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In order to ensure the accuracy of oilfield development dynamic data, the author starts from analyzing the characteristics of development dynamic data, and conducts in-depth research on the characteristics of development dynamic data, the algorithm set for accuracy detection of development dynamic data, and comprehensive analysis methods. Firstly, in response to the spatiotemporal heterogeneity in developing dynamic data, combined with the design concept of a multi detector combination algorithm based on spatiotemporal mixed patterns, the accuracy detection algorithm is evaluated and selected. Based on this, the author proposes a development dynamic data accuracy detection method that considers the influence of multiple factors (FAGTN); Secondly, ARIMA, MGLN, STGCN, and FAGTN algorithms were selected as the algorithm sets for developing dynamic data accuracy detection, in order to complete the data accuracy detection based on monthly oil well data as the research object; Then, a combined weighting based analysis method was proposed to comprehensively analyze the accuracy detection results of dynamic data development, and the results showed: The dynamic data accuracy detection method based on ARIMA has the worst performance, with detection accuracy below 70% in different detection attributes, which is relatively not high enough; The development of dynamic data accuracy detection method based on MGLN achieved an accuracy rate of 80.53% when detecting sleeve pressure, but the accuracy rate did not reach 80% when detecting oil pressure, dynamic liquid level, monthly oil and water production, and the detection effect was relatively unstable; The accuracy of developing dynamic data accuracy detection methods based on STGCN fluctuates around 80%; Realize comprehensive evaluation of detection results; Finally, the experiment and evaluation of the comprehensive detection method for developing dynamic data accuracy were completed using real sample data. [ABSTRACT FROM AUTHOR]

When various urban functions are integrated into one location, they form a mixture of functions. The emerging big data promote an alternative way to identify mixed functions. However, current methods are largely unable to extract deep features in these data, resulting in low accuracy. In this study, we focused on recognizing mixed urban functions from the perspective of human activities, which are essential indicators of functional areas in a city. We proposed a framework to comprehensively extract deep features of human activities in big data, including activity dynamics, mobility interactions, and activity semantics, through representation learning methods. Then, integrating these features, we employed fuzzy clustering to identify the mixture of urban functions. We conducted a case study using taxi flow and social media data in Beijing, China, in which five urban functions and their correlations with land use were recognized. The mixture degree of urban functions in each location was revealed, which had a negative correlation with taxi trip distance. The results confirmed the advantages of our method in understanding mixed urban functions by employing various representation learning methods to comprehensively depict human activities. This study has important implications for urban planners in understanding urban systems and developing better strategies.

Understanding human periodic behaviors is crucial in many applications. Existing research has shown the existence of periodicity in human behaviors, but has achieved limited success in leveraging location periodicity and obtaining satisfactory accuracy for oscillations in human periodic behaviors. In this article, we propose the Mobility Intention and Relative Entropy (MIRE) model to address these challenges. We employ tensor decomposition to extract mobility intentions from spatiotemporal datasets, thereby revealing hidden structures in users' historical records. Subsequently, we utilize subsequences associated with the same mobility intention to mine human periodic behaviors. Furthermore, we introduce a novel periodicity detection algorithm based on relative entropy. Our experimental results, conducted on real-world datasets, demonstrate the effectiveness of the MIRE model in accurately uncovering human periodic behaviors. Comparative analysis further reveals that the MIRE model significantly outperforms baseline periodicity detection algorithms. [ABSTRACT FROM AUTHOR]

The prediction of wind in terms of both wind speed and direction, which has a crucial impact on many real-world applications like aviation and wind power generation, is extremely challenging due to the high stochasticity and complicated correlation in the weather data. Existing methods typically focus on a sub-set of influential factors and thus lack a systematic treatment of the problem. In addition, fine-grained forecasting is essential for efficient industry operations, but has been less attended in the literature. In this work, we propose a novel data-driven model, multi-horizon spatiotemporal network (MHSTN), generally for accurate and efficient fine-grained wind prediction. MHSTN integrates multiple deep neural networks targeting different factors in a sequence-to-sequence (Seq2Seq) backbone to effectively extract features from various data sources and produce multi-horizon predictions for all sites within a given region. MHSTN is composed of four major modules. First, a temporal module fuses coarse-grained forecasts derived by numerical weather prediction (NWP) and historical on-site observation data at stations so as to leverage both global and local atmospheric information. Second, a spatial module exploits spatial correlation by modeling the joint representation of all stations. Third, an ensemble module weighs the above two modules for final predictions. Furthermore, a covariate selection module automatically choose influential meteorological variables as initial input. MHSTN is already integrated into the scheduling platform of one of the busiest international airports of China. The evaluation results demonstrate that our model outperforms competitors by a significant margin. [ABSTRACT FROM AUTHOR]

Urban vibrancy describes the prosperity, diversity, and accessibility of urban areas, which is vital to a city's socio-economic development and sustainability. While many efforts have been made for statically measuring and evaluating urban vibrancy, there are few studies on the evolutionary process of urban vibrancy, yet we know little about the relationship between urban vibrancy evolution and sophisticated spatiotemporal dynamics. In this article, we make use of multi-sourced urban data to develop a data-driven framework, U-Evolve, to investigate urban vibrancy evolution. Specifically, we first exploit the spatiotemporal characteristics of urban areas to create multi-view time-dependent graphs. Then, we analyze the contextual features and graph patterns of multi-view time-dependent graphs in terms of informing future urban vibrancy variations. Our analysis validates the informativeness of multi-view time-dependent graphs for characterizing and informing future urban vibrancy evolution. After that, we construct a feature based model to forecast future urban vibrancy evolution and quantify each feature's importance. Moreover, to further enhance the forecasting effectiveness, we propose a graph learning based model to capture spatiotemporal autocorrelation of urban areas based on multi-view time-dependent graphs in an end-to-end manner. Finally, extensive experiments on two metropolises, Beijing and Shanghai, demonstrate the effectiveness of our forecasting models. The U-Evolve framework has also been deployed in the production environment to deliver real-world urban development and planning insights for various cities in China. [ABSTRACT FROM AUTHOR]

Spatiotemporal companion pattern (STCP) mining is one of the means to identify and detect group behavioral activities. To detect the spatiotemporal traveling pattern of ships from massive spatiotemporal trajectory data and to understand the movement law of group ships, this article proposes a feature-driven approach for STCP mining that consists of (1) generating the grid index via the rasterizing of geospace and characterizing trajectory points via the spatiotemporal trajectory grid sequences (STTGSs) of ships; (2) designing filtering rules with the constraints of range, time and distance to construct a candidate set for ship STCP mining; and (3) measuring the STTGS similarity of the associated ships and setting the confidence threshold to realize spatiotemporal companion mining. The effectiveness of the proposed method is practically validated on a real trajectory dataset which is collected from the Taiwan Strait waters. The experimental results are as follows: 825 pairs of associated ships and 225 pairs of accompanying ships are mined when the grid size is 0.05° and the confidence is 0.5. Larger grid sizes can increase the inclusiveness of the associated ship trajectory similarity measurement, which can result in an increase in confidence of pattern. A large number of pseudo-accompaniment ships are extracted to the result set, resulting in a more dispersed distribution of pattern confidence. By verifying the proposed method, accompanying behavioral activities such as ship cooperative operation, companion navigation method, and so on, can be detected. These results can provide a reference for the research of ship group behavior identification and have an important application value for water transportation management. [ABSTRACT FROM AUTHOR]

Drought is the most widespread natural disaster in the world. How to monitor regional drought scientifically and accurately has become a hot topic for many scholars. In this paper, Geographically Integrated Dryness Index (GIDI) was integrated from an assortment source including Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), Vegetation Condition Index (VCI), and Standardized Precipitation Evapotranspiration Index (SPEI) (as the dependent variable) based on geographically weighted regression method. Besides, the comprehensive drought situation and changing trends in China from 2001 to 2019 were also examined. The results showed that (1) GIDI has excellent performance in monitoring medium- and long-term droughts and the monitoring results shows 2003, 2016, and 2019 were relatively wet years, while 2007, 2009, and 2011 were major drought years, and spring and March were the most frequent droughts season and month, respectively, and (2) except for the middle and upper reaches of the Yellow River and Northeastern China, which have a tendency to become wet, other places have a tendency to fluctuating dry. This study took advantage of simple and efficient methods to integrate existing indices to obtain a new index for monitoring a wider range of droughts, taking into account the physical mechanism of drought formation and the time scale of drought development, so it can scientifically evaluate the spatial and temporal distribution characteristics of drought and changes. [ABSTRACT FROM AUTHOR]

When various urban functions are integrated into one location, they form a mixture of functions. The emerging big data promote an alternative way to identify mixed functions. However, current methods are largely unable to extract deep features in these data, resulting in low accuracy. In this study, we focused on recognizing mixed urban functions from the perspective of human activities, which are essential indicators of functional areas in a city. We proposed a framework to comprehensively extract deep features of human activities in big data, including activity dynamics, mobility interactions, and activity semantics, through representation learning methods. Then, integrating these features, we employed fuzzy clustering to identify the mixture of urban functions. We conducted a case study using taxi flow and social media data in Beijing, China, in which five urban functions and their correlations with land use were recognized. The mixture degree of urban functions in each location was revealed, which had a negative correlation with taxi trip distance. The results confirmed the advantages of our method in understanding mixed urban functions by employing various representation learning methods to comprehensively depict human activities. This study has important implications for urban planners in understanding urban systems and developing better strategies. [ABSTRACT FROM AUTHOR]

Global warming has caused the Arctic Ocean ice cover to shrink. This endangers the environment but has made traversing the Arctic channel possible. Therefore, the strategic position of the Arctic has been significantly improved. As a near-Arctic country, China has formulated relevant policies that will be directly impacted by changes in the international relations between the eight Arctic countries (regions). A comprehensive and real-time analysis of the various characteristics of the Arctic geographical relationship is required in China, which helps formulate political, economic, and diplomatic countermeasures. Massive global real-time open databases provide news data from major media in various countries. This makes it possible to monitor geographical relationships in real-time. This paper explores key elements of the social development of eight Arctic countries (regions) over 2013–2019 based on the GDELT database and the method of labeled latent Dirichlet allocation. This paper also constructs the national interaction network and identifies the evolution pattern for the relationships between Arctic countries (regions). The following conclusions are drawn. (1) Arctic news hotspot is now focusing on climate change/ice cap melting which is becoming the main driving factor for changes in geographical relationships in the Arctic. (2) There is a strong correlation between the number of news pieces about ice cap melting and the sea ice area. (3) With the melting of the ice caps, the social, economic, and military activities in the Arctic have been booming, and the competition for dominance is becoming increasingly fierce. In general, there is a pattern of domination by Russia and Canada. [ABSTRACT FROM AUTHOR]

The accurate trajectory of the train ahead with more dynamic behaviour, such as train position, speed, acceleration, etc., is the critical issue of virtual coupling for future railways, which can drastically reduce their headways and increase line capacity. This paper presents an integrated convolutional neural network (CNN) and long short-term memory (LSTM) hybrid model for the task of trajectory prediction. A CNN–LSTM hybrid algorithm has been proposed. The model employs CNN and LSTM to extract the spatial dimension feature of the trajectory and the long-term dependencies of train trajectory data, respectively. The proposed CNN–LSTM model has superiority in achieving collaborative data mining on spatiotemporal measurement data to simultaneously learn spatial and temporal features from phasor measurement unit data. Therefore, the high-precision prediction of the train trajectory prediction is achieved based on the sufficient fusion of the above features. We use real automatic train operation (ATO) collected data for experiments and compare the proposed method with recurrent neural networks (RNN), recurrent neural networks (GRU), LSTM, and stateful-LSTM models on the same data sets. Experimental results show that the prediction performance of long-term trajectories is satisfyingly accurate. The root mean square error (RMSE) error can be reduced to less than 0.21 m, and the hit rate achieves 93% when the time horizon increases to 4S, respectively. [ABSTRACT FROM AUTHOR]

Traffic accident has become a significant health and development threat with rapid urbanizations. An accurate urban accident forecasting enables higher-quality police force pre-allocation and safe route planning for both traffic administrations and travelers, maximumly reducing injuries and damages. Off-the-shelf short-term accident forecasting methods, which focus on modeling static region-wise correlations with existing neural networks, mostly performed on hour levels and with single step. However, given the dynamic nature of road networks and expanding urban areas, it is challenging when the spatiotemporal granularity of forecasting improves as the rareness of accident records and complexity of long-term future dependencies. To address these challenges, we propose a unified framework RiskSeq, to foresee sparse urban accidents with finer granularities and multiple steps in spatiotemporal perspective. In particular, we design region-wise proximity measurements and temporal feature differential operations, and embed them into a novel Differential Time-varying Graph Convolution Network to dynamically capture traffic variations. Considering the hierarchical spatial dependencies and obvious context influences, a hierarchical sequence learning structure is devised by introducing contextual factors into a step-wise decoder. The multi-scale spatial risks are learned jointly to boost the risk predictions based on risk-gather and risk-assign networks. Extensive experiments demonstrate our RiskSeq can increase 5 to 15 percent performances on two datasets. [ABSTRACT FROM AUTHOR]

Regarded as one of the most dangerous types of natural disaster, tropical cyclones threaten the life and health of human beings and often cause enormous economic loss. However, intensity forecasting of tropical cyclones, especially rapid intensification forecasting, remains a scientific challenge due to limited understanding regarding the intensity change process. We propose an automatic knowledge discovery framework to identify potential spatiotemporal precursors to tropical cyclone rapid intensification from a set of tropical cyclone environmental fields. Specifically, this framework includes (1) formulating RI and non-RI composite environmental fields from historical tropical cyclones using NASA MERRA2 data; (2) utilizing the shared nearest neighbor-based clustering algorithm to detect regions representing relatively homogeneous behavior around tropical cyclone centers; (3) determining candidate precursors from significantly different regions in RI and non-RI groups using a spatiotemporal statistical method; and (4) comparing candidates to existing predictors to select potential precursors. The proposed knowledge discovery framework is applied separately to different factors, including 200 hPa zonal wind, 850–700 hPa relative humidity, and 850–200 hPa vertical shear, to detect potential precursors. Compared to the existing predictors manually labeled, i.e., U200 and U20C, RHLO, and SHRD in the Statistical Hurricane Intensity Prediction Scheme, our automatically discovered precursors have a comparable or better capability for estimating the probability of rapid intensification. [ABSTRACT FROM AUTHOR]

The emerging paradigm of the modern city demands big data analytics in urban studies. Data mining techniques for big data have the potential to ascertain strategic insights for current and future cities. Clustering is increasingly growing in data mining, serving as a knowledge discovery tool. This article proposes a spatiotemporal clustering method, called spatiotemporal data-adaptive clustering (STDAC), to cope with clustering challenges, such as feature type, parameter setting, and treating spatial and temporal dimensions as equals. The proposed algorithm takes a dual-structure approach to obtain high-quality clusters and discover temporal changes not detected by other techniques. STDAC refers to the feature type and uses k-fold cross-validation (KCV) to replace the user-defined parameters with the data-driven values. The data-driven threshold from KCV performs as the endogenous variable, thus not requiring a priori assumptions or parameter settings. This article used the bus, taxi passengers, and de facto population data as illustrative case studies. The clustering performances were evaluated based on validation indexes, such as the Davies–Bouldin index (DBI) and Dunn index (DI), where STDAC generally had lower DBI and greater DI. The findings showed that STDAC could yield better clustering performances than other established algorithms. Hence, the proposed method would be promising to future studies requiring spatiotemporal big data analytics in urban studies, administrative management, and other fields. [ABSTRACT FROM AUTHOR]

Summary: Structural health monitoring (SHM) has been widely employed to reveal the hidden safety information and to diagnose the safety status in dam engineering fields. As one of the most important parameters of SHM, crack opening displacement (COD) is often used to evaluate the cracks or joints of concrete dams. In this paper, a new dam health analytic perspective is introduced by integrating the data mining method into SHM field, focusing on revealing the association rules in COD monitoring data. The association rules are investigated systematically, considering the cause–effect relations between external loads and structural response, the temporal characteristics of time series for a single sensor, the spatial characteristics of monitoring data for multisensors, and the abnormal characteristics for different items of structural responses. The association relation is quantified by proposing the quantitative indexes, including support degree, confidence degree, and promotion degree. The methods are used in the COD monitoring data of the Baishan concrete gravity‐arch dam, which is located in a severely cold area in northeastern China. Results show that 4 out of 24 cause–effect association rules are extracted by calculating the association degree of monitored COD values, and 21 out of 24 crack sensors present a temporal association relationship, among which the confidence degree of two sensors reaches 100%. The variation trend of COD values is relevant with the locations of the crack sensors. These results are consistent with the dam safety monitoring theories and models, which would be very useful for extracting the SHM information between different sensors, predicting the trend of COD value and repairing the monitoring data series of COD sensors, or even for discovering an abnormal signal for the operation safety of dams. [ABSTRACT FROM AUTHOR]

Trajectory classification is a hot topic in the field of spatiotemporal data mining. Existing models exert spatial or temporal computation on trajectory data, which require huge efforts and are often time consuming and lack of efficiency. This article proposes a model to classify unknown ship trajectories through a syntax recognition approach. By using the background semantic information in the rasterized sea chart, the model transforms the ship trajectories into symbolic sentences containing both spatiotemporal and semantic information, and reduces their scale. The class feature is expressed as a context-free grammar and the data classification is implemented through syntax parsing. The parsing requires less computation and is more efficient. Experiments are carried out to verify the model's practicability, and the results show that it is valid and effective.

Spatiotemporal companion pattern (STCP) mining is one of the means to identify and detect group behavioral activities. To detect the spatiotemporal traveling pattern of ships from massive spatiotemporal trajectory data and to understand the movement law of group ships, this article proposes a feature-driven approach for STCP mining that consists of (1) generating the grid index via the rasterizing of geospace and characterizing trajectory points via the spatiotemporal trajectory grid sequences (STTGSs) of ships; (2) designing filtering rules with the constraints of range, time and distance to construct a candidate set for ship STCP mining; and (3) measuring the STTGS similarity of the associated ships and setting the confidence threshold to realize spatiotemporal companion mining. The effectiveness of the proposed method is practically validated on a real trajectory dataset which is collected from the Taiwan Strait waters. The experimental results are as follows: 825 pairs of associated ships and 225 pairs of accompanying ships are mined when the grid size is 0.05° and the confidence is 0.5. Larger grid sizes can increase the inclusiveness of the associated ship trajectory similarity measurement, which can result in an increase in confidence of pattern. A large number of pseudo-accompaniment ships are extracted to the result set, resulting in a more dispersed distribution of pattern confidence. By verifying the proposed method, accompanying behavioral activities such as ship cooperative operation, companion navigation method, and so on, can be detected. These results can provide a reference for the research of ship group behavior identification and have an important application value for water transportation management.

Situation awareness (SA) has been recognized as a critical guarantee for the stable and secure operation of electric power systems, especially under complex uncertainties after renewable energy integration. In this article, an artificial-intelligence-powered solution is presented to reach a full realization of SA covering perception, comprehension, and prediction, the last of which is more advanced but challenging and hence has not been discussed in any literature before. A novel SA model is proposed by aggregating two powerful deep learning structures: convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network. The proposed CNN-LSTM model has superiority to achieve collaborative data mining on spatiotemporal measurement data, i.e., to learn both spatial and temporal features simultaneously from phasor measurement units data. Two functional branches are designed within the SA model: a contingency locator to detect the exact fault location at present and a stability predictor to predict stability status of the system in the future. Test results have shown high performance (accuracy) of the model even on a low level of data adequacy. The proposed SA model can promisingly facilitate very fast postfault actions by the system operators to prevent the power system from any unstable operational status. [ABSTRACT FROM AUTHOR]

The pervasiveness of mobile and sensing technologies today has facilitated the creation of Big Crowdsourced Geotagged Data (BCGD) from individual users in real time and at different locations in the city. Such ubiquitous user-generated data allow us to infer various patterns of human behavior, which helps us understand the interactions between humans and cities. In this article, we aim to analyze BCGD, including mobile consumption check-ins, urban geography data, and human mobility data, to learn a model that can unveil the impact of urban geography and human mobility on the vibrancy of residential communities. Vibrant communities are defined as places that show diverse and frequent consumer activities. To effectively identify such vibrant communities, we propose a supervised data mining system to learn and mimic the unique spatial configuration patterns and social interaction patterns of vibrant communities using urban geography and human mobility data. Specifically, to prepare the benchmark vibrancy scores of communities for training, we first propose a fused scoring method by fusing the frequency and the diversity of consumer activities using mobile check-in data. Besides, we define and extract the features of spatial configuration and social interaction for each community by mining urban geography and human mobility data. In addition, we strategically combine a pairwise ranking objective with a sparsity regularization to learn a predictor of community vibrancy. And we develop an effective solution for the optimization problem. Finally, our experiment is instantiated on BCGD including real estate, point of interests, taxi and bus GPS trajectories, and mobile check-ins in Beijing. The experimental results demonstrate the competitive performances of both the extracted features and the proposed model. Our results suggest that a structurally diverse community usually shows higher social interaction and better business performance, and incompatible land uses may decrease the vibrancy of a community. Our studies demonstrate the potential of how to best make use of BCGD to create local economic matrices and sustain urban vibrancy in a fast, cheap, and meaningful way.

Although Twitter is used for emergency management activities, the relevance of tweets during a hazard event is still open to debate. In this study, six different computational (i.e. Natural Language Processing) and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event. Primarily, tweets containing information about the flooding events and its impacts were analysed. Examination of the relationships between tweet volume and its content with precipitation amount, damage extent, and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert messages. However, only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event. By providing insight into the quality of social media data and its usefulness to emergency management activities, this study contributes to the literature on quality of big data. Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness.

Accurate lightning forecast is significant for disaster prevention and reduction. However, the mainstream lightning forecast methods, which mainly rely on numerical simulations and parameterizations, can hardly cope with the spatiotemporal deviations. Meanwhile, the rapid and complex evolution of lightning regions go beyond the traditional extrapolation-based forecast methods. In this work, we propose a data-driven neural network model for hourly lightning forecast, which exploits both the numerical simulations and the recent historical lightning observations. The two kinds of data complement each other and play different roles at different stages of the forecast. The use of dual-source data greatly increases the amount of information available to improve the forecasting performance. To handle the variability of deviation patterns in numerical simulations, we introduce a channel-wise attention mechanism, which adaptively adjusts the proportion of each simulated parameter to maximize the useful information. The attention mechanism also enables the model to reveal the contribution of each simulated parameter for the forecast. Experimental results on a real-world dataset show that the proposed method outperforms several baseline methods. Ablation studies further demonstrate the effectiveness of our data fusion approach and attention module.

The accurate trajectory of the train ahead with more dynamic behaviour, such as train position, speed, acceleration, etc., is the critical issue of virtual coupling for future railways, which can drastically reduce their headways and increase line capacity. This paper presents an integrated convolutional neural network (CNN) and long short-term memory (LSTM) hybrid model for the task of trajectory prediction. A CNN–LSTM hybrid algorithm has been proposed. The model employs CNN and LSTM to extract the spatial dimension feature of the trajectory and the long-term dependencies of train trajectory data, respectively. The proposed CNN–LSTM model has superiority in achieving collaborative data mining on spatiotemporal measurement data to simultaneously learn spatial and temporal features from phasor measurement unit data. Therefore, the high-precision prediction of the train trajectory prediction is achieved based on the sufficient fusion of the above features. We use real automatic train operation (ATO) collected data for experiments and compare the proposed method with recurrent neural networks (RNN), recurrent neural networks (GRU), LSTM, and stateful-LSTM models on the same data sets. Experimental results show that the prediction performance of long-term trajectories is satisfyingly accurate. The root mean square error (RMSE) error can be reduced to less than 0.21 m, and the hit rate achieves 93% when the time horizon increases to 4S, respectively.

Objectives: Based on taxi movement trajectories, urban traffic operating conditions can be analyzed. At present, the relevant research mainly focuses on the investigation of traffic origin and destination points and the extraction of traffic hotspots, but seldom analyzes the interaction between traffic hotspots. We focus on exploring the temporal and spatial patterns of urban traffic hotspots, with the purpose of providing a scientific basis for traffic management departments for traffic management in different periods and sections.Methods: We take the main urban city of Nanjing City as the research area, and use taxi GPS data in 2015, 2016, and 2017 for experiments. Firstly, the effective trajectory flow is extracted from the taxi GPS data through the local anomaly test method. Secondly, based on the GeoJSON geocoding to realize map matching, the Pathlet method is used to process the trajectory flow into a sequence which can be optimally solved by dynamic programming. Finally, based on the standard for evaluation of urban traffic operation status, the macroscopic fundamental diagram model is constructed according to the relationship be‑ tween traffic flow, speed and density in the road network, and the evaluation index is established by combining travel time ratio and the delay time ratio.Results: (1) Judging from the average of the traffic operation indicators, the main urban area of Nanjing is the most congested in the morning and evening rush hours, and the traffic is smooth in the early morning hours, and the traffic hotspots are more active on working days than on rest days. (2) Judging from the traffic operation indicators of the nine different nodes, the traffic congestion near the parking lot and office building nodes is relatively increased on weekdays, and the traffic congestion near the nodes of shopping malls and residential quarters is relatively increased on the rest days.(3) Judging from the perspective of spatial distribution of traffic hotspots in five representative periods, traffic hotspots mostly appear near intersections and ramps, and there are more interactions between main roads and express roads, but between townships and village roads and county roads. There is less interaction. (4) Judging from the perspective of the temporal and spatial patterns of traffic hotspots near the school during the morning rush hour, the temporal and spatial interaction characteristics of traffic hotspots are significant in areas with high node coverage, while the spatial distribution characteristics of traffic hotspots change unclearly with time in areas with low node coverage.Conclusions: Urban traffic hotspots show significant differences in time and space distribution characteristics, and their spatiotemporal interactions be‑ come stronger with time and space aggregation, which has a significant positive correlation. [ABSTRACT FROM AUTHOR]

The conditions and context in which pedestrians are exposed to the risk of road accidents, air pollution or epidemy are important for improving safety. Spatial information on pedestrian mobility was difficult and expensive to collect so far. This is changing with the popularization of several apps either for guidance or self-monitoring. Produced Space-Time Trajectories (STT) perfectly match the concept of temporal geography, as to assess pedestrian exposure to the above risks. In this article, we propose a modeling approach of pedestrian STT. The abstract model provides a high semantic expressiveness through spatiotemporal queries and a high-level graph representation of the data. We depict a whole framework for collecting and analyzing GPS logs generated by pedestrians' devices, and discuss the system components. Among the targeted applications, a new generation of mobile applications can be developed that allow pedestrians to organize their daily activities and mobility, and minimize their exposure risks. [ABSTRACT FROM AUTHOR]

Regarded as one of the most dangerous types of natural disaster, tropical cyclones threaten the life and health of human beings and often cause enormous economic loss. However, intensity forecasting of tropical cyclones, especially rapid intensification forecasting, remains a scientific challenge due to limited understanding regarding the intensity change process. We propose an automatic knowledge discovery framework to identify potential spatiotemporal precursors to tropical cyclone rapid intensification from a set of tropical cyclone environmental fields. Specifically, this framework includes (1) formulating RI and non-RI composite environmental fields from historical tropical cyclones using NASA MERRA2 data; (2) utilizing the shared nearest neighbor-based clustering algorithm to detect regions representing relatively homogeneous behavior around tropical cyclone centers; (3) determining candidate precursors from significantly different regions in RI and non-RI groups using a spatiotemporal statistical method; and (4) comparing candidates to existing predictors to select potential precursors. The proposed knowledge discovery framework is applied separately to different factors, including 200 hPa zonal wind, 850–700 hPa relative humidity, and 850–200 hPa vertical shear, to detect potential precursors. Compared to the existing predictors manually labeled, i.e., U200 and U20C, RHLO, and SHRD in the Statistical Hurricane Intensity Prediction Scheme, our automatically discovered precursors have a comparable or better capability for estimating the probability of rapid intensification.

Spatiotemporal event sequences (STESs) are the ordered series of event types whose instances frequently follow each other in time and are located close-by. An STES is a spatiotemporal frequent pattern type, which is discovered from moving region objects whose polygon-based locations continiously evolve over time. Previous studies on STES mining require significance and prevalence thresholds for the discovery, which is usually unknown to domain experts. The quality of the discovered sequences is of great importance to the domain experts who use these algorithms. We introduce a novel algorithm to find the most relevant STESs without threshold values. We tested the relevance and performance of our threshold-free algorithm with a case study on solar event metadata, and compared the results with the previous STES mining algorithms. [ABSTRACT FROM AUTHOR]

• Urban flow prediction from spatiotemporal data is systematically reviewed. • Some existing urban flow prediction methods based on machine learning are overviewed. • The difficulties and some ideas of urban flow prediction are discussed briefly. Urban spatiotemporal flow prediction is of great importance to traffic management, land use, public safety. This prediction task is affected by several complex and dynamic factors, such as patterns of human activities, weather, events, and holidays. Datasets evaluated the flow come from various sources in different domains, e.g. mobile phone data, taxi trajectories data, metro/bus swiping data, bike-sharing data. To summarize these methodologies of urban flow prediction, in this paper, we first introduced four main factors affecting urban flow. Second, in order to further analyze urban flow, we partitioned the preparation process of multi-source spatiotemporal data related with urban flow into three groups. Third, we chose the spatiotemporal dynamic data as a case study for the urban flow prediction task. Fourth, we analyzed and compared some representative flow prediction methods in detail, classifying them into five categories: statistics-based, traditional machine learning-based, deep learning-based, reinforcement learning-based, and transfer learning-based methods. Finally, we showed open challenges of urban flow prediction and discussed many recent research works on urban flow prediction. This paper will facilitate researchers to find suitable methods and public datasets for addressing urban spatiotemporal flow forecast problems. [ABSTRACT FROM AUTHOR]

Spatiotemporal data mining has many important applications in environmental modelling. This paper introduces a modified algorithm for spatiotemporal data mining based on density-based spatial clustering of applications with noise (DBSCAN). Based on the modified algorithm, a geographic information system (GIS) add-in was developed using ArcObjects and C#, and is freely available for download. Compared with some existing methods, the new method can perform automated detection of spatiotemporal clusters using limited user input parameters. The application of the add-in was demonstrated using summer temperature data collected from 104 weather stations in southwest China from 1961 to 2011. The results suggest that the modified algorithm can provide more detailed partition of temperature zones compared with some existing methods. [ABSTRACT FROM AUTHOR]

The black hole pattern is a landmark achievement in the study of human moving patterns. However, the black hole pattern has limitations in the evolution modeling of human moving patterns. This paper proposes a black hole pattern with time evolution characteristics. The definition of the new pattern needs to meet the three requirements of group scale, spatial locality and time persistence. This paper proposes a dynamic spatial network model with time evolution characteristics. Based on this model, we define a new black hole pattern and propose a corresponding mining algorithm. In order to improve the efficiency of the pattern mining algorithm, we design a candidate pattern pruning algorithm based on spatiotemporal partitioning, which effectively reduces the searching cost of the mining algorithm in spatiotemporal dimension. Finally, experiments based on real data verify the effectiveness and efficiency of the proposed black hole pattern and mining algorithm. [ABSTRACT FROM AUTHOR]

As urban populations grow, cities need new strategies to maintain a good standard of living while enhancing services and infrastructure development. A key area for improving city operations and spatial layout is the transportation of people and goods. While conventional transportation systems (i.e., fossil fuel based) are struggling to serve mobility needs for growing populations, they also represent serious environmental threats. Alternative-fuel vehicles can reduce emissions that contribute to local air pollution and greenhouse gases as mobility needs grow. However, even if alternative-powered vehicles were widely employed, road congestion would still increase. This paper investigates ridesharing as a mobility option to reduce emissions (carbon, particulates and ozone) while accommodating growing transportation needs and reducing overall congestion. The potential of ridesharing to reduce carbon emissions from personal vehicles in Changsha, China, is examined by reviewing mobility patterns of approximately 8,900 privately-owned vehicles over two months. Big data analytics identify ridesharing potential among these drivers by grouping vehicles by their trajectory similarity. The approach includes five steps: data preprocessing, trip recognition, feature vector creation, similarity measurement and clustering. Potential reductions in vehicle emissions through ridesharing among a specific group of drivers are calculated and discussed. While the quantitative results of this analysis are specific to the population of Changsha, they provide useful insights for the potential of ridesharing to reduce vehicle emissions and the congestion expected to grow with mobility needs. Within the study area, ridesharing has the potential to reduce total kilometers driven by about 24% assuming a maximum distance between trips less than 10 kilometers, and schedule time less than 60 minutes. For a more conservative maximum trip distance of 2 kilometers and passenger schedule time of less than 40 minutes, the reductions in traveled kilometers could translate to the equivalent of approximately 4.0 tons CO2 emission reductions daily.

Understanding human periodic behaviors is crucial in many applications. Existing research has shown the existence of periodicity in human behaviors, but has achieved limited success in leveraging location periodicity and obtaining satisfactory accuracy for oscillations in human periodic behaviors. In this article, we propose the Mobility Intention and Relative Entropy (MIRE) model to address these challenges. We employ tensor decomposition to extract mobility intentions from spatiotemporal datasets, thereby revealing hidden structures in users' historical records. Subsequently, we utilize subsequences associated with the same mobility intention to mine human periodic behaviors. Furthermore, we introduce a novel periodicity detection algorithm based on relative entropy. Our experimental results, conducted on real-world datasets, demonstrate the effectiveness of the MIRE model in accurately uncovering human periodic behaviors. Comparative analysis further reveals that the MIRE model significantly outperforms baseline periodicity detection algorithms., Competing Interests: The authors declare that they have no competing interests., (© 2024 Yi et al.)

Most of the large databases, which are currently available, have a strong spatiotemporal component, particularly for, transport databases. Spatiotemporal data mining is a complex process, since it takes into account the specificities of spatial and temporal information. Unlike the spatiotemporal data mining process, the visual data mining one uses visualization for exploratory purposes at all levels of the knowledge discovery process. The automatic and visual extractions of data have their respective advantages. Computers can process large amounts of data much faster than humans, while humans are able to recognize objects and visually explore data much more efficiently than computers. This article proposes a new process for the spatiotemporal data mining which integrates visualization as a confirmatory and exploratory tool in the drilling process of large spatiotemporal databases. This consists in combining the spatiotemporal data mining and the visual data mining process in one and the same global approach. On the other hand, it proposes a methodological study that allows judging the utility of combining the spatiotemporal data mining methods with several visualization techniques. For each applied spatiotemporal data mining method, several visualization techniques have been used. In order to validate the presented approach, this study proposes a case study that applies the proposed spatiotemporal data mining process to traffic analysis in the city of Oran. [ABSTRACT FROM AUTHOR]

Although Twitter is used for emergency management activities, the relevance of tweets during a hazard event is still open to debate. In this study, six different computational (i.e. Natural Language Processing) and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event. Primarily, tweets containing information about the flooding events and its impacts were analysed. Examination of the relationships between tweet volume and its content with precipitation amount, damage extent, and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert messages. However, only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event. By providing insight into the quality of social media data and its usefulness to emergency management activities, this study contributes to the literature on quality of big data. Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness. [ABSTRACT FROM AUTHOR]

Modern retail chains, such as Starbucks and McDonald's, seek for geographical locations that have higher possibility to bring the maximum profit to establish and open their new stores. For a retail store, the common indicator of profit is its popularity which can be measured by the number of people who had ever consumed there. If one can know the potential popularity of locations along the time or within a certain time period (e.g., Christmas holiday or some memorial days) in advance, it would facilitate the determination of locations that are more profitable for the geographical placement of new retail stores. In this paper, given a targeted retail chain as well as some candidate locations, we propose to predict the popularity values of these locations over time to support their decisions on placing new stores. We devise a semi-supervised learning model, Affinity-based Popularity Inference, to tackle the Temporal Location Popularity Prediction problem. The basic idea is that locations that share similar properties of geo-spatial venue characteristics and human mobility tend to have closer popularity values. Experiments conducted on Foursquare check-in data in New York City exhibit promising results on predicting the temporal popularity of retail chains Starbucks, McDonald's, and Dunkin' Donuts, comparing to interpolation, regression, and state-of-the-art supervised learning methods. [ABSTRACT FROM AUTHOR]

Spatiotemporal co-occurrence patterns (STCOPs) are subsets of Boolean features whose instances frequently co-occur in both space and time. The detection of STCOPs is crucial to the investigation of the spatiotemporal interactions among different features. However, prevalent STCOPs reported by available methods do not necessarily indicate the statistically significant dependence among different features, which is likely to result in highly erroneous assessments in practice. To improve the reliability of results, this paper develops a statistical method to detect STCOPs and discern their statistical significance. The proposed method detects STCOPs against the null hypothesis that the spatiotemporal distributions of different features are independent of each other. To construct the null hypothesis, suitable spatiotemporal point-process models considering spatiotemporal autocorrelation are employed to model the distributions of different features. The performance of the proposed statistical method is assessed by synthetic experiments and a case study aimed at identifying crime patterns among multiple crime types in Portland City. The experimental results demonstrate that the proposed method is more effective for detecting meaningful STCOPs than the available alternative methods. [ABSTRACT FROM AUTHOR]

Finding anomalous regions in spatiotemporal climate data is an important problem with a need for greater accuracy. The collective and contextual nature of anomalies (e.g., heat waves) coupled with the real‐valued, seasonal, multimodal, highly correlated, and gridded nature of climate variable observations poses a multitude of challenges. Existing anomaly detection methods have limitations in the specific setting of real‐valued areal spatiotemporal data. In this paper, we develop a method for extreme event detection in meteorological datasets that follows from well known distribution‐based anomaly detection approaches. The method models spatial and temporal correlations explicitly through a piecewise parametric assumption and generalizes the Mahalanobis distance across distributions of different dimensionalities. The result is an effective method to mine contiguous spatiotemporal anomalous regions from meteorological fields which improves upon the current standard approach in climatology. The proposed method has been evaluated on a real global surface temperature dataset and validated using historical records of extreme events. [ABSTRACT FROM AUTHOR]

Periodic detection in spatiotemporal data is one of the research focuses in data mining. Most previous works only focused on mining periodic patterns and hardly recognized misaligned presence of a pattern due to the intervention of repetitive data. A more flexible asynchronous periodic patterns mining model (AP2M2) based on clustering algorithm and SMCA algorithm is proposed which mainly has three steps, discovering the invisible repetitive data and clustering them into a single record to generate standard and usable dataset, finding the set of stopovers and mining the periodic patterns of moving objects at each stopover. In the experiment, the Chinese bird-watching data is used to check the effectiveness of AP2M2 model and the results show that the AP2M2 model can precisely mine the asynchronous periodic patterns with low time complexity. [ABSTRACT FROM AUTHOR]