Representation Of Spatio-temporal Data Research Articles

STSNet: a novel spatio-temporal-spectral network for subject-independent EEG-based emotion recognition.

How to use the characteristics of EEG signals to obtain more complementary and discriminative data representation is an issue in EEG-based emotion recognition. Many studies have tried spatio-temporal or spatio-spectral feature fusion to obtain higher-level representations of EEG data. However, these studies ignored the complementarity between spatial, temporal and spectral domains of EEG signals, thus limiting the classification ability of models. This study proposed an end-to-end network based on ManifoldNet and BiLSTM networks, named STSNet. The STSNet first constructed a 4-D spatio-temporal-spectral data representation and a spatio-temporal data representation based on EEG signals in manifold space. After that, they were fed into the ManifoldNet network and the BiLSTM network respectively to calculate higher-level features and achieve spatio-temporal-spectral feature fusion. Finally, extensive comparative experiments were performed on two public datasets, DEAP and DREAMER, using the subject-independent leave-one-subject-out cross-validation strategy. On the DEAP dataset, the average accuracy of the valence and arousal are 69.38% and 71.88%, respectively; on the DREAMER dataset, the average accuracy of the valence and arousal are 78.26% and 82.37%, respectively. Experimental results show that the STSNet model has good emotion recognition performance.

Spatiotemporal Database Schema for Data Driven Applications in Smart Agriculture

Background: Data driven sustainable agriculture involves the collection, storage, processing, and analysis of enormous spatiotemporal data related to crop production processes, systems, infrastructure, and environment. Literature survey on smart agriculture research projects indicates that there is a need for improving handling of spatiotemporal data. There are gaps in handling spatial and temporal variability of parameters, division of crop field into management zones to reduce the variability for optimizing application of inputs such as fertilizers, water or pesticides. These gaps are to be addressed at design level of spatiotemporal database for smart agriculture. Objective: To engineer a spatiotemporal database schema that can be used for data driven sustainable agriculture. Methods: The methodology involves spatiotemporal data representation and modeling, Object oriented analysis and design of the database and Verification of the database using life cycle model and algorithmic steps. Findings: The resulting database is capable to store spatial and temporal variability of soil, plant and water parameters as well as handle spatial split, spatial merge, geometry and location changes of spatiotemporal objects. Novelty: Novel Use cases in smart agriculture along with spatiotemporal attributes are identified so that efficient applications can be realized. Adaption of the spatiotemporal database schema for Smart Irrigation System and its implementation methodology are presented. Keywords: Spatiotemporal Database Design; Data-Driven Agriculture; Agriculture Informatics; Database Verification; Spatiotemporal Data Analysis

Personalized Recommendation of Location-Based Services Using Spatio-Temporal-Aware Long and Short Term Neural Network

User behavioral data are critical for predicting the next item in a recommendation system, which can be acquired by location-based services. However, existing approaches directly use latitude and longitude information, rather than fully exploitation of the location information. In order to better utilize location information and user behavior data, considering the shortcomings of existing models, a spatial-temporal long and short-term neural network(SLSTNN) is proposed in this paper for location-based personalized service recommendation. SLSTNN is the first attempt to comprehensively characterize the long and short term sequences of users, which is achieved by a double-layer attention mechanism and integrated with a deep neural network to improve the representation of spatio-temporal data. In addition, the explicit feature cross-network is employed to characterize user profiles and context features. Experimental results demonstrate that the proposed SLSTNN framework outperforms the state-of-the-art methods with an improvement of online conversion rate by 2.14%. SLSTNN addresses the insufficient feature crossing problem in the simple sequence model and can be potentially used in many recommendation systems.

A hybrid data model for dynamic GIS: application to marine geomorphological dynamics

ABSTRACT The search for the most appropriate GIS data model to integrate, manipulate and analyse spatio-temporal data raises several research questions about the conceptualisation of geographic spaces. Although there is now a general consensus that many environmental phenomena require field and object conceptualisations to provide a comprehensive GIS representation, there is still a need for better integration of these dual representations of space within a formal spatio-temporal database. The research presented in this paper introduces a hybrid and formal dual data model for the representation of spatio-temporal data. The whole approach has been fully implemented in PostgreSQL and its spatial extension PostGIS, where the SQL language is extended by a series of data type constructions and manipulation functions to support hybrid queries. The potential of the approach is illustrated by an application to underwater geomorphological dynamics oriented towards the monitoring of the evolution of seabed changes. A series of performance and scalability experiments are also reported to demonstrate the computational performance of the model.

Geovisualizing space and time in a science-art exhibit

Geovisualizing space and time in a science-art exhibit

SPATIOTEMPORAL DATA REPRESENTATION IN R

Recent advances in devices that collect geospatial information have produced massive spatiotemporal data sets. Earth observation and GPS satellites, sensor networks and mobile gadgets are examples of technologies that have created large data sets with better spatial and temporal resolution than ever. This scenario brings a challenge for Geoinformatics: We need software tools to represent process and analyze these large data sets effi ciently. R is an environment widely used for data analysis. In this work, we present a study of spatiotemporal data representation in R. We evaluate R packages to access and create three spatiotemporal data types as diff erent views on the same observation set: time series, trajectories and coverage.

On repairing and querying inconsistent probabilistic spatio-temporal databases

We formally introduce the concept of repair and consistent answer for inconsistent probabilistic spatio-temporal databases. We start by defining the syntax and semantics of SPOT databases, a declarative framework that has been explored in recent years for the representation of spatio-temporal data with uncertainty expressed as probability intervals. In this framework we define and study multiple types of repairs. We also extend the concept of consistent answer to this framework and find that this can be done in several different ways. In emphasizing tractable cases we propose polynomial-time algorithms for computing consistent answers and repairs based on probability interval expansion, and experimentally validate our approach.

An Enhanced Tree Mining Algorithm for Finding Maximal Periodic Movements from Spatiotemporal Databases

Objectives: To find effective periodic patterns throughthe symbolic database representation of spatiotemporal data by using an efficient algorithm, ETMA (Enhanced Tree-based Mining Algorithm for large Databases). Methods/Statistical Analysis: There are distinct types of notions used to store and manage transactions data horizontally such as segment, sequence and individual symbols. The proposed algorithm can mine periodic patterns in full-series and subsection series also.The proposed algorithm based on prefix and suffix tree concurrently discover signal, series and section periodicity furthermore recognize and report only the efficient and non-redundant periods through the pruning techniques to abolish redundant (repetitive) periods. Findings: This algorithm finds interesting results with the help of a mixture of testing have been performed to validate the peroformance, strength, scalability, and correctness of the produced results in comparison with traditional algorithms. It is used to identify the three distinctcategories of maximal patterns effectively on various synthetic and reallife datasets. All tests are completedon distinct types of noisy such as insertion, deletion and replacement. ETMA reduces the running time and buildsservice of the proficient symbolic process. Moreover, ETMA simply report time-series instances dynamically, interms of symbol, sequence and segment approaches respectively. The length of the pattern, and proving efficiency of the pruning and searching strategies from synthetic and real datasets is a really open & challenging mining problem. Application/Improvements: This algorithm is 50% better than MAFIA and 20% better than STNR algorithms on Accident dataset and 50% better than STNR and 74% better than ECLAT algorithms on Mushroom dataset.

Spatiotemporal data representation and its effect on the performance of spatial analysis in a cyberinfrastructure environment – A case study with raster zonal analysis

This paper conducts a systematic research to uncover the impact of spatiotemporal data representation on the performance of raster analysis in a cyberinfrastructure environment. Two broad categories of data organization based on file system and database system are presented and discussed. In particular, these include five specific approaches of storing time-series raster data involving tiling (partitioning the entire image file into non-overlapping pieces), stacking (compositing multiple single-band images into a large multi-band image) techniques, and a combination of tiling and stacking in files or database tables. Raster zonal statistics, which have been used to support a variety of GIS applications ranging from watershed analysis to summarizing forest products, is selected as an example raster analysis algorithm. A series of experiments were conducted to evaluate the performance of the five proposed approaches using different spatial and spatiotemporal queries. The results show that spatiotemporal data representation, though largely ignored in the design of a cyberinfrastructure system, does play an important role in system performance. Specifically, tiling techniques with the support of spatial database outperforms all other approaches, especially those adopting stacking techniques in the data organization. For illustration, the best raster analysis solution was implemented and integrated into an operational cyberinfrastructure in the context of providing spatial decision support in polar science. We expect this work to offer insights to develop efficient cyberinfrastructure modules to support spatial analysis through a thorough analysis of spatiotemporal data representation.

Spatiotemporal Data Mining: Issues, Tasks And Applications

Spatiotemporal data usually contain the states of an object, an event or a position in space over a period of time. Vast amount of spatiotemporal data can be found in several application fields such as traffic management, environment monitoring, and weather forecast. These datasets might be collected at different locations at various points of time in different formats. It poses many challenges in representing, processing, analysis and mining of such datasets due to complex structure of spatiotemporal objects and the relationships among them in both spatial and temporal dimensions. In this paper, the issues and challenges related to spatiotemporal data representation, analysis, mining and visualization of knowledge are presented. Various kinds of data mining tasks such as association rules, classification clustering for discovering knowledge from spatiotemporal datasets are examined and reviewed. System functional requirements for such kind of knowledge discovery and database structure are discussed. Finally applications of spatiotemporal data mining are presented.

Directions in Population GIS

This paper presents a review of contemporary population geographical information systems (GIS), highlighting the enduring connection between mainstream applications and traditional shaded area census maps. The review highlights challenges in underlying data collection, visualisation techniques and the representation of spatio-temporal data which have all been inherited from traditional cartography. Each of these areas is examined in turn to show how new approaches and technologies, emerging from several different domains, are coming together to offer some very different future directions in population GIS development.

Spatio-Temporal Tube data representation and Kernel design for SVM-based video object retrieval system

In this article, we propose a new video object retrieval system. Our approach is based on a Spatio-Temporal data representation, a dedicated kernel design and a statistical learning toolbox for video object recognition and retrieval. Using state-of-the-art video object detection algorithms (for faces or cars, for example) we segment video object tracks from real movies video shots. We then extract, from these tracks, sets of spatio-temporally coherent features that we call Spatio-Temporal Tubes. To compare these complex tube objects, we design a Spatio-Temporal Tube Kernel (STTK) function. Based on this kernel similarity we present both supervised and active learning strategies embedded in Support Vector Machine framework. Additionally, we propose a multi-class classification framework dealing with unbalanced data. Our approach is successfully evaluated on two real movies databases, the french movie "L'esquive" and episodes from "Buffy, the Vampire Slayer" TV series. Our method is also tested on a car database (from real movies) and shows promising results for car identification task.

Dynamic GIS Case Studies: Wildfire Evacuation and Volunteered Geographic Information

AbstractIncorporating the temporal element into traditional GIS is a challenge that has been researched for many years and has many proposed solutions. The implemented system “Extended Dynamic GIS” or EDGIS is based on the “geo‐atom” and Space Time Point (STP). EDGIS provides a platform for spatiotemporal data representation, storage, and query in order to address the need for a dynamic GIS to manage complex geographic data types. The system has the capability of executing spatiotemporal object interaction queries (OIQs) such as crossing and coincidence of field‐objects and object‐fields. In this article existing dynamic GIS analysis techniques are further improved and enhanced through exploration of more in‐depth case studies. Further examined here are applications to wildfire evacuation modeling and travel scenarios of urban environments with individuals providing volunteered geographic information (VGI). The EDGIS platform provides a means for interacting with a range of dynamic geographic phenomena. The areas of transportation, location based services (LBS), hazards, and geo‐sensor networks provide challenges intertwined with the above applications as well as additional challenges pertinent to the ongoing GIScience research topic of spatiotemporal GIS. Using EDGIS to explore the described case studies of wildfire evacuation as well as VGI provides the advancements described above and demonstrates implemented uses for dynamic GIS.

A spatio-temporal geometry-based model for digital documentation of historical living systems

Representations used in digital documentation applications today usually assume a world that only exists in the present. Information contained within a database may be added-to or modified over time, but change through time is seldom maintained. This limitation of current IT has recently received attention, given the increasingly urgent need to understand geographical processes and cause-and-effect inter-relationships between human activities and the environment. Models proposed for the representation of spatio-temporal data are extensions of traditional raster and vector representations that can be seen as location- or feature-based, respectively, and are therefore best organized for performing, either location- or feature-based queries. In this paper, a new spatio-temporal data model suitable for digital documentation of historical living systems (artefacts, monuments and sites) is defined: it is based on 3D geometry and intended to facilitate analysis of temporal relationships and patterns of 3D modeling changes through time. This is particularly useful to both IT and IS managers, researchers and practitioners. It is shown that time-based queries related to 3D models of objects can be processed in an efficient and straightforward manner using the model. Finally, analytical time efficiency estimations are given, showing that the model is also an efficient and compact representation of spatio-temporal information.

An event-based spatiotemporal data model (ESTDM) for temporal analysis of geographical data

Abstract Representations historically used within GIS assume a world that exists only in the present. Information contained within a spatial database may be added-to or modified over time, but a sense of change or dynamics through time is not maintained. This limitation of current GIS capabilities has recently received substantial attention, given the increasingly urgent need to better understand geographical processes and the cause-and-effect interrelationships between human activities and the environment. Models proposed so-far for the representation of spatiotemporal data are extensions of traditional raster and vector representations that can be seen as location- or feature-based, respectively, and are therefore best organized for performing either location-based or feature-based queries. Neither form is as well-suited for analysing overall temporal relationships of events and patterns of events throughout a geographical area as a temporally-based representation. In the current paper, a new spatio-tem...