Maps: Exploring location data with aggregation

Keshif's design brings unprecedented speed and ease to navigate and analyze location data that aggregates (groups) records.


Using synchronized selections

Keshif's rich highlighting, filtering, and comparison features allows you to explore your data with simple mouse moves and clicks. Learn more.

For example, in the Nobel Prize Winners dataset, multiple winners (records) can have the same the birth country. Therefore, the birth country groups winners, and can be used to create aggregating Keshif maps (learn how). The sequence above shows how you can quickly point to a Nobel Prize category to see the number of Novel winners across the European countries. Likewise, selecting a country automatically selects the winners born in that country, and synchronously reveals the number of winners per category.


Aggregating trends by count or sum/average of a numeric attribute

Analyzing data by count (such as, of people) is just one of the ways to analyze distribution trends of data. You can also analyze data by using total or average of a numeric feature of a dataset.

As an example, we use a list of 5000 companies by inc5000 below. The sequence shows:

  1. The count of companies per state,
  2. The total number of workers per state,
  3. The average number of workers per state, and
  4. The average revenue of companies per state.

Pointing to a location (state) on the map shows a tooltip that describes the underlying data based on active aggregation.


Choosing your categorical data view: Map vs. List

Once the map is enabled for the categorical feature, you can easily switch between the list and map view using the icons on upper-right corner.

  • The categorical list view allows you to make richer filtering selections with multiple regions, and to compare data across multiple regions. For example, you can select multiple countries (UK and France), or compare UK and France across time or prize categories.
  • The map view allows you to explore the geographic trends in your data, such as hot-spots or regional differences.

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