Posted by Bernhard on January 23rd 9:
Thanks Zach for contributing to to dataminingblog. The increasing amount of information being displayed in visual form helps viewers understand the often convoluted relationships between ranges of data.
However, when presented with the choice to visually represent data relationships, it can be difficult to choose which model to apply. Different types of data work better with specific visualization models. Geospatial Information on maps used to be set in stone once the project was completed.
With geospatial visualizationssometimes referred to as geovisualizations, users can explore huge amounts of data sorted by geographic details. Interactive elements are common to this model of visualization, and provide insight for a range of uses cases, from political campaigns to large sales organizations.
This visualization model is used primarily by larger companies since it requires a large amount of data to create real value. Geospatial visualizations often focus on particular silos of information, like data from particular cities or regions of a country.
Temporal Displaying data from a range of time is another common model for visualizing information.
Like geospatial models, temporal visualizations include a wide array of applications and specific use cases.
For example, steamgraphs highlight the ebb and flow of data around a central x axis. Temporal visualizations allow viewers to zoom out from particular location, and focus on the increase in absolute activity as represented by the data.
Temporal visualizations have been around for a while. Gantt charts are perhaps one of the first use cases in the business community. Multidimensional One of the most powerful capabilities of data visualization softwaremultidimensional models create representations of data clusters, which can be difficult to comprehend because of their abstract nature.
These new visualizations often take the form of bubble chartswhich represent data clusters in bubbles of varying size, as well as tree mapswhich group data in categorical fashion.
However, pie charts are very limited in their ability to show the relationships of a large number of categories because of their 2D structure. Newer multidimensional visualizations are much more adept at addressing large amounts of data covering a variety of categories.
Hierarchical Hierarchical data visualizations display data as the name implies: Common examples include dendrogramsand variations of tree diagrams, which are distinct from tree maps in that they display data in branches rather than in rectangular spaces. Hierarchical visualizations excel at imposing rank on data sets, and also work well for displaying sub-categories of broader sets of data.
Radial tree models and wedge stack graphs are some of the more visually impressive methods of displaying data in hierarchical fashion. Network Perhaps the most intuitive model for showcasing relationships between data, network visualizations focus on displaying connections between various data sets.
Common models are dependency graphs and node-link diagrams. As the amount of data available to organizations of all types increases, network visualizations will only become more important.
The utility of network visualizations comes from their ability to easily connect disparate types of data. Such connections provide a simple method for exploring a number of data sources. Connecting different data sets has become a science over the past several years, and visualizations represent the result of long hours of labor.
Visualizations are quickly becoming the common ground between data experts and laypeople. He covers gamification, healthcare IT, business intelligence, and other emerging technology. Connect with him on LinkedIn.July - June Doctorate of Philosophy Massachusetts Institute of Technology.
I worked with Professor Sanjay Sarma and the members of the Field Intelligence Laboratory, researching vehicle prognostics, sensors, data mining, and Internet of Things architecture. Much of my research related to the value in open data standards, data interoperability, and the use of machine learning to.
The growth of data both structured and unstructured will present challenges as well as opportunities for industries and academia over the next few years. Project Abstract: By , two thirds of the world’s population will live in urban areas, with the greatest growth in the developing world.
Urban areas already account for a majority of primary energy use and energy-related CO2 emissions (IEA ).
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Ecological economics (also called eco-economics, ecolonomy or bioeconomics of Georgescu-Roegen) is both a transdisciplinary and an interdisciplinary field of academic research addressing the interdependence and coevolution of human economies and natural ecosystems, both intertemporally and spatially.
By treating the economy as a subsystem of Earth's larger ecosystem, and by emphasizing . Introduction. CSHALS is the premier annual event focused on the practical application of Semantic Web and other semantic technologies to problems in the Life Sciences, including pharmaceutical industry and related areas, such as hospitals/healthcare institutions and academic research labs.