地理知识可视化中知识图特征与应用——以小流域淤地坝系规划为例

依据心智表达的双重编码理论、地理的空间图形思维特点以及支持可视化知识探析需求,讨论地理知识可视化中知识图的定义与特点,并把地理知识图分为地理概念命题图与地理相似图解图,地理相似图解图又可分为逻辑拓扑相似图、空间结构相似图、变量关系相似图以及地理过程相似图.以黄土高原的小流域淤地坝系规划为案例,研究了支持坝系空间规划知识图中的概念命题图与相似图解图的形式、种类及其相互关系.     

A Spatio‐Temporal Graph Model for Marine Dune Dynamics Analysis and Representation

Defining a model for the representation and the analysis of spatio‐temporal dynamics remains an open domain in geographical information sciences. In this article we investigate a spatio‐temporal graph‐based model dedicated to managing and extracting sets of geographical entities related in space and time. The approach is based on spatial and temporal local relations between neighboring entities during consecutive times. The model allows us to extract sets of connected entities distant in time and space over long periods and large spaces. From GIS concepts and qualitative reasoning on space and time, we combine the graph model with a dedicated spatial database. It includes information on geometry and geomorphometric parameters, and on spatial and temporal relations. This allows us to extend classical measurements of spatial parameters, with comparisons of entities linked by complex relations in space and time. As a case study, we show how the model suggests an efficient representation of dunes dynamics on a nautical chart for safe navigation.     

A Patch‐based Cellular Automaton for Simulating Land‐use Changes at Fine Spatial Resolution

While cellular automata have become popular tools for modeling land‐use changes, there is a lack of studies reporting their application at very fine spatial resolutions (e.g. 5 m resolution). Traditional cell‐based CA do not generate reliable results at such resolutions because single cells might only represent components of land‐use entities (i.e. houses or parks in urban residential areas), while recently proposed entity‐based CA models usually ignore the internal heterogeneity of the entities. This article describes a patch‐based CA model designed to deal with this problem by integrating cell and object concepts. A patch is defined as a collection of adjacent cells that might have different attributes, but that represent a single land‐use entity. In this model, a transition probability map was calculated at each cell location for each land‐use transition using a weight of evidence method; then, land‐use changes were simulated by employing a patch‐based procedure based on the probability maps. This CA model, along with a traditional cell‐based model were tested in the eastern part of the Elbow River watershed in southern Alberta, Canada, an area that is under considerable pressure for land development due to its proximity to the fast growing city of Calgary. The simulation results for the two models were compared to historical data using visual comparison, K_simulation indices, and landscape metrics. The results reveal that the patch‐based CA model generates more compact and realistic land‐use patterns than the traditional cell‐based CA. The K_simulation values indicate that the land‐use maps obtained with the patch‐based CA are in higher agreement with the historical data than those created by the cell‐based model, particularly regarding the location of change. The landscape metrics reveal that the patch‐based model is able to adequately capture the land‐use dynamics as observed in the historical data, while the cell‐based CA is not able to provide a similar interpretation. The patch‐based approach proposed in this study appears to be a simple and valuable solution to take into account the internal heterogeneity of land‐use classes at fine spatial resolutions and simulate their transitions over time.     

Weka‐STPM: a Software Architecture and Prototype for Semantic Trajectory Data Mining and Visualization

Enormous quantities of trajectory data are collected from many sources, such as GPS devices and mobile phones, as sequences of spatio‐temporal points. These data can be used in many application domains such as traffic management, urban planning, tourism, bird migration, and so on. Raw trajectory data, as generated by mobile devices have very little or no semantics, and in most applications a higher level of abstraction is needed to exploit these data for decision making. Although several different methods have been proposed so far for trajectory querying and mining, there are no software tools to help the end user with semantic trajectory data analysis. In this article we present a software architecture for semantic trajectory data mining as well as the first software prototype to enrich trajectory data with both semantic information and data mining. As a prototype we extend the Weka data mining toolkit with the module Weka‐STPM, which is interoperable with databases constructed under OGC specifications. We tested Weka‐STPM with real geographic databases, and trajectory data stored under the Postgresql/PostGIS DBMS.     

铁路隧道钻爆法施工智能管理的安全质量进度知识图谱构建方法

复杂艰险的山区环境和不确定的地理地质条件是影响铁路隧道施工建设安全、质量和进度的关键因素,面向智能化、精准化的施工管理,提出一种铁路隧道钻爆法施工安全质量进度知识图谱构建方法。首先,根据铁路隧道施工建设过程中与安全质量进度关联的人机料法环5类关键要素的概念与语义关系,设计了模式层自上而下和数据层自下而上双向协同的构建方式;然后,抽取实体及关系并进行融合、存储,完成模式-数据关联的知识图谱构建;最后,以某新建铁路隧道出口工区施工事件为例构建实例图谱。结果表明,该方法构建的知识图谱精细刻画了影响安全、质量和进度的关键要素属性、要素间语义关联关系以及互馈作用关系等,为铁路隧道钻爆法施工全过程的安全质量进度整体性、系统性的智能化管理提供了关键支撑,也为铁路隧道工程数字孪生奠定了基础。     

Big location‐based social media messages from China's Sina Weibo network: Collection,storage, visualization,and potential ways of analysis

China's social media platform, Sina Weibo, like Twitter, hosts a considerable amount of big data: messages, comments, pictures. Collecting and analyzing information from this treasury of human behavior data is a challenge, although the message exchange on the network is readable by everyone through the web or app interface. The official Application Programming Interface (API) is the gateway to access and download public content from Sina Weibo and is used to collect messages for all mainland China. The nearby_timeline() request is used to harvest only messages with associated location information. This technical note serves as a reference for researchers who do not speak Mandarin but want to collect data from this rich source of information. Ways of data visualization are presented as a point cloud, density per areal unit, or clustered using Density‐Based Spatial Clustering of Applications with Noise (DBSCAN). The relation of messages to census information is also given.     

A multi‐label cellular automata model for land change simulation

The use of cellular automata (CA) has for some time been considered among the most appropriate approaches for modeling land‐use changes. Each cell in a traditional CA model has a state that evolves according to transition rules, taking into consideration its own and its neighbors’ states and characteristics. Here, we present a multi‐label CA model in which a cell may simultaneously have more than one state. The model uses a multi‐label learning method—a multi‐label support vector machine, Rank‐SVM—to define the transition rules. The model was used with a multi‐label land‐use dataset for Luxembourg, built from vector‐based land‐use data using a method presented here. The proposed multi‐label CA model showed promising performance in terms of its ability to capture and model the details and complexities of changes in land‐use patterns. Applied to historical land use data, the proposed model estimated the land use change with an accuracy of 87.2% exact matching and 98.84% when including cells with a misclassification of a single label, which is comparably better than a classical multi‐class model that achieved 83.6%. The multi‐label cellular automata outperformed a model combining CA and artificial neural networks. All model goodness‐of‐fit comparisons were quantified using various performance metrics for predictive models.     

SCOPED‐W: SCalable Online Platform for extracting Environmental Data and Water‐related model outputs

This article presents SCOPED, an innovative approach for extracting environmental data using OGC services. In the field of water resource management, SCOPED‐W (‘W’ for ‘Water’) is a method that was developed in the framework of EU/FP7 IASON and EOPOWER projects. This platform supports the collection of data required to build a Soil and Water Assessment Tool (SWAT) model and the uptake, spatialization and dissemination of raw data generated from the outputs of different SWAT models for the Black Sea region. Scientists are documenting the data served by the platform in ISO standardized metadata to support informed use. SCOPED‐W primarily targets the community of SWAT users in the Black Sea region but it can easily be replicated in other geographical areas. Additionally, the SCOPED approach is based on data interoperability that makes it fully compatible with other domains of application as demonstrated here with three original use cases. The article also highlights the benefits of the approach for the GEO community and discusses future improvements for supporting integration with other platforms such as UNEP Live.     

A geospatial case‐based reasoning model for oil–gas reservoir evaluation

Delineating the distribution of oil and natural gas resources is a prerequisite of exploitation. The delineation methods usually include conventional techniques of reservoir evaluation and mathematical models. The conventional reservoir evaluation results generally depend on the experts' knowledge and experience in the field. The mathematical methods mostly require accurate models to be proposed. Considering spatial relationships and characteristics of geological reservoir problems, including nonlinearity, complexity and uncertainty, a novel model called geospatial case‐based reasoning for oil–gas reservoir evaluation was proposed in this article. The key components of the new model, including: (1) the joint representation of spatial relationship and attribute features; (2) the model of spatial relationship and attribute similarity joint reasoning; and (3) the methods of establishing weights for the spatial relationship and attribute features, are completely constructed. A case study of the proposed model for gas reservoir evaluation was carried out. Compared with the backpropagation artificial neural network (BP‐ANN) and the geological empirical evaluation (GEE) methods, the model presented in this article performs 6.38% and 46.81% better than BP‐ANN and GEE, respectively. Furthermore, its execution is simpler, more convenient, and importantly, its utilization hardly requires any professional knowledge of the field.     

Writing Geospatial Concepts: Assessing Student Knowledge Accrued Through Both Writing‐Intensive and Methods‐Oriented Coursework

While writing is touted as a way to learn, little has been written about the use of writing in courses focused on the geospatial technologies: geographic information systems (GIS), remote sensing, and the Global Positioning System (GPS). This study, therefore, aimed to assess student knowledge gained through two types of coursework, writing‐intensive and methods‐oriented. The study used a pre‐/post‐test strategy to measure gains in student knowledge. Results illustrate that student learning took place in both types of courses. The results, while encouraging, point to the need for more refined study of the use of writing in courses on geospatial technologies.     

NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages

Social media messages, such as tweets, are frequently used by people during natural disasters to share real‐time information and to report incidents. Within these messages, geographic locations are often described. Accurate recognition and geolocation of these locations are critical for reaching those in need. This article focuses on the first part of this process, namely recognizing locations from social media messages. While general named entity recognition tools are often used to recognize locations, their performance is limited due to the various language irregularities associated with social media text, such as informal sentence structures, inconsistent letter cases, name abbreviations, and misspellings. We present NeuroTPR, which is a Neuro‐net ToPonym Recognition model designed specifically with these linguistic irregularities in mind. Our approach extends a general bidirectional recurrent neural network model with a number of features designed to address the task of location recognition in social media messages. We also propose an automatic workflow for generating annotated data sets from Wikipedia articles for training toponym recognition models. We demonstrate NeuroTPR by applying it to three test data sets, including a Twitter data set from Hurricane Harvey, and comparing its performance with those of six baseline models.     

Geo‐Social Model: A Conceptual Framework for Real‐time Geocollaboration

Synchronous geocollaboration helps geographically dispersed people to work together in a shared geospatial environment. Its real‐time nature, multiple users' interaction and diversity of work context impose some special social, organizational and technological requirements, making the development of such real‐time geocollaboration systems a challenging task. A conceptual framework is therefore needed to specify and describe what synchronous geocollaboration is, considering its social, spatial and technical aspects. The geo‐social model presented in this article describes a conceptual framework for synchronous geocollaboration systems addressing the above aspects, identifies the core elements of the system and describes how these elements collaborate with each other. This model is presented using application‐level ontology and is then applied to a multi‐agent system based prototype in which multiple users can interact and negotiate in a shared 3D geospatial environment.     

An Ontology‐Driven Framework and Web Portal for Spatial Decision Support1

Numerous systems and tools have been developed for spatial decision support (SDS), but they generally suffer from a lack of re‐usability, inconsistent terminology, and weak conceptualization. We introduce a collaborative effort by the SDS Consortium to build a SDS knowledge portal. We present the formal representation of knowledge about SDS, the various ontologies captured and made accessible by the portal, and the processes used to create them. We describe the portal in action, and the ways in which users can search, browse, and make use of its content. Finally, we discuss the lessons learned from this effort, and future development directions. Our work demonstrates how ontologies and semantic technologies can support the documentation and retrieval of dynamic knowledge in GIScience by offering flexible schemata instead of fixed data structures.     

A GIS-Based Tool for Spatial and Distributed Hydrological Modelling: CLASS Spatial Analyst

CLASS Spatial Analyst is a GIS tool which can be used to support spatially distributed hydrological modelling. The tool generates a number of spatial layers that can be used for many practical applications. These include climate zoning, multi‐resolution DEMs, Compound Topographic Index (CTI) (also referred to as Topographic Wetness Index, TWI), lateral multiple flow paths, accumulation and dispersion of water and solutes from hazard areas, estimation of soil depth, soil material/horizon distribution and soil moisture storage capacity in different parts of the landscape. Although each of these tasks can be undertaken separately using spatial software packages such as ArcGIS, GRASS, TIME, and MapInfo, this tool puts together all these tasks into a single application which provides the user with an option of undertaking some or all of them within this application. The technology used in this tool is supported by various peer‐reviewed publications (e.g. Tarboton 1997 , Gallant and Dowling 2003 ).     

Street‐level Spatial Scan Statistic and STAC for Analysing Street Crime Concentrations

This study develops new types of hotspot detection methods to describe the micro‐space variation of the locations of crime incidents at the street level. It expands on two of the most widely used hotspot detection methods, Spatial and Temporal Analysis of Crime and Spatial Scan Statistic, and applies them to the analysis of the network space. The study first describes the conceptual and the methodological framework of the new methods followed by analyses using: (1) a simulated distribution of points along the street network; and (2) real street‐crime incident data. The simulation study using simulated point distributions confirms that the proposed methods is more accurate, stable and sensitive in detecting street‐level hotspots than their conventional counterparts are. The empirical analysis with real crime data focuses on the distribution of the drug markets and robberies in downtown Buffalo, NY in 1995 and 1996. The drug markets are found to form hotspots that are dense, compact and stable whereas hotspots of the robberies are observed more thinly across a wider area. The study also reveals that the location of the highest risk remains on the same spot over time for both types of crimes, indicating the presence of hotbeds which requires further attention.     

Positioning Disaster Relief Teams Given Dynamic Service Demand: A Hybrid Agent‐Based and Spatial Optimization Approach

During extreme events, like hurricanes, it is essential to position relief services for non‐evacuees throughout the affected region in optimal locations. While previous research has explored a variety of spatial optimization models to accomplish such a task, most work assumes that the population with demand is relatively static. However, this assumption neglects to account for potential feedbacks in the relief distribution system. For example, a population's behavior can both affect and be affected by the placement of relief services, resulting in a dynamic spatial distribution of demand. This article presents a hybrid modeling approach that utilizes GIS data with agent‐based and spatial optimization models to position emergency relief teams in Bay County, Florida during a hurricane event. Non‐evacuating household agents choose to remain at home or seek shelter, while relief team agents are periodically repositioned to account for changes in the spatial distribution of household agents. A total of 220 simulations are run to explore a variety of scenarios. Results show different repositioning timing strategies and the magnitude of the feedback effect drastically changes the level of access households have to relief teams. Ultimately, this work demonstrates the importance of accounting for behavioral and spatial dynamics in disaster relief distribution systems.     

A topographically preserved road‐network tile model and optimal routing method for virtual globes

Virtual globes enable the combination of heterogeneous datasets for optimal routing analyses in transportation, environmental ecology, and construction engineering. In this study, considering the advantages of the hierarchical tiling structure and topography of virtual globes, we propose a tile‐based optimal routing method for large‐scale road networks in a virtual globe. This method designs a topographically preserved road‐network tile model by partitioning roads into tiles and constructs the road‐network pyramid from the bottom to the top. During construction, a TileArc is calculated and flagged as the shortest path in a tile. Based on the built road‐network pyramid carrying hierarchical TileArcs, a multi‐level and flexible shortest path query can be executed efficiently. The proposed method is implemented with large road networks with different road grades in a virtual globe. Experimental results verify its validity, efficiency, and exactness. Moreover, the length of the shortest path with surface distance is approximately 1.3 times longer than that with Euclidean distance.     

Cartoblography: Experiments in Using and Organising the Spatial Context of Micro‐blogging

Blogs, micro‐blogs and online forums underpin a more interconnected world. People communicate ever more and are increasingly keen to explain and illustrate their lives; showing where they are and what they are doing. Desktop, online and mobile mapping landscapes have never been as rich or diverse yet this challenges cartography to adapt and remain relevant in the modern mapping world. We explore the spatial expression and potential value of micro‐blogging and Twitter as a social networking tool. Examples of “twitter maps” are reviewed that leverage the Twitter API and online map services to locate some component of the “tweet”. Scope, function and design are illustrated through development of two proof‐of‐concept map mashups that support collaborative real‐time mapping and the organisation and display of information for mass user events. Through the experiments in using and organising data in this way we demonstrate the value of “cartoblography”– a framework for mapping the spatial context of micro‐blogging.