zoom levels on the map. The result is that we were able to combine the highly customizable qualities of Leaflet with a better performing display of the data. Like the Canvas solution, we used D3 to load in all the data points from a CSV file. However, with Markercluster we did not need to render them all on a map at the same time. As the user zooms in, each cluster breaks apart into smaller subsets of clusters at predefined zoom scales. As the user zooms all the way in, the clusters eventually break apart into the individual data points. As the user zooms out, the opposite happens, with clusters forming labeled with the number of data points contained in them. The noticeable performance improvement stems from the fact that at any given zoom level, the system is only displaying a small subset of the overall dataset, whereas with the other implementation, hundreds of thousands of data points are plotted right from the beginning.
The main goal for this thesis has been investigating design’s possible contributions to the current discourse on how information is generated, communicated and assimilated by people through different media. Research on information and perception theories led me to recognize the relationship between people’s perception and actual data on the same phenomenon as a problematic one in today’s rapidly changing information scenario—and, in turn, to hypothesize how this issue could be addressed through design theory and practice. Focusing on the difference between perceptions and measures of data also represented an opportunity to reflect on the discipline of design as an instrument for social innovation. In fact, I recognized its potential to highlight and question the biases that are inherent both in human perception and in any kind of data. I found a valid paradigm to inform my exploration of those topics in Nathan Shedroff’s understanding of interaction as a necessary experiential element to allow a deeper engagement of people with information. Starting from these principles, I proposed a methodology for the communication of information that would not just allow people to interact with a visual interface, but with information itself. To do so, I worked to systematize a strategy originally developed in visual journalism that is based on asking readers to answer questions on a specific topic by interacting with visualizations of data. I therefore focused my research on the discipline of datavisualization, which provided me with key support in devising the visual language at the core of the proposed methodology—one that could be as effective, perceptually accurate and semantically meaningful as possible.
planning-decisions. By visualizing the urbandata in an interactive immersive VR environment, we aim to provide a framework for an interdisciplinary look into urban complexity and path the way for targeted dis- cussions. The intense immersive virtual reality and the possibilities of interaction implement a fun fac- tor, transmitting complex data in a fun and abstract but therefore comprehensive way, without visual dis- traction from outside. The goal of this project is to represent clearly the diﬀerent urban parameters; en- vironmental factors (ecology), land-use (economy), and inhabitants (social aﬀairs), in an abstract simula- tion. We started the ﬁrst prototype using data from Berlin; in a next step, we are planning to insert data
Displaying multiple data attributes together is the defining characteristic, and one of the key challenges in multi-dimensional datavisualization. The explosion of large, com- plex, multi-dimensional datasets from numerous application domains has generated sig- nificant interest in this area. There are many problems that need to be considered in order to effectively visualize multiple data attributes simultaneously. For example, the maxi- mum number of data attributes that can be represented at once is critical. New techniques should try to increase this upper limit. Previous research based on human perception and human cognition has studied ways to measure and manipulate limits on our ability to “see” information. This research reveals what can limit our understanding of multi-dimen- sional displays. It also motivates us to find new methodologies to design more efficient and more expressive visualizations. Some researchers have suggested that a robust rule- base is needed to support multi-dimensional datavisualization. The study of human per- ception, visual effects, and data analysis can be used to construct these rules. The desire to help a broad range of practitioners to visualize their data suggests the need for a seamless support system. A rule base for the design of multi-dimensional visualizations could be used as a foundation for exactly this kind of system. To date there are only a few previous attempts to build a visualization assistant. In order to be flexible, such a system must work in an application independent manner. This leads to a number of interesting and important questions, for example: How can we decompose or classify the data elements? How should we allocate visual features to each data attribute? Is it possible to measure the con- flicts or interference that may occur between different visual features? Finally, how can results from human perception be harnessed to help to address these needs? A significant body of research has been conducted or is currently ongoing to find a way in which to design perceptually salient visualizations.
With the rapid development of technology, there has been a rapid explosion in data and it is only going to grow faster. According to one prediction; by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet  . This will lead to a substantial increase in data available to us. Because of this reason, information visualization is becoming increasingly relevant. As the amount and dimensionality of data increases, the traditional ways of visualizing data may not be sufficient anymore and we may have to explore new and innovative methods and technologies to visualize data.
As businesses investigate criminal activity or employee misconduct, their manual processes pose inherent limitations that can result in costly losses and delays. Even when technology is applied, the tendency to use text-EDVHGGDWDSUHVHQWDWLRQLQWHUIHUHVZLWKWKHKXPDQEUDLQ¶VQDWXUDOFDSDELOLWLHVWR decipher information. While human processes are still needed to evaluate data, companies are in dire need of technology that takes advantage of natural human cognitive processes to speed analysis along. VANTOS offers just such a technology product. The company has made robust visualization tools one of the core features of its comprehensive enterprise investigation management product. Visualization tools such as those provided by VANTOS V-Flex im PDNHLWIDUHDVLHUIRUDFRPSDQ\¶VVHFXULW\WHDPWRDQDO\]H textual data. Datavisualization allows analysts to easily notice unusual patterns and trends in disparate data sets ² without the time, expense and inaccuracy of manual efforts.
Open Archives Initiative Protocol for Metadata Harvesting, generally referred to as OAI-PMH provides an application independent framework that can be used for presenting and harvesting of metadata . OAI-PMH made its origin in July 2001 with the aim of increasing the efficiency of print/pre-print servers that hosted scientific and technical papers. As the size of these repositories kept increasing over the years, it became hard to maintain the database leading to incorrect data and loss of data. Thus evolved OAI-PMH that provided certain standards that made easy, the maintenance of metadata repositories and presentation of machine readable metadata to public . There are many applications and citation indexes that comply with OAI-PMH for presenting their metadata and Citeseer is one such database. OAI-PMH primarily has two categories of providers namely
There was a great possibility to develop a task specific application (GeoBogi v1.0) based on groundwater modeling results to support the well field protection efforts. Numerous wells were used in the study area to obtain a streamline system, so huge amount of data were produced. The groundwater modeling was executed by DHI-WASY’s FEFLOW software package.
Several advancements in technology have made datavisualization more salient now than ever before. Massive amounts of data, known as “big data,” can now be collected, stored, and made accessible, and these methods are improving every day. In fact, there are an increasing number of public big data assets, which can be used to supplement private data sources. Therefore, for those companies who have chosen to invest in big data, they run the risk of getting lost in an inundation of data. In this case, datavisualization becomes even more valuable because it is a way for companies to “see” their data, make sense of it, and communicate new insights.
FLEXIBILITY WITHIN - THE - TOOL : We saw in Sect. 4.2.3 that the HCI concepts can expand the flexibility of a tool within the outside world (e.g., not to disrupt other activities with the embodiment concept) as well as within our internal world (i.e. to satisfy psychological needs with the experience concept). Visualization differs in that it requires flexibility within the tool itself. Interacting with a visualization often results in changing tasks on the fly, to iteratively build an understanding of the data through a series of smaller data questions. So while with HCI the user would mostly interact at the view level of the visualization pipeline, with visualization they might need to interact with all levels of the pipeline. This means that the user likely benefits from having access to a large variety of means of interacting with the visualization. To summarize, by comparing the visualization and HCI literature we extend the view of interaction in visualization in terms of: scope of user intentionality, user profiling, and our notion of what good interaction means – especially with respect to the external world. Yet some questions cannot be answered by the HCI literature, mainly because HCI lacks an explicit tie to the data component. Specifically, it is unclear how to conceptualize interaction when (i) there is a data- related user intent and (ii) there is a need for interaction flexibility within-the-tool with respect to that intent. We address the first point about data-related intent next, through our definition of interaction. We will clarify the second point about flexibility within-the-tool in Sect. 6.
Visual representations of data play a central role in the recent expansion of data-driven news. From simple bar charts and line charts to more sophis- ticated chart types, data visualizations (or dataviz) are assumed to have the capacity to engage audiences, a view that extends beyond the news. At the same time, the news is experiencing other changes and challenges. At the time of writing, the global political climate is characterized by claims that we are living in a ‘post-truth’ world, in which people have had enough of objective facts and data. In this context, transparency, seen for some time as a trust-generating mechanism appropriate to the networked age, is believed to make it possible for audiences to see how the news is produced and therefore to establish trust (Singer, 2010).
In , Suonsyrjä et al. evaluate the potential of automatic collection of PDD to fulfill the needs for such knowledge in the Finnish software development industry. Post-deployment data is here used to consider data that is automatically collected after the commercial launch of a product. The results highlight that most of the different types of analyses desired from PDD were value analysis, users’ problems detection, and use path analysis. Furthermore, the data types desired were time spent, performance, amount of use, and errors. Yet the main knowledge source were still revolving mainly around direct (meet- ings) or indirect (questionnaires) customer contact, which are not automated. Challenges mentioned in the study were broad, concerning acquiring and processing the data, as well as the maturity of processes and customers, and privacy concerns. Suonsyrjä et al. conclude that automatic collection of PDD has the potential to help teams improve their processes and products, but still lacks more research and development.
Information aesthetics is an emerging field that analyzes the aesthetics of information access as well as the creation of new media works that beautify information processing (Manovich 2001). Its practice consists of data representation and interfacing applications that are situated between the realm of functional datavisualization, and the more subjectively loaded nature of fine arts. One should note that many ‘traditional’ datavisualization algorithms, solutions and visual styles demonstrate levels of genius and creativity. However, until today, the field has shown little understanding of typical creative design considerations such as the subjective value or purpose of visual aesthetics, intrigue or pleasure. Tensions seem to exist between the ‘traditional’ datavisualization field, typically focused on developing high- end applications for research and commercial enterprises, and more ‘artistic’ approaches, which aim for a non-expert audience attending art exhibitions or browsing theme-specific websites. Although both fields investigate new ways of representing data, misunderstandings tend to arise when respective works are reviewed with identical assessment criteria. Therefore, a basic understanding about the commonalities and inequalities between both fields is required to better appreciate their motivation, purpose and significance, to facilitate accurate reviewing processes, and to allow for a more useful exchange of relevant knowledge that would benefit both.
Color usage for displaying categorical data is intended to show clear distinction between elements. This distinction is visible without persuading or showing value; saturation levels are roughly equal to each other. Colors should be used in the order presented.
Despite solving for the fundamental capabilities of big data and providing easy-to-use tools for visualization, organizations are still struggling with the basics: graduating from static reporting to interactive, online presentation tools. The datavisualization discipline needs to be seen as an analytic process, not a reporting outcome. This is the first barrier to overcome on the business intelligence (BI) maturity model.