A framework to enhance semantic flexibility for analysis of distributed phenomena

While some geographic phenomena hold uniform properties, such as land‐use zones, many geographic phenomena are distributed such that their properties vary across an extended area. While such distributed phenomena are best represented as continuous surfaces, individual objects (or features) often emerge among clusters of high or low values in a field. For example, areas of relatively high elevation may be viewed as hills, while flat low‐lying areas are perceived as plains in a terrain. A comprehensive spatial analysis of distributed phenomena should examine both the spatial variance of its attribute surfaces and the characteristics of individual objects embedded in the field. An immediate research challenge to meet such spatial analysis needs is that these emerging features often have vague boundaries that vary according to the use and the user. The nature, and even existence, of these objects depend upon the range of values, or thresholds, used to define them. We propose a representation framework that takes a dual raster‐vector approach to capture both field‐ and object‐like characteristics of distributed phenomena and maintain multiple representations of embedded features delineated by boundaries that are likely to be relevant for the expected uses of the data. We demonstrate how boundaries influence the analysis and understanding of spatiotemporal characteristics of distributed phenomena. Using precipitation as a proof of concept, we show how the proposed framework enhances semantic flexibility in spatiotemporal query and analysis of distributed phenomena in geographic information systems.     

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

There 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.     

Thematic signatures for cleansing and enriching place-related linked data

There has been significant progress transforming semi-structured data about places into knowledge graphs that can be used in a wide variety of geographic information systems such as digital gazetteers or geographic information retrieval systems. For instance, in addition to information about events, actors, and objects, DBpedia contains data about hundreds of thousands of places from Wikipedia and publishes it as Linked Data. Repositories that store data about places are among the most interlinked hubs on the Linked Data cloud. However, most content about places resides in unstructured natural language text, and therefore it is not captured in these knowledge graphs. Instead, place representations are limited to facts such as their population counts, geographic locations, and relations to other entities, for example, headquarters of companies or historical figures. In this paper, we present a novel method to enrich the information stored about places in knowledge graphs using thematic signatures that are derived from unstructured text through the process of topic modeling. As proof of concept, we demonstrate that this enables the automatic categorization of articles into place types defined in the DBpedia ontology (e.g., mountain) and also provides a mechanism to infer relationships between place types that are not captured in existing ontologies. This method can also be used to uncover miscategorized places, which is a common problem arising from the automatic lifting of unstructured and semi-structured data.     

Mining spatiotemporal association patterns from complex geographic phenomena

ABSTRACT

Spatiotemporal association pattern mining can discover interesting interdependent relationships among various types of geospatial data. However, existing mining methods for spatiotemporal association patterns usually model geographic phenomena as simple spatiotemporal point events. Therefore, they cannot be applied to complex geographic phenomena, which continuously change their properties, shapes or locations, such as storms and air pollution. The most salient feature of such complex geographic phenomena is the geographic dynamic. To fully reveal dynamic characteristics of complex geographic phenomena and discover their associated factors, this research proposes a novel complex event-based spatiotemporal association pattern mining framework. First, a complex geographic event was hierarchically modeled and represented by a new data structure named directed spatiotemporal routes. Then, sequence mining technique was applied to discover the spatiotemporal spread pattern of the complex geographic events. An adaptive spatiotemporal episode pattern mining algorithm was proposed to discover the candidate driving factors for the occurrence of complex geographic events. Finally, the proposed approach was evaluated by analyzing the air pollution in the region of Beijing-Tianjin-Hebei. The experimental results showed that the proposed approach can well address the geographic dynamic of complex geographic phenomena, such as the spatial spreading pattern and spatiotemporal interaction with candidate driving factors.     

Toward an inclusive semantic interoperability: the case of Cree hydrographic features

There has been ample work in GIScience on the formalization of ontologies but a relatively neglected area is the influence of language and culture on ontologies of geography. Although this subject has been investigated for conceptual ontologies using indigenous words denoting geographic features, this article represents the first attempt to develop a logical ontology with an indigenous group. The process of developing logical ontologies is here referred to as formalization. A methodology for formalizing ontologies with indigenous peoples is presented. A conceptual (human readable) ontology and a logical (axioms specified in mathematical logic) ontology were developed using this methodology. Research was conducted with the Cree, the largest indigenous language grouping in Canada. Results show that the geospatial ontology developed from Cree geographic concepts possesses unique design considerations: no superordinate classes were found from archival sources or Cree speakers so ontologies are structurally flat; the ontology contains some unique classes of water bodies; and the ontology challenges our notions of the generalizability of ontologies within indigenous groups. Whereas these difficulties are not insurmountable to the establishment of a cross-cultural Geospatial Semantic Web, the current plans of the World Wide Web Consortium do not adequately address them. We suggest future directions toward an inclusive semantic interoperability.     

论地理信息的尺度特性

尺度是地理信息一种固有的本质属性,尺度问题是当前地理信息科学研究的核心内容之一。该文根据地理信息在获取、处理、传输、表达和分析中尺度显现的基本规律,将地理信息的尺度特性概括为尺度依赖性、空间形态可分性与可聚合性、语义层次性与语义连通性、尺度不变性、尺度一致性,并探讨其具体内涵。     

语义引导的图标式地理概念建模环境初探

以地理问题求解和地理科学研究环境建设的实际需求为引导,针对当前地理建模过程中存在的建模思想难以交流与重用、建模方式复杂、多领域专家协同建模困难等问题,研究地理概念建模过程中地理概念场景、概念实体及其相互作用关系的表达与元数据描述方法,利用空间数据表达规范与地理模型元数据表达规范逐步引导数据与模型的选择与匹配,构建地理概念模型,并在此基础上提出一种可视化、引导式的面向地理问题表达的概念建模方法,为地理研究提供一个语义引导的图标式地理概念建模环境。     

Criteria of geographic relevance: an experimental study

The relevance of geographic information has become an emerging problem in geographic information science due to an enormous increase in volumes of data at high spatial, temporal, and semantic resolution, because of ever faster rates of new data capturing. At the same time, it is not clear whether the concept of relevance developed in information science and implemented for document-based information retrieval can be directly applied to this new, highly dynamic setting. In this study, we analyze the criteria users apply when judging the relevance of geographic entities in a given mobile usage context. Two different experiments have been set up in order to gather users' opinions on a set of possible criteria, and their relevance judgements in a given scenario. The importance ascribed to the criteria in both experiments clearly implies that a new concept of relevance is required when dealing with geographic entities instead of digital documents. This new concept of ‘Geographic Relevance’ is highly dependent on personal mobility and user's activity, whose understanding may in turn be refined by the assimilation of ‘Geographic Relevance’ itself.     

Using geolifespans to model dynamic geographic domains

Geographic objects are characterized by having different durations of existence, or geolifespans. A typology based on the concept of a geolifespan is developed to model variations in the longevity of entities that are stored in geographic information systems. The typology consists of two upper-level classes: Persistent and Ephemeral. The Ephemeral class is composed of three subclasses: Temporary, Transient and Brief. The set of possible transitions between classes in the typology is described, capturing how objects can change from one class to another, e.g., from Temporary to Brief or from Transient to Persistent. A transition sequence models the geolifespan class(es) to which an object belongs over a period of time and captures the evolution of dynamic geographic objects with respect to their longevity. Geolifespan classes are applied to scenarios of spatial change as well as a geosensor network to illustrate their role in modelling geographic dynamics.     

Exploring the Past and Future of Our Planet: Not Bit-by-Bit but All at Once

Understanding the Changing Planet lacks the spark of wonder and synthesis that initially attracted me into geography. Its structure around compelling questions reflects disciplinary fragmentation and the model of interdisciplinary science favored in the late twentieth century. It is suggested that geography can move beyond the “big tent with three poles” approach, to a twenty-first-century hyperdisciplinary model where content, science, analysis, and synthesis blur and a new bottom-up science can emerge. In this science, new theory and even grand unified theory might be possible. As discussed in Understanding the Changing Planet, the Web and the emergence of volunteered geographic information are examples. This would make study of the whole earth and its systems “a respectable object for scholarly efforts.”     

Modelling the history of semi-structured geographical entities

Geographical entities are characterized by rather complex structures. They involve space and thematic information, which is subject to change in time, while history should be maintained. On the other hand, these structures may be irregular (i.e. they do not necessarily conform to a fixed schema), because associated data is usually collected based on different specifications and multiple resolutions. Hence, the representation of geographical entities in traditional data models, such as the relational or object-oriented, is not always feasible. In this respect, this paper investigates the use of semi-structured data (SSD) models—an innovative approach recently developed in Information Technology—for modelling dynamic geographical entities. A framework for the representation of geographic entities in Object Exchange Model (OEM), a popular model for semi-structured data, is introduced. Additionally, it is shown how useful information can be extracted from such a representation using the LOREL query language for SSD. A simplified case study in the application domain of cadastre involving SSD is examined closely.     

Framework for probabilistic geospatial ontologies

Partial knowledge about geospatial categories is important for practical use of ontologies in the geospatial domain. Degree of overlaps between geospatial categories, especially those based on geospatial actions concepts and geospatial enitity concepts, need to be specified in ontologies. Conventional geospatial ontologies do not enable specification of such information, and this presents difficulties in ontology reasoning for practical purposes. We present a framework to encode probabilistic information in geospatial ontologies based on the BayesOWL approach. The approach enables rich inferences such as most similar concepts within and across ontologies. This paper presents two case studies of using road‐network ontologies to demonstrate the framework for probabilistic geospatial ontologies. Besides inferences within the probabilistic ontologies, we discuss inferences about most similar concepts across ontologies based on the assumption that geospatial action concepts are invariable. The results of such machine‐based mappings of most similar concepts are verified with mappings of concepts extracted from human subjects testing. The practical uses of probabilistic geospatial ontologies for concept matching and measuring naming heterogeneities between two ontologies are discussed. Based on our experiments, we propose such a framework for probabilistic geospatial ontologies as an advancement of the proposal to develop semantic reference systems.     

A comprehensive methodology for discovering semantic relationships among geospatial vocabularies using oceanographic data discovery as an example

It is challenging to find relevant data for research and development purposes in the geospatial big data era. One long-standing problem in data discovery is locating, assimilating and utilizing the semantic context for a given query. Most research in the geospatial domain has approached this problem in one of two ways: building a domain-specific ontology manually or discovering automatically, semantic relationships using metadata and machine learning techniques. The former relies on rich expert knowledge but is static, costly and labor intensive, whereas the second is automatic and prone to noise. An emerging trend in information science takes advantage of large-scale user search histories, which are dynamic but subject to user- and crawler-generated noise. Leveraging the benefits of these three approaches and avoiding their weaknesses, a novel methodology is proposed to (1) discover vocabulary-based semantic relationships from user search histories and clickstreams, (2) refine the similarity calculation methods from existing ontologies and (3) integrate the results of ontology, metadata, user search history and clickstream analysis to better determine their semantic relationships. An accuracy assessment by domain experts for the similarity values indicates an 83% overall accuracy for the top 10 related terms over randomly selected sample queries. This research functions as an example for building vocabulary-based semantic relationships for different geographical domains to improve various aspects of data discovery, including the accuracy of the vocabulary relationships of commonly used search terms.     

OpenGIS参考模型ORM及地理信息服务应用模式

地理信息语义互操作是地理信息共享应用的基础,为解决OpenGIS抽象规范与实施规范在地理信息语义描述上的不足,OGC建立了OpenGIS参考模型(OpenGIS Reference Model,ORM),旨在通过ORM实现地理信息共享与互操作。该文从空间信息应用政策、空间信息语义描述、空间信息服务定义与分类、多网络服务配置、共享开发标准五个层面介绍ORM,并分析基于ORM的地理信息服务应用模式。     

GIS应用中认知阶层性对地理信息有效传输的影响

GIS应用的最终目的是使其所包含的地理信息有效传递。该文从信息科学、认知科学及地理信息科学角度出发,指出认知空间和地理空间的阶层对应性是实现地理信息有效传递的基础;提出两类空间重叠区的概念,并阐明其在信息有效传递过程中的重要作用;确立认知科学在GIS应用中的两个基本原则是建立这种对应性的理论依据;强调信息制造者(制图者)是构建这种对应性的实施者,而信息接收者(用户)对认知空间分类阶层的正确识别则是构建这种对应性的出发点。     

NEAT approach for testing and validation of geospatial network agent-based model processes: case study of influenza spread

ABSTRACT

Agent-based models (ABM) are used to represent a variety of complex systems by simulating the local interactions between system components from which observable spatial patterns at the system-level emerge. Thus, the degree to which these interactions are represented correctly must be evaluated. Networks can be used to discretely represent and quantify interactions between system components and the emergent system structure. Therefore, the main objective of this study is to develop and implement a novel validation approach called the NEtworks for ABM Testing (NEAT) that integrates geographic information science, ABM approaches, and spatial network representations to simulate complex systems as measurable and dynamic spatial networks. The simulated spatial network structures are measured using graph theory and compared with empirical regularities of observed real networks. The approach is implemented to validate a theoretical ABM representing the spread of influenza in the City of Vancouver, Canada. Results demonstrate that the NEAT approach can validate whether the internal model processes are represented realistically, thus better enabling the use of ABMs in decision-making processes.     

Street‐based topological representations and analyses for predicting traffic flow in GIS

It is well received in the space syntax community that traffic flow is significantly correlated to a morphological property of streets, which are represented by axial lines, forming a so called axial map. The correlation co‐efficient (R ²) approaches 0.8 and even a higher value according to the space syntax literature. In this paper, we study the same issue using the Hong Kong street network and the Hong Kong Annual Average Daily Traffic datasets, and find surprisingly that street‐based topological representations (or street–street topologies) tend to be better representations than the axial map. In other words, vehicle flow is correlated to a morphological property of streets better than that of axial lines. Based on the finding, we suggest the street‐based topological representations as an alternative GIS representation, and the topological analyses as a new analytical means for geographic knowledge discovery.     

Points of interest recommendation from GPS trajectories

Recently, points of interest (POIs) recommendation has evolved into a hot research topic with real-world applications. In this paper, we propose a novel semantics-enhanced density-based clustering algorithm SEM-DTBJ-Cluster, to extract semantic POIs from GPS trajectories. We then take into account three different factors (popularity, temporal and geographical features) that can influence the recommendation score of a POI. We characterize the impacts caused by popularity, temporal and geographical information, by using different scoring functions based on three developed recommendation models. Finally, we combine the three scoring functions together and obtain a unified framework PTG-Recommend for recommending candidate POIs for a mobile user. To the best of our knowledge, this work is the first that considers popularity, temporal and geographical information together. Experimental results on two real-world data sets strongly demonstrate that our framework is robust and effective, and outperforms the baseline recommendation methods in terms of precision and recall.