模型与方法元数据及地理信息服务研究

资源与环境信息系统中数据的元数据标准业已产生,但对于数据使用十分重要的模型与方法元数据尚未有完整的描述,本文提出并分析了模型与方法元数据的概念与内容,同时针对近年提出的地理信息服务,提出了模型与方法元数据的服务模型,以实现对模型与方法的有效管理与分发服务,并得出在资源环境信息系统中基于模型与方法元数据的地理信息服务是切实可行的结论.     

基于DEM三维地层建模及一体化显示

目前三维地层表面建模方法构建的三维地层模型上表面粗糙,与地形不一致.在分析当前建模方法缺陷的基础上,基于多层DEM建模思想,提出了利用DEM修正主TIN的建模方法;该方法可保证模型与地形的空间一致性,实现模型与地理数据的一体化显示.并提出了利用虚拟钻孔进行剖面图离散化处理,将建模数据源统一为钻孔数据,从而将剖面图融入实际建模流程,解决了单一钻孔数据导致建模结果不精确及模型无法修正的问题,也简化了多源数据导致建模方法复杂的问题.最后以合肥市某区为例,展示了该方法的实现过程及建模效果.     

时空数据模型的研究现状与展望

分析了时空表达与建模理论的5个发展阶段:基于静态数据模型扩展、面向对象表达、基于对象变化(事件序列)、时空集成和以过程为核心的时空数据模型;阐述了时空数据模型在时空动态语义、地理时空认知表达和地球信息科学解决的基本问题方面的研究现状,并指出由于现有时空数据模型主要以地理实体存在状态的"对象视图"或"事件视图"(而不是演变特性的"过程视图")作为表达载体,割裂了其内在联系,无法实现复杂地理实体的时空语义描述和动态过程分析。最后,提出以地理实体演变为核心的时空语义描述方法和融入地理对象、变化机制、对象变化的时空动态表达将是时空表达与建模理论的发展趋势。     

人口统计数据空间化的一种方法

人口空间分布信息在环境健康风险诊断、自然灾害损失评估和现场抽样调查比较等地理学和相关学科研究中占有重要的地位。目前随着对地观测技术和地理信息科学的飞速发展, 如何精确地进行人口数据空间化成为了研究的难点和热点。针对采用传统方法解决人口空间化问题所遇到的困难和不足, 设计了遗传规划(genetic programming, GP)、遗传算法(genetic algorithms, GA) 和GIS 相结合的方法, 以GIS 确定量化影响因子权重, 以GP 建立模型结构, 以GA 优化模型参数, 成功建立研究区-山西省和顺县的人口数据格网分布表面。实验证明与传统建模方法(如逐步回归分析模型和重力模型)相比, 所提方法建模过程更为智能化与自动化, 模型结构更为灵活多样, 而且数据拟合精度更高。     

地理概念教学的重要性及策略——从“清明时节雨纷纷”谈起

从"‘清明时节雨纷纷’属何种天气系统"这一有争议的问题说起,指出地理概念教学的重要性,并指出目前地理概念教学中存在重现象轻本质、重结论轻过程、重应用轻思维等问题。在概念教学中,可使用以下策略:运用直观手段,建立概念;抓住要点设疑,剖析概念;采用比较方法,辨别概念;借助典型范例,巩固概念。     

基于模型需求模板匹配的多源地理数据推送方法研究

针对现有地理分析模型同多源复杂地学数据之间耦合困难、模型运算数据处理过程复杂等问题,构建了以模型需求模板匹配为基础的多源地理数据自动处理与推送方法。利用元数据对多源地理数据进行统一描述,并从模型的数据需求和任务需求两个角度生成模型需求模板,最后通过基于XML的元数据与模型需求模板的匹配求得数据操作模板。在数据操作模板中应用算子库作为转换工具,实现了数据库数据到模型需求数据的转换,完成模型运算数据的自动推送。基于江苏沿海滩涂数据库的分析案例表明,该文提出的模型模板匹配方法可对模型运行数据进行有效解析,并通过数据操作流的构建实现数据的自动推送。该研究可为服务型GIS的发展及地理模型的集成提供理论参考与方法借鉴。     

地理概念获得的几种策略

概念教学是地理教学的重点内容。本文针对地理概念教学中概念获得环节存在的问题,依据实际教学经验和教育教学理论总结归纳出几种地理概念获得的策略和方法。     

指向教学内容整合的地理概念体系建构研究——以“河流”为例

近年来地理教学中概念呈现碎片化、概念教学片段化、概念结构不清晰等问题逐渐凸显,考试评价对学生地理概念理解的要求提高。因此,教师应当在教学中系统整合地理概念,以促进学生深度学习,提升学生核心素养水平。本文从系统整合的角度切入,梳理地理概念体系建构的路径;以“河流”为例,通过提炼地理概念、联系地理概念、划分概念层级,形成围绕学生理解认知、实现教学内容重组的地理概念体系;讨论指向教学内容整合的地理概念体系的价值,即理清地理概念教学的逻辑线索、增强初高中地理教学的衔接效果、提供深度教学与深度学习的支架。     

数字地形分析应用适配性知识的案例表达与推理方法

数字地形分析(Digital Terrain Analysis, DTA)在应用时依赖于建模知识,尤其是关于所建的应用模型是否与研究区特点、数据等条件相适配的知识(称为“应用适配性知识”);由于这类知识难以形式化表达,现有的数字地形分析工具对此类知识缺乏利用,从而导致普通用户在应用数字地形分析时建模困难。针对该问题,设计了一套数字地形分析领域应用适配性知识的案例表达与相应的推理方法。以美国32个河网提取案例为例,通过交叉验证,初步表明案例及其推理应用方法适合于数字地形分析领域应用适配性知识的形式化表达与应用,该方法通过与建模环境的集成,可大幅降低数字地形分析应用建模难度。     

三维地质建模若干基本问题探讨

由于地质数据及其应用本身所具有的复杂性、不确定性、信息不完备等特点,使得三维地质建模成为制约三维GIS研究及其应用的主要瓶颈之一。讨论三维空间数据模型的设计,提出“多元数据—多方法集成—多层次干预”的“三多”建模思路;从建模尺度、数据源、建模工具等方面对建模方法进行分类;分析三维地质建模的关键技术,包括三维地质模型构建、海量模型数据操作和多元数据一体化耦合显示;针对三维地质建模系统的开发提出6项建议,分析近期三维地质建模的研究热点。     

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.     

A Pedagogic Framework to Link GIS to the Intellectual Core of Geography

Abstract

This paper aims to develop a new pedagogic framework for teaching GIS at the college and university level using Berry's geographic matrix. By synthesizing different schools of thought, this paper argues that GIS education essentially involves two aspects—how to teach about GIS and how to teach with GIS. Berry's geographic matrix can be used to tie these dual aspects of GIS education together neatly. As an abstract representation of geographical phenomena, the geographic matrix embeds all three entities of GIS—location, attribute, time—and thus can help GIS instructors teach about GIS. As a synthesis of geographical approaches, the geographic matrix can assist GIS instructors teach with GIS. This paper demonstrates that GIS is actually an implementation of Berry's geographic matrix. Furthermore, the 10 approaches to geographical analysis, originally proposed by Berry for the geographic matrix, can be executed routinely in a GIS environment. By incorporating Berry's geographic matrix into GIS education, teachers can enable students to surpass technical issues and to appreciate the conceptual and functional linkages between GIS and geography's intellectual core.     

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.     

基于多粒度时空对象数据模型的高速公路智能监控系统建模

多粒度时空对象数据模型更注重现实世界复杂、动态现象的表达,能更好地应用于智能设施管理和智能决策支持系统。该文基于多粒度时空对象数据模型及其建模理论,对高速公路智能监控系统中实体对象的特征和关系进行概念建模,设计数据存储方案,开发高速公路智能监控多粒度时空对象建模原型系统,并验证数据模型的实用性。结果表明:多粒度时空对象数据模型可以很好地支撑高速公路智能监控系统中动态智能实体及实体间复杂关系的表达、管理和分析,实现对路情的智能分析与决策。     

虚拟地理环境中尺度适宜性问题的探讨

本文沿着地理学语言的演化过程,讨论了地理学中尺度概念的演变;针对虚拟地理环境的框架结构,依据尺度概念的维度、类别和组成因素分析了四组尺度适宜性问题,并讨论了在虚拟地理环境搭建和应用过程中各种尺度适宜性之间的关系。以香港区域气象过程模拟为例,应用多尺度地形数据和模型,简要分析了在空间维上,测量尺度类别中组成因素（分辨率） 层次的尺度适宜性。该案例不只表明尺度适宜性对动态地理过程问题求解的重要影响,同时表明对尺度适宜性的讨论有助于其认知与分解,丰富虚拟地理环境的理论与方法。     

元胞自动机的地理过程模拟机制及扩展

地理空间、地理梯度、地理流和空间关系是经典地理学进行地理过程分析常用的4个基本概念,元胞自动机(CA)作为复杂空间系统研究的重要工具。分析表明,其与经典地理过程分析理论具有类似地表达机制,因而能有效地进行地理过程模拟。但由于标准CA是一种更广泛抽象的空间模型,其对地理特征的描述存在一定局限,限制了其更真实地模拟地理过程的能力。论文提出了基于地理特征的CA概念模型,深圳特区土地利用演化的实证研究表明,地理特征CA概念模型具有极大的应用价值。     

多源地理大数据支撑下的地理事件建模与分析

地理事件作为描述地理过程的基本单元,逐渐成为地理信息科学（GIS）核心研究内容。由于受人类活动数据获取限制,GIS对地理事件的建模和分析主要关注事件所引起的地理空间要素变化及要素之间的相互影响与作用机制。然而,近年来随着基于位置服务数据（LBS）爆炸式的增长和人类活动大数据定量刻画手段的快速发展,地理事件对人类活动的影响以及公众对地理事件的网络参与度都引起了多个领域的广泛关注,对地理事件的时空认知、建模方法和分析框架提出了巨大的挑战。对此,本文首先深入分析了大数据时代地理事件的概念与分类体系;其次,基于地理事件的时空语义给出了基于图模型的事件数据建模,建立了事件本体及其次生或级联事件的“节点—边”表达结构,开展了事件自身时空演化及其前“因”后“果”的形式化描述;第三,从时空数据分析与挖掘的角度,给出了大数据时代地理事件建模与分析的整体框架,拟突破传统“地理实体空间”事件探测与分析方法的局限性,融合“虚拟空间”事件发现与传播模拟思路,实现多源地理大数据支撑下的面向地理事件的人类活动多尺度时空响应与区域差异分析;最后,本文以城市暴雨事件为例诠释了本文所提出的地理事件建模与分析方法,从城市和城市内部两个尺度进行了暴雨事件与人类活动的一致性响应及区域差异分析,得到了明确的结论,验证了前文分析框架的可行性与实用性。     

基于特征的土地利用空间数据库模型

目前普遍使用的基于图层的空间数据库模型存在许多问题。基于特征的空间数据库模型是一种新的空间数据存储模式,它在很大程度上代表了GIS的发展方向。采用超图数据模型分析、设计和描述土地利用特征在概念和逻辑层次的提取与定义,建立土地特征分类体系,进行基于特征的土地利用空间数据库建模。     

Coupling maximum entropy modeling with geotagged social media data to determine the geographic distribution of tourists

Modeling the geographic distribution of tourists at a tourist destination is crucial when it comes to enhancing the destination’s resilience to disasters and crises, as it enables the efficient allocation of limited resources to precise geographic locations. Seldom have existing studies explored the geographic distribution of tourists through understanding the mechanisms behind it. This article proposes to couple maximum entropy modeling with geotagged social media data to determine the geographic distribution of tourists in order to facilitate disaster and crisis management at tourist destinations. As one of the most popular tourist destinations in the United States, San Diego was chosen as the study area to demonstrate the proposed approach. We modeled the tourist geographic distribution in the study area by quantifying the relationship between the distribution and five environmental factors, including land use, land parcel, elevation, distance to the nearest major road and distance to the nearest transit stop. The geographic distribution’s dependency on and sensitivity to the environmental factors were uncovered. The model was subsequently applied to estimate the potential impacts of one simulated tsunami disaster and one simulated traffic breakdown due to crisis events such as a political protest or a fire hazard. As such, the effectiveness of the approach has been demonstrated with specific disaster and crisis scenarios.     

Inferring network paths from point observations

The use of geographically referenced point data, such as that obtained from global positioning systems (GPS), is rapidly increasing. However, due to error and uncertainty inherent in most geographic datasets, the ability to accurately associate these point locations with other layers of geographic data is still a challenge. One difficulty in particular is how to associate spatially and temporally referenced point-based observations of a network activity with a network topology such that a continuous network path can be best inferred. In this article, an optimization method for inferring a network path from a temporal sequence of point observations of location is presented. An application to GPS data is provided to highlight various characteristics of the proposed modeling approach relative to several other available techniques.