There are a lot of different pods available in SpatialKey, huh?  This article will walk through some examples of how raw data can be visualized in SpatialKey and explain all of the different types of visualization pods that SpatialKey offers.

The file we will be working with contains information about real estate transactions in the Sacramento area reported over a five-day period, as reported by the Sacramento Bee.  If you want to follow along, you can download the file (download .csv file).

This file contains address information, which SpatialKey will use to visualize where the data should be placed on the map.

The dataset also contains other information that we can visualize in SpatialKey.  The “Type” field contains information about what type of property was sold.  From what we can see on the first page in excel, there are a couple different types of properties and many more of one type than the other.

We can use a Unique Value List pod in SpatialKey to quickly show how many sales there were for each type of property.  With this pod, we are able to see that there are actually 4 different values in the type column and we can easily see how many of each type there are without having to apply filters or sort in excel.

We also have date information in our dataset.

The Timeline pod shows frequency of the real estate transactions over time.  We can see that over the weekend, there were no real estate transactions completed but there is higher activity on Fridays and Mondays.  With more data, the histogram could show us trends throughout the year or across multiple years.

We can also add a Day of Week pod to visualize this data in a slightly different way.  If we had more than 1 week worth of date, the Day of Week pod could help us understand sales patterns based on the day of the week.

Our dataset also contains numeric values like price.

There are many different ways to visualize numeric data on your map.  For example, you can change how a map is rendered so our heatmap could be showing concentrations of total price or average price instead of the number of sales.

  

But, since we are on the topic of pods, let’s check out a couple of great pods for visualizing numeric data.  In the image below, you can see two joined Statistics pods showing the sum and the average of the price column for the dataset.  Also, there is a Histogram pod showing the distribution of the price for real estate transactions.  You can easily see that most transactions were in the $120K to $240K range.

You can also identify any outliers in the data.  The Histogram is telling us that there are approximately 50 real estate transactions that have extremely low values.  We can add a Record List pod and filter the histogram to only show those low-value transactions.  You can see that after applying the filter, our Statistics pod updates to show stats for the filtered values only.

We just started scratching the surface on pods – there are many other pods to use with your data.  The pods that are available for your dataset will depend on the types of data your dataset contains.  For example, if there were no date fields in your dataset, you wouldn’t have an option to add a Timeline pod.  From this above example, you can see that the real power of SpatialKey is realized when you have multiple pods, all working together to help you best visualize your data.

For additional details on pods, check out the following articles: