Data Visualization

What i s Da ta Visu ali za tion`  Data visualization is the graphical representation of information.  Bar charts, scatter graphs, and maps are examples of simple data visualizations.  Information technology combines the principles of visualization with powerful applications and large data sets to create sophisticated images and animations.

De fin itio ns  Visualization is the graphical presentation of information, with the goal of providing the viewer with a qualitative understanding of the information contents.  Information may be data, processes, relations, or concepts.  Graphical presentation may entail manipulation of graphical entities (points, lines, shapes, images, text) and attributes (color, size, position, shape).  Understanding may involve detection, measurement, and comparison, and is enhanced via interactive techniques and providing the information from multiple views and with multiple techniques.

How d oes i t wo rk?  Visualizations encompass a wide and growing range of projects, reflecting creative ways of representing all sorts of data visually, with virtually no limit to what kind of information can be translated into an image.  The designer of a visualization determines which visual element (color, shape, size, motion, and so forth) will represent individual data points. Images can be 2D or 3D, can be fixed or dynamic, and can allow user interaction.

Ch ara cterist ic s of Da ta                

Numeric, symbolic (or mix) Scalar, vector, or complex structure Various units Discrete or continuous Spatial, quantity, category, temporal, relational, structural Accurate or approximate Dense or sparce Ordered or non-ordered Disjoint or overlapping Binary, enumerated, multilevel Independent or dependent Multidimensional Single or multiple sets May have similarity or distance metric May have intuitive graphical representation (e.g. temperature with color) Has semantics which may be crucial in graphical consideration

Co mmon Techniques          

Charts: bar or pie Graphs: good for structure, relationships Plots: 1- to n-dimensional Maps: one of most effective Images: use color/intensity instead of distance (surfaces) 3-D surfaces and solids isosurfaces/slices translucency stereopsis animation

Why is it si gnific ant?  Computer systems generate and store massive and growing amounts of data.  Data visualizations offer one way to harness this infrastructure to find trends and correlations.  Representing large amounts of disparate information in a visual form often allows you to see patterns that would otherwise be buried in vast, unconnected data sets.

 Data visualizations bring themes and ideas to the surface, where they can be easily discerned.  Visualizations allow you to understand and process enormous amounts of information quickly because it is all represented in a single image or animation.  Moreover, virtually any kind of data from a broad range of academic disciplines can be represented visually, making data visualization a potentially valuable approach to learning for a large number of students and researchers.

Ti me Magaz ine us es vi sual hi lls (spi kes ) to emphasi ze t he densi ty of A mer ican popul ati on in its map.

Cr azyEgg lets you expl ore the behavi or of your visi tors wi th a heat map. More popul ar sect ions , whi ch are cl icked mor e of ten, are hi ghl ighted as “war m” - in red col or.

Newsmap is an appl icati on that visual ly ref lects the constantl y changi ng landscape of the Googl e New s news aggregator . The si ze of data bl ocks i s def ined by thei r popul ari ty at the mom ent.

What a re the downsi des?

 Visualizations rely on accurate and matched data.  If data are incomplete or faulty, or if data sets use different definitions or units, these issues must be resolved in order to create a valid visualization.  Even if the data are reliable and consistent, a poorly conceived visualization might show nothing of consequence or exaggerate the significance of certain trends, resulting in flawed or misleading conclusions.  In some cases, a lot of time and trouble go into a visualization that adds nothing to an understanding of the data that you wouldn’t find in a simple table or even a textual description.