Spatiotemporal Event Sequences Research Articles

A Novel Similarity Measure of Spatiotemporal Event Setting Sequences: Method Development and Case Study

Examining the similarity of event environments or surroundings—more precisely, settings—provides additional insight in analyzing event sequences, as it provides information about the context and potential common factors that may have influenced them. This article proposes a new similarity measure for event setting sequences, which involve the space and time in which events occur. While similarity measures for spatiotemporal event sequences have been studied, the settings and setting sequences have not yet been studied. While modeling event setting sequences, we consider spatial and temporal scales to define the bounds of the setting and incorporate dynamic variables alongside static variables. Using a matrix-based representation and an extended Jaccard index, we developed new similarity measures that allow for the use of all variable data types. We successfully used these similarity measures coupled with other multivariate statistical analysis approaches in a case study involving setting sequences and pollution event sequences associated with the same monitoring stations, which validate the hypothesis that more similar spatial-temporal settings or setting sequences may generate more similar events or event sequences. In conclusion, the developed similarity measures have wide application beyond the case study to other disciplinary contexts and geographical settings. They offer researchers a powerful tool for understanding different factors and their dynamics corresponding to occurrences of spatiotemporal event sequences.

Clustering-Assisted Regional Spatio-Temporal Sequence Pattern Mining in Crime Database

With the recent advances in IoT and other smart devices, an explosive amount of data, both spatially and temporally significant are generated. Discovering interesting or useful patterns from these spatiotemporal data is referred to as spatiotemporal data mining. These patterns could be unordered, totally ordered or partially ordered based on the temporal ordering. This work focusses on the totally ordered patterns or sequential patterns from spatiotemporal event database. Spatiotemporal event sequence miner finds sequence of events that overlaps spatially and temporally. Traditional approaches discover patterns that are frequent in the entire dataset. In this work a clustering-assisted approach to find regionally or locally frequent spatiotemporal pattern is proposed. The proposed Clustering assisted Regional Spatiotemporal Event Sequence (CReST) mining approach overcomes the bias caused by uneven distribution of spatiotemporal events while mining patterns. Chicago crime dataset is used for evaluating the proposed approach with traditional sequence mining algorithm.

A Unifying Framework for Analysis of Spatial-Temporal Event Sequence Similarity and Its Applications

Measures of similarity or differences between data objects are applied frequently in geography, biology, computer science, linguistics, logic, business analytics, and statistics, among other fields. This work focuses on event sequence similarity among event sequences extracted from time series observed at spatially deployed monitoring locations with the aim of enhancing the understanding of process similarity over time and geospatial locations. We present a framework for a novel matrix-based spatiotemporal event sequence representation that unifies punctual and interval-based representation of events. This unified representation of spatiotemporal event sequences (STES) supports different event data types and provides support for data mining and sequence classification and clustering. The similarity measure is based on the Jaccard index with temporal order constraints and accommodates different event data types. The approach is demonstrated through simulated data examples and the performance of the similarity measures is evaluated with a k-nearest neighbor algorithm (k-NN) classification test on synthetic datasets. As a case study, we demonstrate the use of these similarity measures in a spatiotemporal analysis of event sequences extracted from space time series of a water quality monitoring system.

Spatiotemporal event sequence discovery without thresholds.

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.

ST Sequence Miner: visualization and mining of spatio-temporal event sequences

As a promising field of research, event sequence analysis seems to assist in facilitating clear reasoning behind human decisions by mining reality behind the sequential actions. Mining frequent patterns from event sequences has proved to be promising in extracting actionable insights, which plays an important role in many application domains. Much of the related work challenges the problem solely from the temporal perspective omitting the information that could be gained from the spatial part. This could be in part due to the fact that analysis of event sequences with references to both time and space is attributed as a challenging task due to the additional variance in the data introduced by the spatial aspect. We propose a visual analytics approach that incorporates spatio-temporal pattern extraction leveraging an extended sequential pattern mining algorithm and a pattern discovery guidance mechanism operating on geographic query and selection capabilities. As an implementation of our approach, we introduce a visual analytics tool, namely ST Sequence Miner, enabling event pattern exploration in time-location space. We evaluate our approach over a credit card transaction dataset by adopting case study methodology. Our study unveils that patterns mined from event sequences can better explain possible relationships with proper visualization of time-location data.

Temporal contextual cuing of visual attention.

Previous research has shown how spatial attention is guided to a target location, but little is understood about how attention is allocated to an event in time. The authors introduce a paradigm to manipulate the sequential structure of visual events independent of responses. They asked whether this temporal context could be implicitly learned and used to guide attention to a relative point in time or location, or both, in space. Experiments show that sequentially structured event durations, event identities, and spatiotemporal event sequences can guide attention to a point in time as well as to a target event's identity and location. Cuing was found to rely heavily on the element immediately preceding the target, although cuing from earlier items also was evident. Learning was implicit in all cases. These results show that the sequential structure of the visual world plays an important role in guiding visual attention to target events.