# Data Abstraction to Visualization

As mentioned earlier, knowledge about the types of the attributes can provide guidance in selecting graphical encoding strategies.

Some examples:

* **Line chart** is _inappropriate_ for **unordered** attributes i.e. categorical attributes. This is mainly because categorical attributes can be reordered/sorted arbitrarily, thereby changing the line pattern. Instead, we must use **bar chart** for categorical attributes.\
  ![](https://612062878-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M5-0RG_uqhxyMNjpMUW%2F-M5-0SvQ45Rarxg8L2lA%2F-M5-0U_Rg2Cr_bw7DhDH%2FBar%20Chart.PNG?generation=1586990818743124\&alt=media)
* **Spatial** attributes can be visualized using spatial representations like **maps**.\
  ![](https://612062878-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M5-0RG_uqhxyMNjpMUW%2F-M5-0SvQ45Rarxg8L2lA%2F-M5-0U_TrqAXSF8BMFkX%2FSpatial%20Visualization.PNG?generation=1586990818660118\&alt=media)
* **Divergent quantitative** attributes can be represented using **heat maps** (divergent color scales).\
  ![](https://612062878-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M5-0RG_uqhxyMNjpMUW%2F-M5-0SvQ45Rarxg8L2lA%2F-M5-0U_VRD8t2yobCcks%2FHeat%20Map.PNG?generation=1586990818622909\&alt=media)
