Space-time Data Models Research Articles

Building a Unified Spatio-Temporal Data Model for Grid Resources Based on Microservice Architecture

Under the background of accelerating the process of power grid construction, the unified spatial-temporal data model of power grid resources has become a necessary means to describe the relationship between spatial objects and power grid data. Affected by the defect of the information island, some unified spatiotemporal data models of power grid resources have poor updating performance. Therefore, a unified spatiotemporal data model of power grid resources based on microservice architecture is designed. The architecture can obtain the spatial structure elements of the power grid area, identify the spatial correlation characteristics of the modeling object through the distribution of power energy supply lines, eliminate the dimension of meteorological data variables, design a unified resource scheduling scheme based on the microservice architecture, calculate the space-time weight matrix, and build a space-time data model. Test results are that under the two update task scenarios, the average update performance of the unified spatiotemporal data model of power grid resources based on the Internet of things is 11363 times/second. The average update performance of the unified spatiotemporal data model of power grid resources based on the Internet of things is 9958 times/second). And the average update performance of the unified spatiotemporal data model of power grid resources based on the genetic algorithm is 9771 times/second. It shows that the designed unified spatiotemporal data model of power grid resources is perfect after combining the microservice architecture.

Big Data Analysis of the Ecological Environment in the Dianchi Watershed

To analyze the massive data on the ecological environment in Dianchi Watershed scientifically, understand its situation and dynamic trend over time, we plan to establish a feature-based four-dimensional space-time data model and build an integrated space-time database. The SuperMap map database is used to establish a space-time visual query and analysis system of the ecological environment information based on WebGIS with the B/S structure. The ecological environment information of the Dianchi Watershed is taken as the research object for scientific and comprehensive analysis and management of the ecological environment situation, thereby providing intuitive and reliable data information support for the decision-making of the ecological environment construction.

Foundations of sustainability information representation theory: spatial–temporal dynamics of sustainable systems

A critical need exists to broaden and deepen sustainability information foundations that can foster growth of actionable knowledge about human–environment relations to address grand challenges in sustainable system domains such as sustainable development, social–ecological systems, and hazards influencing global environmental change. Broad-based information is needed to integrate across domains to address sustainability problems cast as complex systems problems that vary widely across space and time. Deep-based information is needed to address nuanced and contextual relationships that exist within and across domains. Both broad and deep information together are needed to better address spatial–temporal dynamics in complex sustainable systems. According to many publications, the concept of sustainable systems is considered to be at the core of self-organizing systems; and in turn, the concept of self-organizing systems is at the core of social–ecological systems, coupled natural–human systems, and human–environment systems. The sustainable systems concept is elucidated in terms of ontological and epistemological foundations from geographic information representation theory. A framework for Measurement-informed Ontology and Epistemology for Sustainability Information Representation, drawing from research about ontology and epistemology in geographic information science, provides the foundation for elucidating concepts and relations about sustainability information representation in general and sustainable systems in particular. The framework is developed to form a core of sustainability information representation theory, and consequently provides a basis for articulating first principles about the character of space–time data models that can be used to create computational models within geographic information systems (GIS). An example applies the framework to common pool resources as sustainable systems. Developing the framework and exploring an example fosters intellectual bridge building between sustainability science and sustainability management in the form of a sustainability information science. That intellectual bridge building of sustainability information science supports societal progress moving knowledge into decision action toward sustainable development, encouraging new insight for designs of space–time data models, and extending GIS as an information technology foundation for sustainability management. Conclusions and directions for next steps in research about sustainability information representation theory in general and sustainable systems in particular are offered.

실시간 대중교통 경로안내를 위한 Space Time Point 모델의 설계와 구현

최근 환경문제로 인해 보행, 자전거, 대중교통과 같은 친 환경 교통수단에 대한 관심이 높아지고 있다. 하지만 보행과 자전거는 경우에 따라 장거리 이동이 불가능하기 때문에 이용범위가 제한적이라는 단점이 있다. 반면에 대중교통은 교통체증이라는 사회적 문제점까지 해결할 수 있는 교통수단이다. 이러한 대중교통은 시간에 따라 운행여부가 달라진다는 특징을 갖고 있다. 하지만 현재 웹상에서 서비스중인 대중교통정보 안내 서비스는 이러한 특징을 반영하지 못하기 때문에 경우에 따라 사용자에게 잘못된 정보를 제공할 수 있다. 이러한 한계점을 해결하고 대중교통의 운행정보를 고려한 정보제공을 위해 본 연구에서는 STP(Space Time Point)데이터 모델을 제안하였다. STP데이터 모델은 객체의 최하위 개념을 포인트 단위로 인식하고 이들을 계층적으로 구성하여 객체를 표현할 수 있으며, 기존의 시공간 데이터 모델과는 달리 시간에 따라 객체정보가 변하는 다양한 동적 공간객체의 구현이 가능하다. 대전지역을 대상으로 대중교통 운행정보로 버스 및 지하철에 대한 STP모델을 설계 및 구축하고 이를 이용하여 동적 경로 안내 기능을 구현하였다. 구현한 경로안내 기능을 통해 대전국립현충원에서 한남대학교로 가는 경로를 시간대별로 탐색해 본 결과 대중교통의 운행에 제한이 발생하는 심야시간에는 운행이 제한되는 노선을 우회하는 경로를 안내함으로써 시간에 따라 서로 다른 경로를 나타내었다. 이는 기존 데이터 모델에서는 제공할 수 없는 결과로 실시간 운행정보를 고려한 경로안내가 가능함을 확인하였다. 이러한 기능은 실시간 교통정보나 통행이력정보등과 같은 다양한 데이터 모델의 적용을 통해 추가적인 기능 확장이 가능할 것으로 기대한다. Recent environmental concerns have made interest in environment-friendly transportation means such as walking, biking, and public transportation. However, since it is difficult to move long distance by walking or biking, their scope of application is rather limited. On the other hand, public transportation can solve traffic congestion, a recent social issue, though its usability may depend on its time schedule. Currently available information services on public transportation in the Web do not reflect well such traits of the public transportation; thus, in some cases, they may provide wrong information to end users. To solve such problems and provide information based on timetable of public transportations, this paper proposes a STP(Space Time Point) data model. Unlike existing space-time data models, this model recognizes the bottommost element of an object as a point and structures these points in hierarchical way to define an object. In particular, It can make it possible to implement a variety of dynamic spatial objects changing object information according to time. An objective of this study is to design a STP model for bus and subway based on timetables of public transportation in Daejeon area and builds a system to provide path navigation. With the designed navigation function, a path from the Daejeon National Cemetery to Hannam University was searched by time slot. The result showed that the system provided different paths by time, as the system guided different paths when bus operation was limited in midnight. As existing data model could not provide such results, it is confirmed that the system can provide path navigation based on real-time traffic information. It is expected that based on such functionality, it is possible to provide additional functionalities by applying diverse data models such as real-time transport information or traffic history information.

Interpreting self-organizing maps through space–time data models

Self-organizing maps (SOMs) are a technique that has been used with high-dimensional data vectors to develop an archetypal set of states (nodes) that span, in some sense, the high-dimensional space. Noteworthy applications include weather states as described by weather variables over a region and speech patterns as characterized by frequencies in time. The SOM approach is essentially a neural network model that implements a nonlinear projection from a high-dimensional input space to a low-dimensional array of neurons. In the process, it also becomes a clustering technique, assigning to any vector in the high-dimensional data space the node (neuron) to which it is closest (using, say, Euclidean distance) in the data space. The number of nodes is thus equal to the number of clusters. However, the primary use for the SOM is as a representation technique, that is, finding a set of nodes which representatively span the high-dimensional space. These nodes are typically displayed using maps to enable visualization of the continuum of the data space. The technique does not appear to have been discussed in the statistics literature so it is our intent here to bring it to the attention of the community. The technique is implemented algorithmically through a training set of vectors. However, through the introduction of stochasticity in the form of a space--time process model, we seek to illuminate and interpret its performance in the context of application to daily data collection. That is, the observed daily state vectors are viewed as a time series of multivariate process realizations which we try to understand under the dimension reduction achieved by the SOM procedure.

Survival Analysis in Land Change Science: Integrating with GIScience to Address Temporal Complexities

Although land changes are characterized by dimensionality in both space and time, and a multitude of methods and techniques have been developed to model them, the temporal dimension has seldom been adequately addressed by commonly used methods. In the context of temporal complexities represented in different space–time data models, this study aims to establish a framework for applying survival analysis theory and techniques to geographical land change modeling. Our efforts focus on (1) introducing basic concepts in survival analysis and their connections to space–time data commonly used in land change analysis, (2) using survival metrics to describe temporal patterns that are not easily detected by other methods, and (3) applying survival analysis methods to disclose effects of varying temporal patterns and uncertainties. Our findings suggest that survival analysis, coupled with geographic information systems (GIS) and remote sensing data, can effectively disclose relationships in land changes, and in many instances excel in shedding light on the temporal patterns of land changes.

Making Space for Time: Issues in Space-Time Data Representation

Even with much activity over the past decade, including organized efforts on both sides of the Atlantic, the representation of both space and time in digital databases is still problematic and functional space-time systems have not gone beyond the limited prototype stage. Why is this the case? Why did it take twenty years from the first GIS for the for representation and analysis in the temporal, as well as the spatial dimension, to begin? I explore the answers to these questions by giving a historical overview of the development of space-time representation in the geographic information systems and database communities and a review of the most recent research. Within the context of this perspective, I also question what seems to be a spirit of self-accusation in which the lack of functional space-time systems has been discussed in the literature and in meetings of GIS researchers. I close by offering my own interpretation of current research issues on space-time data models and languages.

Geomedical Systems: Intervention and Control

Part 1 Overview: Geomedical Systems - a Perspective. Part 2 Epidemic Systems: Principles of Deterministic Modelling Modelling Alternative Modes of Transmission Space-Time Models Forecasting and Control. Part 3 Environmental Systems: Aggregative Analysis of Non-Infectious Disease: Statitistical Models for Space-Time Data Models for Planning Health Care Delivery. Part 4 Retrospect: Geomedical Systems in Perspective.