Translating Bertin into Arabic today: new hidden facets of Semiology of Graphics

ABSTRACT

Semiology of Graphics is a seminal work of contemporary cartography. Published in 1967 by Jacques Bertin, the book attracted as much mistrust as it did interest even if some claims seem obsolete or outdated with the advent of GIS. This article discusses some underlying perspectives regarding an Arabic translation of Sémiologie Graphique. First, one may question the usefulness and the language of translation, and what issues readers should learn or be aware of before reading the book. In the Arab world, little research has engaged graphical semiology and its paradigms are rarely encountered. Second, only a limited number of graphical procedures have been experimented or implemented by digital means. There is a gap between some theoretical statements and their practical applications. Third, many other semiological aspects remain, in substance, little known in detail. This article considers whether these should be revisited by visualization and graphical analytics.     

Jacques Bertin,from classical training to systematic thinking of graphic signs

ABSTRACT

Nearly 50 years after the publication of the first French edition of Sémiologie Graphique, Jacques Bertin’s thought is more than ever alive, in cartography as well as in various other disciplines, such as graphic design or visual data analysis. This article recalls the main elements of Bertin’s career and puts forward the salient points of his reflection on graphics. Beyond many conceptual or technical innovations, Bertin’s essential originality appears to be his attempt to propose a general framework for graphical representations.     

Design and evaluation of a geovisual analytics system for uncovering patterns in spatio-temporal event data

ABSTRACT

It remains difficult to develop a clear understanding of geo-located events and their relationships to one another, particularly when it comes to identifying patterns of events in less-structured textual sources, such as news feeds and social media streams. Here we present a geovisualization tool that can leverage computational methods, such as T-pattern analysis, for extracting patterns of interest from event data streams. Our system, STempo, includes coordinated-view geovisualization components designed to support visual exploration and analysis of event data, and patterns extracted from those data, in terms of time, geography, and content. Through a user evaluation, we explore the usability and utility of STempo for understanding patterns of recent political, social, economic, and military events in Syria.     

A geovisual analytics exploration of the OpenStreetMap crowd

It is sometimes easy to forget that massive crowdsourced data products such as Wikipedia and OpenStreetMap (OSM) are the sum of individual human efforts stemming from a variety of personal and institutional interests. We present a geovisual analytics tool called Crowd Lens for OpenStreetMap designed to help professional users of OSM make sense of the characteristics of the “crowd” that constructed OSM in specific places. The tool uses small multiple maps to visualize each contributor’s piece of the crowdsourced whole, and links OSM features with the free-form commit messages supplied by their contributors. Crowd Lens allows sorting and filtering contributors by characteristics such as number of contributions, most common language used, and OSM attribute tags applied. We describe the development and evaluation of Crowd Lens, showing how a multiple-stage user-centered design process (including testing by geospatial technology professionals) helped shape the tool’s interface and capabilities. We also present a case study using Crowd Lens to examine cities in six continents. Our findings should assist institutions deliberating OSM’s fitness for use for different applications. Crowd Lens is also potentially informative for researchers studying Internet participation divides and ways that crowdsourced products can be better comprehended with visual analytics methods.     

轨迹大数据云存储与热力图生成方法

针对目前轨迹大数据基于WebGIS的热力图展示中的成图耗时较长、用户移动缩放交互差等问题,本文建立了基于HBase的轨迹数据与地图数据存储模型,为轨迹大数据在电子地图上进行热力图展示提供了存储方法,同时提出一种基于聚类处理的轨迹数据热力图可视化方法,针对不同缩放级别能够有效地减少数据绘制计算量与数据传输量,较大地提升了热力图渲染与展示的效率。试验证明,该方案能够实现轨迹数据、电子地图数据的存储,提高热力图可视化绘制效率,能够为轨迹数据挖掘与分析提供技术支撑。     

Challenges and strategies for the visual exploration of complex environmental data

ABSTRACT

In this opinion paper, we, a group of scientists from environmental-, geo-, ocean- and information science, argue visual data exploration should become a common analytics approach in Earth system science due to its potential for analysis and interpretation of large and complex spatio-temporal data. We discuss the challenges that appear such as synthesis of heterogeneous data from various sources, reducing the amount of information and facilitating multidisciplinary, collaborative research. We argue that to fully exploit the potential of visual data exploration, several bottlenecks and challenges have to be addressed: providing an efficient data management and an integrated modular workflow, developing and applying suitable visual exploration concepts and methods with the help of effective and tailored tools as well as generating and raising the awareness of visual data exploration and education. We are convinced visual data exploration is worth the effort since it significantly facilitates insight into environmental data and derivation of knowledge from it.     

大数据时代的农情监测与预警

农情信息是世界粮农组织、各国政府、粮食贸易企业以及农场管理迫切需要掌握的信息。大数据时代的农情监测与预警正在由模型驱动向数据驱动转变,大数据正逐渐成为监测与预警的核心驱动力。伴随着农情监测与预警大数据的爆炸式增长,大数据与云计算技术的发展为农情监测与预警提供了全新的技术手段。2013年以来,全球农情遥感速报系统(CropWatch)已逐步引入聚类分析、时间序列分析、关联分析、时空变化异常诊断等大数据分析方法,并应用于业务化运行的农情监测与预警中。大数据技术提升了CropWatch的数据挖掘能力,对CropWatch农情监测与预警时空尺度的拓展以及农情监测内容的精细化起到推动作用,促进了面向需求的CropWatch农情信息与预警精准云服务的发展,促成了大数据时代CropWatch农情监测与预警技术体系的升级。未来,大数据时代的农情监测与预警将逐渐向全自动化监测、实时化精准农业管理与智能化信息服务方向发展;通过众源采集技术高效低廉的获取农情观测大数据将成为未来的发展趋势;大数据技术跨领域数据挖掘的能力,使得丰富多元化的跨界信息服务将成为大数据时代农情监测与预警的主流发展方向。大数据时代的CropWatch正在向基于大数据的农情监测与预警系统全速迈进。     

大数据背景下的地理信息数据网络安全探讨

地理信息数据是大数据的重要组成部分和数据来源。大数据背景下,地理信息数据呈现出泛在化、普适化、在线化的特征,同时也面临着各种网络安全问题。在全面分析地理信息数据面临的网络安全问题基础上,提出在物理层面、技术层面、管理层面上的应对策略。     

基于大数据的世界文化遗产属性深度融合与分析

全球世界文化遗产本体及其环境数据是遗产价值认知和保护的基础,更是大数据时代遗产研究、展示以及可持续利用的依据.本文基于网络和地球大数据,利用自然语言处理、空间分析、领域知识图谱等技术,构建了包括领域知识图谱5万个节点,94万条三元组的全球世界文化遗产知识图谱,支持世界文化遗产的属性、节点关系查询以及图结构表达.基于知识...     

大数据时代下测绘地理信息检验检测面临的问题与思考

大数据时代下,测绘地理信息数据作为承载各类自然资源信息、社会信息、经济信息、人文信息等重要基础数据,其质量作用举足轻重.然而,当前测绘地理信息的质量检验检测从理论基础、方式方法、服务模式等各个方面都不能完全满足时代的需求.本文分析了大数据时代的一些典型特征和思维方式,从质量度量方式、抽样检查评价、检验与检测等方面分析了...     

Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use

Data are increasingly spatio‐temporal—they are collected some‐where and at some‐time. The role of proximity in spatial process is well understood, but its value is much more uncertain for many temporal processes. Using the domain of land cover/land use (LCLU), this article asserts that analyses of big data should be grounded in understandings of underlying process. Processes exhibit behaviors over both space and time. Observations and measurements may or may not coincide with the process of interest. Identifying the presence or absence of a given process, for instance disentangling vegetation phenology from stress, requires data analysis to be informed by knowledge of the process characteristics and, critically, how these manifest themselves over the spatio‐temporal unit of analysis. Drawing from LCLU, we emphasize the need to identify process and consider process phase to quantify important signals associated with that process. The aim should be to link the seriality of the spatio‐temporal data to the phase of the process being considered. We elucidate on these points and opportunities for insights and leadership from the geographic community.     

基于ArcView的基坑监测数据可视化

介绍GIS软件ArcView的功能和应用特点以及运用ArcView软件来实现基坑监测数据的可视化,对基坑监测数据的可视化以及数据挖掘整个过程进行详细的研究,最后结合工程实例进行应用与分析得到如下结论:arcview应用于基坑监测项目中,可以实现监测的可视化以及基坑监测数据的管理、分析、查询、输出等功能。     

3维虚拟环境下场景专题信息表达方法研究

技术的不断进步与人们对现实认知的逐渐深化促使场景信息的表达向专题方向发展。基于场景信息表达专题化、高效化的考虑,结合3维场景中信息表达的特点,本文重点研究在3维虚拟环境下的场景专题信息的表达分类与表现方法,目的是使用户只关注专题信息即可获取所需信息,可以在最短时间内形成对场景信息的总体把握与正确认识。     

大数据智能时代地图学课程内容改革探索

为了适应当前智能时代大数据的特点和地图应用的发展,本文结合当前地图学课程内容设置现状,分析在大数据时代地图学教学的特点,多源地理信息的参考体系差异、多维度地理信息高效可视化、多尺度表达的灵性化、地图用户需求的多样化等对地图学教学提出的新要求,在此基础上,结合地图学课程教学的理论与实践目标,提出在地图数学基础、地图可视化...     

供给侧结构性改革下测绘大数据中心建设分析——以陕西省地理空间大数据中心为例

在高质量发展的要求下,针对分散的IT基础设施、"孤岛化"的数据资源和生产能力瓶颈等方面的供给侧问题,本文以陕西省地理空间大数据中心建设为例,通过3个阶段性工作推进了测绘事业供给侧结构性改革。首先通过IT基础设施建设与升级实现了集约利用和灵活配置;然后通过数据资源改造与共享实现了大数据分析和开放互联;最后通过产业链升级与高效供给实现了按需更新与服务。经过建设,大数据中心硬件资源利用率接近99%,在地理国情监测、全国GNSS基准站网平差、第三次全国国土调查等方面发挥了提质增效的作用。     

Semantic and syntactic interoperability in online processing of big Earth observation data

ABSTRACT

The challenge of enabling syntactic and semantic interoperability for comprehensive and reproducible online processing of big Earth observation (EO) data is still unsolved. Supporting both types of interoperability is one of the requirements to efficiently extract valuable information from the large amount of available multi-temporal gridded data sets. The proposed system wraps world models, (semantic interoperability) into OGC Web Processing Services (syntactic interoperability) for semantic online analyses. World models describe spatio-temporal entities and their relationships in a formal way. The proposed system serves as enabler for (1) technical interoperability using a standardised interface to be used by all types of clients and (2) allowing experts from different domains to develop complex analyses together as collaborative effort. Users are connecting the world models online to the data, which are maintained in a centralised storage as 3D spatio-temporal data cubes. It allows also non-experts to extract valuable information from EO data because data management, low-level interactions or specific software issues can be ignored. We discuss the concept of the proposed system, provide a technical implementation example and describe three use cases for extracting changes from EO images and demonstrate the usability also for non-EO, gridded, multi-temporal data sets (CORINE land cover).     

Mapping the porosity of international border to pedestrian traffic: a comparative data classification approach to a study of the border region in Austria,Italy, and Slovenia

National borders play an important role in everyday life. Interest in border studies has increased with recent changes in geographical locations of the border or the fluctuation of the permeability of the border between some countries, such as in the European Union. Whether the nations are trying to increase traffic flow of the border or to implement stricter border control, having appropriate information of the border is crucial for effective policymaking.

The objective of this research was to identify areas of high porosity, or high permeability, for pedestrians along the southern national border region in Carinthia, Austria using terrain, land use, and road data along with geocomputational methods. Two unsupervised classification methods, the fuzzy K-means clustering and the Self-Organizing Map, were applied to segment the border into homogeneous zones according to topographic and infrastructural attributes. The fuzzy K-means clustering method was chosen for its ability to allow for a continuous approach to classification. With this method, an object can belong, with different degrees of membership, to multiple classes, which is a more realistic reflection of the natural world than discrete clustering, where each object can only belong to one class. However, the fuzzy K-means clustering method does have disadvantages, i.e. the user must determine the number of classes and the input parameters are required to be in continuous format. The second classification method, the Self-Organizing Map, is a type of artificial neural network and was chosen for its ability to automatically determine the number of classes and handle categorical data. The Self-Organizing Map is unique because it can transform high dimensional data into low dimensional display while preserving the topology and spatial distribution of the input parameters. The results of the two classification methods suggest that the fuzzy K-means classification is more effective than the Self-Organizing Map for this situation. However, more research is needed to determine the fit of these algorithms for particular spatial data classification tasks.

The results obtained from this research provide an insight into the permeability of the border region of Carinthia, Slovenia, and Italy to pedestrian traffic and can be potentially useful for decision making processes for tourism development and road transportation management in that region. Furthermore, the approach presented in this article can be applied to other national borders to identify zones permeable to pedestrian traffic.     

智慧时代的空间信息人才培养:挑战与应对

以智慧地理信息服务新兴产业应用岗位需求为目标导向,分析了智慧时代空间信息认知特征与需求,揭示了地理空间信息理解和空间认知在智慧地理空间信息服务中的重要地位,以及未来空间信息人才面临的机遇与挑战;提出了应对智慧地理空间信息服务需求的成果导向创新应用能力本为教育新模式,构建了以智慧地理信息服务项目驱动的OCCE培养途径/任务群,积极探索了新时代测绘地理信息工程教育教学改革方向。