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。     

地理时空三向聚类分析方法的构建与实践

随着地理数据获取能力的不断提升,地理数据体量呈指数增长,数据种类、数据性质更加多元化。对数据的有效甄别和归类成为理解地理现象时空特征、演化过程和行为机制的关键。传统聚类方法面临数据体量大、维数高、质量差的挑战,加之对地理空间与时间关联分析的需求,对聚类方法改进和提升研究的要求越来越迫切。本文介绍了从单向到三向聚类构建思路的变革。单向聚类是仅在样本或属性方向上进行聚类,易忽视非常相似的局部特征、易犯“横看成岭侧成峰”的错误。双向聚类是基于数据矩阵内元素值的相似性,形成一个子矩阵分割方案,使子矩阵内元素相似度尽可能高,子矩阵间元素相似度尽可能低,从而实现行列两方向的同时聚类,避免了单向聚类的不足。鉴于双向聚类难以满足地理研究超出双向的解译需求,本文提出并研发了一个全新的三向聚类方法,给出了运用该方法开展地理时空格局过程探测的流程,总结了如何根据研究涉及的“空间—时间—尺度—属性”构建三维数据体;最后,展示了三向聚类的地理实践案例。结果表明：① 三向聚类是一种大数据时代探测地理数据时空分异规律的有效方法,可以解决数据维度高、质量低等问题;② 面对不同的地理问题,三向聚类在算法层面上是通用的,不同之处仅在于：根据不同问题涉及的空间、时间、尺度、属性的不同,构建不同的数据体;不同数据体聚类得到的不同结果回答不同的地理问题;③ 三向聚类可以实现地理数据的时空分异规律多方向、多尺度、多层次的联合解译,揭示地理特征时空尺度叠加效应。最后,论文强调根据地理问题组织数据的重要性,期待未来能够提升三向聚类在多空间尺度、多属性方面的地理研究实践。     

Patterns and causes of uncertainty in the American Community Survey

In 2010 the American Community Survey (ACS) replaced the long form of the United States decennial census. The ACS is now the principal source of high-resolution geographic information about the U.S. population. The margins of error on ACS census tract-level data are on average 75 percent larger than those of the corresponding 2000 long-form estimate. The practical implications of this increase is that data are sometimes so imprecise that they are difficult to use. This paper explains why the ACS tract and block group estimates have large margins of error. Statistical concepts are explained in plain English. ACS margins of error are attributed to specific methodological decisions made by the Census Bureau. These decisions are best seen as compromises that attempt to balance financial constraints against concerns about data quality, timeliness, and geographic precision. In addition, demographic and geographic patterns in ACS data quality are identified. These patterns are associated with demographic composition of census tracts. Understanding the fundamental causes of uncertainty in the survey suggests a number of geographic strategies for improving the usability and quality ACS.     

Multicriteria generalization (MCG): a decision-making framework for formalizing multiscale environmental data reduction

Environmental simulation models need automated geographic data reduction methods to optimize the use of high-resolution data in complex environmental models. Advanced map generalization methods have been developed for multiscale geographic data representation. In the case of map generalization, positional, geometric and topological constraints are focused on to improve map legibility and communication of geographic semantics. In the context of environmental modelling, in addition to the spatial criteria, domain criteria and constraints also need to be considered. Currently, due to the absence of domain-specific generalization methods, modellers resort to ad hoc methods of manual digitization or use cartographic methods available in off-the-shelf software. Such manual methods are not feasible solutions when large data sets are to be processed, thus limiting modellers to the single-scale representations. Automated map generalization methods can rarely be used with confidence because simplified data sets may violate domain semantics and may also result in suboptimal model performance. For best modelling results, it is necessary to prioritize domain criteria and constraints during data generalization. Modellers should also be able to automate the generalization techniques and explore the trade-off between model efficiency and model simulation quality for alternative versions of input geographic data at different geographic scales. Based on our long-term research with experts in the analytic element method of groundwater modelling, we developed the multicriteria generalization (MCG) framework as a constraint-based approach to automated geographic data reduction. The MCG framework is based on the spatial multicriteria decision-making paradigm since multiscale data modelling is too complex to be fully automated and should be driven by modellers at each stage. Apart from a detailed discussion of the theoretical aspects of the MCG framework, we discuss two groundwater data modelling experiments that demonstrate how MCG is not just a framework for automated data reduction, but an approach for systematically exploring model performance at multiple geographic scales. Experimental results clearly indicate the benefits of MCG-based data reduction and encourage us to continue expanding the scope of and implement MCG for multiple application domains.     

Reflections and speculations on the progress in Geographic Information Systems (GIS): a geographic perspective

Great strides have been made in Geographic Information Systems (GIS) research over the past half-century. However, this progress has created both opportunities and challenges. From a geographic perspective, certain challenges remain, including the modelling of geographic-featured environments with GIS data model, the enhancement of GIS’s analysis functions for comprehensive geographic analysis and achieving human-oriented geographic information presentation. Several basic theoretical and technical ideas that follow the workflow and processes of geographic information induction, geographic scenario modelling, geographic process analysis and geographic environment representation are proposed to fill the gaps between GIS and geography. We also call for designing methods for big geographic data-oriented analysis, making best use of videos and developing virtual geographic scenario-based GIS for further evolution.     

GIS空间数据库的"一库两用"策略研究

GIS有两种普遍的应用需求:空间分析和数字制图,但两种需求对空间数据的要求不完全一致,导致空间数据在同一GIS中无法充分共享。该文探讨能够同时满足两种需求的GIS构建方法。分析地理空间数据存在的冲突,引入"一库两用"思想,建立空间分析型数据模型;利用专家系统机制实现空间分析数据向数字制图数据的转换,转换过程中补充的信息源于专家知识库。该研究有助于解决GIS中数据不一致问题,使GIS更实用、功能更完善。     

对地理信息分类编码的认识与思考

面向GIS的地理信息分类编码是在概念水平上解决空间信息分层组织与属性数据库结构设计的重要方法。该文从地理实体、地理数据与地理信息概念角度 ,探讨地理信息分类编码对象的本质特征 ;简要分析大多数商品化GIS软件中属性数据的表达方式 ,指出地理信息分类编码实质上是从GIS数据组织的概念层次上对属性信息的分类分级 ;提出地理信息分类粒度概念 ,并建立地理信息分类模型 ,列举了在不同视图下的粗粒地理信息分类方案和代码用途 ;提出并讨论地理信息分类编码尚待进一步研究的问题 ,以及国内长期以来难以形成地理信息分类标准的原因。     

Field Work in the Classroom

To make sense of spatial data, information and concepts strong geographic literacy skills are a prerequisite, yet existing studies suggest a global decline in these skills. Current geographic tools predominantly focus on mapping tasks that do not necessarily lead to an understanding of the broader geographic relevance of location. This article argues that in order to improve geographic literacy exercises should test an understanding of location and employ techniques to correct deficiencies. To support this argument this study offers evidence on the effectiveness of online quizzes in improving geographic literacy skills of first-year undergraduate geography students.     

Predicting potential distributions of geographic events using one-class data: concepts and methods

One common problem with geographic data is that, for a specific geographic event, only occurrence information is available; information about the absence of the event is not available. We refer to these specific types of geospatial data as geographic one-class data (GOCD). Predicting the potential spatial distributions that a particular geographic event may occur from GOCD is difficult because traditional binary classification methods that require availability of both positive and negative training samples cannot be used. The objective of this research is to define GOCD and propose novel approaches for modelling potential spatial distributions of geographic events using GOCD. We investigate the effectiveness of one-class support vector machine (OCSVM), maximum entropy (MAXENT) and the newly proposed positive and unlabelled learning (PUL) algorithm for solving GOCD problems using a case study: species distribution modelling from synthetic data. Our experimental results indicate that generally OCSVM, MAXENT and PUL are effective in modelling the GOCD. Each method has advantages and disadvantages, but PUL seems to be the most promising method.     

虚拟地理环境的多维数据模型与地理过程表达

该文通过剖析虚拟地理环境所表达的地球表层空间与其在计算机空间的映射之间的关系,定义了一维、二维、三维及多维虚拟地理环境;指出虚拟地理环境与传统地图、GIS的区别及联系。阐述近年来虚拟地理环境研究对地理学乃至地球科学研究方法和手段的影响以及对地理科学思维方式和重点研究领域的影响。     

桂西山地灾害的监测、预测及其对策

广西百色市是自然灾害频发的地区,降雨是激发山地灾害的关键因素。通过收集大量的地质灾害资料和气象资料,运用天气学、数值预报产品、MOS法等,结合卫星探测资料,建立各种地理环境和地质特征条件下山地灾害预警的气象模型和各种灾害等级的预警指标,并就山地灾害的监测与对策提出了一些建议和措施。     

Automatic alignment of contemporary vector data and georeferenced historical maps using reinforcement learning

ABSTRACT

With large amounts of digital map archives becoming available, automatically extracting information from scanned historical maps is needed for many domains that require long-term historical geographic data. Convolutional Neural Networks (CNN) are powerful techniques that can be used for extracting locations of geographic features from scanned maps if sufficient representative training data are available. Existing spatial data can provide the approximate locations of corresponding geographic features in historical maps and thus be useful to annotate training data automatically. However, the feature representations, publication date, production scales, and spatial reference systems of contemporary vector data are typically very different from those of historical maps. Hence, such auxiliary data cannot be directly used for annotation of the precise locations of the features of interest in the scanned historical maps. This research introduces an automatic vector-to-raster alignment algorithm based on reinforcement learning to annotate precise locations of geographic features on scanned maps. This paper models the alignment problem using the reinforcement learning framework, which enables informed, efficient searches for matching features without pre-processing steps, such as extracting specific feature signatures (e.g. road intersections). The experimental results show that our algorithm can be applied to various features (roads, water lines, and railroads) and achieve high accuracy.     

地理大数据挖掘的本质

针对地理大数据的内在本质以及地理大数据挖掘对于地理学研究的意义,本文解释了地理大数据的含义,并在大数据“5V”特征的基础上提出了粒度、广度、密度、偏度和精度等“5度”的特征,揭示了地理大数据的本质特点。在此基础上,从地理大数据的表达方式、地理大数据挖掘的目标、地理模式的叠加与尺度性、地理大数据挖掘与地理学的关系等4个方面阐述了地理大数据挖掘的本质与作用,并从挖掘目标的角度对地理大数据挖掘方法进行分类。未来地理大数据挖掘的研究将面临地理大数据的聚合、挖掘结果的有效性评价以及发现有价值的知识而非常识等几方面的挑战。     

地理视频数据模型及其应用开发研究

讨论地理视频的基本概念和数据模型,尝试提出地理视频数据的实体-关系图,并在网络环境中进行地理视频应用开发.地理视频数据模型的核心要素是:对视频帧进行位置和语义描述,建立视频片段的轨迹图层和元数据,并在轨迹图层中扩充视频帧线性参照.通过空间参照、线性参照和语义参照将视频数据与地理数据集成,实现地理视频的查询、检索、播放和地图跟踪.以河南大学校园和开封市公路网络为例,采集地理视频数据,建立地理视频数据库,发布网络地图和视频服务,并分别采用最新的Adobe Flex框架、ArcGIS Server ADF和JavaScript Mashup方式,开发网络视频GIS应用系统.原型系统开发表明,地理视频数据模型适用于网络视频GIS开发,且在技术实现上较为简单.     

Mining query-driven contexts for geographic and temporal search

The explosive growth of geographic and temporal data has attracted much attention in information retrieval (IR) field. Since geographic and temporal information are often available in unstructured text, the IR task becomes a non-straightforward process. In this article, we propose a novel geo-temporal context mining approach and a geo-temporal ranking model for improving the search performance. Queries target implicitly ‘what’, ‘when’ and ‘where’ components. We model geographic and temporal query-dependent frequent patterns, called contexts. These contexts are derived based on extracting and ranking geographic and temporal entities found in pseudo-relevance feedback documents. Two methods are proposed for inferring the query-dependent contexts: (1) a frequency-based statistical approach and (2) a frequent pattern mining approach using a support threshold. The derived geographic and temporal query contexts are then exploited into a probabilistic ranking model. Finally, geographic, temporal and content-based scores are combined together for improving the geo-temporal search performance. We evaluate our approach on the New York Times news collection. The experimental results show that our proposed approach outperforms significantly a well-known baseline search, namely the probabilistic BM25 ranking model and state-of-the-art approaches in the field as well.     

地理大数据为地理复杂性研究提供新机遇

大数据之风自2010年席卷全球,已在科学、工程和社会等领域产生深远影响。本文首先从地理大数据、第四范式以及非线性复杂地理系统3组基本概念出发,剖析上述3组概念之间的科学联系与相互支撑作用,提出大数据和第四范式为地理复杂性研究提供新机遇。其后,探讨如何利用大数据和复杂性科学的理论方法开展地理复杂性研究。基于地理大数据,可以通过统计物理学的系列指标描述现实地理世界的复杂非线性特征,同时,还可利用深度学习、复杂网络、多智能体等方法,实现复杂非线性地理系统的推演和模拟。上述方法对认知地理现象和过程的复杂性,对复杂地理系统的分析、模拟、反演与预测有重要作用。最后,提出地理大数据和复杂性科学相互支撑可能成为21世纪地理学的主流科学方法。     

我国地理信息产业的发展现状及思考

文章讨论了地理信息和地理信息产业的涵义,以及发展我国地理信息产业的重要性,对我国地理信息产业的现状进行了思考与分析,并提出了建议。认为我国地理信息产业面临机遇与挑战,通过扶持国产GIS软件,制定空间数据标准和交换格式标准,扩大地理信息技术服务的应用领域,珍惜人才,重视教育,我国的地理信息产业将有很大的发展空间。     

Construction of interfaces for the exchange of geographic data

Transferring data from one geographic information system to another commonly requires a sequence of interfaces, software packages that convert data from one format to another. Construction is described of a model interface that uses a relational database management system and compiler-building tools that work from a machine-readable definition of a data format. Parallels are drawn with conversion and translation problems in other areas from which software tools might be obtained for automation of interface construction. Three interfacing strategies are examined. In particular, the advantages and limitations are discussed for the strategy of using a standard interchange format. The principal conclusion is that achieving a widely-accepted method of defining geographic data formats should be an important objective of efforts at standardization.     

Big Data from the Ground Up: Mobile Maps and Geographic Knowledge

Consumer users of maps on mobile devices are producing noteworthy geographic knowledges in the contexts of their own lives that are distinct from those of professional data scientists. By leveraging the streaming nature of big data in mobile maps and zooming multiscalar views, consumer users' mobile map practices produce a popular, multiscalar form of visual geographic knowledge that is both enabled and limited by its big data assemblage and associated technologies. The first half of this article outlines the role of consumer user practices amidst spatial big data assemblages, not for volunteered geographic information or aggregate analysis but for contextual, everyday use. Consumer users and their knowledges are coconstituted through mobile map viewing and as materially limited technological practices. This article focuses specifically on the consumer users' concept of scale in this context, for Web-based maps' multiscalar views differentiate them from older maps. The second half analyzes mobile map consumer users' concepts of scale in a series of focus groups that involved both questions and observing participants' actions with maps on their own phones. Instead of passively accepting maps at predetermined optimized scales from the map application, consumer users actively viewed the map across scales while searching but not while navigating.