Spatio-temporal Trajectories Research Articles

An outline of future-oriented dialectics: Conceptualising dialectical positions, trajectories and processes in the context of futures research

The paper outlines a dialectical approach to analyse societal tendencies by emphasising future tensions, contradictions and antagonisms. First, the theoretical baseline of the approach is a commitment to the basic tenet of dialectical thinking: a perspective of the world as a process and a becoming. Second, the approach focuses on societal and socio-technical change trajectories and various dynamics in between. Third, it is based on systems and complexity thinking, particularly the idea of a societal system consisting of various parallel and contradictory trajectories. Methodologically, the approach emphasises dialectical thinking in two lenses: positions and processes. Dialectical positionality refers to basic dialectical positions as outcomes of the dynamics between the spatiotemporal trajectories in the societal system. Dialectical processuality refers to the processual nature of future-oriented dialectics, that is, the unravelling of the spatio-temporal processes and trajectories that lead to changes in the societal system. Dialectical processes are divided into two dynamic levels: trajectory and system. The paper presents stylised examples of positions and processes on these two levels.

A Long Short-Term Memory-Based Approach for Detecting Turns and Generating Road Intersections from Vehicle Trajectories.

Owing to the widespread use of GPS-enabled devices, sensing road information from vehicle trajectories is becoming an attractive method for road map construction and update. Although the detection of intersections is critical for generating road networks, it is still a challenging task. Traditional approaches detect intersections by identifying turning points based on the heading changes. As the intersections vary greatly in pattern and size, the appropriate threshold for heading change varies from area to area, which leads to the difficulty of accurate detection. To overcome this shortcoming, we propose a deep learning-based approach to detect turns and generate intersections. First, we convert each trajectory into a feature sequence that stores multiple motion attributes of the vehicle along the trajectory. Next, a supervised method uses these feature sequences and labeled trajectories to train a long short-term memory (LSTM) model that detects turning trajectory segments (TTSs), each of which indicates a turn occurring at an intersection. Finally, the detected TTSs are clustered to obtain the intersection coverages and internal structures. The proposed approach was tested using vehicle trajectories collected in Wuhan, China. The intersection detection precision and recall were 94.0% and 91.9% in a central urban region and 94.1% and 86.7% in a semi-urban region, respectively, which were significantly higher than those of the previously established local G* statistic-based approaches. In addition to the applications for road map development, the newly developed approach may have broad implications for the analysis of spatiotemporal trajectory data.

TripleConvTransformer: A deep learning vessel trajectory prediction method fusing discretized meteorological data

The shipping industry is increasingly threatened by global climate change. Reliable trajectory prediction can be used to perceive potential risks and ensure navigation efficiency. However, many existing studies have not fully considered the impact of complex ocean environmental factors and have only focused on local regions, which are difficult to extend to a global scale. To this end, we propose a deep learning vessel trajectory prediction method fusing discretized meteorological data (TripleConvTransformer). First, we clean the automatic identification system data to form a high-quality spatiotemporal trajectory dataset. Then, we fuse the trajectory data with the meteorological data after feature discretization to deeply mine the motion information of ocean-going ships. Finally, we design three modules, the global convolution, local convolution, and trend convolution modules, based on the simplified transformer model to capture multiscale features. We compare TripleConvTransformer with state-of-the-art prediction models. The experimental results show that in the prediction of the trajectory points in the next 90 min, the smallest root mean square error in terms of longitude and latitude and the highest overall prediction accuracy are achieved using TripleConvTransformer. Our method not only fully considers the influence of meteorological factors in the ocean-going process but also effectively extracts the important information hidden in the data, thus achieving accurate trajectory prediction on a global scale.

Mining regular behaviors based on spatiotemporal trajectory multi‐dimensional features

In the military and civilian surveillance domain, it is of great significance to mine regular behaviours of targets for situation awareness and command decision support. Most of the existing trajectory clustering algorithms only consider the similarity of spatial position of the trajectory, without sufficient multi-dimensional information such as time, course and velocity. Some approaches based on information fusion take these multi-dimensional information into account, but the features with different dimensions fused by weight coefficients are not robust and universal for different scenarios. In this paper, a regular behaviour mining method based on spatiotemporal trajectory multi-dimensional features and density clustering is proposed. Firstly, multi-dimensional Hausdorff similarity is defined to measure spatiotemporal trajectory from different feature dimensionalities. Different from methods based on information fusion, the proposed method defines trajectory density in feature similarity of different dimensions and adaptively determines parameters according to feature distribution in different dimensions. Experimental results in simulated and radar measured trajectory data show that the proposed method can be accurate and robust in clustering evaluation indexes such as Purity, Precision, Recall and Rand Index from different scenarios, which has a good application prospect in intelligent surveillance tasks.

Large-scale influence of numerical noises as artificial stochastic disturbances on a sustained turbulence

We investigate the large-scale influence of numerical noises as tiny artificial stochastic disturbances on a sustained turbulence. Using two-dimensional (2-D) turbulent Rayleigh–Bénard convection (RBC) as an example, we solve numerically the Navier–Stokes equations, separately, by means of a traditional algorithm with double precision (denoted RKwD) and the so-called clean numerical simulation (CNS). The numerical simulation given by RKwD is a mixture of the ‘true’ physical solution and the ‘false’ numerical noises that are random and can be regarded as a kind of artificial stochastic disturbances; unfortunately, the ‘true’ physical solution is mostly at the same level as the ‘false’ numerical noises. By contrast, the CNS can greatly reduce the background numerical noise to any a required level so that the ‘false’ numerical noises are negligible compared with the ‘true’ physical solution, thus the CNS solution can be used as a ‘clean’ benchmark solution for comparison. It is found that the numerical noises as tiny artificial stochastic disturbances could indeed lead to large-scale deviations of simulations not only in spatio-temporal trajectories but also even in statistics. In particular, these numerical noises (as artificial stochastic disturbances) even lead to different types of flows. The shearing convection occurs for the RKwD simulations, and its corresponding flow field turns to a kind of zonal flow thereafter; however, the CNS benchmark solution always sustains the non-shearing vortical/roll-like convection during the whole process of simulation. Thus we provide rigorous evidence that numerical noises as a kind of small-scale artificial stochastic disturbances have quantitatively and qualitatively large-scale influences on a sustained turbulence, i.e. the 2-D turbulent RBC considered in this paper.

Study protocol to explore the social effects of environmental exposure and lifestyle behaviours on pregnancy outcome: an overview of cohort of pregnant women study

IntroductionA growing number of international studies have highlighted the adverse consequences of lived experience in the first thousand days of pregnancy and early life on the probability of stillbirth, child...

Spatiotemporal Identification of Cell Divisions Using Symmetry Properties in Time-Lapse Phase Contrast Microscopy

A variety of biological and pharmaceutical studies, such as for anti-cancer drugs, require the quantification of cell responses over long periods of time. This is performed with time-lapse video microscopy that gives a long sequence of frames. For this purpose, phase contrast imaging is commonly used since it is minimally invasive. The cell responses of interest in this study are the mitotic cell divisions. Their manual measurements are tedious, subjective, and restrictive. This study introduces an automated method for these measurements. The method starts with preprocessing for restoration and reconstruction of the phase contrast time-lapse sequences. The data are first restored from intensity non-uniformities. Subsequently, the circular symmetry of the contour of the mitotic cells in phase contrast images is used by applying a Circle Hough Transform (CHT) to reconstruct the entire cells. The CHT is also enhanced with the ability to “vote” exclusively towards the center of curvature. The CHT image sequence is then registered for misplacements between successive frames. The sequence is subsequently processed to detect cell centroids in individual frames and use them as starting points to form spatiotemporal trajectories of cells along the positive as well as along the negative time directions, that is, anti-causally. The connectivities of different trajectories enhanced by the symmetry of the trajectories of the daughter cells provide as topological by-products the events of cell divisions together with the corresponding entries into mitoses as well as exits from cytokineses. The experiments use several experimental video sequences from three different cell lines with many cells undergoing mitoses and divisions. The quantitative validations of the results of the processing demonstrate the high performance and efficiency of the method.

User‐centered road network traffic analysis with MobilityDB

AbstractPerformance indicators of road networks are a long‐lasting topic of research. Existing schemes assess network properties such as the average speed on road segments and the queuing time at intersections. The increasing availability of user trajectories, collected mainly using mobile phones with a variety of applications, creates opportunities for developing user‐centered performance indicators. Performing such an analysis on big trajectory data sets remains a challenge for the existing data management systems, because they lack support for spatiotemporal trajectory data. This article presents an end‐to‐end solution, based on MobilityDB, a novel moving object database system that extends PostgreSQL with spatiotemporal data types and functions. A new class of indicators is proposed, focused on the users' experience. The indicators address the network design, the traffic flow, and the driving comfort of the motorists. Furthermore, these indicators are expressed as analytical MobilityDB queries over a big set of real vehicle trajectories.

Intraneuronal sortilin aggregation relative to granulovacuolar degeneration, tau pathogenesis and sorfra plaque formation in human hippocampal formation.

Extracellular β-amyloid (Aβ) deposition and intraneuronal phosphorylated-tau (pTau) accumulation are the hallmark lesions of Alzheimer’s disease (AD). Recently, “sorfra” plaques, named for the extracellular deposition of sortilin c-terminal fragments, are reported as a new AD-related proteopathy, which develop in the human cerebrum resembling the spatiotemporal trajectory of tauopathy. Here, we identified intraneuronal sortilin aggregation as a change related to the development of granulovacuolar degeneration (GVD), tauopathy, and sorfra plaques in the human hippocampal formation. Intraneuronal sortilin aggregation occurred as cytoplasmic inclusions among the pyramidal neurons, co-labeled by antibodies to the extracellular domain and intracellular C-terminal of sortilin. They existed infrequently in the brains of adults, while their density as quantified in the subiculum/CA1 areas increased in the brains from elderly lacking Aβ/pTau, with pTau (i.e., primary age-related tauopathy, PART cases), and with Aβ/pTau (probably/definitive AD, pAD/AD cases) pathologies. In PART and pAD/AD cases, the intraneuronal sortilin aggregates colocalized partially with various GVD markers including casein kinase 1 delta (Ck1δ) and charged multivesicular body protein 2B (CHMP2B). Single-cell densitometry established an inverse correlation between sortilin immunoreactivity and that of Ck1δ, CHMP2B, p62, and pTau among pyramidal neurons. In pAD/AD cases, the sortilin aggregates were reduced in density as moving from the subiculum to CA subregions, wherein sorfra plaques became fewer and absent. Taken together, we consider intraneuronal sortilin aggregation an aging/stress-related change implicating protein sorting deficit, which can activate protein clearance responses including via enhanced phosphorylation and hydrolysis, thereby promoting GVD, sorfra, and Tau pathogenesis, and ultimately, neuronal destruction and death.

Multimodal and multicontrast image fusion via deep generative models

Recently, it has become progressively more evident that classic diagnostic labels are unable to accurately and reliably describe the complexity and variability of several clinical phenotypes. This is particularly true for a broad range of neuropsychiatric illnesses such as depression and anxiety disorders or behavioural phenotypes such as aggression and antisocial personality. Patient heterogeneity can be better described and conceptualized by grouping individuals into novel categories, which are based on empirically-derived sections of intersecting continua that span both across and beyond traditional categorical borders. In this context, neuroimaging data (i.e. the set of images which result from functional/metabolic (e.g. functional magnetic resonance imaging, functional near-infrared spectroscopy, or positron emission tomography) and structural (e.g. computed tomography, T1-, T2- PD- or diffusion weighted magnetic resonance imaging) carry a wealth of spatiotemporally resolved information about each patient's brain. However, they are usually heavily collapsed a priori through procedures which are not learned as part of model training, and consequently not optimized for the downstream prediction task. This is due to the fact that every individual participant usually comes with multiple whole-brain 3D imaging modalities often accompanied by a deep genotypic and phenotypic characterization, hence posing formidable computational challenges.In this paper we design and validate a deep learning architecture based on generative models rooted in a modular approach and separable convolutional blocks (which result in a 20-fold decrease in parameter utilization) in order to a) fuse multiple 3D neuroimaging modalities on a voxel-wise level, b) efficiently convert them into informative latent embeddings through heavy dimensionality reduction, c) maintain good generalizability and minimal information loss. As proof of concept, we test our architecture on the well characterized Human Connectome Project database (n = 974 healthy subjects), demonstrating that our latent embeddings can be clustered into easily separable subject strata which, in turn, map to different phenotypical information (including organic, neuropsychological, personality variables) which was not included in the embedding creation process.The ability to extract meaningful and separable phenotypic information from brain images alone can aid in creating multi-dimensional biomarkers able to chart spatio-temporal trajectories which may correspond to different pathophysiological mechanisms unidentifiable to traditional data analysis approaches. In turn, this may be of aid in predicting disease evolution as well as drug response, hence supporting mechanistic disease understanding and also empowering clinical trials.

A graph based approach for mining significant places in trajectory data

Significant place mining in spatiotemporal trajectory data is a key task for mobile pattern mining, useful for supporting location-aware services. State-of-the-art trajectory clustering algorithms utilize a density-based distance measure. However, some major problems with this approach are that (1) results are often inaccurate, especially on data of varying density, (2) the user must fine-tune many thresholds that are unintuitive to set, and (3) boundary points between clusters are often assigned to the wrong locations. Performance is also a major issue as many state-of-the-art algorithms have a very high time complexity. Motivated by these issues, this paper proposes an approach inspired by the data field theory and community detection. It is a graph-based significant place mining algorithm, called GB-SPM, for capturing and characterizing the essence of similarity between nodes. GB-SPM first applies a novel low index neighborhood velocity point filtration method to extract characteristic points. Then, a characteristic point index neighborhood is used to map them to graph nodes. In this way, the original problem is transformed into a community detection problem in complex community networks. Finally, a new edge weight metric is proposed to capture and characterize the nature of similarity between nodes. To evaluate clustering quality, we used the Silhouette (SI) for unannotated data to value inter-cluster separation and intra-cluster homogeneity. To evaluate mining effectiveness, we used Matthew’s correlation coefficient (MCC) for annotated data. Numerous experiments were carried out on real world datasets, and the accuracy and performance of the designed algorithm was compared with the state-of-the-art algorithms. Results show that GB-SPM improves on average SI by 13.9%, MCC by 20.7%, and runtime by 5.15 times.

Transforming Gait: Video-Based Spatiotemporal Gait Analysis.

Human pose estimation from monocular video is a rapidly advancing field that offers great promise to human movement science and rehabilitation. This potential is tempered by the smaller body of work ensuring the outputs are clinically meaningful and properly calibrated. Gait analysis, typically performed in a dedicated lab, produces precise measurements including kinematics and step timing. Using over 7000 monocular video from an instrumented gait analysis lab, we trained a neural network to map 3D joint trajectories and the height of individuals onto interpretable biomechanical outputs including gait cycle timing and sagittal plane joint kinematics and spatiotemporal trajectories. This task specific layer produces accurate estimates of the timing of foot contact and foot off events. After parsing the kinematic outputs into individual gait cycles, it also enables accurate cycle-by-cycle estimates of cadence, step time, double and single support time, walking speed and step length.

Geographic information science in the era of geospatial big data: A cyberspace perspective

Geographic information science in the era of geospatial big data: A cyberspace perspective

DIGITAL ELEVATION MODELS FOR VEGETATION MONITORING: A CASE STUDY OF A FOREST DISTRICT IN POLAND

Abstract. Monitoring vegetation cover is one of the prime aims of remote sensing. Spatio-temporal trajectories of the vegetation state are key findings used by various branches of sciences for research and planning of human activities purposes. Among available vegetation monitoring methods, including forests, are multispectral and microwave indices. However, there are other ways to monitor vegetation/forest cover using surveys performed from satellite orbit. These approaches, including LiDAR and InSAR-derived digital elevation data products (further DEMs), are relatively less discussed in the literature on forest monitoring. Here, we present the results of a study of forest monitoring using the difference between two DEMs captured approximately 15 years apart. DEMs involved are the Shuttle RadarTopography Mission (SRTM) and Copernicus. The study area is located in a forestry district in the southwestern part of Poland. Spatial constraints and forest stand age, and dominant species were available from a forest stand map of the district. The study concludes that DEMs difference can be used for preliminary forest change assessment.

Quantitative Evaluation of Psychological Tolerance under the Haze: A Case Study of Typical Provinces and Cities in China with Severe Haze.

The interplay of specific weather conditions and human activity results due to haze. When the haze arrives, individuals will use microblogs to communicate their concerns and feelings. It will be easier for municipal administrators to alter public communication and resource allocation under the haze if we can master the emotions of netizens. Psychological tolerance is the ability to cope with and adjust to psychological stress and unpleasant emotions brought on by adversity, and it can guide human conduct to some extent. Although haze has a significant impact on human health, environment, transportation, and other factors, its impact on human mental health is concealed, indirect, and frequently underestimated. In this study, psychological tolerance was developed as a psychological impact evaluation index to quantify the impact of haze on human mental health. To begin, data from microblogs in China’s significantly haze-affected districts were collected from 2013 to 2019. The emotion score was then calculated using SnowNLP, and the subject index was calculated using the co-word network approach, both of which were used as social media evaluation indicators. Finally, utilizing ecological and socioeconomic factors, psychological tolerance was assessed at the provincial and prefecture level. The findings suggest that psychological tolerance differs greatly between areas. Psychological tolerance has a spatio-temporal trajectory in the timeseries as well. The findings offer a fresh viewpoint on haze’s mental effects.

GsstSIM: A high‐performance and synchronized similarity analysis method of spatiotemporal trajectory based on grid model representation

AbstractExisting spatiotemporal similarity analysis methods for trajectories have the problems of spatiotemporal unsynchronization and low efficiency in processing large‐scale datasets, which cannot satisfy the increasingly urgent requirements of real‐time or quasi‐real‐time applications. To address these problems, this article proposes a grid‐based and synchronized spatiotemporal similarity analysis method based on the spatiotemporal grid model called gsstSIM. First, a low‐dimensional and multi‐scale trajectory coding representation is implemented based on the spatiotemporal grid model. Second, a synchronized spatiotemporal similarity measure is proposed based on trajectory codes. It transforms the similarity analysis from complex geometric calculations to simple algebraic operations of code sets, which reduces the computational complexity. In addition, the trajectory encoding representation with space‐time collinearity enables gsstSIM to measure the synchronized spatiotemporal similarity. Third, the efficient Multi‐scale grid index, called MSGrid, is established to realize fast query of top‐K similar trajectories for large‐scale datasets. Experimental results demonstrate that gsstSIM is more robust to noise positioning points and various sampling rates than the state‐of‐the‐art algorithms STLCSS, TWS and SWS. It can achieve a second‐level response of spatiotemporal similarity query in processing large‐scale datasets, which is much faster than existing algorithms. The proposed method has promising to support the applications with high time‐efficiency requirements such as epidemic tracking and traffic condition calculation.

Tai Chi Movement Recognition Method Based on Deep Learning Algorithm

The current action recognition method has good effect when applied to static recognition, but, when applied to dynamic action sequence recognition, the temporal and spatial feature segmentation is too dependent on sample template, resulting in low recognition accuracy. To address the inadequacies of standard movement detection techniques in the application of comparable domains, a deep learning algorithm is utilised to recognise Tai Chi Chuan motions. For Tai Chi Chuan movement human body skeleton framework, add image depth parameter is added, and OpenPose model is utilised to estimate joint point coordinates. The ST-GCN deep learning model was created to recognise Tai Chi Chuan motions by extracting movement features from the spatiotemporal trajectory of human joints during Tai Chi Chuan movements. Instance test results show that rate of using the deep learning algorithm of gesture recognition is 89.22%, with significantly lower error detection rate, which is good for Tai chi chuan movement recognition effect.

Automatic Mining Method of Group Behavior Patterns Based on Incremental Spatiotemporal Trajectory Big Data

There is a problem of unclear group clustering in group behavior pattern mining, which leads to a long mining time. An automatic group behavior pattern mining method based on incremental spatiotemporal trajectory big data is proposed. The grid sequence of each road segment and the road segment information included in each grid are obtained using the group behavior pattern trajectory network. Using incremental trajectory data, the properties of incremental spatiotemporal trajectory big data are retrieved, and the group behavior pattern is grouped. In the obtained class, all data element records are categorized according to their data elements. Multiple attribute dimensions, such as data definition, limitations, and feature words, are used to standardize the spatiotemporal trajectory data pieces. To complete the autonomous mining of group behavior patterns, all subsequences are visited, computed, and compared. The test results show that when the group size threshold is 20, the running time of the group behavior pattern automatic mining method based on incremental spatiotemporal trajectory big data is 311.66, which is 141.29 s and 148.66 s shorter than that based on DBSCAN and K-means, respectively. Therefore, this method has higher execution efficiency.

Spatio-temporal trajectory evolution and cause analysis of air pollution in Chengdu, China

ABSTRACT This study comprehensively analyzed air pollution in Chengdu (CD), a megacity in southwest China, evaluated the Variation Characteristics of air quality during 2015–2018, and conducted Random Forest classification of air pollution data of 2017. The classification results showed three pollution periods: severe (December, January and February), ozone (May‒August), and slight (March and November). These features were combined with potential source contribution function (PSCF), concentration weighted trajectory (CWT) and backward trajectory model (HYSPLIT) for simulating spatio-temporal trajectory of air polluted during each pollution periods. The results show that PM2.5 mainly comes from CD and surrounding cities, and some may be from India, Myanmar and Chongqing; PM10 mainly comes from CD and surrounding cities and some may be from India and Myanmar; NO2 mainly comes from CD and surrounding cities and cities and Some of the pollution may come from the input of India, Myanmar, Chongqing and Inner Mongolia; O3 mainly comes from the urban agglomeration of Sichuan Basin and some areas from Chongqing, Sichuan Liangshan and Yunnan Guizhou. Combined with the meteorological data of temperature, relative humidity and wind speed, aerosol optical depth, planetary boundary layer height and thermal anomaly data, the Monthly, daily and hourly spatio-temporal characteristics and the possible occurred cause of the main air pollution during each pollution period in CD were revealed detail. The research in this paper is critical for pollution control and prevention and provides a scientific basis for studying the spatio-temporal characteristics and sources of pollution in megacities in terrain such as basins and mountains. Implications: Air pollution has a significant impact on human and ecological health. In 2013, Chengdu was one of the five cities with the most serious PM2.5 pollution in the world. In the previous study of air pollution in Chengdu, it was only for a short period of pollution. It is impossible to fully understand the spatio-temporal trajectory and cause of air pollution. Chengdu is surrounded by mountains, and the meteorological conditions have been stagnant for a long time. The research on the spatio-temporal evolution of the main air pollution trajectories in each pollution period in Chengdu is particularly important. Quantifying the pollution trajectory and air pollution concentration is helpful to fully understand the air quality in Chengdu. The comprehensive analysis of multi-source data such as air pollution and meteorology has focused on strengthening the in-depth research on the transmission law of air pollution, the spatio-temporal change trend of air pollution, the sources of air pollution and the causes of air pollution, so as to help people fully understand the sources and causes of pollution in Chengdu. Aiming at the trajectory law, causes and occurrence time of air pollution, it is conducive for the government to formulate corresponding policies, carry out regional emission reduction and joint prevention and control, improve air quality and minimize the harm of air pollution to the public.

Visual Analysis of Vessel Behaviour Based on Trajectory Data: A Case Study of the Yangtze River Estuary

The widespread of shipborne Automatic Identification System (AIS) equipment will continue to produce a large amount of spatiotemporal trajectory data. In order to explore and understand the hidden behaviour patterns in the data, an interactive visual analysis method combining multiple views is proposed. The method mainly includes four parts: using a trajectory compression algorithm that takes into account the vessel motion characteristics to preprocess the vessel trajectory data; displaying and replaying vessel trajectories based on Electronic Chart System (ECS), and proposing a detection algorithm for vessel stay points based on the principle of spatiotemporal density to semantically label vessel trajectories; using the Fast Dynamic Time Warping (FastDTW) similarity measurement algorithm and the Ordering Points to Identify the Clustering Structure (OPTICS) clustering algorithm to cluster vessel trajectories to show the differences and similarities between vessel traffic flows; and showing the distribution of vessels and the variation trend of vessel density based on the vessel heatmap. Based on the AIS data of the Yangtze River Estuary, three cases are used to prove the usefulness and effectiveness of the system in vessel behaviour analysis.