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Data visualisation
Published in Catherine Dawson, A–Z of Digital Research Methods, 2019
Data visualisation is a research method that uses visualisation to help researchers better understand data, see patterns, recognise trends and communicate findings. Researchers approaching their work from a variety of epistemological positions and theoretical perspectives use data visualisation as a research method, and it is used in both quantitative and qualitative approaches. It can be both exploratory and explanatory, helping the viewer to understand information quickly, pinpointing emerging trends, identifying relationships and patterns and communicating the story to others. It enables data to become useful and meaningful, providing insight into data and helping researchers to develop hypotheses to explore further. Large datasets can often appear overwhelming: data visualisation methods help to make them more manageable and present data in a visually-engaging way to a much wider audience. Hand-drawn and printed visualisation has been used for hundreds of years, but recent advances in software and visualisation tools, along with the increasing collection and use of big data (Chapter 3), have led to a rapid increase in the use of data visualisation as a digital research method. Information about the history of visualisation can be found on the website of the Centre for Research and Education in Arts and Media (CREAM) at the University of Westminster (http://data-art.net).
Data Modeling for Systems Integration
Published in Adedeji B. Badiru, Systems Engineering Using the DEJI Systems Model®, 2023
Data visualization is the presentation of data in a pictorial or graphical format. It enables decision-makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. With interactive visualization, technology can be used to drill down into charts and graphs for more detail, interactively changing what data is seen and how it’s processed. With big data there’s potential for great opportunity, but many retail banks are challenged when it comes to finding value in their big data investment. For example, how can they use big data to improve customer relationships? How—and to what extent—should they invest in big data?
Business modeling and forecasting
Published in Adedeji B. Badiru, Ibidapo-Obe Oye, Babatunde J. Ayeni, Manufacturing and Enterprise, 2018
Adedeji B. Badiru, Ibidapo-Obe Oye, Babatunde J. Ayeni
Data visualization is the presentation of data in a pictorial or graphical format. It enables decision-makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. With interactive visualization, technology can be used to drill down into charts and graphs for more detail, interactively changing what data is seen and how it’s processed. With Big Data there’s potential for great opportunity, but many retail banks are challenged when it comes to finding value in their Big Data investment. For example, how can they use Big Data to improve customer relationships? How – and to what extent – should they invest in Big Data ?
Factors affecting MOOC teacher effectiveness from the perspective of professional capital
Published in Behaviour & Information Technology, 2023
In terms of MOOC instructional design, to improve the personalisation of the MOOC curriculum, the researchers investigated the resources, tools, and methods often used by teachers in curriculum teaching through questionnaires and interviews (Bozkurt, Kilgore, and Crosslin 2018). By analyzing the course teachers’ team, the effects of social presence, teaching presence, and attitude differences of different teachers in the teaching process on teaching results were compared, and further improvements to MOOC teaching design were proposed (Douglas et al. 2019). The importance of learners’ assessment information on the course for the teacher should be emphasised; thus, suggestions for improving the existing assessment function were proposed (Emmons, Light, and Borner 2017). Data visualisation can be used to help teachers effectively understand specific student information, including age distribution, performance and feedback for the purpose of adjusting and optimising course materials and guidance (Castano-Munoz et al. 2018).
Risk reduction via spatial and temporal visualization of road accidents: a way forward for emergency response optimization in developing countries
Published in International Journal of Injury Control and Safety Promotion, 2023
Aqsa Qalb, Hafiz Syed Hamid Arshad, Muhammad Shafaat Nawaz, Asra Hafeez
Data visualization is the extremely effective approach to encode the information so that our brains can interpret the results and take informed decisions (Alrajhi & Kamel, 2019). With the help of Folium Library, heat maps were generated for different time periods using spatial and temporal data, which helped in determining time series pattern of road accidents. The maps generated are dynamic and interactive, and can be zoomed in and out at any level while a dynamic display of time-series would enable the user to play and monitor the real-time spread of accidents in the city. Moreover, Pandas library and Matplotlib were used for the data manipulation and Seaborn library was engaged for the data visualization by plotting the charts or graphs. All these libraries are used to calculate and describe the traffic accidents in Jupyter Notebook.
Machine Learning Techniques and Big Data Analysis for Internet of Things Applications: A Review Study
Published in Cybernetics and Systems, 2022
Fei Wang, Hongxia Wang, Omid Ranjbar Dehghan
Big data, machine learning, and IoT all face a similar set of problems: The volume of data acquired or extracted is called the “data volume.” We’ll soon be talking about zettabytes and yottabytes instead of gigabytes.For IoT applications, keeping up with the rapid evolution of data is a major challenge.IoT has long been plagued by problems accessing data quickly enough.As we speak of “data value,” we mean making sure that data organization always results in value being extracted.IoT applications face challenges related to privacy and security. Increasing data security reliability while reducing the attack potential is the objective of this challenge.The use of graphs and charts to convey huge amounts of data in an understandable manner is known as data visualization.Using methods such as knowledge extraction, IoT devices generate huge volumes of data that can be used to pick out the most significant facts. We can reduce the storage implications of ever growing IoT data by implementing real-time analysis in various applications.