Data Visualization

 

             Wong details the three essential elements of sophisticated use of elements, clear meaning, and refined execution. Wong reveals that evaluating visual form has three dimensions, each of equal importance. The first is separating essential data from the rest via visual context, communicating the insight intended to be conveyed, and applying visual polish to bring deep attention to the visual details.

            The example of the data visualization I found is the chart adopted to track national job gains and losses. The categories have their distinction, with distinguishing context sometimes. The categories have adjacent properties that complement other aspects of the categories. The first rule is using contrast to capture the reader’s attention. The chart uses different colors and sizes to track the number of sectors losing or gaining jobs each month. The authors use various color schemes, with red indicating the most significant job losses and blue the biggest gains.

            The second rule of data visualization is using common chart elements to convey exact meaning. The chart has a clear title to convey to the reader the purpose of the visual representation. The aim communicated clearly by the authors is tracking monthly job gains and losses in each sector of the economy. The plot area clearly displays monthly job gains and losses per sector with data points tracking payrolls' rise and fall. The legend used in the visualization indicates what the color schemes imply. Each color used shows a certain percentage of rising or fall.   

            The third rule of comprehension is applying refinement to the execution to eliminate distractors. With the many distractions often demanding the reader's attention, it is vital to keep charts simple. The refinement of the chart area helps the reader to focus on the information presented by the authors. It is easy to track the percentage of payroll rises and falls in the chart's format.