SensePlace3: a geovisual framework to analyze place–time–attribute information in social media

SensePlace3 (SP3) is a geovisual analytics framework and web application that supports overview + detail analysis of social media, focusing on extracting meaningful information from the Twitterverse. SP3 leverages social media related to crisis events. It differs from most existing systems by enabling an analyst to obtain place-relevant information from tweets that have implicit as well as explicit geography. Specifically, SP3 includes not just the ability to utilize the explicit geography of geolocated tweets but also analyze implicit geography by recognizing and geolocating references in both tweet text, which indicates locations tweeted about, and in Twitter profiles, which indicates locations affiliated with users. Key features of SP3 reported here include flexible search and filtering capabilities to support information foraging; an ingest, processing, and indexing pipeline that produces near real-time access for big streaming data; and a novel strategy for implementing a web-based multi-view visual interface with dynamic linking of entities across views. The SP3 system architecture was designed to support crisis management applications, but its design flexibility makes it easily adaptable to other domains. We also report on a user study that provided input to SP3 interface design and suggests next steps for effective spatiotemporal analytics using social media sources.     

基于线阵水下定位的解析求解算法

当信号接收器位于同一直线时,DGPS水下立体定位系统的解析算法并不能解算出目标空间立体坐标。提出一种新方法,利用它们的几何关系,计算出它们所在平面的相对位置。得到平面位置可以作为求解立体坐标的一部分,还可以应用于平面内水下目标定位的测试。解算中四个信号接收器的数据可以解算出唯一解,当信号接收器数目大于四个时,利用最小二乘理论获最优解。该方法采用解析法直接求解,不存在定位结果的发散问题。     

Application of Web-GIS and Geovisual Analytics to Monitoring of Seabed Evolution in South Baltic Sea Coastal Areas

Regular surveys of coastal zone seabed deliver important information about geomorphologic processes such as silting of waterways. The recent introduction of the Sentinel series of sensors has allowed for the use of satellite sensing for shallow bathymetry morphology monitoring. In this context, this article presents a dedicated Geographic Information System for Baltic Sea shallow water depth monitoring on the basis of Sentinel-2 imagery. The system employs Geovisual Analytics for differential analysis of bathymetry changes as well as monitoring the visibility of known wrecks in the coastal waters of Southern Baltic Sea. Results are verified with regard to known changes in shallow water bathymetry between 28 June 2015 and 3 March 2017.     

An IEEE value loop of human-technology collaboration in geospatial information science

ABSTRACT

Geosensing and social sensing as two digitalization mainstreams in big data era are increasingly converging toward an integrated system for the creation of semantically enriched digital Earth. Along with the rapid developments of AI technologies, this convergence has inevitably brought about a number of transformations. On the one hand, value-adding chains from raw data to products and services are becoming value-adding loops composed of four successive stages – Informing, Enabling, Engaging and Empowering (IEEE). Each stage is a dynamic loop for itself. On the other hand, the “human versus technology” relationship is upgraded toward a game-changing “human and technology” collaboration. The information loop is essentially shaped by the omnipresent reciprocity between humans and technologies as equal partners, co-learners and co-creators of new values.

The paper gives an analytical review on the mutually changing roles and responsibilities of humans and technologies in the individual stages of the IEEE loop, with the aim to promote a holistic understanding of the state of the art of geospatial information science. Meanwhile, the author elicits a number of challenges facing the interwoven human-technology collaboration. The transformation to a growth mind-set may take time to realize and consolidate. Research works on large-scale semantic data integration are just in the beginning. User experiences of geovisual analytic approaches are far from being systematically studied. Finally, the ethical concerns for the handling of semantically enriched digital Earth cover not only the sensitive issues related to privacy violation, copyright infringement, abuse, etc. but also the questions of how to make technologies as controllable and understandable as possible for humans and how to keep the technological ethos within its constructive sphere of societal influence.     

Big spatial data for urban and environmental sustainability

ABSTRACT

Eighty percent of big data are associated with spatial information, and thus are Big Spatial Data (BSD). BSD provides new and great opportunities to rework problems in urban and environmental sustainability with advanced BSD analytics. To fully leverage the advantages of BSD, it is integrated with conventional data (e.g. remote sensing images) and improved methods are developed. This paper introduces four case studies: (1) Detection of polycentric urban structures; (2) Evaluation of urban vibrancy; (3) Estimation of population exposure to PM2.5; and (4) Urban land-use classification via deep learning. The results provide evidence that integrated methods can harness the advantages of both traditional data and BSD. Meanwhile, they can also improve the effectiveness of big data itself. Finally, this study makes three key recommendations for the development of BSD with regards to data fusion, data and predicting analytics, and theoretical modeling.     

Mapping collective human activity in an urban environment based on mobile phone data

Identifying and characterizing variations of human activity – specifically changes in intensity and similarity – in urban environments provide insights into the social component of those eminently complex systems. Using large volumes of user-generated mobile phone data, we derive mobile communication profiles that we use as a proxy for the collective human activity. In this article, geocomputational methods and geovisual analytics such as self-organizing maps (SOM) are used to explore the variations of these profiles, and its implications for collective human activity. We evaluate the merits of SOM as a cross-dimensional clustering technique and derived temporal trajectories of variations within the mobile communication profiles. The trajectories’ characteristics such as length are discussed, suggesting spatial variations in intensity and similarity in collective human activity. Trajectories are linked back to the geographic space to map the spatial and temporal variation of trajectory characteristics. Different trajectory lengths suggest that mobile phone activity is correlated with the spatial configuration of the city, and so at different times of the day. Our approach contributes to the understanding of the space-time social dynamics within urban environments.     

The space-time cube as part of a GeoVisual analytics environment to support the understanding of movement data

This paper reports the results of an empirical usability experiment on the performance of the space-time cube in a GeoVisual analytics environment. It was developed to explore movement data based on the requirements of human geographers. The interactive environment consists of multiple coordinated views incorporating three graphical representations. For the experiment, two groups of the user, domain experts and non-domain experts, had to execute several map-use tasks to answers specific question. The data collected during the experiment were analysis resulting in a set of usability metrics related to the effectiveness, efficiency and user satisfaction of developed application. The comparison of both groups showed that domain experts were able to operate the visual analytical environment more effectively and efficiently due to their interest to explore their data. The user feedback derived from the analysis of both experiments was further processed for the improvement of the application.     

面向不同主题的交通大数据可视分析

大数据时代,交通数据如何更快更准确地实时反映交通状况成为研究热点。使用不同交通数据对不同的研究主题进行可视分析,进而揭示蕴含的交通信息知识,为交通部门提供决策支持。首先,分析了大数据的基本特征,总结了大数据可视分析的研究现状;其次,对交通大数据的基本构成、获取方式及数据类型特点进行详细描述;最后,按照不同的研究主题选择不同的数据源与不同的可视化表达方式,揭示不同研究主题蕴含的交通规律,为交通大数据的自动模型分析提供可靠的可视化知识。     

Assessing the Effectiveness and Efficiency of Map Colour for Colour Impairments Using an Eye-tracking Approach

Colour impairments influences access to geographical information which is usually represented by colour maps. Three dimensions of colour: Hue, Saturation and Value (HSV), are intuitive and most critical visual variables in map design. In this paper, we specifically focus on colour deficiency of red-green colour impairments. A controlled experiment was designed and conducted to explore how three colour dimensions (HSV) affect the abilities of people with normal colour vision or with red-green colour impairments to distinguish colours in maps. An eye-tracking approach was applied to quantify the accuracy and response time by capturing user eye movements to analyse the effectiveness and efficiency. In this study, we used one section of the administrative map of Hebei Province to test participant responses to area features. Differences of effectiveness and efficiency across normal colour vision and red-green colour impairments were compared. Multiple comparisons among Hue, Saturation and Value were analysed. Results show that for both normal colour vision and red-green colour impairments, Hue is the most differentiable than Saturation and Value. Saturation and Value are at the same level to be differentiated and more difficult to be distinguished. Guidelines of designing maps for both normal colour vision and red-green colour impairments were derived. The results of this study can be helpful to improve the map designs for colour deficiency.     

栅格数据地学可视化分析环境的构建分析

以遥感数据、数字高程数据等为代表的栅格数据获取技术的进步,以及栅格数据本身适合地学模拟的特点,使得栅格数据应用越来越广泛.当前以定量计算为主的方法难以有效支撑栅格数据分析任务,将可视化引入,充分利用人机协同优势,形成栅格数据地学可视化分析环境是一个较好的解决途径.但是,栅格数据大数据量的特征会引起属性空间可视化时的遮挡...