When interacting with data in SpatialKey, you will almost always apply a filter – it’s actually hard to avoid applying filters!
Filters can be applied in many different areas within SpatialKey.
- Filter with pods in a dashboard
- Filter on a joined dataset
- Custom shape filters
- Filter with buffers
- Filter records near an address
- Filter by visible area in map
- Synchronize your dashboard filters
- Compare distribution of filtered and unfiltered data in pods
All of the filters mentioned above occur in dashboards. There is one other type of dataset filter you can apply, but this happens outside of a dashboard by managing the individual dataset settings.
Let’s start with an overview of the different ways to apply filters so you can see which way is best for your data.
This is the most common way to filter your data – so common that you may not even notice that you are applying filters since it is just a normal part of your data exploration process. Simply double-click on a record in the Unique Value List pod for example, and you have applied a filter. Pods provide a quick way to filter your data, and all pods stay in sync when filters are applied.
One of the many ways to filter your data is to filter based on a joined field. Maybe you joined two point datasets together to enhance them with each other’s fields; or maybe you joined your point dataset with a shapefile in order to see which of your points fall within the shape boundaries.
You can quickly filter your data geographically by adding shapes to your map. You can add simple circles and rectangles as well as more complex polygons. When you add these shapes you can turn data filtering on to see your data filtered within or outside of these shape boundaries.
Filtering with buffers is a powerful way to perform analysis on your datasets. For example, you can set up criteria to show only locations from Dataset A that fall within [x] miles of Dataset B. Now plug your use case in that example…
- I want to see prospects within 10 miles of my sales reps
- I want to see clients that are farther than 30 miles from my repair shops
- I want to see houses for sale within 500 meters of a school
- I want to see houses for sale within 500 meters of a golf course
- I want to see my insurance policies within 250 meters of terrorism targets
- I want to see my insurance policies within 100 feet of the Mississippi river
There are endless possibilities of how to use this analytic capability.
SpatialKey offers filtering based on proximity to a specified address. Simply enter an address, choose a radius for the buffer around the address, and apply your filter.
When you enable area filters, you are automatically filtering the other pods in the dashboard by the visible area on the map. By default this is not enabled.
(Learn more – see “Filter by Visible Area in Map” section)
Use synchronized filters to filter multiple datasets with a single filter pod. Synchronized filters work in dashboards where multiple datasets have been added. Once a synchronization is set up, any filters applied to the selected dataset/column combination will also be applied to all other synchronized datasets.
SpatialKey allows you to see the impact that filters have on an attribute of your data by showing the filtered and unfiltered distributions in the same view.
Applying dataset filters is a handy way to cut you dataset into a smaller subset before you get to the dashboard. You could use this feature to filter out null records or records that didn’t geocode well, just to name a couple of examples. When you enter a dashboard, you will only see the records that meet the set filter criteria.