The flood of data, the speed of (near) real-time updates, very complex relations between any piece of information, high bias, and less time for decision making have become common features of today’s world. Cartography, geographic information systems, and web-based applications address such issues in many ways, visual analytics being one such application [1
Within a few years, visual analytics has gone beyond the state-of-the-art through the utilization of geographic information gained from (big) data. Such an approach stimulates innovative thinking about complex challenges [4
]. Visual analytics techniques and tools are used to synthesize information and derive insights from big and dynamic data, detect the expected and discover the unexpected, and provide understandable assessments and communicate them effectively [5
]. A strong focus is on presenting the spatial-temporal aspect of the data in an “interactive” and “connected” way. Interactivity means that the user can interact through the GUI (graphical user interface) and the system will respond in (near) real-time. Connectivity is realized by integrated, linked, and synchronized graphs, (heat) maps, tables, or 3D views. The use of interactive maps, heat maps, and charts to understand user behavior (e.g., shifts in traffic flows/volume due to changing events) enables inter-disciplinary actors to explore new ideas together in a holistic, comprehensive, systematic, analytical, and visual manner before deploying costly solutions.
Using interactive visual analytics tools, the analysis of problems can have greater depth as many layers of data relating to the physical and social world can be considered together. With big data tools, impacts can be explored across a whole region, rather than just one or two small localities. Instead of providing spreadsheets of uninspiring figures to illustrate the impact of, for example, urban transportation (e.g., https://movement.uber.com/explore/paris
), road routing decisions in general [6
], or different types of hazards [7
], visualizations provide one version of the truth for all to use.
The visual analytics tools presented in this paper are the results of several European research and innovation projects. Their development between November 2017 and October 2020 is being funded from the European Horizon 2020 project titled PoliVisu (Policy Development based on Advanced Geospatial Data Analytics and Visualization; https://www.polivisu.eu/
). PoliVisu develops the traditional public policy making cycle (outlined by Patton and Sawicki [8
]) using big data and visual analytics tools. The aim is to provide an open set of digital tools to leverage data to help public sector decision-making become more democratic by (a) experimenting with different policy options through impact visualization and (b) using the resulting visualizations to engage and harness the collective intelligence of policy stakeholders for collaborative solution development [9
1.1. User Evaluation of Tools for Visual Analysis
Visual analytics, in general, should allow users to focus their full perceptual and cognitive capabilities on analytical processes. To allow this, visual analytics tools must be user-friendly and understandable to users. It is therefore logical that these tools should be evaluated by their users. Visual analytics tools are usually assessed on the basis of their use in experimental situations that employ some kind of analysis. Only a few studies have addressed their evaluation by actual users.
The evaluation of visual analytics tools usually detects design problems in the layout of their user interface as well as in the interaction with this interface [10
]. Freitas et al. [10
] and Scholtz [11
] define the sets of criteria for evaluating visual analytics tools, addressing both the evaluation of visual representations and interaction mechanisms. Scholz et al. [12
] provide further discussion of the development of an evaluation methodology for visual analytics environments. Some authors, e.g., [13
] focus on the heuristic (expert) evaluation of visual analytics tools.
A special type of visual analysis is geospatial visual analytics, which deals with problems involving geographical space and the various objects, events, phenomena, and processes populating it [1
]. Cartographic research of geovisual analytics has stressed interest in user studies informed by psychology and related cognitive science [1
]. As Roth et al. [16
] states, future work is needed to fully define high-level, insight-based tasks for geovisual analytics and interactive cartography.
In accordance with the above discussion, the user testing of geospatial visual analytics tools is important. Such user testing is presented, for example, by Robinson et al. [17
] and Roth, Ross, and MacEachren [18
]. Roth, Ross, and MacEachren [18
] evaluated interactive maps for the visual analysis of criminal activities. They used both evaluation by experts (the think aloud method) and typical users (an online survey). Robinson et al. [16
] evaluated the usability of geospatial visual analytics tools for epidemiology by means of various research methods (verbal protocol analysis, card-sorting, focus groups, and in-depth case study). Schnűrer, Sieber, and Çöltekin [19
] compared four variants of the GUI of an interactive map by analyzing the number and distribution of mouse clicks. A number of other user studies verified the usability of web map portals (i.e., [20
However, web map portals themselves do not enable the visual analytical functions of geospatial data, though they may serve as base maps for visual analytics.
Because visual analytics tools are based on visual stimuli that are perceived by sight, eye-tracking is a suitable method for evaluating these tools. A review of eye-tracking applications for the evaluation of visual analytics is presented by Kurzhals et al. [26
] and Krassanakis and Cybulski [27
]. Kiefer et al. [28
] state that eye-tracking evaluation of interactive is challenging. Other papers, for example, Çöltekin et al. [29
] and Çöltekin, Fabrikant, and Lacayo [30
] describe application of eye-tracking to compare two variants of GUI for an interactive map. Golebiowska, Opach, and Rød [31
] tested web-based analytical tool consisting of choropleth map, table, and parallel coordinates plot. Eye-tracking was also used to evaluate the geoportal MyFireWatch, which is based on an interactive map displaying the distribution of bushfires in Australia [32
] or for evaluation of weather forecast maps [33
The presented paper focuses on the user evaluation of interactive maps based on WebGLayer and HSLayers NG technology. The eye-tracking method was chosen for evaluation because it allows users to study the visual perception of users in combination with direct observation, which allows user interaction with interactive maps to be evaluated.
1.2. Description of the Investigated Tools
Visual analytics of spatiotemporal changes can bring new insights into the consequences of human decisions in many areas. We may identify several application domains, starting from smart mobility policy [6
], through the monitoring of machinery and sensor data in agriculture [36
], the monitoring of population movement and distribution [38
], and noise mapping [40
], to spatiotemporal visualizations of crime scenes [41
]. The main focus of visual analytics is, therefore, placed on interactivity through utilizing the concept of multiple coordinated views [43
] and dynamic queries to emphasize the impact of changes in various phenomena.
]. It is an open-source software released under the BSD (Berkley software distribution) license. The library is based on WebGL and uses the device’s GPU for the fast rendering and filtering of data. It can render data on a map provided by third party libraries (e.g., OpenLayers, Leaflet, GoogleMap API). The library is focused on spatial data and large datasets—so far, up to tens of millions of data records. It was developed and supported by the EU CIP (Competitiveness and Innovation Framework Programme) project OpenTransportNet for traffic flows and traffic accidents [5
] and was later expanded and enhanced within the European Horizon 2020 PoliVisu project to facilitate a wider range of visualizations that meet cities’ needs when working with smart mobility policy in the era of big data.
The WebGLayer allows the development of interactive heatmap visualizations of large datasets (see a comparison of its performance to other contemporary used solutions in [45
]) by implementing multiple linked views to present data. Each of the views enables different interactions (such as brushing, filtering, or relationship analysis) that trigger an instant update of the other views. Thus, users benefit from immediate and dynamic data visualizations, gain a better understanding of data by applying filters, and develop the opportunity to discover relationships and patterns in the data.
The three following cases’ usage (two of WebGLayer, one of HSLayers NG) are evaluated and described in this paper:
WebGLayer-based Chicago Map
—crimes in Chicago (United States of America). The use case visualizes the reported incidents of crime that occurred in the city of Chicago, the United States of America, between 1 January 2017 and 31 December 2017. In total, 63,216 crimes were reported in this period and published in the form of an application that is available at the URL: http://innoconnect.net/demo/chicago-criminality/
HSLayers NG-based Pilsen Map
—traffic intensity monitoring in Pilsen (Czech Republic). The use case visualizes historical, present, and estimated future traffic flows. The historical data in 2017 and 2018 comprise 500 million records (150 GB of data), while another million records (1 GB) from online detectors are generated each day. The developed application is available at the URL: https://intenzitadopravy.plzen.eu
. The new traffic intensity application is in its prototype phase. Once finished, it will be migrated to the mentioned address.
WebGLayer-based Flanders Map
—traffic accidents in Flanders (Belgium). The use case visualizes 63,532 traffic accidents that happened in Flanders in recent years. These represent approximately 85% of all accidents that involved injury or death registered by the police. The data were provided by the Federal Police and processed in collaboration with Informatie Vlaanderen. The developed application is available at the URL: http://innoconnect.net/apps/flanders-traffic-accidents/