Spatio-temporal Data Model Research Articles

Hair-Oriented Data Model for Spatio-Temporal Data Mining

Spatio-temporal data are complex in terms of number of attributes for spatial and temporal values, and the data are changing towards time. Traditional method to mining the spatio-temporal data is the fact that the data is stored in data warehouse in un-normalization form as union of spatial and temporal data know as tabular data warehouse. A Hair-Oriented Data Model (HODM) has been proved as a suitable data model for spatio-temporal data. It has reduced the file size and decreased query execution time. The spatio-temporal data stored using the HODM known as Hair-Oriented Data warehouse. However, this paper aims to presents a method to develop spatio-temporal data mining model using the Hair-Oriented data warehouse. The Hair-Oriented data model also provide with various functions for easy maintenance on spatio-temporal data warehouse. Experiment conducted using Climate-change spatio-temporal data set benchmark. Two Climate-change spatio-temporal models been developed using regression and k-nearest neighbor techniques. The performance of the Hair-Oriented Data Warehouse is evaluated by comparing its performance with traditional tabular data warehouse. The result shows that developing data mining spatio-temporal model using Hair-Oriented data warehouse is faster compare using the tabular data warehouse, therefore it can be concluded that the Hair-Oriented Data Model is suitable for Spatio-temporal data mining.

Bayesian Inference and Data Augmentation Schemes for Spatial, Spatiotemporal and Multivariate Log-Gaussian Cox Processes inR

Log-Gaussian Cox processes are an important class of models for spatial and spatiotemporal point-pattern data. Delivering robust Bayesian inference for this class of models presents a substantial challenge, since Markov chain Monte Carlo (MCMC) algorithms require careful tuning in order to work well. To address this issue, we describe recent advances in MCMC methods for these models and their implementation in the R package lgcp. Our suite of R functions provides an extensible framework for inferring covariate effects as well as the parameters of the latent field. We also present methods for Bayesian inference in two further classes of model based on the log-Gaussian Cox process. The first of these concerns the case where we wish to fit a point process model to data consisting of event-counts aggregated to a set of spatial regions: we demonstrate how this can be achieved using data-augmentation. The second concerns Bayesian inference for a class of marked-point processes specified via a multivariate log-Gaussian Cox process model. For both of these extensions, we give details of their implementation in R.

A class of Matérn-like covariance functions for smooth processes on a sphere

There have been noticeable advancements in developing parametric covariance models for spatial and spatio-temporal data with various applications to environmental problems. However, literature on covariance models for processes defined on the surface of a sphere with great circle distance as a distance metric is still sparse, due to its mathematical difficulties. It is known that the popular Matérn covariance function, with smoothness parameter greater than 0.5, is not valid for processes on the surface of a sphere with great circle distance. We introduce an approach to produce Matérn-like covariance functions for smooth processes on the surface of a sphere that are valid with great circle distance. The resulting model is isotropic and positive definite on the surface of a sphere with great circle distance, with a natural extension for nonstationarity case. We present extensive numerical comparisons of our model, with a Matérn covariance model using great circle distance as well as chordal distance. We apply our new covariance model class to sea level pressure data, known to be smooth compared to other climate variables, from the CMIP5 climate model outputs.

An Event‐driven Spatiotemporal Data Model (E‐ST) Supporting Dynamic Expression and Simulation of Geographic Processes

AbstractThe wide use of various sensors makes real‐time data acquisition possible. A new spatiotemporal data model, the Event‐driven Spatiotemporal Data Model (E‐ST), is proposed to dynamically express and simulate the spatiotemporal processes of geographic phenomena. In E‐ST, a sensor object is introduced into the model as a flexible real‐time data source. An event type that is generating and driving conditions is registered into a geographic object, so an event can not only express spatiotemporal change in a geographic object, but also drive spatiotemporal change in some geographic objects. As a dynamic GIS data model, the E‐ST has five characteristics – Temporality and Spatiality, Real‐time, Extendability, Causality, and Realizability. Described and realized in UML, a test‐case deployment demonstrating the impact of urban waterlogging on traffic confirms that a spatiotemporal change process in a geographic phenomena is expressed and simulated by this model. Summarizing this work, four directions for future research are outlined.

Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes

The paper offers a new methodology for modelling, recognition and understanding of electroencephalography (EEG) spatio-temporal data measuring complex cognitive brain processes during mental tasks. The key element is that mental tasks are performed through complex spatio-temporal brain processes and they can be better understood only if we model properly the spatio-/spectro temporal data that measures these processes. The proposed methodology is based on a recently proposed novel spiking neural network architecture, called NeuCube as a general framework for spatio-temporal brain data modelling. The methodology is demonstrated on benchmark cognitive EEG data. The new approach leads to a faster data processing, improved accuracy of the EEG data classification and improved understanding of this data and the cognitive processes that generated it. The paper concluded that the new methodology is worth exploring further on other spatio-temporal data, measuring complex cognitive brain processes, aiming at using this method for the development of the next generation of brain–computer interfaces and systems for early diagnosis of degenerative brain disease, such as Alzheimer’s Disease (AD), and for personalised neuro-rehabilitation systems.

Spatio-temporal topological relationships between land parcels in cadastral database

Abstract. There are complex spatio-temporal relationships among cadastral entities. Cadastral spatio-temporal data model should not only describe the data structure of cadastral objects, but also express cadastral spatio-temporal relationships between cadastral objects. In the past, many experts and scholars have proposed a variety of cadastral spatio-temporal data models, but few of them concentrated on the representation of spatiotemporal relationships and few of them make systematic studies on spatiotemporal relationships between cadastral objects. The studies on spatio-temporal topological relationships are not abundant. In the paper, we initially review current approaches to the studies of spatio-temporal topological relationships, and argue that spatio-temporal topological relation is the combination of temporal topology on the time dimension and spatial topology on the spatial dimension. Subsequently, we discuss and develop an integrated representation of spatio-temporal topological relationships within a 3-dimensional temporal space. In the end, based on the semantics of spatiotemporal changes between land parcels, we conclude the possible spatio-temporal topological relations between land parcels, which provide the theoretical basis for creating, updating and maintaining of land parcels in the cadastral database.

Discrete nonhomogeneous and nonstationary logistic and Markov regression models for spatiotemporal data with unresolved external influences

Dynamical systems with different characteristic behavior at multiple scales can be modeled with hybrid methods combining a discrete model (e.g., corresponding to the microscale) triggered by a continuous mechanism and vice versa. A data-driven black-box-type framework is proposed, where the discrete model is parametrized with adaptive regression techniques and the output of the continuous counterpart (e.g., output of partial differential equations) is coupled to the discrete system of interest in the form of a fixed exogenous time series of external factors. Data availability represents a significant issue for this type of coupled discrete-continuous model, and it is shown that missing information/observations can be incorporated in the model via a nonstationary and nonhomogeneous formulation. An unbiased estimator for the discrete model dynamics in presence of unobserved external impacts is derived and used to construct a data-based nonstationary and nonhomogeneous parameter estimator based on an appropriately regularized spatiotemporal clustering algorithm. One-step and long-term predictions are considered, and a new Bayesian approach to discrete data assimilation of hidden information is proposed. To illustrate our method, we apply it to synthetic data sets and compare it with standard techniques of the machine-learning community (such as maximum-likelihood estimation, artificial neural networks and support vector machines).

A spatio-temporal process data model for characterizing marine disasters

Marine disasters are a more prevalent problem in China than in many other countries. Based on the development of a status quo of China's marine disaster the space-time process model is used. The model uses the ocean's temperature field, salinity field, water density field, surface wind field, wave field and other four-dimensional spatio-temporal quantities. This paper studies that model in detail. This study aims at using the theory to provide support during marine disasters in an effort to prevent or decrease their frequency in the future.

A Service-Oriented Spatio-Temporal Data Model of the Marine Environment

On the basis of analyzing of the marine environment spatio-temporal data characteristics and the existing data model, proposes a conceptual model for marine environment data based on the feature and field to achieve integration of description and expression of feature, field, time, space and semantic domains. Designs and implements the service-oriented marine environment data organization and storage using Geography Markup Language (GML). Efficient data organization and management is crucial to marine environment visual services.

Spatial and Spatio-Temporal Log-Gaussian Cox Processes: Extending the Geostatistical Paradigm

In this paper we first describe the class of log-Gaussian Cox processes (LGCPs) as models for spatial and spatio-temporal point process data. We discuss inference, with a particular focus on the computational challenges of likelihood-based inference. We then demonstrate the usefulness of the LGCP by describing four applications: estimating the intensity surface of a spatial point process; investigating spatial segregation in a multi-type process; constructing spatially continuous maps of disease risk from spatially discrete data; and real-time health surveillance. We argue that problems of this kind fit naturally into the realm of geostatistics, which traditionally is defined as the study of spatially continuous processes using spatially discrete observations at a finite number of locations. We suggest that a more useful definition of geostatistics is by the class of scientific problems that it addresses, rather than by particular models or data formats.

Forecasting province-level $${\text {CO}}_{2}$$ CO 2 emissions in China

Due to criticisms of potential identification issues within spatial panel data models, this study contributes to the literature by comparing forecasts of province-level carbon dioxide emissions against empirical reality using dynamic, spatial panel data models with and without fixed effects. From a policy standpoint, understanding how to predict emissions is important for designing climate change mitigation policies. From a statistical standpoint, it is important to test spatial econometrics models to see if they are a valid strategy to describe the underlying data. We find that the best model is the spatio-temporal panel data model which controls for fixed effects. Our findings demonstrate the importance of considering not only spatial and temporal dependence but also the individual or heterogeneous characteristics within each province.

Cartography and Maps beyond Disciplines

Based on a retrospective view of the research activities conducted at Department of Cartography of Technische Universitat Munchen (TUM) in the recent decade and an analysis of emerging challenges and opportunities for professional cartographers, the author presents a perspective of cartographic research in the next decade. The research highlights in more than a dozen accomplished doctoral theses are synoptically demonstrated. The involved international research team shares an interdisciplinary knowledge profile that covers geodetic engineering, geography, electrical engineering and communication technology, environmental engineering, transportation engineering, and computer sciences. The conducted research works were essentially driven by the media revolutions, the exponentially growing geodata and the changing behaviour of map users. The addressed research topics range from geospatial data integration, spatio-temporal data modelling and event detection, routing and navigation services, and 3D visualization to user-centred design of map services. These seemingly diverse grass-root efforts have prototypically mirrored the mainstreams in cartographic science, technology and art. For upcoming years, the two related new driving forces - big data and crowdsourcing will likely dominate the cartographic research agenda, forcing cartographers to think big and think outside the box. A shift of map production and updating workflow from coverage-orientation to feature-orientation is anticipated. Map services will be increasingly created from evolving data streams that are neither complete nor certain. Some of the long-lasting research gaps in usability studies may be bridged by means of controlled crowdsourcing.

Parametric database approach integration for handling temporal data in GIS

In large spatial, temporal, and spatio-temporal databases, the concept of dimensions – time and space – is central and their efficient processing has always been a great concern. A Geographic Information System (GIS) is widely known for its efficient and sophisticated handling of the spatial databases; however, integration of the time domain introduces additional complexities. Considerable research is ongoing in this area. Though numerous approaches have already been proposed in the literature to address the time-related challenges in GIS community, such as the development of temporal GIS data models, this study contributes to these ongoing efforts through extensions to the Parametric Data Model. Parametric database approaches (Parametric Data Model) have been developed to handle dimensional data such as space, time, belief, etc. In this study, the time dimension is of specific interest and we propose the integration of parametric approaches to GIS to absorb the temporal burden. The temporal processing of the data is done using parametric approaches that address the limitation of GIS to support temporal queries and temporal analysis of phenomena. Using the AutoConViz tool, developed for spatio-temporal data modeling and visualization, results can be directly integrated into standard GIS software programs such as ArcGIS and further processed seamlessly along with and in a similar manner to other attributes.

Covariance structure of spatial and spatiotemporal processes

An important aspect of statistical modeling of spatial or spatiotemporal data is to determine the covariance function. It is a key part of spatial prediction (kriging). The classical geostatistical approach uses an assumption of isotropy, which yields circular isocorrelation curves. However, this is inappropriate for many applications, and several nonstationary approaches have been developed. Adding the temporal aspect, there is often interaction between time and space, requiring classes of nonseparable covariance structures. WIREs Comput Stat 2013, 5:279–287. doi: 10.1002/wics.1259This article is categorized under: Data: Types and Structure > Image and Spatial Data Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data

An Algebra for Spatiotemporal Data: From Observations to Events

AbstractRecent technological advances in geospatial data gathering have created massive data sets with better spatial and temporal resolution than ever before. These large spatiotemporal data sets have motivated a challenge for Geoinformatics: how to model changes and design good quality software. Many existing spatiotemporal data models represent how objects and fields evolve over time. However, to properly capture changes, it is also necessary to describe events. As a contribution to this research, this article presents an algebra for spatiotemporal data. Algebras give formal specifications at a high‐level abstraction, independently of programming languages. This helps to develop reliable and expressive applications. Our algebra specifies three data types as generic abstractions built on real‐world observations: time series, trajectory and coverage. Based on these abstractions, it defines object and event types. The proposed data types and functions can model and capture changes in a large range of applications, including location‐based services, environmental monitoring, public health, and natural disasters.

A Semantic-based Spatio-temporal Data Model for Internet of Things

A Semantic-based Spatio-temporal Data Model for Internet of Things

Spatial data model for underground mine 3D visual production management and control system

In order to realize the visualization,spatial analysis,automatic modeling and dynamic updating of model,the mine spatial environment and traditional 3D spatial data models were analyzed.Then an entity-oriented spatio-temporal hybrid data model was designed.It integrated the 3D parameter skeleton model,Topology-concerned and Entity-oriented 3D Vector Data Model(TEVDM) and Octree-Block Model(OBM).The TEVDM and OBM were used to express the boundary and internal property of orebodys.The Parametric Entity-Network Data Model(PENDM) was introduced into the model to describe the skeleton of the roadway engineering and production systems.Particularly,a behavior model was introduced into the model to describe the personnel and devices with behavior characteristics.The model also included the complete spatial feature which could describe the set of semantically related entities.Finally,some samples of using this model were given to describe the mines' spatial phenomenon and carry out spatial analysis.The samples show this model has better practicability in mining than traditional models.

A FRAMEWORK FOR SPATIO-TEMPORAL DATA WAREHOUSE

Data Warehouse (DW) is topic-oriented, integrated, static datasets which are used to support decision-making. Driven by the constraint of mass spatio-temporal data management and application, Spatio-Temporal Data Warehouse (STDW) was put forward, and many researchers scattered all over the world focused their energy on it.Although the research on STDW is going in depth , there are still many key difficulties to be solved, such as the design principle, system framework, spatio-temporal data model (STDM), spatio-temporal data process (STDP), spatial data mining (SDM) and etc. In this paper, the concept of STDW is discussed, and analyzes the organization model of spatio-temporal data. Based on the above, a framework of STDW is composed of data layer, management layer and application layer. The functions of STDW should include data analysis besides data process and data storage. When users apply certain kind of data services, STDW identifies the right data by metadata management system, then start data processing tool to form a data product which serves the data mining and OLAP. All varieties of distributed databases (DDBs) make up data sources of STDW, including Digital Elevation Model (DEM), Diagnosis-Related Group (DRG), Data Locator Group (DLG), Data Objects Management (DOM), Place Name and other databases in existence. The management layer implements heterogeneous data processing, metadata management and spatio-temporal data storage. The application layer provides data products service, multidimensional data cube, data mining tools and on-line analytical process.

Spatio-Temporal Database and Its Models: A Review

This paper focus on the study of spatial temporal database and its models and different types of applications where dynamic modeling of spatial temporal database can be used, this is an emerging field which places lot of contribution in DBMS with the aspects of the real world. Spatio-Temporal data models are the heart of a Spatio-Temporal Information System (STIS); they describe object data types, relationships, operations and rules to maintain database integrity. A rigorous data model must anticipate spatio-temporal queries and analytical methods to be performed in the STIS. Spatio-temporal database models are proposed to deal with real world applications, where spatial changes occur over the time line. A serious weakness of existing models is that each of them deals with few common characteristics found across a number of specific applications. Thus the applicability of the model to different cases, fails on spatio-temporal behaviors not anticipated by the application used for the initial model development. Keywords- Land Information System (LIS), Geographical Information System (GIS), Spatial-temporal databases, STIS.

Spatiotemporal Modeling for Disaster in Indonesia A Conceptual Model

Abstract Disaster management in Indonesia requires data management which is based on the whole cycle of disaster that occurs in Indonesia. A Disaster Data Management Research in Indonesia, named DIMaS (Disaster Management Information System), has been carried out in 2009. This model has been developed as a disaster information system model which is based only on textual mode without observing the geographic and timing information aspects. Nowadays the role of Spatial Temporal Information Systems has widely used to support data management. This spatial and timing aspect will complete the disaster information not only in textual mode but it will give disaster information with a graphical mode and specific time frame. An opportunity arise to analyze historical disaster information and to forecast disaster with spatial and temporal information. Finally how a spatiotemporal data model for disaster management can be applied to the Dimas system which is unique for Indonesia disaster information system and its environment will be described.