实时GIS时空数据模型

为满足动态目标与传感器等实时观测数据获取、存储、管理、分析与可视化的要求,需要发展一种新型地理信息系统—实时GIS。本文根据实时GIS中各种地理要素的特点以及存贮管理要求,提出了一种面向动态地理对象与动态过程模拟的实时GIS时空数据模型,它将时空过程、地理对象、事件、事件类型、状态、观测等相关要素整合在一个时空数据模型中。基于该模型研发了新一代实时GIS,并以四种动态地理对象（包括移动对象、原位传感器对象、视频对象和过程模拟对象）的时空数据的实时接入、存储与可视化为例,验证的模型的可行性。     

A UML‐based Representation of Spatio‐Temporal Evolution in Road Network Data

Geographic features change over time, this change being the result of some kind of event. Most database systems used in GIS are relational in nature, capturing change by exhaustively storing all versions of data, or updates replace previous versions. This stems from the inherent difficulty of modelling geographic objects and associated data in relational tables, and this is compounded when the necessary time dimension is introduced to represent how these objects evolve. This article describes an object‐oriented (OO) spatio‐temporal conceptual data model called the Feature Evolution Model (FEM), which can be used for the development of a spatio‐temporal database management system (STDBMS). Object versioning techniques developed in the fields of Computer Aided Design (CAD) and engineering design are utilized in the design. The model is defined using the Unified Modelling Language (UML), and exploits the expressiveness of OO technology by representing both geographic entities and events as objects. Further, the model overcomes the limitations inherent in relational approaches in representing aggregation of objects to form more complex, compound objects. A management object called the evolved feature maintains a temporally ordered list of references to features thus representing their evolution. The model is demonstrated by its application to road network data.     

Extending F‐Perceptory to model fuzzy objects with composite geometries for GIS

When analyzing spatial issues, geographers are often confronted with many problems with regard to the imprecision of the available information. It is necessary to develop representation and design methods which are suited to imprecise spatiotemporal data. This led to the recent proposal of the F‐Perceptory approach. F‐Perceptory models fuzzy primitive geometries that are appropriate in representing homogeneous regions. However, the real world often contains cases that are much more complex, describing geographic features with composite structures such as a geometry aggregation or combination. From a conceptual point of view, these cases have not yet been managed with F‐Perceptory. This article proposes modeling fuzzy geographic objects with composite geometries, by extending the pictographic language of F‐Perceptory and its mapping to the Unified Modeling Language (UML) necessary to manage them in object/relational databases. Until now, the most commonly used object modeling tools have not considered imprecise data. The extended F‐Perceptory is implemented under a UML‐based modeling tool in order to support users in fuzzy conceptual data modeling. In addition, in order to properly define the related database design, an automatic derivation process is implemented to generate the fuzzy database model.     

A Space‐Time Raster GIS Data Model for Spatiotemporal Analysis of Vegetation Responses to a Freeze Event

Many past space‐time GIS data models viewed the world mainly from a spatial perspective. They attached a time stamp to each state of an entity or the entire area of study. This approach is less efficient for certain spatio‐temporal analyses that focus on how locations change over time, which require researchers to view each location from a temporal perspective. In this article, we present a data model to organize multi‐temporal remote sensing datasets and track their changes at the individual pixel level. This data model can also integrate raster datasets from heterogeneous sources under a unified framework. The proposed data model consists of several object classes under a hierarchical structure. Each object class is associated with specific properties and behaviors to facilitate efficient spatio‐temporal analyses. We apply this data model to a case study of analyzing the impact of the 2007 freeze in Knoxville, Tennessee. The characteristics of different vegetation clusters before, during, and after the 2007 freeze event are compared. Our findings indicate that the majority of the study area is impacted by this freeze event, and different vegetation types show different response patterns to this freeze.     

以事件为核心的面向对象时空数据模型

GIS中时态问题的解决不仅要在数学上寻求方法的支持，更应该在现实世界的时空现象中去探求其内在的变化规律。事件是驱动时空数据发生变化的根本原因，但每一次事件对数据对象的影响总是局限于一定的范围，这内在地表现为对象的属性之间的时空内聚性并决定了时态数据管理的对象粒度。时空数据模型的设计必须参照这一规律才能在冗余与效率之间取得平衡。在简要分析现有的几种典型时空数据模型的基础上，根据上述观点设计了一种以事件为核心的面向对象时空数据模型。房产信息系统的设计与实现证明，该模型对于时空数据管理效果较好，为进一步抽取变化的模式和预测决策奠定了基础。     

Assessing Similarity of Geographic Processes and Events

The increased availability of spatiotemporal data collected from satellite imagery and other remote sensors provides opportunities for enhanced analysis of geographic phenomena. Much of the new data includes regular snapshots of the environment. Comparison of these snapshots can provide information about changes to the phenomena of interest. However, conventional GIS data models and analytical tools lack capabilities to adequately handle massive multidimensional data. One of the fundamental tools necessary to meet such challenges is query support to retrieve and summarize data according to dynamic geographic phenomena, such as geographic events and processes, of interest. Such query support depends upon abilities to assess spatiotemporal similarity so that data representing geographic events that exhibit the spatiotemporal characteristics of interest can be identified in a GIS database. To this end, this paper introduces a method to assess similarity of geographic events and processes (such as storms) based on their spatiotemporal characteristics (such as distribution of precipitation). We developed six indices to capture static and dynamic characteristics of geographic events and applied the Dynamic Time Warping method to temporal sequences of the six indices to examine the similarity among these events. With a case study, we demonstrated the proposed indices and method capable of comparing spatiotemporal characteristics of events as recorded in a GIS database and categorizing spatiotemporal data into groups of events according to their behavior in space and time.     

A Multiscale Approach for Spatio-Temporal Outlier Detection

A spatial outlier is a spatially referenced object whose thematic attribute values are significantly different from those of other spatially referenced objects in its spatial neighborhood. It represents an object that is significantly different from its neighbourhoods even though it may not be significantly different from the entire population. Here we extend this concept to the spatio‐temporal domain and define a spatial‐temporal outlier (ST‐outlier) to be a spatial‐temporal object whose thematic attribute values are significantly different from those of other spatially and temporally referenced objects in its spatial or/and temporal neighbourhoods. Identification of ST‐outliers can lead to the discovery of unexpected, interesting, and implicit knowledge, such as local instability or deformation. Many methods have been recently proposed to detect spatial outliers, but how to detect the temporal outliers or spatial‐temporal outliers has been seldom discussed. In this paper we propose a multiscale approach to detect ST‐outliers by evaluating the change between consecutive spatial and temporal scales. A four‐step procedure consisting of classification, aggregation, comparison and verification is put forward to address the semantic and dynamic properties of geographic phenomena for ST‐outlier detection. The effectiveness of the approach is illustrated by a practical coastal geomorphic study.     

CAUSTA: Clifford Algebra‐based Unified Spatio‐Temporal Analysis

Introducing Clifford algebra as the mathematical foundation, a unified spatio‐temporal data model and hierarchical spatio‐temporal index are constructed by linking basic data objects, like pointclouds and Spatio‐Temporal Hyper Cubes of different dimensions, within the multivector structure of Clifford algebra. The transformation from geographic space into homogeneous and conformal space means that geometric, metric and many other kinds of operators of Clifford algebra can be implemented and we then design the shortest path, high‐dimensional Voronoi and unified spatial‐temporal process analyses with spacetime algebra. Tests with real world data suggest these traditional GIS analysis algorithms can be extended and constructed under Clifford Algebra framework, which can accommodate multiple dimensions. The prototype software system CAUSTA (Clifford Algebra based Unified Spatial‐Temporal Analysis) provides a useful tool for investigating and modeling the distribution characteristics and dynamic process of complex geographical phenomena under the unified spatio‐temporal structure.     

基于模糊理论的时空数据模型及算法解决

针对当前时空数据模型中存在的主要问题,本文提出了一种基于模糊理论的时空数据模型.该模型从时空对象演化的角度出发,结合模糊隶属度的概念,力求精确表达对象演化的整个过程及对象之间的相互关系.在此基础上,进一步分析了该模型的数据结构以及算法解决,为时空数据模型的研究提供了新的思路.     

Time‐geographic Derivation of Feasible Co‐presence Opportunities from Network‐constrained Episodic Movement Data

Certain datasets on moving objects are episodic in nature – that is, the data is characterized by time gaps during which the position of the object is unknown. In this article, a model is developed to study the sparsely sampled network‐constrained movement of several objects by calculating both potential and feasible (i.e. more likely) co‐presence opportunities over time. The approach is applied to the context of a static sensor network, where the location of an object is only registered when passing a sensor location along a road network. Feasibility is incorporated based on the deviation from the shortest path. As an illustration, the model is applied to a large Bluetooth tracking dataset gathered at a mass event. The model output consists of maps showing the temporal evolution of the distribution of feasible co‐presence opportunities of tracked visitors over the network (i.e. the number of visitors that could have been present together). We demonstrate the model's usefulness in studying the movement and distribution of a crowd over a study area with relatively few sampling locations. Finally, we discuss the results with a special emphasis on the distinction between feasible and actual presence, the need for further validation and calibration, and the performance of the implementation.     

顾及信誉的众源时空数据模型

提出了一种顾及信誉度的众源时空数据模型。在分析众源时空数据中地理要素、目标状态、对象版本、贡献者、信誉度、改变现实空间实体或信息空间对象状态的事件等要素间的相互作用机理的基础上,采用面向对象方法设计了一种顾及信誉度的众源时空数据组织方法,用UML对其进行描述,分析了与信誉度相关操作及其联动关系,得出了8条联动规则。开发了顾及信誉度的众源时空数据管理原型系统,验证了所提模型的有效性。     

A Review and Evaluation of Uncertainty Classification and the Error‐Band Geometry Model

Advances in computer technologies have improved the quality of maps, making map comparison and analysis easier, but uncertainty and error still exist in GIS when overlaying geographic data with multiple or unknown confidence levels. The goals of this research are to review current geospatial uncertainty literature, present the Error‐Band Geometry Model (EBGM) for classifying the size and shape of spatial confidence intervals for vector GIS data, and to analyze the interpretability of the model by looking at how people use metadata to classify the uncertainty of geographic objects. The results from this research are positive and provide important insight into how people interpret maps and geographic data. They suggest that uncertainty is more easily interpreted for well defined point data and GPS data. When data is poorly defined, people are unable to determine an approach to model uncertainty and generate error‐bands. There is potential for using the EBGM to aid in the development of a GIS tool that can help individuals parameterize and model spatial confidence intervals, but more research is needed to refine the process by which people use the decision tree. A series of guiding questions or an “uncertainty wizard” tool that helps one select an uncertainty modeling approach might improve the way people apply this model to real‐world applications.     

多尺度时空数据的集成与对象进化模型

多尺度时空数据集成的根本目的是增加信息量.本文通过对时空对象变化过程的分析,指出对象进化的结果不仅是特征的变化,还包括机制的变化,其直接动力是对象行为,其根本原因是其内在机制,时空事件起着对象间信息通道的作用,据此提出对象进化模型,该模型可作为多尺度时空数据集成的结构性基础,在反映时空现象的演变过程,特别是异构演变过程,以及在这个过程中产生的因果关系的同时,还可以保留时空数据的不同时间尺度和空间尺度特点,从而达到增加时空数据库的信息量的目的.文中给出了该模型基于关系型数据库的一个结构示例.根据多空间尺度数据的特点,本文最后还提出了一个时空数据组织体系,用于指导数据访问过程.     

Dynamic GIS Case Studies: Wildfire Evacuation and Volunteered Geographic Information

Incorporating 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.     

Asynchronous Geospatial Processing: An Event‐Driven Push‐Based Architecture for the OGC Web Processing Service

Geospatial processing tasks like solar potential analyses or floodplain investigations within flood scenarios are often complex and deal with large amounts of data. If such analysis operations are performed in distributed web‐based systems, technical capabilities are mostly not sufficient. Major shortcomings comprise the potentially long execution times and the vast amount of messaging overhead that arise from common poll‐based approaches. To overcome these issues, an approach for an event‐driven architecture for web‐based geospatial processing is proposed within this article. First, this article presents a thorough qualitative discussion of different available technologies for push‐based notifications. The aim of this discussion is to find the most suitable push‐based messaging technologies for application with OGC Web Processing Services (WPS). Based on this, an event‐driven architecture for asynchronous geospatial processing with the WPS is presented, building on the Web Socket Protocol as the transport protocol and the OGC Event Service as the message‐oriented middleware. The proposed architecture allows pushing notifications to clients once a task has completed. This paradigm enables the efficient execution of web‐based geospatial processing tasks as well as the integration of geographical analyses into event‐driven real‐time workflows.     

时态GIS的空间实体关联技术应用研究

在自然界中大多数现实世界的实体都是动态的,地图上的公路、铁路、居民地等只是相对于一个很短的时间里是静态的,经过一个长的时间这些实体都将发生改变。本文根据不同时态地理实体的各种特征比较,将两个时态的数据进行关联,建立几何、位置和属性的变化模型,将这个动态的过程表现出来。试验表明,本文的算法是有效的。     

A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods

Rapid flood mapping is critical for local authorities and emergency responders to identify areas in need of immediate attention. However, traditional data collection practices such as remote sensing and field surveying often fail to offer timely information during or right after a flooding event. Social media such as Twitter have emerged as a new data source for disaster management and flood mapping. Using the 2015 South Carolina floods as the study case, this paper introduces a novel approach to mapping the flood in near real time by leveraging Twitter data in geospatial processes. Specifically, in this study, we first analyzed the spatiotemporal patterns of flood-related tweets using quantitative methods to better understand how Twitter activity is related to flood phenomena. Then, a kernel-based flood mapping model was developed to map the flooding possibility for the study area based on the water height points derived from tweets and stream gauges. The identified patterns of Twitter activity were used to assign the weights of flood model parameters. The feasibility and accuracy of the model was evaluated by comparing the model output with official inundation maps. Results show that the proposed approach could provide a consistent and comparable estimation of the flood situation in near real time, which is essential for improving the situational awareness during a flooding event to support decision-making.     

Use of a Three-Domain Repesentation to Enhance GIS Support for Complex Spatiotemporal Queries

As the development of Geographic Information Systems (GIS) proceeds to advanced scientific and societal applications, there is an emerging need to enhance GIS support for complex spatiotemporal queries. Dynamic GIS representations (as opposed to static, map-based representations) that can integrate proper data elements in the production of geographic information are required. This paper demonstrates the use of a three-domain representation that facilitates compilation of higher-level information (such as frequency and rate) from preliminary data records (such as time and location) stored in a database. The three-domain representation is compared with snapshot, space-time composite, and spatiotemporal object models using a sample data set for forest transitions. While the three-domain representation is a normalization of these data models, it offers a conceptual alternative that enables GIS to represent spatiotemporal behaviors of geographic entities, in addition to entities as well as histories at locations as emphasized in most GIS data models. The comparison shows that the three-domain representation has combined the strengths of the space-time composite and spatiotemporal object models. Moreover, it enables aggregations of analytical use along with dynamic mappings between geographic concepts and locations, a distinct capability that takes GIS query processing beyond the level of information support offered by static map-based data models.