Building geospatial infrastructure

ABSTRACT

Many visions for geospatial technology have been advanced over the past half century. Initially researchers saw the handling of geospatial data as the major problem to be overcome. The vision of geographic information systems arose as an early international consensus. Later visions included spatial data infrastructure, Digital Earth, and a nervous system for the planet. With accelerating advances in information technology, a new vision is needed that reflects today’s focus on open and multimodal access, sharing, engagement, the Web, Big Data, artificial intelligence, and data science. We elaborate on the concept of geospatial infrastructure, and argue that it is essential if geospatial technology is to contribute to the solution of problems facing humanity.     

Big Earth data: disruptive changes in Earth observation data management and analysis?

ABSTRACT

Turning Earth observation (EO) data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community. Recently, the term ‘big Earth data’ emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges. We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains. The disruptive element is that analysts and end-users increasingly rely on Web-based workflows. In this contribution we study selected systems and portals, put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.     

(re)Considering Bertin in the age of big data and visual analytics

ABSTRACT

This paper highlights a selection of core ideas articulated by Bertin and leveraged by many researchers over time, with particular attention to how the ideas relate to developments in cartography, big data, and visual analytics. A primary contribution is a bibliometric analysis of the impact of Bertin’s Semiology of Graphics at its 50th anniversary. A briefer bibliometric assessment of Graphics and Graphic Information Processing impacts is also provided. The bibliometric analysis includes exploration of citations to Semiology of Graphics over the entire time span (in both English and French editions) as well as more focused analysis by topic and outlet since the advent of visual analytics as a research domain. Then, very recent research related to cartography, visual analytics, and big data is examined in detail to determine if and how Bertin’s ideas continue to be leveraged and extended for current data representation and analysis challenges. After outlining some limitations of the bibliometric analysis, discussion reflects on the current relevance of Bertin’s ideas, potential applications in visual analytics, and the need for a complement to Sémiologie Graphique focused on interactive visual interfaces to an increasingly diverse array of display forms. The paper concludes with thoughts on next steps.     

Spatially simplified scatterplots for large raster datasets

Abstract

Scatterplots are essential tools for data exploration. However, this tool poorly scales with data-size, with overplotting and excessive delay being the main problems. Generalization methods in the attribute domain focus on visual manipulations, but do not take into account the inherent nature of information redundancy in most geographic data. These methods may also result in alterations of statistical properties of data. Recent developments in spatial statistics, particularly the formulation of effective sample size and the fast approximation of the eigenvalues of a spatial weights matrix, make it possible to assess the information content of a georeferenced data-set, which can serve as the basis for resampling such data. Experiments with both simulated data and actual remotely sensed data show that an equivalent scatterplot consisting of point clouds and fitted lines can be produced from a small subset extracted from a parent georeferenced data-set through spatial resampling. The spatially simplified data subset also maintains key statistical properties as well as the geographic coverage of the original data.     

Resolution Control for Balancing Overview and Detail in Multivariate Spatial Analysis

Abstract

Parallel coordinates, re-orderable matrices, and dendrograms, widely used methods for visual exploration of multivariate data, are systematically integrated in a complementary manner for supporting multi-resolution visual data analysis with an enhanced overview + detail exploratory strategy. There are three main topics: (1) dynamic control across resolutions at which data are explored; (2) coordination and color mapping among the views; and (3) enhanced features of each view designed for the overview + detail exploratory tasks. A case study analysis is used to demonstrate the potential for boosting productivity for exploration tasks by coordinating the views through user-controlled resolutions within a highly interactive analysis environment. The case study is focused on a complex, geographically referenced dataset including public health, demographic and environmental components.     

Redefining the possibility of digital Earth and geosciences with spatial cloud computing

Abstract

Global challenges (such as economy and natural hazards) and technology advancements have triggered international leaders and organizations to rethink geosciences and Digital Earth in the new decade. The next generation visions pose grand challenges for infrastructure, especially computing infrastructure. The gradual establishment of cloud computing as a primary infrastructure provides new capabilities to meet the challenges. This paper reviews research conducted using cloud computing to address geoscience and Digital Earth needs within the context of an integrated Earth system. We also introduce the five papers selected through a rigorous review process as exemplar research in using cloud capabilities to address the challenges. The literature and research demonstrate that spatial cloud computing provides unprecedented new capabilities to enable Digital Earth and geosciences in the twenty-first century in several aspects: (1) virtually unlimited computing power for addressing big data storage, sharing, processing, and knowledge discovering challenges, (2) elastic, flexible, and easy-to-use computing infrastructure to facilitate the building of the next generation geospatial cyberinfrastructure, CyberGIS, CloudGIS, and Digital Earth, (3) seamless integration environment that enables mashing up observation, data, models, problems, and citizens, (4) research opportunities triggered by global challenges that may lead to breakthroughs in relevant fields including infrastructure building, GIScience, computer science, and geosciences, and (5) collaboration supported by cloud computing and across science domains, agencies, countries to collectively address global challenges from policy, management, system engineering, acquisition, and operation aspects.     

Semantic Visual Variables for Augmented Geovisualization

ABSTRACT

The human–cyber–physical world produces a considerable volume of multi-modal spatio-temporal data, thus leading to information overload. Visual variables are used to transform information into visual forms that are perceived by the powerful human vision system. However, previous studies of visual variables focused on methods of ‘drawing information’ without considering ‘intelligence’ derived from balancing ‘importance’ and ‘unimportance’. This paper proposes semantic visual variables to support an augmented geovisualization that aims to avoid exposing users to unnecessary information by highlighting goal-oriented content over redundant details. In this work, we first give definitions of several concepts and then design a semiotic model for depicting the mechanisms of augmented geovisualization. We also provide an in-depth discussion of semantic visual variables based on a hierarchical organization of the original visual variables, and we analyse the critical influencing factors that affect the choice of visualization forms and visual variables. Finally, a typical application is used to illustrate the relevance of this study.     

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.     

Applying VGI to collaborative research in the humanities: the case of ARTL@S

ABSTRACT

Using Artl@s as an example of a project that relies on volunteered geographic information (VGI), this article examines the specific challenges that exist, beyond those frequently discussed in general VGI systems (e.g., participants’ motivation and data quality control) in regard to sharing research data in humanities: (1) most data from the humanities is qualitative and collected from multiple data sources which are often inconsistent and unmappable; (2) data is usually interconnected with multiple relationships among different tables which creates challenges for both mapping and query functionality; (3) data is both geographical and historical. Consequently addresses that no longer exist have to be geolocated and visualized on historical basemaps and spaces must be represented diachronically; (4) the design of web map application needs to balance both sophisticated research requirements and a user-friendly interface; (5) finally contributors expect their data to be cited or acknowledged when used in other studies and users need metadata and citation information in order to reuse and repurpose datasets.

In this article, we discuss how Artl@s, a project which developed a georeferenced historical database of exhibition catalogues, addresses these challenges. Artl@s provides a case study for VGI adoption by digital humanities scholars for research data sharing, as it offers features, such as flexible batch data contribution, interrelated spatial query, automatic geolocalization of historical addresses, and data citation mechanisms.     

Sonifying data uncertainty with sound dimensions

ABSTRACT

The communication of data uncertainty is a crucial problem in data science, information visualization, and geographic information science (GIScience). Effective ways to communicate the uncertainty of data enables data consumers to interpret the data as intended by the producer, reducing the possibilities of misinterpretation. In this article, we report on an empirical investigation of how sound can be used to convey information about data uncertainty in an intuitive way. To answer the research question How intuitive are sound dimensions to communicate uncertainty? we carry out a cognitive experiment, where participants were asked to interpret the certainty/uncertainty level in two sounds A and B (N = 33). We produce sound stimuli by varying sound dimensions, including loudness, duration, location, pitch, register, attack, decay, rate of change, noise, timbre, clarity, order, and harmony. In the stimuli, both synthetic and natural sounds are used to allow comparison. The experiment results identify three sound dimensions (loudness, order, and clarity) as significantly more intuitive to communicate uncertainty, providing guidelines for sonification and information visualization practitioners.     

Contemporary American cartographic research: a review and prospective

ABSTRACT

We review recent developments in cartographic research in North America, in the context of informing the 29th International Cartographic Conference, and 18th General Assembly in 2019. The titles of papers published since 2015 in four leading cartographic journals yielded a corpus of 245 documents containing 1109 unique terms. These terms were analyzed using Latent Dirichlet Allocation and by visual analytics to produce 14 topic groups that mapped onto five classes. These classes were named as information visualization, cartographic data, spatial analysis and applications, methods and models, and GIScience. The classes were then used as themes to discuss the recent cartographic literature more broadly, first, to review recent trends in the research and to identify research gaps, and second, to examine prospects for new research over the next 20 years. A conclusion draws some broad findings from the review, suggesting that cartographic research in the future will be aimed less at dealing with data, and more at generating insight and knowledge to better inform society about global challenges.     

Retrieving Hierarchical Information from Maps: The Role of Metric and Configurational Variables

Abstract

One of the most important challenges for cartographers is to be able to transmit information appearing in maps in a simple manner to users. The commonest strategy to do so consists in displaying visual information in a hierarchical way, that is, making it some elements to appear as being more important than others. Nevertheless, recent research has shown that people also pay attention to configurational information, or information about relationships among elements appearing in a map, to retrieve hierarchical information of it. This is the topic of this paper. It aims to investigate the role of metric and configurational information in enabling people to retrieve hierarchical information from maps. The main problem consisted in identifying ‘the main street’ of different layouts whose paths were sometimes widened to make them appear more important. The main findings show that people retrieved hierarchical information by paying attention to a combination of metric and configurational factors.

One of the main challenges that cartographers face is to transmit information in a way that is simple to understand for everyone. The most frequent strategy for this is to display the information in a hierarchical way; that is, by exaggerating the size or width of specific elements and thus assigning them a greater degree of importance. Nevertheless, recent research has shown that when reading maps, people also read configurational information. This is the topic of this paper. It aims to investigate the role of metric and configurational information in enabling people to retrieve hierarchical information from maps. For this, a set of exercises was designed and carried out where people were asked to identify the main street of different specially designed layouts. The main findings show that people retrieved hierarchical information by paying attention to a combination of metric and configurational factors.     

Exhibiting the Exhibitors: Spatial Visualization for Heterogeneous Cinema Venue Data

Abstract

Cinema data is characteristically complex, heterogeneous and interlinked. Rather than relying on simple information retrieval techniques, researchers are increasingly turning to the creative exploration and reapplication of data in order to more fully explore the meaning of newly available and diverse data sets. In this context, the cinema historian becomes the creator of visual texts which can be assessed for both their interpretive insight and their aesthetic qualities. This paper presents four research projects that use different spatio-temporal visualization techniques to understand the industrial dynamics of post-war film exhibition and distribution in Australia. The research integrates work by a group of inter-disciplinary investigators into the effectiveness of techniques such as dendritic mapping, Circos circular visualizations, animation, cartogram mapping, and multivariate visualization for the study of cinema circuits and operations at a number of scales.     

Research on SAR data integrated processing methodology oriented on earth environment factor inversions

ABSTRACT

As an important advanced technique in the field of Earth observations, Synthetic Aperture Radar (SAR) plays a key role in the study of global environmental change, resources exploration, disaster mitigation, urban environments, and even lunar exploration. However, studies on imaging, image processing, and Earth factor inversions have often been conducted independently for a long time, which significantly limits the application effectiveness of SAR remote sensing due to the lack of an overall integrated design scheme and integrated information processing. Focusing on this SAR application issue, this paper proposes and describes a new SAR data processing methodology – SAR data integrated processing (DIP) oriented on Earth environment factor inversions. The simple definition, typical integrated modes and overall implementation ideas are introduced. Finally, focusing on building information extraction (man-made targets) and sea ice classification (natural targets) applications, three SAR DIP methods and experiments are conducted. Improved results are obtained under the guidance of the SAR DIP framework. Therefore, the SAR DIP theoretical framework and methodology represent a new SAR science application mode that has the capability to improve the SAR remote sensing quantitative application level and promote the development of new theories and methodologies.     

Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?

Abstract

The geospatial sciences face grand information technology (IT) challenges in the twenty-first century: data intensity, computing intensity, concurrent access intensity and spatiotemporal intensity. These challenges require the readiness of a computing infrastructure that can: (1) better support discovery, access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries; (2) provide real-time IT resources to enable real-time applications, such as emergency response; (3) deal with access spikes; and (4) provide more reliable and scalable service for massive numbers of concurrent users to advance public knowledge. The emergence of cloud computing provides a potential solution with an elastic, on-demand computing platform to integrate – observation systems, parameter extracting algorithms, phenomena simulations, analytical visualization and decision support, and to provide social impact and user feedback – the essential elements of the geospatial sciences. We discuss the utilization of cloud computing to support the intensities of geospatial sciences by reporting from our investigations on how cloud computing could enable the geospatial sciences and how spatiotemporal principles, the kernel of the geospatial sciences, could be utilized to ensure the benefits of cloud computing. Four research examples are presented to analyze how to: (1) search, access and utilize geospatial data; (2) configure computing infrastructure to enable the computability of intensive simulation models; (3) disseminate and utilize research results for massive numbers of concurrent users; and (4) adopt spatiotemporal principles to support spatiotemporal intensive applications. The paper concludes with a discussion of opportunities and challenges for spatial cloud computing (SCC).     

Digital Earth from vision to practice: making sense of citizen-generated content

Abstract

The vision of Digital Earth (DE) put recently forward under the auspices of the International Society for DE extends the paradigm of spatial data infrastructures by advocating an interactive and dynamic framework based on near-to-real time information from sensors and citizens. This paper contributes to developing that vision and reports the results of a two-year research project exploring the extent to which it is possible to extract information useful for policy and science from the large volumes of messages and photos being posted daily through social networks. Given the noted concerns about the quality of such data in relation to that provided by authoritative sources, the research has developed a semi-automatic workflow to assess the fitness for purpose of data extracted from Twitter and Flickr, and compared them to that coming from official sources, using forest fires as a case study. The findings indicate that we were able to detect accurately six of eight major fires in France in the summer of 2011, with another four detected by the social networks but not reported by our official source, the European Forest Fire Information Service. These findings and the lessons learned in handling the very large volumes of unstructured data in multiple languages discussed in this study provide useful insights into the value of social network data for policy and science, and contribute to advancing the vision of DE.     

A comparison of three image-object methods for the multiscale analysis of landscape structure

Within the conceptual framework of Complex Systems, we discuss the importance and challenges in extracting and linking multiscale objects from high-resolution remote sensing imagery to improve the monitoring, modeling and management of complex landscapes. In particular, we emphasize that remote sensing data are a particular case of the modifiable areal unit problem (MAUP) and describe how image-objects provide a way to reduce this problem. We then hypothesize that multiscale analysis should be guided by the intrinsic scale of the dominant landscape objects composing a scene and describe three different multiscale image-processing techniques with the potential to achieve this. Each of these techniques, i.e., Fractal Net Evolution Approach (FNEA), Linear Scale-Space and Blob-Feature Detection (SS), and Multiscale Object-Specific Analysis (MOSA), facilitates the multiscale pattern analysis, exploration and hierarchical linking of image-objects based on methods that derive spatially explicit multiscale contextual information from a single resolution of remote sensing imagery. We then outline the weaknesses and strengths of each technique and provide strategies for their improvement.     

Harveys and Walking Maps

Abstract

Geographical information is connected to everyday life in many ways. Web cartography has made geospatial data more available to the public. In the next few years, the average age of the population will be over 51 years, with more than 15% of the population over the age of 65 years. The fact that the senior population is increasing suggests that online mapping and map viewer design should be aware of the senior population’s visual restrictions, as well as restrictions of other potential map users, including colour-vision-impaired users. This paper describes the senior population’s visual restrictions that can be compared with colour-vision-impaired users and provides guidelines with regard to online mapping and map viewer design for this growing segment of the elderly population.