Spatio-temporal Sequential Patterns Research Articles

Discovery of crime event sequences with constricted spatio-temporal sequential patterns

In this article, we introduce a novel type of spatio-temporal sequential patterns called Constricted Spatio-Temporal Sequential (CSTS) patterns and thoroughly analyze their properties. We demonstrate that the set of CSTS patterns is a concise representation of all spatio-temporal sequential patterns that can be discovered in a given dataset. To measure significance of the discovered CSTS patterns we adapt the participation index measure. We also provide CSTS-Miner: an algorithm that discovers all participation index strong CSTS patterns in event data. We experimentally evaluate the proposed algorithms using two crime-related datasets: Pittsburgh Police Incident Blotter Dataset and Boston Crime Incident Reports Dataset. In the experiments, the CSTS-Miner algorithm is compared with the other four state-of-the-art algorithms: STS-Miner, CSTPM, STBFM and CST-SPMiner. As the results of the experiments suggest, the proposed algorithm discovers much fewer patterns than the other selected algorithms. Finally, we provide the examples of interesting crime-related patterns discovered by the proposed CSTS-Miner algorithm.

Disruption of the Auxin Gradient in the Abscission Zone Area Evokes Asymmetrical Changes Leading to Flower Separation in Yellow Lupine.

How auxin transport regulates organ abscission is a long-standing and intriguing question. Polar auxin transport across the abscission zone (AZ) plays a more important role in the regulation of abscission than a local concentration of this hormone. We recently reported the existence of a spatiotemporal sequential pattern of the indole-3-acetic acid (IAA) localization in the area of the yellow lupine AZ, which is a place of flower detachment. In this study, we performed analyses of AZ following treatment with an inhibitor of polar auxin transport (2,3,5-triiodobenzoic acid (TIBA)). Once we applied TIBA directly onto the AZ, we observed a strong response as demonstrated by enhanced flower abscission. To elucidate the molecular events caused by the inhibition of auxin movement, we divided the AZ into the distal and proximal part. TIBA triggered the formation of the IAA gradient between these two parts. The AZ-marker genes, which encode the downstream molecular components of the inflorescence deficient in abscission (IDA)-signaling system executing the abscission, were expressed in the distal part. The accumulation of IAA in the proximal area accelerated the biosynthesis of abscisic acid and ethylene (stimulators of flower separation), which was also reflected at the transcriptional level. Accumulated IAA up-regulated reactive oxygen species (ROS) detoxification mechanisms. Collectively, we provide new information regarding auxin-regulated processes operating in specific areas of the AZ.

Spatiotemporal Analysis of Taxi-Driver Shifts Using Big Trace Data

In taxi management, taxi-driver shift behaviors play a key role in arranging the operation of taxis, which affect the balance between the demand and supply of taxis and the parking spaces. At the same time, these behaviors influence the daily travel of citizens. An analysis of the distribution of taxi-driver shifts, therefore, contributes to transportation management. Compared to the previous research using the real shift records, this study focuses on the spatiotemporal analysis of taxi-driver shifts using big trace data. A two-step strategy is proposed to automatically identify taxi-driver shifts from big trace data without the information of drivers’ identities. The first step is to pick out the frequent spatiotemporal sequential patterns from all parking events based on the spatiotemporal sequence analysis. The second step is to construct a Gaussian mixture model based on prior knowledge for further identifying taxi-driver shifts from all frequent spatiotemporal sequential patterns. The spatiotemporal distribution of taxi-driver shifts is analyzed based on two indicators, namely regional taxi coverage intensity and taxi density. Taking the city of Wuhan as an example, the experimental results show that the identification precision and recall rate of taxi-driver shift events based on the proposed method can achieve about 95% and 90%, respectively, by using big taxi trace data. The occurrence time of taxi-driver shifts in Wuhan mainly has two high peak periods: 1:00 a.m. to 4:00 a.m. and 4:00 p.m. to 5:00 p.m. Although taxi-driver shift behaviors are prohibited during the evening peak hour based on the regulation issued by Wuhan traffic administration, experimental results show that there are still some drivers in violation of this regulation. By analyzing the spatial distribution of taxi-driver shifts, we find that most taxi-driver shifts distribute in central urban areas such as Wuchang and Jianghan district.

Spatio-temporal IAA gradient is determined by interactions with ET and governs flower abscission

The abscission zone (AZ) is a specialized tissue that usually develops at the base of an organ and is highly sensitive to phytohormones, e.g., abscisic acid (ABA), ethylene (ET), and gibberellins (GAs). A current model of organ abscission assumes that the formation of an auxin gradient around the AZ area determines the time of shedding; however, that thesis is supported by studies that are primarily concerned with auxin transporters. To better understand the events underlying the progression of abscission, we focused for the first time on indole-3-acetic acid (IAA) distribution following AZ activation. We performed a series of immunolocalization studies in proximal and distal regions of floral AZ cells in yellow lupine, which is an agriculturally important legume. The examined phytohormone was abundant in natural active AZ cells, as well as above and below parts of this structure. A similar gradient of IAA was observed during the early steps of abscission, which was induced artificially by flower removal. Surprisingly, IAA was not detected in inactive AZ cells. This paper is also a consequence of our comprehensive studies concerning the phytohormonal regulation of flower abscission in yellow lupine. We present new data on interactions between IAA and ET, previously pointed out as a strong modulator of flower separation. The detailed analysis shows that disruption of the natural auxin gradient around the AZ area through the application of synthetic IAA had a positive effect on ET biosynthesis genes. We proved that these changes are accompanied by an accumulation of the ET precursor. On the other hand, exposure to ET significantly affected IAA localization in the whole AZ area in a time-dependent manner. Our results provide insight into the existence of a spatio-temporal sequential pattern of the IAA gradient related to the abscission process; this pattern is maintained by interactions with ET. We present new valuable evidence for the existence of conservative mechanisms that regulate generative organ separation and can help to improve the yield of agronomically significant species in the future.

Discovery of closed spatio-temporal sequential patterns from event data

In the paper, we first thoroughly examine and prove properties of the participation index of spatio-temporal sequential patterns. Then, we introduce notions of a closure of a spatio-temporal sequential pattern and a closed spatio-temporal sequential pattern, as well as investigate and prove their properties. In particular, we prove that the set of all participation index strong closed spatio-temporal sequential patterns constitues a lossless representation of all participation index strong spatio-temporal sequential patterns. We also propose an algorithm, called CST-SPMiner, for discovering all participation index strong closed spatio-temporal sequential patterns. CST-SPMiner is an adaptation of the STBFM algorithm, which was proposed recently for the discovery of spatio-temporal sequential patterns with high participation index. While STBFM uses the CSP-tree structure for time-efficient candidate patterns generation and evaluation, CST-SPMiner uses it also for fast identification of closed patterns. Efficiency and effectiveness of our algorithm were verified on real crime data for Boston.

A Non-Parametric Algorithm for Discovering Triggering Patterns of Spatio-Temporal Event Types

Temporal or spatio-temporal sequential pattern discovery is a well-recognized important problem in many domains like seismology, criminology, and finance. The majority of the current approaches are based on candidate generation which necessitates parameter tuning, namely, definition of a neighborhood, an interest measure, and a threshold value to evaluate candidates. However, their performance is limited as the success of these methods relies heavily on parameter settings. In this paper, we propose an algorithm which uses a nonparametric stochastic de-clustering procedure and a multivariate Hawkes model to define triggering relations within and among the event types and employs the estimated model to extract significant triggering patterns of event types. We tested the proposed method with real and synthetic data sets exhibiting different characteristics. The method gives good results that are comparable with the methods based on candidate generation in the literature.

Grade-based Spatio-Temporal Sequential Pattern Mining using Support and Event Index Measures

Objectives: The knowledge on cause-effect relationships between instances of real-world entities can be gathered by extracting sequential patterns from spatio-temporal databases. The discovery of the patterns in the context of space and time is a challenging issue. The sequential pattern mining algorithms designed for traditional databases may result in the loss of spatio-temporal correlations due to the improper estimations of properties related to the time and space. The proposed work approaches the problem of designing sequential pattern mining algorithm specifically for spatio-temporal event datasets. Methods/Statistical Analysis: An algorithm is proposed which is based on frequency-based measures for mining frequent spatio-temporal sequential patterns. The spatio-temporal sequential pattern mining based on Support index and Event index algorithm proposes two new parameters support index and event index which are used to scrutinize the sequences extracted from the database. A data structure is also proposed to represent the spatio-temporal data for efficient pattern mining. Findings: The proposed algorithm generates the interesting set of frequent sequential patterns. The proposed algorithm is compared with Slicing-STS-Miner and MST-ITP and the experimental results proved that the proposed algorithm performs well with the order of two to three. Application/Improvements: The proposed algorithm uses frequency-based measures rather than density-based measures. Frequency-based measures take less computational time when compared to density-based measures. The proposed technique is suitable for extracting knowledge in the form of sequential patterns from spatio-temporal point databases.

Spatiotemporal Sequential Influence Modeling for Location Recommendations

Recommending to users personalized locations is an important feature of Location-Based Social Networks (LBSNs), which benefits users who wish to explore new places and businesses to discover potential customers. In LBSNs, social and geographical influences have been intensively used in location recommendations. However, human movement also exhibits spatiotemporal sequential patterns, but only a few current studies consider the spatiotemporal sequential influence of locations on users’ check-in behaviors. In this article, we propose a new gravity model for location recommendations, called LORE, to exploit the spatiotemporal sequential influence on location recommendations. First, LORE extracts sequential patterns from historical check-in location sequences of all users as a Location-Location Transition Graph (L 2 TG), and utilizes the L 2 TG to predict the probability of a user visiting a new location through the developed additive Markov chain that considers the effect of all visited locations in the check-in history of the user on the new location. Furthermore, LORE applies our contrived gravity model to weigh the effect of each visited location on the new location derived from the personalized attractive force (i.e., the weight) between the visited location and the new location. The gravity model effectively integrates the spatiotemporal, social, and popularity influences by estimating a power-law distribution based on (i) the spatial distance and temporal difference between two consecutive check-in locations of the same user, (ii) the check-in frequency of social friends, and (iii) the popularity of locations from all users. Finally, we conduct a comprehensive performance evaluation for LORE using three large-scale real-world datasets collected from Foursquare, Gowalla, and Brightkite. Experimental results show that LORE achieves significantly superior location recommendations compared to other state-of-the-art location recommendation techniques.

Event R-Tree Miner: an Efficient Approach to Mine Sequential Patterns from Spatio-Temporal Event Dataset

With the advances of technologies such as GPS, remote sensing, RFID, indoor locating devices, and geosensor networks, mining of spatio-temporal patterns seems more important to track spatio-temporal phenomena with increasingly finer spatial resolutions. Accordingly, we have presented an efficient approach to mine sequential patterns from spatio-temporal event datasets. Here, we make use of three major challenges, such as 1) definition of significance measure for finding spatio-temporal sequential pattern, 2) event tree, to reduce the running time and, 3) the definition of adaptive neighborhood to improve the accuracy. Based on these three challenges, R-tree data structure is utilized to construct the event R-tree for avoiding the accessing of database every time. Furthermore, follow ratio, a significance measure is proposed for finding the significant spatio-temporal sequential patterns and the sequence behavior of spatio-temporal events are identified with the help of adaptive neighborhood that is bounded with MBR condition. Finally, the experimentation is made with the real and synthetic dataset and we have proved the efficiency of the proposed event R-tree miner than the STS miner in terms of computation time

A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets

Given a large spatio-temporal database of events, where each event consists of the fields event ID, time, location, and event type, mining spatio-temporal sequential patterns identifies significant event-type sequences. Such spatio-temporal sequential patterns are crucial to the investigation of spatial and temporal evolutions of phenomena in many application domains. Recent research literature has explored the sequential patterns on transaction data and trajectory analysis on moving objects. However, these methods cannot be directly applied to mining sequential patterns from a large number of spatio-temporal events. Two major research challenges still remain: 1) the definition of significance measures for spatio-temporal sequential patterns to avoid spurious ones and 2) the algorithmic design under the significance measures, which may not guarantee the downward closure property. In this paper, we propose a sequence index as the significance measure for spatio-temporal sequential patterns, which is meaningful due to its interpretability using spatial statistics. We propose a novel algorithm called Slicing-STS-miner to tackle the algorithmic design challenge using the spatial sequence index, which does not preserve the downward closure property. We compare the proposed algorithm with a simple algorithm called STS-miner that utilizes the weak monotone property of the sequence index. Performance evaluations using both synthetic and real-world data sets show that the slicing-STS-miner is an order of magnitude faster than STS-Miner for large data sets.