Maps: Exploring in aggregating (grouping) location data

Analyzing geographic data using synchronized selections

You can use the rich selection, highlighting and filtering capabilities in Keshif to quickly explore your data.  Read more.

For example, in the  Nobel Prize Winners dataset, multiple Nobel winners (rows) can have the same country of birth. Therefore, the winners (rows) can be grouped by their country of birth, and you can use the country names to prepare your map. The screen capture above shows how you can quickly highlight a Nobel Prize category to see the number of Novel winners across the European countries. Selecting countries (regions) on the map also automatically selects the winners of that country, and reveals the number winners there are per category in the selected country. Now, all you need to do is to think about what you want to explore!


Analyzing geographic trends by aggregating sum/average of a numeric attribute

The examples above enables the analysis of count of records (people) in each country. Note that it is the default aggregate metric in Keshif. You can also analyze records by total or average of a numeric value for all data in that country/region, such as average age of Nobel winners per country.

To demonstrate this, we use an example using a  list of 5000 companies, collected by inc5000. The sequence below first shows 1) the count of companies in each state, then shows 2) the total number of workers, and then 3) the average number of workers. You can see the data in each state by mouse-over using the tooltip.


Choosing your View: Map vs. Categorical List

Once the map is added to the categorical attribute, you can easily switch between list and map view. 

  • The categorical list view allows you to make richer filtering selections using multiple and/or/not selections, and to compare your data by region using locking.
  • The map view reveals the geographic distribution of your data,  and can help you understand if there exists trends across north/south or east/west.

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