thresholding technique in the pixel intensity space. The second step traces a path through the midline of the cell through a skeletonization operation. Finally, the third step provides the processed data as a ratiometric timelapse and yields a ratiometric kymograph (i.e., a 1D spatial profile through time). Data from ratiometric images acquired with genetically encoded fluorescent reporters from growing pollen tubes were used to benchmark the method. This pipeline allows for faster, less biased, and more accurate representation of the spatiotemporal dynamics along the midline of polarized cells, thus advancing the quantitative toolkit available to investigate cell polarity. The AMEBaS Python source code is available at: https://github.com/badain/amebas.git

Data-driven analysis of soil consolidation with prefabricated vertical drains considering stratigraphic variation

In coastal cities such as Hong Kong, rapid reclamation using prefabricated vertical drains (PVDs) is preferred as it can accelerate land supply to meet the urgent demand for houses. A robust PVD design relies on the correct identification of permeable soil layers and accurate delineation of their stratigraphic connectivity with surrounding drainage boundaries. The current engineering practice often ignores the physical locations of minor drainage boundaries (e.g., sand lenses) in the subsurface stratigraphy and might lead to a false interpretation of potential drainage and consolidation mechanisms. In this study, a data-driven analysis framework that takes stratigraphic uncertainty into consideration is proposed to investigate the spatiotemporal consolidation of PVD-improved ground using sparse site investigation data often encountered in engineering practice. The method adaptively develops multiple geological cross-sections from limited measurements and prior knowledge that is reflected by a single training image. The resulting multiple geological realizations serve as the input for PVD analysis. The proposed data-driven framework allows for a probabilistic evaluation of soil spatiotemporal behavior in terms of the degree of consolidation and future ground settlement. More importantly, the proposed method accurately predicts the spatial distribution of stratigraphic boundaries with quantified uncertainty, which significantly influences the consolidation mechanism.

Forced sympatry? Spatiotemporal interactions of ursids, the Himalayan brown bear and the Asiatic black bear, along a gradient of anthropic disturbances in Western Himalaya

AbstractThe coexistence of species with similar ecological niches implies that species must segregate along one or more niche axes in order to avoid competition. Partitioning of time, space, or resources is an essential niche dimension along which competitor species tend to coexist. The intensity of interspecific competition may vary under varying anthropogenic pressures, but such knowledge is scarce. We investigated the co‐occurrence patterns of two bear species, Asiatic Black Bear Ursus thibetanus and Himalayan Brown Bear Ursus arctos isabellinus, at two sites in the Western Himalayan region of India with similar ecological conditions and varying anthropogenic pressures. Camera trap datasets from the Great Himalayan National Park (GHNP) in Himachal Pradesh and the Bhagirathi basin in Uttarakhand were used to assess the species interactions. We used two species interaction models and kernel density estimates to understand the co‐occurrence pattern of the bear species and their interactions with humans. Our results provide insights into the spatiotemporal behavior of the two bear species and reveal their sympatric and allopatric relationships in two different anthropogenic environments. We found (1) a prominent association between the two bear species (98%) indicating spatial overlap in GHNP, but found spatial separation among the two species (93%) in Bhagirathi basin (2). In GHNP, the spatially co‐occurring bear species were found to be temporarily segregated, whereas in Bhagirathi, they showed spatial avoidance and similar activity patterns (3). In GHNP, human activities in the alpine habitat had a significant negative impact on brown bear presence. The comparative study revealed that the brown bear uses the forested habitats of GHNP due to high anthropogenic disturbances in the alpine areas, whereas no spatial association was observed in the Bhagirathi basin with comparatively low anthropogenic disturbances. The results highlight that, in addition to biological factors, anthropogenic pressures can also influence interspecific competition.

Decomposing solar and geomagnetic activity and seasonal dependencies to examine the relationship between GPS loss of lock and ionospheric turbulence

Ionospheric irregularities are plasma density variations that occur at various altitudes and latitudes and whose size ranges from a few meters to a few hundred kilometers. They can have a negative impact on the Global Navigation Satellite Systems (GNSS), on their positioning accuracy and even cause a signal loss of lock (LoL), a phenomenon for which GNSS receivers can no longer track the satellites’ signal. Nowadays, the study of plasma density irregularities is important because many of the crucial infrastructures of our society rely on the efficient operation of these positioning systems. It was recently discovered that, of all possible ionospheric plasma density fluctuations, those in a turbulent state and characterized by extremely high values of the Rate Of change of the electron Density Index appear to be associated with the occurrence of LoL events. The spatial distributions of this class of fluctuations at mid and high latitudes are reconstructed for the first time using data collected on Swarm satellites between July 15th, 2014 and December 31st, 2021, emphasizing their dependence on solar activity, geomagnetic conditions, and season. The results unequivocally show that the identified class of plasma fluctuations exhibits spatio-temporal behaviours similar to those of LoL events.

Statistical modeling of the space–time relation between wind and significant wave height

Abstract. Many marine activities, such as designing ocean structures and planning marine operations, require the characterization of sea-state climate. This study investigates the statistical relationship between wind and sea states, considering its spatiotemporal behavior. A transfer function is established between wind fields over the North Atlantic (predictors) and the significant wave height (predictand) at three locations: southwest of the French coast (Gironde), the English Channel, and the Gulf of Maine. The developed method considers both wind seas and swells by including local and global predictors. Using a fully data-driven approach, the global predictors' spatiotemporal structure is defined to account for the non-local and non-instantaneous relationship between wind and waves. Weather types are constructed using a regression-guided clustering method, and the resulting clusters correspond to different wave systems (swells and wind seas). Then, in each weather type, a penalized linear regression model is fitted between the predictor and the predictand. The validation analysis proves the models skill in predicting the significant wave height, with a root mean square error of approximately 0.3 m in the three considered locations. Additionally, the study discusses the physical insights underlying the proposed method.

To study the effect of ER flux with buffer on the neuronal calcium.

Calcium signaling is decisive for cellular functions. This calcium random walk stipulates neuronal functions. Calcium concentration could provoke gene transcription, apoptosis, neuronal plasticity, etc. A malformation in calcium could change the neuron's intracellular behavior. Calcium concentration balancing is a complex cellular mechanism. This occurrence can be handled with the Caputo fractional reaction-diffusion equation. In this mathematical modeling, we have included the STIM-Orai mechanism and Endoplasmic Reticulum (ER) flux, Inositol Triphosphate Receptor (IPR), SERCA, plasma membrane flux, voltage-gated calcium entry, and different buffer interactions. A hybrid integral transform and Green's function approach were taken to solve the initial boundary problem. A closed-form solution of a Mittag-Leffler family function plotted using MATLAB software. Different parameters impact changes in the spatiotemporal behavior of the calcium concentration. Specific roles of organelles involved in Alzheimer's disease-affected neurons are computed. Ethylene glycol tetraacetic acid (EGTA), 1,2-bis(o-aminophenoxy)ethane N,N,N,N-tetraacetic acid (BAPTA), and S100B protein effects are also observed. In all simulations, we can say S100B and the STIM-Orai effect cannot be neglected. This model lights up the different approaches for calcium signaling pathway simulation. As a consequence, we determine that a generalized reaction-diffusion approach is a better fit realistic model.

An analysis on the spatiotemporal behavior of inbound tourists in Jiaodong Peninsula based on Flickr geotagged photos

Exploring the spatiotemporal behavior characteristics of inbound tourists is of great practical significance to the management and planning of attractions. Based on Flickr geotagged photos and metadata, the research analyzes the spatiotemporal behavior characteristics of inbound tourists in Jiaodong Peninsula from the perspective of tourist flow network. We decompose the time series characteristics of tourists with STL decomposition algorithm, and predict the inbound tourism trend through the time series model. We propose the Hot Status Index (HSI) based on network centrality, extract and analyze the distribution pattern of inbound tourism hotspots with the structural hole method. The k-core decomposition algorithm is introduced to analyze the hierarchical characteristics of the tourist flow network. And Fast Unfolding algorithm is adopted to analyze the characteristics of community aggregation under different scales, dividing the scenic area into four communities. The results show that the time series model can accurately estimate the trend of the number of tourists. And there is a “competition” effect among attractions in Jiaodong Peninsula. The attractions show a hierarchy effect, of which the core layer has obvious small-world characteristics, with a clustering coefficient of 0.768. The inter-city tourist flow in Jiaodong Peninsula reveals a closed “multi-triangle” distribution pattern, mainly in the marginal coastal cities. Qingdao old town presents community stability, and other urban communities have a scale effect, mainly comprising two tourism circles.

A noisy neighbour: acoustic competition drives changes in song traits and spatiotemporal behaviour of orthopterans

Competition for acoustic space is key for sound-communicating animals, yet little is known about whether and how it may drive evolutionary pathways, for example signal structure, as well as species distributions or individual spatial behaviour. Here we assessed the occurrence of interspecific acoustic competition in sound-communicating taxa by testing three nonmutually exclusive hypotheses explaining variability of song structure and singing activity, in two sibling species of tree crickets of the genus Oecanthus. By comparing song acoustic parameters, diel activity patterns and relative densities of singing crickets in both sympatry and allopatry, we found that the local co-occurrence of the two species drives changes in all the considered factors of song production. Namely, we report changes in temporal and structural song traits, as well as in singing activity patterns and apparent population density, with strongly asymmetrical effects, only evident for one of the two species considered. Our results highlight the role of acoustic space as a key resource that shapes biotic interactions, and eventually behaviours, in sound-communicating taxa, and represent a replicable approach in the study of acoustic assemblages.

Direct Observation of Nanotracer Transport in Swarming Bacteria during Antibiotic Adaptation.

Microorganisms inevitably encounter environmental variations and thus need to develop necessary strategies to adjust the colonies for survival. Here, we use cooperating Serratia marcescens bacteria to reveal how the whole population responds to a gradually deteriorating habitat. When subjected to antibiotics with increasing doses, the swarming bacteria transform weak homogeneous turbulent flows to nematic jet flows with defects and vortices on a large scale, by which bacteria exploit these coherent flows to transfer material and/or information. We elucidate a complete view of detailed spatiotemporal transport behavior in such microscale active turbulence with single-nanoparticle tracking. The nanoparticles in these active flows are brought into the state with the up limit of superdiffusion by the bacterial collective response to the stronger antibiotic stimulation. Strikingly, we found that, under the strengthening stimulation from antibiotics, bacteria with only a small fraction of their community get elongated and facilitate the drastic turbulence transition and an enhanced superdiffusion. These findings imply a possible collective response mechanism against environmental variations.

Interurban mobility: Eurythmic relations among metropolitan cities monitored by mobile phone data

The main aim of this article is to identify and explain the spatio-temporal pattern of interurban mobility in Czechia. The research is based upon analysis of mobile phone location data. More precisely, the data set about more than 3 million mobile-phone stations from 2019 is analysed to investigate mobility patterns and spatiotemporal behaviour among Prague, Brno, and Ostrava, three major agglomerations of Czechia. To achieve the goal, the paper uses proven concepts from time geography and chronogeography, such as constraints and pacemakers. The results reveal that, firstly, Prague's dominant position in the settlement hierarchy is crucial to mobility rhythms even for long-distance journeys. Secondly, journey purpose and means of transport are also proven to be key pacemakers in intraurban mobility.

Changes in global teleconnection patterns under global warming and stratospheric aerosol intervention scenarios

Abstract. We investigate the potential impact of stratospheric aerosol intervention (SAI) on the spatiotemporal behavior of large-scale climate teleconnection patterns represented by the North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), El Niño–Southern Oscillation (ENSO) and Atlantic Multidecadal Oscillation (AMO) indices using simulations from the Community Earth System Model versions 1 and 2 (CESM1 and CESM2). The leading empirical orthogonal function of sea surface temperature (SST) anomalies indicates that greenhouse gas (GHG) forcing is accompanied by increases in variance across both the North Atlantic (i.e., AMO) and North Pacific (i.e., PDO) and a decrease over the tropical Pacific (i.e., ENSO); however, SAI effectively reverses these global-warming-imposed changes. The projected spatial patterns of SST anomaly related to ENSO show no significant change under either global warming or SAI. In contrast, the spatial anomaly pattern changes pertaining to the AMO (i.e., in the North Atlantic) and PDO (i.e., in the North Pacific) under global warming are effectively suppressed by SAI. For the AMO, the low contrast between the cold-tongue pattern and its surroundings in the North Atlantic, predicted under global warming, is restored under SAI scenarios to similar patterns as in the historical period. The frequencies of El Niño and La Niña episodes modestly increase with GHG emissions in CESM2, while SAI tends to compensate for them. All climate indices' dominant modes of inter-annual variability are projected to be preserved in both warming and SAI scenarios. However, the dominant decadal variability mode changes in the AMO, NAO, and PDO induced by global warming are not suppressed by SAI.

When death comes: linking predator-prey activity patterns to timing of mortality to understand predation risk.

The assumption that activity and foraging are risky for prey underlies many predator-prey theories and has led to the use of predator-prey activity overlap as a proxy of predation risk. However, the simultaneous measures of prey and predator activity along with timing of predation required to test this assumption have not been available. Here, we used accelerometry data on snowshoe hares (Lepus americanus) and Canada lynx (Lynx canadensis) to determine activity patterns of prey and predators and match these to precise timing of predation. Surprisingly we found that lynx kills of hares were as likely to occur during the day when hares were inactive as at night when hares were active. We also found that activity rates of hares were not related to the chance of predation at daily and weekly scales, whereas lynx activity rates positively affected the diel pattern of lynx predation on hares and their weekly kill rates of hares. Our findings suggest that predator-prey diel activity overlap may not always be a good proxy of predation risk, and highlight a need for examining the link between predation and spatio-temporal behaviour of predator and prey to improve our understanding of how predator-prey behavioural interactions drive predation risk.

Evaluating the Effectiveness of Environmental Interpretation in National Parks Based on Visitors’ Spatiotemporal Behavior and Emotional Experience: A Case Study of Pudacuo National Park, China

Problems such as global environmental pollution and climate change have made the public’s desire for nature and closeness to greenery increasingly strong amid rapid urbanization. Improving the ability of experiential environmental interpretation products and services is the basis for national parks to meet the public’s needs, and the evaluation of their effectiveness is a necessary basis for optimizing the quality of environmental interpretation services in response to the current problems of unsynchronized environmental interpretation facilities and service levels. Using Pudacuo National Park as a case study, 365 visitors’ spatio-temporal trajectories with GPS devices and questionnaire data were collected, and the interaction changes of visitors’ external spatiotemporal behaviors and internal emotional experiences were analyzed using cluster analysis, GPS geoprocessing model construction, and emotional mean calculation methods, and the results showed that (1) Pudacuo National Park visitors mainly comprise four types of visitor clusters, which are the sightseeing type, trekking + sightseeing type, cruise type, and hiking + cruise type, as well as four types of spatio-temporal behavior patterns; the differences of visitors’ spatiotemporal behavior patterns are reflected in spatial movement, time allocation, and stopping behavior, and correspond to different emotional experience intensity. (2) Emotional experience value is positively correlated with location stay time, and emotional experience intensity is higher, corresponding to spatio-temporal behavior patterns with longer trajectory distance, longer dwell time, and more stopping behaviors, indicating that environmental services promote longer visitor stopping time and generate high-quality recreation experiences. (3) Finally, we proposed the optimization of environmental interpretation mode according to the spatiotemporal emotional differences of different types of visitor clusters. This study provides a basis for improving the quality of visitor experience and optimizing the quality of environmental interpretation services and provides a useful reference for guiding the construction of high-quality and diverse ecological experiences in national parks.

Spatiotemporal behavior pattern differentiation and preference identification of tourists from the perspective of ecotourism destination based on the tourism digital footprint data.

Tourist impact management in ecotourism destinations requires an accurate description of tourists' spatiotemporal behavior patterns and recreation preferences to minimize the ecological environmental impact and maximize the recreation experience. This study classified tourist behaviors into five typical behavior patterns based on the digital footprints of tourists visiting Ziwuyu of the Qinling Mountains, including 348 traveling tracks and 750 corresponding geotagged photographs: short-distance, traversing, reentrant, large loop, and small loop. Furthermore, each behavior pattern's recreation preference was identified using photograph analysis. Tourists with large-loop and reentrant behavior patterns have 89.8% and 30% chances of visiting Jianshanding, respectively. Key protected areas are faced with the risk of ecological environmental damage. Based on the behavior pattern differentiation and preference of tourists, this paper provides a decision-making basis for the classified management and guidance of tourists in ecotourism destinations. It has reference value for the management of similar ecotourism destinations.

Impact of Microclimate on People’s Experiences and Behaviours in the Cultural Consumption Space: A Case Study of Panjiayuan Antique Market in Beijing, China

Antique and cultural consumption spaces make a great contribution to urban vitality where numerous people walk, stay, and trade. However, how these people’s experiences and behaviours are affected by the microclimate of such spaces has not been studied till now. To address this gap, and using the concept of Post-occupancy Evaluation (POE) as a basis, our study investigated microclimate factors, subjective experiences, and spatiotemporal behaviour patterns in Panjiayuan Antique Market in Beijing, China. Using a mixed-methods approach comprising subjective questionnaires (n = 101), face-to-face interviews (n = 81), spatiotemporal behaviour mapping (n = 8455), and on-site observations, our results showed that microclimates impact people’s experiences and behaviours, with visibility and noise being the two primary impact factors. Most female visitors are more sensitive to the microclimate than male visitors. Furthermore, vendors in the Antique Market preferred to amend their nearby environments to increase the microclimate quality to offer a better experience to visitors around them. This study developed a comprehensive methodology that expands POE in relation to microclimatic factors in the context of cultural consumption spaces. These findings suggest that microclimates have different impacts on people’s experiences and behaviours in different spaces, which should be considered when designing and renewing urban antique markets in the future.

Extreme precipitation return levels for multiple durations on a global scale

Quantifying the magnitude and frequency of extreme precipitation events is key in translating climate observations to planning and engineering design. Past efforts have mostly focused on the estimation of daily extremes using gauge observations. Recent development of high-resolution global precipitation products, now allow estimation of global extremes. This research aims to quantitatively characterize the spatiotemporal behavior of precipitation extremes, by calculating extreme precipitation return levels for multiple durations on the global domain using the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. Both classical and novel extreme value distributions are used to provide insight into the spatial patterns of precipitation extremes. Our results show that the traditional Generalized Extreme Value (GEV) distribution and Peak-Over-Threshold (POT) methods, which only use the largest events to estimate precipitation extremes, are not spatially coherent. The recently developed Metastatistical Extreme Value (MEV) distribution, that includes all precipitation events, leads to smoother spatial patterns of local extremes. For durations of 5 and 10 days, however, there are less events per year to fit the distribution (37 and 22 on average, respectively), leading to larger inter-annual variability and possible overestimation of the extremes. While the GEV and POT methods predict a consistent shift from heavy to thin tails with increasing duration, the MEV method predicts a relatively constant heaviness of the tail for any precipitation duration, opening up an important research question on what is the ‘correct’ tail behavior of extreme precipitation for different durations. The generated extreme precipitation return levels and corresponding parameters are provided as the Global Precipitation EXtremes (GPEX) dataset. These data can be useful for studying the underlying physical processes causing the spatiotemporal variations of the heaviness of extreme precipitation distributions.

Exploring Gender Differences through the Lens of Spatiotemporal Behavior Patterns in a Cultural Market: A Case Study of Panjiayuan Market in Beijing, China

Markets are more than commercial places with high mobility, greatly contributing to urban vitality, social inclusion, and even local identity, which have all been studied extensively. However, as one of the market types, the cultural market contains a highly gendered feature in our contemporary cities that has rarely been explored. Therefore, this article presents a survey that uses spatiotemporal behavior mapping (STBM) to collect gender-related behavior patterns across four sites, five activity layers, four age groups, and three time dimensions in Beijing Panjiayuan cultural market, China. Our results show that all sites are generally dominated by males. Although mobility is higher on weekends than weekdays, the number of women decreases and the number of men increases at the weekend, resulting in a higher gender ratio. The gender ratio in the 19–36 and 54+ year groups synchronized decline with mobility, whereas other age groups did not appear to be significantly affected. More specifically, in the buying and common activities layers, the ratio of men to women at the two sites with higher mobility increased, and vice versa. It is the first study to present the effect of spatial mobility on gender differences in a cultural market by using objective spatiotemporal data. Our findings provide a scientific basis for the optimization and design practice of culture-related spaces to create a more equal and inclusive urban environment.

Identification and Spatiotemporal Analysis of Bikesharing-Metro Integration Cycling

An essential function of dockless bikesharing (DBs) is to serve as a feeder mode to the metro. Optimizing the integration between DBs and the metro is of great significance for improving metro travel efficiency. However, the research on DBs–Metro Integration Cycling (DBsMIC) faces challenges such as insufficient methods for identification and low identification accuracy. In this study, we improve the enhanced two-step floating catchment area and incorporate Bayes’ rule to propose a method to identify DBsMIC by considering the parameters of time, distance, environmental competition ratio, and POI service power index. Furthermore, an empirical study is conducted in Shenzhen to verify the higher accuracy of the proposed method. Their spatiotemporal behavior pattern is also explored with the help of the kernel density estimation method. The research results will help managers improve the effective redistribution of bicycles, promote the coupling efficiency between transportation modes, and achieve sustainable development of urban transportation.

Drifting Faraday patterns under localised driving

Physical systems undergoing spontaneous pattern formation are governed by intrinsic length scales that may compete with extrinsic ones, resulting in exceptional spatiotemporal behaviour. In this work, we report experimental and theoretical evidence that spatial nonuniformity sets Faraday-wave patterns in motion, which are noticeable in the zigzag and drift dynamics exhibited by their wave crests. We provide a minimal theoretical model that succeeds in characterising the growth of localised patterns under nonuniform parametric driving. Furthermore, the derivation accounts for symmetry-breaking nonlinear gradients that we show are the source of the drift mechanism, which comes into play right after the system has crossed a secondary bifurcation point. Numerical solutions of the governing equations match our experimental findings and theoretical predictions. Our results advance the understanding of pattern behaviour induced by nonuniformity in generic nonlinear extended systems far from equilibrium.