Spatio-temporal Data Model Research Articles

Fuzzy Spatiotemporal Data Modeling Based on UML

With the wide application of spatiotemporal data, more and more data need to be modeled. Fuzziness is one of the important characteristics of spatiotemporal data, but most of the existing spatiotemporal data models are regarded as accurate data and most of the spatiotemporal data models are static. The purpose of this paper is to build a fuzzy spatiotemporal data model based on UML by expanding the standard modeling language UML. On this basis, the historical topological state, effective time, and transaction time are added to make the model dynamic, that is, the states of multiple times are combined together to form a timeline to represent the development process of a fuzzy spatiotemporal entity. Considering the fuzziness of data and the dynamic development of objects, the model can describe the fuzzy spatiotemporal objects better, and make the model dynamic. At the same time, the operation of the UML class diagram is added to make the model data exercisable and compared according to their own needs, which increases the practicability of the model. Finally, an example of land desertification in Alashan is given, to prove the practicability of the model constructed in this paper.

A Novel Spatiotemporal Data Model for River Water Quality Visualization and Analysis

River water quality (RWQ) data has obvious characteristics of spatial and temporal distribution, and tables are conventionally exploited for storage of multi-period monitoring data of RWQ; however, neither effective visualization nor accurate analysis of the obtained data can be realized due to its dispersion character. In this paper, a novel spatiotemporal data model is proposed for RWQ data to realize conveniently data representation and spatiotemporal analysis. In this model, a spatial point, containing both location and dynamic water quality information, is considered as the basic element of river spaces, and methods of expanding a point to a line segment, a flat surface and a cube are designed respectively so as to make this model be applicable to different generalizations of river spaces. Moreover, a temporal data storage structure is designed so that efficient inquiry and advanced analysis of RWQ data can be guaranteed and the occupied memory space can be reduced. Finally, case studies are conducted by performing 3D visualization, trend analysis and anomaly identification on RWQ data, the result of which showing that tridimensional representation of RWQ data can be realized efficiently, the computational complexity is reduced significantly and the occupied memory space of monitoring data is effectively economized. Accordingly, the proposed spatiotemporal data model can contribute to the efficient visualization and advanced analysis of RWQ data.

Spatial-feature data cube for spatiotemporal remote sensing data processing and analysis

With the rapid development in Earth observation technology, a variety of satellite sensors have provided large and open sets of remote sensing data. However, traditional methods of analysis are no longer available for time-serial remote sensing data analysis that typically handles multidimensional spatio-temporal data models. Moreover, researchers have found it trivial and tedious to obtain ready-to-analyze data for Earth science models from regular Earth observation data. For an easy and efficient time-serial remote sensing data analysis, a spatial-featured data cube analysis tool based on multidimensional data model is proposed for time-serial remote sensing data processing and analysis. For the performance consideration, a distributed execution engine was also used for efficient implementation of large-scale tasks in parallel. Finally, through experiments on both normalized difference vegetation index product and water detection within a 20-year period, we confirmed that our approach is efficient and scalable for a long time-series analysis.

A NEW PROCESS-ORIENTED AND SPATIOTEMPORAL DATA MODEL FOR GIS DATA

Abstract. With the rapid development of wireless sensor and information technology, there is a trend of transition from "digital monitoring" to "intelligence monitoring" advancing process. The traditional model cannot completely match the dynamic data to accurately describe changes of geographical and environmental changes. In this paper, we try to build a process-oriented and real-time spatiotemporal data model to meet the demands. With various types of monitoring devices, detection methods and the utilization of new technologies, the model can simulate the possible waterlog area in a specific year by analyzing the given data. By testing and modifying the spatiotemporal model, we can come to a rational conclusion that our model can forecast the actual situation in certain extent.

A Survey of Modelling Trends in Temporal GIS

The main achievements of spatio-temporal modelling in the field of Geographic Information Science that spans the past three decades are surveyed. This article offers an overview of: (i) the origins and history of Temporal Geographic Information Systems (T-GIS); (ii) relevant spatio-temporal data models proposed; (iii) the evolution of spatio-temporal modelling trends; and (iv) an analysis of the future trends and developments in T-GIS. It also presents some current theories and concepts that have emerged from the research performed, as well as a summary of the current progress and the upcoming challenges and potential research directions for T-GIS. One relevant result of this survey is the proposed taxonomy of spatio-temporal modelling trends, which classifies 186 modelling proposals surveyed from more than 1,450 articles.

Big data and reliability applications: The complexity dimension

ABSTRACTBig data features not only large volumes of data but also data with complicated structures. Complexity imposes unique challenges on big data analytics. Meeker and Hong (2014; Quality Engineering, pp. 102–16) provided an extensive discussion of the opportunities and challenges on big data and reliability; they also described engineering systems which generate big data that can be used in reliability analysis. Meeker and Hong (2014) focused on large-scale system operating and environment data (i.e., high-frequency multivariate time series data) and provided examples on how to link such data as covariates to traditional reliability responses such as time to failure, time to recurrence of events, and degradation measurements. This article intends to extend that discussion by focusing on how to use data with complicated structures to do reliability analysis. Such data types include high-dimensional sensor data, functional curve data, and image streams. We first provide a review of recent developments in those directions, then we provide a discussion on how analytical methods can be developed to tackle the challenging aspects that arise from the complex features of big data in reliability applications. The use of modern statistical methods such as variable selection, functional data analysis, scalar-on-image regression, spatio-temporal data models, and machine-learning techniques will also be discussed.

PortoLivingLab: An IoT-Based Sensing Platform for Smart Cities

Smart cities aim to improve the citizens’ quality of life by leveraging information about urban scale processes extracted from heterogeneous data sources collected on city-wide deployments. The Internet-of-Things (IoT) is, thus, the enabler of smart city technologies at urban scale. In this paper, we present PortoLivingLab, a multisource sensing infrastructure that leverages IoT technology to achieve city-scale sensing of four phenomena: weather, environment, public transport, and people flows. To sense these processes on a city scale, we deployed a vehicular network with over 600 vehicles and 19 static environmental sensors. We also developed an easily reconfigurable crowdsensing platform and carried out several crowdsensing campaigns with more than 600 participants. The data is collected in a common backend and stored using similar spatio-temporal data models to simplify sharing and joint analysis for the characterization of urban dynamics. We describe the architecture and composing elements of PortoLivingLab, highlighting the IoT technologies, and challenges faced. We present several proof-of-concept use cases (e.g., passenger flows from WiFi connections) that provide new insights into different components of an evolving and moving city. Finally, we lay out the future lines of work that will strive for finding hidden phenomena by leveraging data from the three complementary platforms.

Spatio-temporal data mining and modeling: distribution pattern and governance input efficiency of heavy metal emission in industrial wastewater, China

Abstract Heavy metal (HM) in industrial wastewater has been one of the serious environmental issues in China for a long time. This paper analyzes the distribution of HMs and governance input efficiency in industrial wastewater based on the archival data of China Statistical Yearbook on Environment from 2001 to 2014. The empirical analysis shows that the concentrations of Hg, Cd, Pb, As, and Cr(VI) generally decreased from 2001 to 2014. The emissions of Hg, Cd, Pb, and As are mostly concentrated in the central provinces (i.e., Hunan, Hubei, Jiangxi), the southern provinces (i.e., Guangxi and Guangdong), and the northern provinces (i.e., Gansu and Inner Mongolia). The distribution pattern is closely related to local industry due to resources dependence, such as mining and processing of non-ferrous metal ores, smelting and pressing of ferrous or non-ferrous metals. Cr(VI) is mainly located in the eastern coastal provinces, including Zhejiang and Jiangsu, and caused by manufacturing industries such as automobile, metal products, leather, fur, feather and related products, and footware. Furthermore, we find that the annual expenditure on and the capacity to deal with industrial wastewater play significant negative effects on reducing HM concentrations in industrial wastewater.

Transformation of fuzzy spatiotemporal data from XML to object-oriented database

The rapid development of the Internet promotes that XML is increasingly accepted as a medium for integrating and exchanging data. Meanwhile, object-oriented database with many excellent properties has been applied more and more widely. In particular, considering the need of processing the fuzzy spatiotemporal data existing in practical applications, we study the methodology of building fuzzy spatiotemporal data model and transforming fuzzy spatiotemporal data from XML to object-oriented database as well. To establish a suitable model, we devise a fuzzy spatiotemporal data model in XML to capture the semantics of fuzzy spatiotemporal features. In addition, we take the object-oriented schema that best describes the exciting fuzzy XML Schema and the characteristics of the XML tree structure into account to develop a series of rules to map spatiotemporal data from XML to object-oriented database. Such mapping rules could provide a significant consolidation of the interoperability of fuzzy spatiotemporal data from XML to object-oriented databases.

Spatiotemporal incidence rate data analysis by nonparametric regression.

To monitor the incidence rates of cancers, AIDS, cardiovascular diseases, and other chronic or infectious diseases, some global, national, and regional reporting systems have been built to collect/provide population-based data about the disease incidence. Such databases usually report daily, monthly, or yearly disease incidence numbers at the city, county, state, or country level, and the disease incidence numbers collected at different places and different times are often correlated, with the ones closer in place or time being more correlated. The correlation reflects the impact of various confounding risk factors, such as weather, demographic factors, lifestyles, and other cultural and environmental factors. Because such impact is complicated and challenging to describe, the spatiotemporal (ST) correlation in the observed disease incidence data has complicated ST structure as well. Furthermore, the ST correlation is hidden in the observed data and cannot be observed directly. In the literature, there has been some discussion about ST data modeling. But, the existing methods either impose various restrictive assumptions on the ST correlation that are hard to justify, or ignore partially or entirely the ST correlation. This paper aims to develop a flexible and effective method for ST disease incidence data modeling, using nonparametric local smoothing methods. This method can properly accommodate the ST data correlation. Theoretical justifications and numerical studies show that it works well in practice.

Generalized linear mixed models for spatio-temporal data with an application to Leptospirosis in Thailand

Generalized linear mixed models for spatio-temporal data with an application to Leptospirosis in Thailand

Reengineering Object-Oriented Fuzzy Spatiotemporal Data into XML

With the rapid development of the Internet, XML has become the defacto standard for integrating and exchanging data. Since more and more business data are stored in object-oriented database, we study the methodology of modeling fuzzy spatiotemporal data and transforming fuzzy spatiotemporal data from object-oriented databases to XML as well. In order to allow for better and platform independent sharing of fuzzy spatiotemporal data stored in an object-oriented format, we devise a fuzzy spatiotemporal data model in object-oriented database to capture the semantics of spatiotemporal features. In particular, XML schema best describes the existing fuzzy object-oriented schema, and we investigate the transforming rules of spatiotemporal data from fuzzy object-oriented database to XML. Furthermore, an instance demonstrates the validation of our approach. Such approach of transformation can provide a significant consolidation of the interoperability of fuzzy spatiotemporal data from object-oriented databases to XML.

Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales

Distance sampling is a widely used method for estimating wildlife population abundance. The fact that conventional distance sampling methods are partly design-based constrains the spatial resolution at which animal density can be estimated using these methods. Estimates are usually obtained at survey stratum level. For an endangered species such as the blue whale, it is desirable to estimate density and abundance at a finer spatial scale than stratum. Temporal variation in the spatial structure is also important. We formulate the process generating distance sampling data as a thinned spatial point process and propose model-based inference using a spatial log-Gaussian Cox process. The method adopts a flexible stochastic partial differential equation (SPDE) approach to model spatial structure in density that is not accounted for by explanatory variables, and integrated nested Laplace approximation (INLA) for Bayesian inference. It allows simultaneous fitting of detection and density models and permits prediction of density at an arbitrarily fine scale. We estimate blue whale density in the Eastern Tropical Pacific Ocean from thirteen shipboard surveys conducted over 22 years. We find that higher blue whale density is associated with colder sea surface temperatures in space, and although there is some positive association between density and mean annual temperature, our estimates are consistent with no trend in density across years. Our analysis also indicates that there is substantial spatially structured variation in density that is not explained by available covariates.

Factor copula models for data with spatio-temporal dependence

We propose a new copula model for spatial data that are observed repeatedly in time. The model is based on the assumption that there exists a common factor that affects the measurements of a process in space and in time. Unlike models based on multivariate normality, our model can handle data with tail dependence and asymmetry. The likelihood for the proposed model can be obtained in a simple form and therefore parameter estimation is quite fast. Simulation from this model is straightforward and data can be predicted at any spatial location and time point. We use simulation studies to show different types of dependencies, both in space and in time, that can be generated by this model. We apply the proposed copula model to hourly wind data and compare its performance with some classical models for spatio-temporal data.

Spatio-temporal Network-Constrained Trajectory Data Model and Service Reliability Assessment

ABSTRACTDue to urbanization and increase in traffic density, provision of efficient public transport facility is a major component of planning and development. The spatio-temporal data of vehicles’ movement have mainly led to mobility pattern analysis, which eventually reveal new information about the efficiency of transport service. This research focuses on extracting mobility patterns of bus service for students in Lahore City. Trajectory data collected through GPS survey have been processed by using MYSQL spatial database applications and HTML to identify areas in entire route of bus service causing delay. Three different types of delay have been calculated, namely stop delay, simple path delay, and path repeated delay. The entire analysis results reveal that mainly the delay is caused at start segments of planned route or the last segment of stop route. The major cause of delay is bus movement on tertiary roads through congested commercial areas.

Uncertain spatiotemporal data modeling and algebraic operations based on XML

The problem of modeling and operating spatiotemporal data has received a great deal of interest, due to its various applications in the real world such as GIS and sensor database. A wide range of work covering spatial data, temporal data and spatiotemporal data assumes that the data is known, accurate and complete. But in reality, information is often imprecise and imperfect. In addition, traditional data models which are investigating in the context of traditional database suffer from some inadequacy of necessary semantics such as inability to handle imprecise and uncertain information. Consequently, the advent of XML, which has the advantages of simplicity, readability and extensibility, seems to provide an opportunity for modeling and operating uncertain spatiotemporal data. Hence, the new problem that emerges is how to model and operate uncertain spatiotemporal data in XML. Therefore, in this paper, we establish an uncertain spatiotemporal data model based on XML. Then, on the basis of the model we provide a set of algebraic operations for capturing and handling uncertain spatiotemporal data. By employing algebraic operations, we demonstrate how to translate queries expressed in XQuery to our algebra. A translation example shows that our algebraic operations are full of expressive power and illustrates that our algebra can be applied to general data. Apart from this, we also propose a set of equivalence rules to optimize the process of query and give an example to show how the optimization approach works.

Study on the construction of historical toponymic information management system for Essence of the Geographical Content of the History Books

ABSTRACTOver the last decades, to promote the study of historical geography, a number of institutions have constructed historical GIS platforms. Correspondingly, a large number of historical studies require more detailed information on historical toponyms, and there is an urgent need for improving and expanding the toponymic information on the existing historical GIS platforms. Therefore, in this paper, we selected Du Shi Fang Yu Ji Yao, one of the most representative books of historical geography, as the data source. We analyzed the geographic information in the book, especially the structure and characteristics of the toponymic information, according to the particularities of administrative regions in the Ming Dynasty, and designed a spatio-temporal data model for both administrative and non-administrative region toponyms. The both types of toponyms are used to construct an information database on more than 60,000 toponyms in Essence of the Geographical Content of the History Books that provides a convenient destination for inquiries by containing more abundant and precise toponymic information of the Ming Dynasty, and develop a toponymic information management system of the Essence of the Geographical Content of the History Books, in which the toponymys can be rapidly extracted, separated and used to establish relationships between them.

SPATIOTEMPORAL DATA ORGANIZATION AND APPLICATION RESEARCH

Abstract. Organization and management of spatiotemporal data is a key support technology for intelligence in all fields of the smart city. The construction of a smart city cannot be realized without spatiotemporal data. Oriented to support intelligent applications，this paper proposes an organizational model for spatiotemporal data, and details the construction of a spatiotemporal big data calculation, analysis, and service framework for highly efficient management and intelligent application of spatiotemporal data for the entire data life cycle.

An Annotated Bibliography on Spatio-temporal Modelling Trends

This article presents a brief overview and an annotated bibliography on the spatio-temporal modelling approaches and spatio-temporal data models that have been proposed since the late 1980s. While some models are theoretical, others are grounded upon specific case studies that have dealt with the evolution of spatial objects over time applied to scenarios such as land use, wildfire growth, road networks reconfiguration, or the analysis of objects in movement (moving objects). A taxonomy of 14 spatio-temporal modelling trends is described, the annotated bibliography is classified accordingly. Furthermore, three sections closely related to spatio-temporal modelling issues are also included: (i) ontological modelling foundations, (ii) temporal modelling, and (iii) time geography. These sections show the origins and part of the fundamentals of spatio-temporal modelling in GIS. The purpose of this bibliography, which consists of 319 references, is to compile a comprehensive set of references that can be of help to students and researchers, as well as to practitioners, interested in spatio-temporal modelling issues. This article is supported by an extended, dynamic, online bibliography which contains over 1450 references available at http://spaceandtime.wsiabato.info.

Affine-Invariant Triangulation of Spatio-Temporal Data with an Application to Image Retrieval

In the geometric data model for spatio-temporal data, introduced by Chomicki and Revesz [1], spatio-temporal data are modelled as a finite collection of triangles that are transformed by time-dependent affinities of the plane. To facilitate querying and animation of spatio-temporal data, we present a normal form for data in the geometric data model. We propose an algorithm for constructing this normal form via a spatio-temporal triangulation of geometric data objects. This triangulation algorithm generates new geometric data objects that partition the given objects both in space and in time. A particular property of the proposed partition is that it is invariant under time-dependent affine transformations, and hence independent of the particular choice of coordinate system used to describe the spatio-temporal data in. We can show that our algorithm works correctly and has a polynomial time complexity (of reasonably low degree in the number of input triangles and the maximal degree of the polynomial functions that describe the transformation functions). We also discuss several possible applications of this spatio-temporal triangulation. The application of our affine-invariant spatial triangulation method to image indexing and retrieval is discussed and an experimental evaluation is given in the context of bird images.