Discussion 1:
Data viz follows a particular chart type, determined by the data and story you are giving. Temporal graph type is much suited in trends and activities that are shown over time for cross-comparison purposes. This level of data chart has numerous kinds of visualization that include hours, days, months, and years. This type of representation is depicted in a cyclic design because of the nature of the time—day and night.
The primary concern of temporal viz is understanding time-based regular intervals and variances, or prediction query. The time-series data and sequential trends can be visualized using multiple temporal charts like line graphs. The study of mankind’s activities usually follows a sequential event (Ren et al., 2018).
Another type is the Spatial chart type that focuses on understanding data physically using a form of map visualization as a core chart type. The data are represented in various maps serving different functions in improving the visualization (Kirk, et al., 2016). Color encoding in choropleth maps is important in showing data attributes.
The characteristic values are encoded with the area scopes through a distortion of physical space by cartograms. And tile grid maps make the spatial scopes’ shape and size uniform for the encoded color data to be observed with ease and compare. Again, the grid maps select any size of scope and this makes it easier for the smaller ones.
The categorical chart compares the numerical values’ categories and dissemination (Kirk, et al., 2016). It handles multivariate data represented either categorically or numerically. The visualization of these data objects in understanding the patterns of the data attributes. The data can be visualized in more than one form of graphs. Box-plot graph viz in a one-dimension graph and represent data quartiles ranges if the data is numerical. Bar-graphs type is abundant in representing numerical data more effectively e.g. student grade. Another characteristic is color visualization of values in heatmaps.
References
Kirk, A., Timms, S., Rininsland, Ǯ., & Teller, S. (2016). Data Visualization: Representing Information on Modern Web. Packt Publishing Ltd.
Ren, J., Bai, X., Lu, Y. Y., Tang, K., Wang, Y., Reinert, G., & Sun, F. (2018). Alignment-free sequence analysis and applications. Annual Review of Biomedical Data Science, 1, 93-114.
Discussion 2:
Temporal visualizations are simplest, quickest ways to represent the time series data. The reason for choosing this temporal category of chart types is because using the functionality in temporal charts, we can perform trend analysis, gain insight into multidimensional raster data at given locations, and plot values that are changing over time in the form of a line graph (Temporal profile chart. ,n.d.).
A temporal profile chart is interactive (Bethany, 2015); where selecting one point on the temporal profile seamlessly switches the map display to the time slice from which the point value was extracted; which provides a convenient way to navigate through the data. There are several handy temporal visualization styles for time series data. In temporal category X-axis indicates the rime interval size and time aggregation which obviously changes to mean, we can manually change the rime aggregation to count, mean, median. And Y-axis shows the bounds such as minima and maxima based on the range of data values represented on the axis, standardized values. Temporal category charts; Line graph, stacked are chart, bar charts, gantt chart, stream graph, heat map, and polar area diagram these are all comes under temporal visualization.
Representation of geospatial visualizations with heat map. Often geospatial visualization uses heat maps since, they easily and quickly help to identify the hot regions of high concentrations of a given variable. When adapted to temporal visualizations, heat map helps to explore 2 level of time in a 2 dimensional array. Heat maps are perfectly fits for a two-tiered time frame; for instance, seven days of the week spread across 52 weeks in a year, 24 hours in a day spread across 30 days of the month, etc. The limitation, though, is that only one variable can be visualized in a heat map. Comparison between two or more variables is very difficult to represent (Bethany, 2015).
References:
Bethany. (2019). Visualizing Time Series Data: 7 Types of Temporal Visualizations – Atlan: Humans of Data. Retrieved from https://humansofdata.atlan.com/2016/11/visualizing-time-series-data/
Temporal profile chart. (n.d.). Retrieved from https://pro.arcgis.com/en/pro-app/help/analysis/geoprocessing/charts/tempo
