A less‐is‐more approach to geovisualization – enhancing knowledge construction across multidisciplinary teams

The ‘less‐is‐more’ concept in interface design for computer applications has recently gained ground. In this article, the concept is adopted for a user‐centered design of geovisualization application. The premise is that using simple and clear design can lead to successful applications with improved ease of use. Over the last three decades, the development of GIS and geovisualization has seen a marked increase in the levels of interaction between the user, the system and the information. However, these enthusiastic advances in technology have not resulted in a significant increase in the number of users.

This article suggests that types of user interaction should not simply emphasize traditional GIS functions such as zooming and panning but move towards interaction based on facilitating the knowledge construction process. Considerations are made for the complexity of the system, the task at hand and the skills and limitations of the users. These elements are particularly important when maps act as the mediators in collaboration with users across disciplinary backgrounds. In such cases, the emphasis on simplicity and usability becomes as important as functionality. In these situations a geovisualization application designed for specific uses can maximize effective development of geographic knowledge.

In this article, a minimalistic design approach to geovisualization is adopted by creating a geographic profiling tool which shifts the emphasis from technological advances or interaction with the map to the interaction elements key to building the spatial knowledge of GIS experts and non‐experts alike. To evaluate this notion of ‘less‐is‐more geovisualization’ the profiling tool is evaluated according to usability matrices: efficiency, effectiveness and learnability. How well the Suburban Profiler contributes to these elements is assessed by conducting a video analysis of the types and forms of user interaction available. The video analysis demonstrates the usefulness and usability of the Suburban Profiler, providing proof of concept for ‘less‐is‐more geovisualization’.     

Web-based geospatial multiple criteria decision analysis using open software and standards

The emerging ubiquity of geospatial information is providing an unprecedented opportunity to apply Geographical Information Systems (GIS)-based multiple criteria decision analysis (MCDA) to a broad spectrum of use cases. Volunteered geographic information, open GIS software, geoservice-based tools, cloud-based virtualized platforms, and worldwide collaboration of both domain experts and general users have greatly increased the quantity and accessibility of geospatially referenced data resources. Currently, there is a lack of GIS-based MCDA tools that integrate this decision-driven process within a widely accessible, robust geoframework environment, designed for user-friendly interaction. In this contribution, we present a conceptual workflow and proof-of-concept software application, Geocentric Environment for Analysis and Reasoning (GEAR), which provides a viable transition path to enhance geospatial MCDA in the age of open GIS. We propose a Web-based platform that leverages open-source geotechnologies to incorporate a wide variety of geospatial data formats in a common solution space to allow for spatially enhanced and time-relevant decision analysis. Through the proposed workflow, a user can ingest and modify heterogeneous data formats, exploit temporally tagged data sources, create multicriteria decision analysis models, and visualize the results in an iterative and collaborative workspace. A sample case study applied to disaster relief is used to demonstrate the prototype and workflow. This proof-of-concept Web-based application provides a notional pathway of how to connect open-source data to open-source analysis through a geospatially enabled MCDA workflow that could be virtually accessible to many levels of decision makers from individuals to entire organizations.     

Optimization of simulation and visualization analysis of dam-failure flood disaster for diverse computing systems

Simulation and subsequent visualization in a network environment are important to glean insights into spatiotemporal processes. As computing systems become increasingly diverse in hardware architectures, operating systems, screen sizes, human–computer interactions and network capabilities, effective simulation and visualization must become adaptive to a wide range of diverse devices. This paper focuses on the optimization of simulation and visualization analysis of the dam-failure flood spatiotemporal process for diverse computing systems. First, an adaptive browser/server architecture of the dam-failure simulation application was designed to fill the hardware performance and visualization context gap that exists within diverse computing systems. Second, a data flow and an optimization method for multilevel time-series flood data were given to provide more support to network simulation, visualization and analysis on diversified terminals. Finally, a user interaction friendly and plugin-free prototype system was developed. The experiment results demonstrate that the methods addressed in this paper can cope with the challenge in simulation, visualization and interaction of a dam-failure simulation application on diversified terminals.     

A comprehensive methodology for discovering semantic relationships among geospatial vocabularies using oceanographic data discovery as an example

It is challenging to find relevant data for research and development purposes in the geospatial big data era. One long-standing problem in data discovery is locating, assimilating and utilizing the semantic context for a given query. Most research in the geospatial domain has approached this problem in one of two ways: building a domain-specific ontology manually or discovering automatically, semantic relationships using metadata and machine learning techniques. The former relies on rich expert knowledge but is static, costly and labor intensive, whereas the second is automatic and prone to noise. An emerging trend in information science takes advantage of large-scale user search histories, which are dynamic but subject to user- and crawler-generated noise. Leveraging the benefits of these three approaches and avoiding their weaknesses, a novel methodology is proposed to (1) discover vocabulary-based semantic relationships from user search histories and clickstreams, (2) refine the similarity calculation methods from existing ontologies and (3) integrate the results of ontology, metadata, user search history and clickstream analysis to better determine their semantic relationships. An accuracy assessment by domain experts for the similarity values indicates an 83% overall accuracy for the top 10 related terms over randomly selected sample queries. This research functions as an example for building vocabulary-based semantic relationships for different geographical domains to improve various aspects of data discovery, including the accuracy of the vocabulary relationships of commonly used search terms.     

Interpreting maps through the eyes of expert and novice users

The experiments described in this article combine response time measurements and eye movement data to gain insight into the users' cognitive processes while working with dynamic and interactive maps. Experts and novices participated in a user study with a ‘between user’ design. Twenty screen maps were presented in a random order to each participant, on which he had to execute a visual search. The combined information of the button actions and eye tracker reveals that both user groups showed a similar pattern in the time intervals needed to locate the subsequent names. From this pattern, information about the users' cognitive load could be derived: use of working memory, learning effect and so on. Moreover, the response times also showed that experts were significantly faster in finding the names in the map image. This is further explained by the eye movement metrics: experts had significantly shorter fixations and more fixations per second meaning that they could interpret a larger part of the map in the same amount of time. As a consequence, they could locate objects in the map image more efficiently and thus faster.     

Economic-Geological Estimation of Gold and Ore Deposits Using Information-Analytical System for the Selection of Analogues

The computer system is developed for evaluation of gold deposits over the world using data collected by experts in the field of gold deposit geology. With the system in use, gold object of interest can be described on the basis of selected groups of tags. The similarity of the considered object to an object from the database is estimated by designed algorithms on the basis of available information characterizing the considered object. These algorithms allow to apply mathematical methods for analysis of the problem what improves quality and reliability of the results. By the method of analogue determination, user can always evaluate unknown properties of the considered object, as for example, ore formation type, economic geological estimate of the deposit on the basis of known properties of the determined analogue.The software equipped by an intuitive graphical interface is developed with use of GIS technologies and modern methods of object-oriented programming.     

Infometric and statistical diagnostics to provide artificially-intelligent support for spatial analysis: the example of interpolation

The wider uptake of GIS tools by many application areas outside GIScience means that many newer users of GIS will have high-level knowledge of the wider task, and low-level knowledge of specific system commands as given in reference manuals. However, these newer users may not have the intermediate knowledge that experts in GI science have gained from working with GI systems over several years. Such intermediate knowledge includes an understanding of the assumptions implied by the use of certain functions, and an appreciation of how to combine functions appropriately to create a workflow that suits both the data and overall goals of the geographical analysis task.

Focusing on the common but non-trivial task of interpolating spatial data, this paper considers how to help users gain the necessary knowledge to complete their task and minimise the possibility of methodological error. We observe that both infometric (or cognitive) knowledge and statistical knowledge are usually required to find a solution that jointly and efficiently meets the requirements of a particular user and data set. Using the class of interpolation methods as an example, we outline an approach that combines knowledge from multiple sources and argue the case for designing a prototype ‘intelligent’ module that can sit between a user and a given GIS.

The knowledge needed to assist with the task of interpolation is constructed as a network of rules, structured as a binary decision tree, that assist the user in selecting an appropriate method according to task-related knowledge (or ‘purpose’) and the characteristics of the data sets. The decision tree triggers exploratory diagnostics that are run on the data sets when a rule requires to be evaluated. Following evaluation of the rules, the user is advised which interpolation method might be and should not be considered for the data set. Any parameters required to interpolate the particular data set (e.g. a distance decay parameter for Inverse Distance Weighting) are also supplied through subsequent optimisation and model selection routines. The rationale of the decision process may be examined, so the ‘intelligent interpolator’ also acts as a learning tool.     

SOVAS: a scalable online visual analytic system for big climate data analysis

ABSTRACT

Big climate data offers great opportunities for scientific discovery but demands efficient and effective analytics to investigate unknown and complex patterns. Most existing online processing and analytics systems for climate studies only support fixed user interface with predefined functions. These systems are often not scalable to handle massive climate data that could easily accumulate terabytes daily. To address the major limitations of existing online systems for climate studies, this paper presents a scalable online visual analytic system, known as SOVAS, to balance both usability and flexibility. SOVAS, enabled by a set of key techniques, supports large-scale climate data analytics and knowledge discovery in a scalable and sharable environment. This research not only contributes to the community an efficient tool for analyzing big climate data but also contributes to the literature by providing valuable technical references for tackling spatiotemporal big data challenges.     

Usability evaluation for geographic information systems: a systematic literature review

The growing dissemination of geographic information systems (GIS) in recent years has increased the availability and use of geospatial tools. Despite this trend in GIS, usability is often not considered, and studies on usability are relatively rare. However, it has been shown that the effectiveness, efficiency and user satisfaction of these tools when used for geospatial tasks may be impacted by their design and terminology. In this article, we present a comprehensive review of the relevant literature. We analyze and compare publications of GIS usability evaluations, the methods that were used, and the findings reported. We thus produce a more detailed picture of GIS usability evaluations. Our contributions are (1) an overview of methods applied in case studies for evaluating the usability of GIS; (2) the identification of gaps and resulting weaknesses of previous studies; and (3) challenges and opportunities for future GIS usability studies. Our findings reveal a lack of usability guidelines for GIS, a lack of methods for analyzing complex workflows, and a lack of usage studies over longer periods of time. These contributions can help GIS developers and designers to improve the usability of their systems.     

Interoperable information model for geovisualization and interaction in XR environments

ABSTRACT

Since the vision of a Digital Earth (DE) was introduced in 1998, geo-browsers seem to have nearly fulfilled this vision. Virtual reality (VR) for visualizing the DE provides an immersive user experience in a mirror world. Location-based augmented reality (AR) browsers have been introduced and provide content according to user and environmental contexts. However, the content models of traditional geo-browsers and AR browsers have very limited interoperability, because they are described in application-specific formats using their domain standards. Each application is vertically integrated from content to application. The Web is an interoperable and open platform, and hundreds of millions of users are already using it to create and share content. To envision DE browsers for cross-reality (XR) environments that concurrently support geovisualization as well as VR, AR, and mixed-reality environments, we propose a DE content model based on Web standards and architecture that provides full interoperability and openness for XR browsers as a first-class citizen of the Web. This is expected to improve the DE content development efficiency by fully using the Web content development environment.     

The impact of interactive visualization on trade-off-based geospatial decision-making

In a previous work we developed GISwaps, a novel method for geospatial decision-making based on Even Swaps. In this paper, we present the results of an evaluation of a visualization framework integrated with this method, implemented within a decision support system. This evaluation is based on two different studies. In the quantitative study, 15 student participants used GISwaps with no visual features, and 15 participants used GISwaps with the integrated visual framework, as the tool in a solar farm site location case study. The results of the quantitative evaluation show positive impact of the visualization in terms of increased coherency in trade-offs. The results also show a statistically significant difference in average trade-off values between the groups, with users from the non-visual group setting on average 20% higher trade-off values compared with the users in the visual group. In the qualitative study, we had one expert in GIS, two experts in decision-making and two experts in solar energy as a focus user group. Data in this study were obtained by observations and semi-structured interviews with the participants. The impact of the visualization framework was assessed positively by all participants in the expert group.     

Editorial

Abstract

The analysis of geographical information is compared with other production processes in which a user can only accept an end-product if it meets certain quality requirements. Whereas users are responsible for defining the levels of quality they need to use the results of the analyses of geographical information systems in their work, database managers, experts and modellers could greatly assist users to achieve the quality of results they seek by formalizing information on: (1) data collection, level of resolution and quality; (2) the use of the basic analytical functions of the geographical information system; and (3) the data requirements, sensitivity and error propagation in models. These meta-data could be incorporated in a knowledge base alongside the geographical information system where, together with procedures for on-line error propagation, a user could be advised on the best way to achieve a desired aim. If the analysis showed that the original constellation of data, methods and models could not achieve the aim with the desired quality, the intelligent geographical information system would present a range of alternative strategies—better methods, more data, different data, better models, better model calibration, or better spatial resolution—and their costs by which the user's aims could reasonably be achieved.     

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.     

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.     

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。     

High-Resolution Measurement and Analysis of Visitor Experiences in Time and Space: The Case of Aalborg Zoo in Denmark

The notion of consumer and user experience has become dominant in our society in recent years, especially in relation to leisure activities. In this study we used the experience sampling method (ESM) data collection technique in Aalborg Zoo, Denmark, to understand the distribution of subjective experiences within this site. Visitors to the zoo were asked to send phone messages (SMS) about their subjective feelings in real time and to carry with them Global Positioning System (GPS) devices that recorded their movement. This method allowed us to geotag experiences of visitors throughout the zoo compound. The results indicate that the quality of experience of visitors varies both in time and in space. We conclude that there is a need to further explore the effect of place on experiences using repeat, high-resolution measurements. In this regard we believe that geographers, who have a long tradition of studying human–environment relations, have the tools to lead this type of exploration.     

试论地理学实验技术的特点与开发

本文讨论了地理学实验的基本特点与局限性，及其在地理学研究，教学上的应用，把地理学实验技术加以归纳总结，使之理论化，系统化和规范化，并从培养技术人才，着眼于实验手段现代化等方面提出了如何更好地应用和发展地理学实验技术的设想。     

Local Perceptions of the Sustainability of Alaska’s Highly Contested Cook Inlet Salmon Fisheries

Using a mixed set of ethnographic methods including interviews and a structured survey, we evaluate local perceptions about the sustainability of salmon fisheries among fishers of the Cook Inlet region of Alaska. A majority of residents report participating in these fisheries, but we find significant disagreement about their sustainability. Household income and the types of fishing practiced both influence people's responses, and many implicated specific user groups for overharvesting and/or for receiving unfair catch allocations. Drawing on the theoretical areas of information theory and political ecology, we argue that these findings reveal a complex interplay among the practical, social, and political aspects of how local resource users, or “local experts,” develop and communicate their assessments of ecological conditions in a context of conflict. We conclude with notes on future research as it relates to the successful design of local-management and co-management solutions, especially in settings where resources are contested by stakeholders.     

Dislocation Pedagogy

This article describes an experiment in learning with a course on “research and philosophies of place and space” in which students were repeatedly dislocated from an environment familiar to them and replaced in key locations about the city with local place experts as their guide. The article begins with a review of the meaning of place and the opportunities offered by the City of Kingston for reflective engagement. It briefly hints at some of the social trends devaluing place learning and the consequently political nature of this pedagogy. Finally, it summarizes the lessons learned in five of the ten classes.