Spatio-temporal Data Model Research Articles

Integration of Traffic Information into the Path Planning among Moving Obstacles

This paper investigates the integration of traffic information (TI) into the routing in the presence of moving obstacles. When traffic accidents occur, the incidents could generate different kinds of hazards (e.g., toxic plumes), which make certain parts of the road network inaccessible. On the other hand, the first responders, who are responsible for management of the traffic incidents, need to be fast and safely guided to the incident place. To support navigation in the traffic network affected by moving obstacles, in this paper, we provide a spatio-temporal data model to structure the information of traffic conditions that is essential for the routing, and present an extended path planning algorithm, named MOAAstar–TI (Moving Obstacle Avoiding A* using Traffic Information), to generate routes avoiding the obstacles. A speed adjustment factor is introduced in the developed routing algorithm, allowing integration of both the information of vehicles and traffic situations to generate routes avoiding the moving obstacles caused by the incidents. We applied our system to a set of navigation scenarios. The application results show the potentials of our system in future application in real life.

Consistencies of fuzzy spatiotemporal data in XML documents

Researches on spatiotemporal data based on XML has received increasing attention due to that XML has a lot of advantages such as extensibility and flexibility. Although XML has been employed to model and handle spatiotemporal data, relatively little work has been carried out to further investigate the consistencies of spatiotemporal data, especially fuzzy spatiotemporal data in XML documents. In this paper, we first propose a fuzzy spatiotemporal data model, and then present the structure of fuzzy spatiotemporal data in XML document. After studying consistency conditions for fuzzy spatiotemporal data in XML documents, we demonstrate how updating operations, inserting operations, and deleting operations effect on consistencies of fuzzy spatiotemporal data in XML documents. Furthermore, we propose algorithms for fixing these inconsistencies. After investigating several characteristics of the three primitive changing operations on the fuzzy spatiotemporal data model, the performances of inconsistency fixing time are evaluated.

Fixing inconsistencies of fuzzy spatiotemporal XML data

Fuzzy spatiotemporal data models have been used to support spatial and temporal knowledge representation and reasoning in the presence of fuzziness. In the meantime, XML is expected to become the next generation standard language for exchanging data over the Internet, which will become a trend to represent fuzzy spatiotemporal data based on XML. However, fuzzy spatiotemporal XML documents may contain inconsistencies violating predefined spatial and temporal constraints, which cause the data inconsistency problems. Although those consistency problems in XML documents have been widely studied, their studies only take the general data into account, and the studies on consistencies of fuzzy spatiotemporal data are still open issues. In this paper we put forward solutions to the problems of inconsistencies in fuzzy spatiotemporal XML documents. We also analyze inconsistent states which are named discontinuity overlap or cycle of the temporal labels of some incoming edges. Then, we put forward the corresponding approaches to checking and fixing fuzzy spatiotemporal XML documents according to the inconsistent states. Finally, the experimental results show that our proposed algorithms can fix inconsistencies of fuzzy spatiotemporal XML documents significantly.

Modeling Environmental Impacts on Viticultural Ecosystems

This paper focuses on simulating environmental impacts on grapevine behavioral dynamics and vineyard management strategies. The methodology presented uses technology from geomatics object oriented databases and spatio-temporal data models. Our approach has two principle objectives, first, to simulate grapevine phenology and grape ripening under spatial and temporal environmental conditions and constraints and secondly, to simulate viticultural practices and adaptation strategies under various constraints (environmental, economical, socio-technical). The approach is based on a responsive agent-based structure where environmental conditions and constraints are considered as a set of forcing data (biophysical, socio-economic and regulatory data) that influences the modelled activities. The experiment was conducted in the regulated wine producing appellation Grand Cru “Quarts de Chaume”, situated in the middle Loire Valley, France. All of the methodology, from the implementation of the knowledge database to the analysis of the first simulation, is presented in this paper.

Reengineering Fuzzy Spatiotemporal UML Data Model Into Fuzzy Spatiotemporal XML Model

Spatiotemporal information and application have received considerable attention. Spatiotemporal information is often imprecise or uncertain; therefore, the establishment of a reasonable and effective fuzzy spatiotemporal data model is vital. The traditional tool-based modeling method cannot meet the needs of fuzzy spatiotemporal data modeling. XML has become the standard of Web data representation and exchange. Based on the fuzzy set and probability theory, this paper analyzes the characteristics of fuzzy spatiotemporal data and establishes a fuzzy spatiotemporal XML data model. We present a fuzzy spatiotemporal UML data model and provide the rules for converting the fuzzy spatiotemporal UML data model to the fuzzy spatiotemporal XML model. We demonstrate our model and the conversion by applying them to a meteorological event.

Towards a Three-Level Framework for IoT Redundancy Control through an Explicit Spatio-Temporal Data Model

Abstract: In this paper we present an ongoing work towards the implementation of a framework that tackles service redundancy in IoT/WSNs as an explicit spatio-temporal phenomenon. From this perspective, redundancy is measured and explicitly stored using a spatio-temporal data model. The expected advantages of keeping an explicit history of redundancy evolution in space and time are to compare different redundancy control algorithms, to apply different knowledge extraction techniques in order to identify possible redundancy patterns, and to implement more proactive redundancy control strategies. In this paper we focus on the data model that we propose to control service redundancy at three scales: macro, meso and micro scales, respectively.

A Conceptual Data Model for Flood Based on Cellular Automata Using Moving Object Data Model

Flood is considered as the costliest natural disaster in Indonesia due to its frequent occurrences as well as the extensive damage that it causes. Several studies provide different flood prediction models based on various hydrological factors. A lot of these models use grid-to-grid approach, making them suitable to be modelled as cellular automata. This paper presents a conceptual data model for flood based on cellular automata model using spatio-temporal data model, especially the moving object data model, as the modelling approach. The conceptual data model serves as the model of data structures within an environment for flood prediction simulation. We describe two conceptual data models as the alternatives to model the data structures of flood model. We create the data model based on the study to the factors that constitute the flood models. The first conceptual data model alternative focuses on the cell/grid as the main entity type. The changes of the states of the cells are stored as moving integer. The second alternative emphasizes on flood as the main entity type. The changes of the flood area are stored as moving region. Both alternatives introduce some advantages and disadvantages and the choice rely on the purpose of the use of the data model. We present a proposal of the architecture of a flood prediction system using cellular automata as the modelling approach. As the continuation of this work, further design and implementation details must be provided.

Bayesian non-parametric modeling for integro-difference equations

Integro-difference equations (IDEs) provide a flexible framework for dynamic modeling of spatio-temporal data. The choice of kernel in an IDE model relates directly to the underlying physical process modeled, and it can affect model fit and predictive accuracy. We introduce Bayesian non-parametric methods to the IDE literature as a means to allow flexibility in modeling the kernel. We propose a mixture of normal distributions for the IDE kernel, built from a spatial Dirichlet process for the mixing distribution, which can model kernels with shapes that change with location. This allows the IDE model to capture non-stationarity with respect to location and to reflect a changing physical process across the domain. We address computational concerns for inference that leverage the use of Hermite polynomials as a basis for the representation of the process and the IDE kernel, and incorporate Hamiltonian Markov chain Monte Carlo steps in the posterior simulation method. An example with synthetic data demonstrates that the model can successfully capture location-dependent dynamics. Moreover, using a data set of ozone pressure, we show that the spatial Dirichlet process mixture model outperforms several alternative models for the IDE kernel, including the state of the art in the IDE literature, that is, a Gaussian kernel with location-dependent parameters.

Spatiotemporal social (STS) data model: correlating social networks and spatiotemporal data

A location-based social network is a network representation of social relations among actors, which not only allow them to connect to other users/friends but also they can share and access their physical locations. Here, the physical location consists of the instant location of an individual at a given timestamp and the location history that an individual has accumulated in a certain period. This paper aimed to capture this spatiotemporal social network (STS) data of location-based social networks and model it. In this paper, we propose a STS data model which captures both non-spatial and spatial properties of moving users, connected on social network. In our model, we define data types and operations that make querying spatiotemporal social network data easy and efficient. We extend spatiotemporal data model for moving objects proposed in Ferreira et al. (Trans GIS 18(2):253–269, 2014) for social networks. The data model infers individual’s location history and helps in querying social network users for their spatiotemporal locations, social links, influences, their common interests, behavior, activities, etc. We show the some results of applying our data model on a spatiotemporal dataset (GeoLife) and two large real-life spatiotemporal social network datasets (Gowalla, Brightkite) collected over a period of two years. We apply the proposed model to determine interesting locations in the city and correlate the impact of social network relationships on the spatiotemporal behavior of the users.

Hair-oriented data model for spatio-temporal data representation

Having an effective data structure regards to fast data changing is one of the most important demands in spatio-temporal data. Spatio-temporal data have special relationships in regard to spatial and temporal values. Both types of data are complex in terms of their numerous attributes and the changes exhibited over time. A data model that is able to increase the performance of data storage and inquiry responses from a spatio-temporal system is demanded. The structure ofthe relationships between spatio-temporal data mimics the biological structure of the hair, which has a ‘Root’ (spatial values) and a ‘Shaft’ (temporal values) and undergoes growth. This paper aims to show the mathematical formulation of a Hair-Oriented Data Model (HODM) for spatio-temporal data and to demonstrate the model's performance by measuring storage size and query response time. The experiment was conducted by using more than 178,000 records of climate change spatio-temporal data that were implemented in implemented in an object-relational database using nested tables. The data structure and operations are implemented by SQL statements that are related to the concepts of Object-Relational databases. The performances of file storage and execution query are compared using a tabular and normalized entity relationship model that engages various types of queries. The results show that HODM has a lower storage size and a faster query response time for all studied types of spatio-temporal queries. The significances of the work are elaborated by doing comparison with the generic data models. The experimental results showed that the proposed data model is easier to develop and more efficient.

NONSEPARABLE DYNAMIC NEAREST NEIGHBOR GAUSSIAN PROCESS MODELS FOR LARGE SPATIO-TEMPORAL DATA WITH AN APPLICATION TO PARTICULATE MATTER ANALYSIS.

Particulate matter (PM) is a class of malicious environmental pollutants known to be detrimental to human health. Regulatory efforts aimed at curbing PM levels in different countries often require high resolution space-time maps that can identify red-flag regions exceeding statutory concentration limits. Continuous spatio-temporal Gaussian Process (GP) models can deliver maps depicting predicted PM levels and quantify predictive uncertainty. However, GP-based approaches are usually thwarted by computational challenges posed by large datasets. We construct a novel class of scalable Dynamic Nearest Neighbor Gaussian Process (DNNGP) models that can provide a sparse approximation to any spatio-temporal GP (e.g., with nonseparable covariance structures). The DNNGP we develop here can be used as a sparsity-inducing prior for spatio-temporal random effects in any Bayesian hierarchical model to deliver full posterior inference. Storage and memory requirements for a DNNGP model are linear in the size of the dataset, thereby delivering massive scalability without sacrificing inferential richness. Extensive numerical studies reveal that the DNNGP provides substantially superior approximations to the underlying process than low-rank approximations. Finally, we use the DNNGP to analyze a massive air quality dataset to substantially improve predictions of PM levels across Europe in conjunction with the LOTOS-EUROS chemistry transport models (CTMs).

A Spatio-Temporal VGI Model Considering Trust-Related Information

Over the past several years, volunteered geographic information (VGI) has expanded rapidly. VGI collection has been proven to serve as a highly successful means of acquiring timely and detailed global spatial data. However, VGI includes several special properties. For example, the contributor’s reputation affects the quality of objects edited, and a geographic object may have multiple versions. The existing spatio-temporal data model cannot describe the unique properties of VGI. Therefore, a spatio-temporal VGI model considering trust-related information is presented in this paper. In this model, central elements of the VGI environment, e.g., geographic entity, entity state, state version, contributor, reputation, geographic event, and edit event, and their interaction mechanisms are analysed. Major VGI objects and relations are determined using the object-oriented method and trust-related operations, and their relationships are analysed, and nine linkage rules among trust-related operations are found to maintain the consistency of corresponding data. A prototype system for the spatio-temporal VGI model is presented, and the effectiveness of the model is verified.

Spatiotemporal Model Fusion: Multiscale Modelling of Civil Unrest

SummaryCivil unrest is a complicated, multifaceted social phenomenon that is difficult to forecast. Relevant data for predicting future protests consist of a massive set of heterogeneous sources of data, primarily from social media. Using a modular approach to extract pertinent information from disparate sources of data, we develop a spatiotemporal multiscale framework to fuse predictions from algorithms mining social media. This novel multiscale spatiotemporal model is developed to satisfy four essential requirements: be scalable to handle massive spatiotemporal data sets, incorporate hierarchical predictions, accommodate predictions of differing quality and uncertainty, and be flexible, allowing revisions to existing algorithms and the addition of new algorithms. The paper details the challenges that are posed by these four requirements and outlines the benefits of our novel multiscale spatiotemporal model relative to existing methods. In particular, our multiscale approach coupled with an efficient sequential Monte Carlo framework enables scalable rapid computation of richly specified Bayesian hierarchical models for spatiotemporal data.

Spatiotemporal data model for network time geographic analysis in the era of big data

ABSTRACTThere has been a resurgence of interest in time geography studies due to emerging spatiotemporal big data in urban environments. However, the rapid increase in the volume, diversity, and intensity of spatiotemporal data poses a significant challenge with respect to the representation and computation of time geographic entities and relations in road networks. To address this challenge, a spatiotemporal data model is proposed in this article. The proposed spatiotemporal data model is based on a compressed linear reference (CLR) technique to transform network time geographic entities in three-dimensional (3D) (x, y, t) space to two-dimensional (2D) CLR space. Using the proposed spatiotemporal data model, network time geographic entities can be stored and managed in classical spatial databases. Efficient spatial operations and index structures can be directly utilized to implement spatiotemporal operations and queries for network time geographic entities in CLR space. To validate the proposed spatiotemporal data model, a prototype system is developed using existing 2D GIS techniques. A case study is performed using large-scale datasets of space-time paths and prisms. The case study indicates that the proposed spatiotemporal data model is effective and efficient for storing, managing, and querying large-scale datasets of network time geographic entities.

Data environment construction for virtual geographic environment

Virtual geographic environment (VGE) aims to express the real-world naturally, and support the complex geographic analysis. The data environment, fundamental of VGE, is expected to support the data management, analysis, sharing and application requirements of the massive complex geographic spatio-temporal data. In this paper, we summarized the key problems in the construction of the data environment of VGE. The unified spatio-temporal data model and a new data structure were developed according to the geographic rules. The organization and compress storage mechanism of massive spatio-temporal data were also developed. With these foundations, case studies, which integrate the global, regional and city scale data to operate complex data modeling and analysis, are performed. The results showed that the construction of the integrated data environment of VGE can largely improve the efficiency of GIS analysis, which also provides a potential new tool to support the complex geographic analysis.

Preparing The National Map for the 3D Elevation Program – products, process and research

The 3D Elevation Program (3DEP) is a collaborative effort among government entities, academia, and the private sector to collect high-resolution 3-dimensional data over the United States. The United States Geological Survey (USGS) is making preparations for managing, processing, and delivering petabytes of 3DEP elevation products for the Nation. In addition to the existing 1/3, 1, and 2 arc-second seamless elevation data layers of The National Map, new 3DEP products include lidar point cloud data; a standard 1-meter DEM layer; additional source datasets; and, in Alaska, 5-meter digital elevation models. A new product generation system improves the construction and publication of the seamless elevation datasets, prepares the additional 3DEP products for distribution, and automates the data management functions required to accommodate the high-volume 3DEP data collection. Major changes in geospatial data acquisition, such as high resolution lidar data, volunteered geographic information, data processing using parallel and grid computer systems, and user needs for semantic access to geospatial data and products, are driving USGS research associated with the 3DEP. To address the research requirements, a set of inter-related projects including spatiotemporal data models, data integration, geospatial semantics and ontology, high performance computing, multi-scale representation, and hydrological modeling using lidar and other 3DEP data has been developed.

Spatio-Temporal Features of China’s Urban Fires: An Investigation with Reference to Gross Domestic Product and Humidity

Frequent fire accidents pose a serious threat to human life and property. The spatio-temporal features of China’s urban fires, and their drivers should be investigated. Based on the Spatio-temporal Dynamic panel data Model (SDM), and using fire data gathered from 337 Chinese cities in 2000 to 2009, the influence of spatio-temporal factors on the frequency of urban fires was analyzed. The results show that (1) the overall fire incidence of China increased annually before 2002 and reduced significantly after 2003, and then high fire incidence increased in western China; (2) Spatio-temporal factors play a significant role in the frequency of Chinese urban fires; specifically, the fire assimilation effect, fire inertia effect and fire caution effect. The ratio of fire incidence of China has reduced significantly, and the focus of fire incidence moved towards the western region of China. GDP and humidity have a significant effect on urban fire situation change in China, and these effects may be referred to as “fire assimilation effects”, “fire inertia effects” and “fire caution effects”.

2차원 지도용 시계열 공간 데이터 모델과 구축방법

2차원 지도용 시계열 공간 데이터 모델과 구축방법

Incorporating fuzziness in spatiotemporal XML and transforming fuzzy spatiotemporal data from XML to relational databases

Since XML could benefit greatly from database support and more specifically from relational database systems, we study the methodology of modeling fuzzy spatiotemporal data in XML and transforming fuzzy spatiotemporal data from XML to relational databases as well. To accomplish this, we devise a fuzzy spatiotemporal data model in XML to capture the semantics of fuzzy spatiotemporal features. To allow for better and platform independent sharing of fuzzy spatiotemporal data stored in a relational format, we propose a temporal edge approach to transform fuzzy spatiotemporal XML data into relational databases. The unique feature of our approach is that no schema information is required for transformation of fuzzy spatiotemporal data. Moreover, temporal, spatial, and fuzzy features of fuzzy spatiotemporal data in XML documents are taken into consideration. Finally, the experimental results demonstrate the performance advantages of our approach. Such approach of transformation could provide a significant consolidation of the interoperability of fuzzy spatiotemporal data from XML to relational databases.

A scalable framework for spatiotemporal analysis of location-based social media data

In the past several years, social media (e.g., Twitter and Facebook) has experienced a spectacular rise in popularity and has become a ubiquitous location for discourse, content sharing and social networking. With the widespread adoption of mobile devices and location-based services, social media typically allows users to share the whereabouts of daily activities (e.g., check-ins and taking photos), thus strengthening the role of social media as a proxy for understanding human behaviors and complex social dynamics in geographic spaces. Unlike conventional spatiotemporal data, this new modality of data is dynamic, massive, and typically represented in a stream of unstructured media (e.g., texts and photos), which pose fundamental representation, modeling and computational challenges to conventional spatiotemporal analysis and geographic information science. In this paper, we describe a scalable computational framework to harness massive location-based social media data for efficient and systematic spatiotemporal data analysis. Within this framework, the concept of space–time trajectories (or paths) is applied to represent activity profiles of social media users. A hierarchical spatiotemporal data model, namely a spatiotemporal data cube model, is developed based on collections of space–time trajectories to represent the collective dynamics of social media users across aggregation boundaries at multiple spatiotemporal scales. The framework is implemented based upon a public data stream of Twitter feeds posted on the continent of North America. To demonstrate the advantages and performance of this framework, an interactive flow mapping interface (including both single-source and multiple-source flow mapping) is developed to allow real-time and interactive visual exploration of movement dynamics in massive location-based social media data at multiple scales.